BYU PRISM

1. Gunnell, L., Lu, X., Vienna, J.D., Kim, D-S, Riley, B.J., Hedengren, J.D., Uncertainty propagation and sensitivity analysis for constrained optimization of nuclear waste vitrification, 2025, doi: 10.1111/jace.20446 Article (Open-Access)

1. Arce Munoz, S., Hedengren, J.D., Transfer Learning for Thickener Control, Processes, Special Issue: Machine Learning Optimization of Chemical Processes, 2025, 13, 223, doi: 10.3390/pr13010223 Article

2025

Arce Munoz, S., Hedengren, J.D., Transfer Learning for Thickener Control, Processes, Special Issue: Machine Learning Optimization of Chemical Processes, 2025, 13, 223, doi: 10.3390/pr13010223 Article

 Current CV

1. Wallace, J., Hedengren, J.D., Powell, K.M., Memmott, M., Model predictive control of a grid-scale Thermal Energy Storage system in RELAP5-3D, Progress in Nuclear Energy, Volume 177, 2024, 105410, ISSN 0149-1970, doi: 10.1016/j.pnucene.2024.105410 Article
2. Wallace, J., Hill, D., Thurston, D., Hedengren, J.D., Memmott, M., Model predictive control of a Lab-Scale thermal energy storage system in RELAP5-3D, Nuclear Engineering and Design, 2024, doi:10.1016/j.nucengdes.2024.112906 Article

1. Wallace, J., Hill, D., Thurston, D., Hedengren, J.D., Memmott, M., Model predictive control of a Lab-Scale thermal energy storage system in RELAP5-3D, Nuclear Engineering and Design, 2024. Article

1. Ho, A., Billings, B., Hedengren, J.D., Powell, K.M., Flexible operation of nuclear hybrid energy systems for load following and water desalination, Renewable Energy Focus, Volume 51, October 2024, 1006412024, doi: 10.1016/j.ref.2024.100641 Article

 Current CV

1. Arce Munoz, S., Pershing, J., Hedengren, J.D., Physics-Informed Transfer Learning for Process Control Applications, Industrial & Engineering Chemistry Research, 2024, doi: 10.1021/acs.iecr.4c02781 Article

1. Arce Munoz, S., Pershing, J., Hedengren, J.D., Physics-Informed Transfer Learning for Process Control Applications, Industrial & Engineering Chemistry Research, doi: 10.1021/acs.iecr.4c02781 Article

1. Chen, Y., Hill, D., Billings, B., Hedengren, J.D., Powell, K.M., Hydrogen underground storage for grid electricity storage: An optimization study on techno-economic analysis, Energy Conversion and Management, Volume 322, 2024, 119115, ISSN 0196-8904, doi: 10.1016/j.enconman.2024.119115 Article

• APMonitor and GEKKO Optimization Suite: Advanced Process Monitoring (APM) is a software platform to solve large-scale Differential and Algebraic Equation (DAE) models in simulation, process control, and optimization applications. Recipient of the 2014 AIChE David Himmelblau Award, 2018 AIChE Computing Practice Award, and 2024 John R. Ragazzini Award.

1. Ho, A., Billings, B., Hedengren, J.D., Powell, K.M., Flexible Nuclear Hybrid Systems for Load Following and Water Desalination, Renewable Energy Focus, Volume 51, October 2024, 1006412024, doi: 10.1016/j.ref.2024.100641 Article

1. Ho, A., Billings, B., Hedengren, J.D., Powell, K.M., Flexible Nuclear Hybrid Systems for Load Following and Water Desalination, Renewable Energy Focus, 2024.

John D. Hedengren Photo Credit: Alyssa Lyman Dahneke, CC BY-SA 4.0

John D. Hedengren Photo Credit: Alyssa Dahneke, CC BY-SA 4.0

Photo Credit: Alyssa Dahneke, CC BY-SA 4.0

John D. Hedengren Photo: Alyssa Dahneke, CC BY-SA 4.0

John D. Hedengren Photo: Alyssa Dahneke, CC BY-SA 4.0

1. Gunnell, L., Perez, K.X., Castillo, I., Hoogerwerf, A.S., Peng, Y., Hedengren, J.D., Detection of Valve Stiction in Industrial Control Loops through Continuous Wavelet Transformation with a CNN, American Control Conference, Toronto, Canada, July 2024. Preprint

 Current CV

1. Larsen, A., Lee, R., Wilson, C., Hedengren, J.D., Benson, F., Memmott, M., Multi-Objective Optimization of Molten Salt Microreactor Shielding Perturbations Employing Machine Learning, Nuclear Engineering and Design, Volume 426, September 2024, 113372, 2024. Article

1. Larsen, A., Lee, R., Wilson, C., Hedengren, J.D., Benson, F., Memmott, M., Multi-Objective Optimization of Molten Salt Microreactor Shielding Perturbations Employing Machine Learning, Nuclear Engineering and Design, accepted for publication, 2024.

1. Hill, D., Tito, S.R., Walmsley, M., Hedengren, J.D., Techno-ecnonmic optimization of a hybrid energy system with limited grid connection in pursuit of net zero carbon emissions for New Zealand, e-Prime - Advances in Electrical Engineering, Electronics and Energy, 2024, 100564, ISSN 2772-6711, DOI: 10.1016/j.prime.2024.100564. Article (Open Access)

1. Hill, D., Tito, S.R., Walmsley, M., Hedengren, J.D., Techno-ecnonmic optimization of a hybrid energy system with limited grid connection in pursuit of net zero carbon emissions for New Zealand, e-Prime - Advances in Electrical Engineering, Electronics and Energy, 2024, 100564, ISSN 2772-6711, DOI: 10.1016/j.prime.2024.100564. Article (Open-Access)

Dr. John Hedengren is a Professor at Brigham Young University in the Chemical Engineering Department. He leads the BYU Process Research and Intelligent Systems Modeling (PRISM) group with a focus on physics-informed machine learning for optimization of energy systems, unmanned aircraft, and drilling. Prior to BYU he worked in industry for 7 years on nonlinear estimation and predictive control for polymers. His work includes the APMonitor Optimization Suite with a recent extension to Python GEKKO. He led the development of the Temperature Control Lab that is used by many universities for process control and data science education. His publications span topics of data science, machine learning, smart grid optimization, unmanned aerial systems, and predictive control.

Dr. John Hedengren is a Professor at Brigham Young University in the Chemical Engineering Department. He leads the BYU Process Research and Intelligent Systems Modeling (PRISM) group with a current focus on physics-informed machine learning for optimization of energy systems, unmanned aircraft, and drilling. Prior to BYU he worked in industry for 7 years on nonlinear estimation and predictive control for polymers. His work includes the APMonitor Optimization Suite with a recent extension to Python GEKKO. He led the development of the Temperature Control Lab that is used by many universities for process control and data science education. His publications span topics of data science, machine learning, smart grid optimization, unmanned aerial systems, and predictive control.

Dr. John Hedengren is a Professor at Brigham Young University in the Chemical Engineering Department. He leads the BYU Process Research and Intelligent Systems Modeling (PRISM) group with a current focus on physics-informed machine learning for optimization of energy systems, unmanned aircraft, and drilling. Prior to BYU he worked in industry for 7 years on nonlinear estimation and predictive control for polymers. His work includes the APMonitor Optimization Suite with a recent extension to Python GEKKO. He led the development of the Arduino-based Temperature Control Lab that is used by many universities for process control and data science education. His publications span topics of data science, machine learning, smart grid optimization, unmanned aerial systems, and predictive control.

Dr. John Hedengren is a Professor at Brigham Young University in the Chemical Engineering Department. He leads the BYU Process Research and Intelligent Systems Modeling (PRISM) group with a current focus on physics-informed machine learning for optimization of energy systems, unmanned aircraft, and drilling. Prior to BYU he worked in industry for 7 years on nonlinear estimation and predictive control for polymers. His work includes the APMonitor Optimization Suite with a recent extension to the Python GEKKO language. He led the development of the Arduino-based Temperature Control Lab that is currently used by many universities for process control education. His publications span topics of data science, machine learning, smart grid optimization, unmanned aerial systems, and predictive control.

Prof. Hedengren has consulting experience with Meta, Apache, Aramco, ENI Petroleum, HESS, TOTAL, and other companies on machine learning and automation solutions. He worked full-time for 5 years with ExxonMobil supporting advanced control and optimization solutions. His industrial control experience with PLC and DCS systems includes OPTO22, Honeywell TPS/TDC3000, Experion system, OPC, and Modbus. His area of expertise is in process dynamics, control, and optimization with applications in fiber optic monitoring, automation of oil and gas processes, unmanned aerial systems, systems biology, and grid-scale energy systems. In chemicals manufacturing, he has extensive experience in automation and modeling of the production of polymers such as polyethylene, polypropylene, butyl rubber, and polystyrene as well as specialty chemicals (polyalphaolefins). Automation software that he developed has been applied in over 100 industrial applications world-wide in refineries, chemical plants, and offshore oil platforms.

His professional service includes IEEE CSS Chair of Control Education, Trustee of the CACHE Corporation, adjunct professor at the University of Utah, member of the AIChE CAST Committee, and Communications Chair for the American Automatic Control Council (A2C2). In 2005, he received a Ph.D. (Ch.E.) from the University of Texas at Austin for contributions to control and estimation of large-scale dynamic systems. He served as a Society of Petroleum Engineers (SPE) Distinguished Lecturer for 2018-2019, visiting 22 local sections to deliver a presentation on "Drilling Automation and Downhole Monitoring with Physics-based Models". He completed a sabbatical in 2020 to collaboratively develop combined physics-based and machine learned methods for optimization and automation.

1. Chen, Y., Hill, D., Billings, B., Hedengren, J.D., Powell, K.M., Hydrogen Underground Storage for Grid Resilience: A Dynamic Simulation and Optimization Study, American Control Conference, Toronto, Canada, July 2024.
2. Gunnell, L., Perez, K.X., Castillo, I., Hoogerwerf, A.S., Peng, Y., Hedengren, J.D., Detection of Valve Stiction in Industrial Control Loops through Continuous Wavelet Transformation with a CNN, American Control Conference, Toronto, Canada, July 2024.

1. Wallace, J., Hill, D., Thurston, D., Hedengren, J.D., Memmott, M., Model Predictive Control of a Grid-Scale Thermal Energy Storage System in Relap5-3d, Nuclear Engineering and Design, 2024. Article

1. Wallace, J., Hill, D., Thurston, D., Hedengren, J.D., Memmott, M., Model Predictive Control of a Grid-Scale Thermal Energy Storage System in Relap5-3d, Nuclear Engineering and Design, 2024.

2024

1. Gunnell, L., Nicholson, B., Hedengren, J.D., Equation-based and data-driven modeling: Open-source software current state and future directions, Computers & Chemical Engineering, 2024, 108521, ISSN 0098-1354, DOI: 10.1016/j.compchemeng.2023.108521. Preprint | Article

1. Hedengren, J.D., Park, J., Perez, K., Generative AI for Process Systems Engineering: Potential Applications and Pitfalls, Emerging Technologies in Data Analytics, 2024 AIChE Spring Meeting, New Orleans, LA. Presentation

1. Hedengren, J.D., Fry, A., Perez, K., Hacioglu, A., Wang, J., Loftin, J., Machine Learning for Engineering course in MATLAB and Python, Education Division, 2023 AIChE Annual Meeting, Orlando, FL.

1. Hedengren, J.D., Fry, A., Perez, K., Hacioglu, A., Wang, J., Loftin, J., Machine Learning for Engineering course in MATLAB and Python,

Education Division, 2023 AIChE Annual Meeting, Orlando, FL.

1. Hedengren, J.D., Unlock Data to Optimize Industrial Processes, Energy Geoscience Institute (EGI), Invited Talk, University of Utah, Sept 2023.
2. Hedengren, J.D., Data-Driven Engineering Education with Hands-On Learning, Plenary Talk, Past and Future of Process/Product Analytics & Machine Learning, including Education and Workforce Development, FOPAM 2023, Foundations of Process/Product Analytics and Machine learning, University of California, Davis. Presentation

1. Hedengren, J.D., Learn Data-Driven Engineering with Interactive Modules, Industry 4.0 Topical Session, Analytics & AI, 2023 AIChE Spring Meeting, Houston, TX. Presentation
2. Babaei, M.R., Park, J., Venkat, A., Hedengren, J.D., Framework for Hybrid Machine Learning with Open-Source Python Seeq Sysid Package, 2023 AIChE Spring Meeting, Houston, TX. Presentation
3. Babaei, M.R., Mtetwa, F., Stone, R., Knotts, T.A., Hedengren, J.D., Physics-Informed Deep Learning for Prediction of Thermophysical Properties: Normal Boiling Point, 2023 AIChE Spring Meeting, Houston, TX. Presentation
4. VanKeersblick, L., Clark, M., Hunter, I., Hedengren, J.D., Joint Angle Calculations Using Motion Capture and Deep Learning Pose Estimation for Safety Applications, Poster Session: Industry 4.0/Analytics & AI, 2023 AIChE Spring Meeting, Houston, TX. Presentation
5. Hedengren, J.D., Physics-Informed Deep Learning for Optimization and Control, Automated Systems & Soft Computing Lab (ASSCL), College of Computer and Information Sciences (CCIS), Prince Sultan University, Feb 2023.

2024

1. Gunnell, L., Perez, K., Hedengren, J.D., Generative AI for Process Systems Engineering: Potential Applications and Pitfalls, Emerging Technologies in Data Analytics, 2024 AIChE Spring Meeting, New Orleans, LA. Presentation

1. Gunnell, L., Nicholson, B., Hedengren, J.D., Equation-based and data-driven modeling: Open-source software current state and future directions, Computers & Chemical Engineering, 2023, 108521, ISSN 0098-1354, DOI: 10.1016/j.compchemeng.2023.108521. Preprint |Article

1. Gunnell, L., Nicholson, B., Hedengren, J.D., Equation-based and data-driven modeling: Open-source software current state and future directions, Computers & Chemical Engineering, 2023, 108521, ISSN 0098-1354, DOI: 10.1016/j.compchemeng.2023.108521. Article

1. Gunnell, L., Nicholson, B., Hedengren, J.D., Equation-based and data-driven modeling: Open-source software current state and future directions, Computers & Chemical Engineering, 2023, 108521, ISSN 0098-1354,

1. Gunnell, L., Nicholson, B., Hedengren, J.D., Equation-based and data-driven modeling: Open-source software current state and future directions,

Computers & Chemical Engineering, 2023, 108521, ISSN 0098-1354, DOI: 10.1016/j.compchemeng.2023.108521. Article

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• Nonlinear Model Library: One of the major bottlenecks to improve nonlinear Model Predictive Control (Nonlinear MPC) and Real-time Optimization (RTO) is the lack of reliable first-principles or hybrid models. This nonlinear model library is a collaborative platform where chemical process models can be documented and shared.

1. Park, J., Babaei, M.R., Arce Munoz, S., Venkat, A.N., Hedengren, J.D., Simultaneous Multistep Transformer Architecture for Model Predictive Control, Computers & Chemical Engineering, Volume 178, October 2023, 108396, DOI: 10.1016/j.compchemeng.2023.108396 Preprint | Article

1. Park, J., Babaei, M.R., Arce Munoz, S., Venkat, A.N., Hedengren, J.D., Simultaneous Multistep Transformer Architecture for Model Predictive Control, Computers & Chemical Engineering, Volume 178, October 2023, 108396. Preprint | Article

1. Park, J., Babaei, M.R., Arce Munoz, S., Venkat, A.N., Hedengren, J.D., Simultaneous Multistep Transformer Architecture for Model Predictive Control, Computers & Chemical Engineering, 2023, accepted for publication. Preprint | Article

1. Park, J., Babaei, M.R., Arce Munoz, S., Venkat, A.N., Hedengren, J.D., Simultaneous Multistep Transformer Architecture for Model Predictive Control, Computers & Chemical Engineering, 2023, accepted for publication. Preprint Article

1. Park, J., Babaei, M.R., Arce Munoz, S., Venkat, A.N., Hedengren, J.D., Simultaneous Multistep Transformer Architecture for Model Predictive Control, Computers & Chemical Engineering, 2023, accepted for publication. Preprint

1. Park, J., Babaei, M.R., Arce Munoz, S., Venkat, A.N., Hedengren, J.D., Simultaneous Multistep Transformer Architecture for Model Predictive Control, Computers & Chemical Engineering, 2023, accepted for publication.

1. Hedengren, J. D., A Nonlinear Model Library for Dynamics and Control, Computer Aids for Chemical Engineering (CACHE) News, Summer 2008. Link

1. Babaei, M.R., Stone, R., Knotts, T.A., Hedengren, J.D., Physics-Informed Neural Networks with Group Contribution Methods, Journal of Chemical Theory and Computation, American Chemical Society, 2023, DOI: 10.1021/acs.jctc.3c00195. Article

1. Hill, D., McCrea, D., Ho, A., Memmott, M., Powell, K., Hedengren, J., A Multi-Scale method for combined design and dispatch optimization of nuclear hybrid energy systems including storage, e-Prime - Advances in Electrical Engineering, Electronics and Energy, Volume 5, 2023, 100201, ISSN 2772-6711, DOI: 10.1016/j.prime.2023.100201 Article (Open Access)

1. Babaei, M.R., Stone, R., Knotts, T.A., Hedengren, J.D., Journal of Chemical Theory and Computation, American Chemical Society, 2023, DOI: 10.1021/acs.jctc.3c00195. Article

1. Babaei, M.R., Stone, R., Knotts, T.A., Hedengren, J.D.,

Journal of Chemical Theory and Computation, American Chemical Society, 2023, DOI: 10.1021/acs.jctc.3c00195. Article

1. Rossiter, J.A., Cassandras, C.G., Hespanha, J., Dormido, S., Torre, L., Ranade, G., Visioli, A., Hedengren, J.D., Murray, R.M., Antsaklis, P., Lamnabhi-Lagarrigue, F., Parisini, T., Control education for societal-scale challenges: A community roadmap, Annual Reviews in Control, 2023, ISSN 1367-5788, DOI: 10.1016/j.arcontrol.2023.03.007 Article (Open Access)

1. Rossiter, J.A., Cassandras, C.G., Hespanha, J., Dormido, S., Torre, L., Ranade, G., Visioli, A., Hedengren, J.D., Murray, R.M., Antsaklis, P., Lamnabhi-Lagarrigue, F., Parisini, T., Control education for societal-scale challenges: A community roadmap, Annual Reviews in Control,

2023, ISSN 1367-5788, DOI: 10.1016/j.arcontrol.2023.03.007 Article (Open Access)

1. Ho, A., Memmott, M., Hedengren, J.D., Powell, K.M., Exploring the benefits of molten salt reactors: An analysis of flexibility and safety features using dynamic simulation, Digital Chemical Engineering, Volume 7, June 2023, 100091, ISSN 2772-5081, DOI: 10.1016/j.dche.2023.100091 Article

1. Yao, J., Gao, T., Hedengren, J.D., Powell, K.M., A Two-Level Optimization Framework for Battery Energy Storge Systems to Enhance Economics and Minimize Long-Term Capacity Fading, Journal of Energy Storage, Volume 63, July 2023, 106943, 2023, DOI: 10.1016/j.est.2023.106943 Article

Include pending articles here - will not show.

1. Ho, A., Memmott, M., Hedengren, J.D., Powell, K.M., Exploring the benefits of molten salt reactors: An analysis of flexibility and safety features using dynamic simulation, Digital Chemical Engineering, 2023, 100091, ISSN 2772-5081, DOI: 10.1016/j.dche.2023.100091 Article

1. Ho, A., Memmott, M., Hedengren, J.D., Powell, K.M., Exploring the benefits of molten salt reactors: An analysis of flexibility and safety features using dynamic simulation,

1. Ho, A., Memmott, M., Hedengren, J.D., Powell, K.M.,

Exploring the benefits of molten salt reactors: An analysis of flexibility and safety features using dynamic simulation, Digital Chemical Engineering, 2023, 100091, ISSN 2772-5081, DOI: 10.1016/j.dche.2023.100091 Article

1. Hedengren, J.D., Brower, D.V., Kidder, K., Hillman, Z., Data-Driven TLP Tendon Loads from Internal Hull Fiber-Optic Sensors, ASME 42nd International Conference on Ocean, Offshore and Arctic Engineering, OMAE2023/103309, Melbourne, Australia, June 2023. Preprint

1. Yao, J., Gao, T., Hedengren, J.D., Powell, K.M., A Two-Level Optimization Framework for Battery Energy Storge Systems to Enhance Economics and Minimize Long-Term Capacity Fading, Journal of Energy Storage, 2023.

 Current CV

1. Hedengren, J.D., Nicholson, B., Open-Source Modeling Platforms, Keynote Talk at FOCAPO / CPC 2023, San Antonio, TX, 8-12 January 2023. Preprint | Presentation

1. Hedengren, J.D., Nicholson, B., Open-Source Modeling Platforms, Keynote Talk at FOCAPO / CPC 2023, San Antonio, TX, 8-12 January 2023. Preprint | Presentation?

1. Hedengren, J.D., Nicholson, B., Open-Source Modeling Platforms, Kenote address at FOCAPO / CPC 2023, San Antonio, TX, Jan 2023. Preprint | Presentation?

2023

1. Hedengren, J.D., Nicholson, B., Open-Source Modeling Platforms, Kenote address at FOCAPO / CPC 2023, San Antonio, TX, Jan 2023. Preprint Δ | Presentation?

1. Munoz, S.A., Park, J., Stewart, C.M., Martin, A.M., Hedengren, J.D., Deep Transfer Learning for Approximate Model Predictive Control, Processes 2023, 11, 197. Article (Open Access)

1. Hedengren, J.D., Brower, D.V., Kidder, K., Hillman, Z., Data-Driven TLP Tendon Loads from Internal Hull Fiber-Optic Sensors, ASME 42nd International Conference on Ocean, Offshore and Arctic Engineering, OMAE2023/103309, Melbourne, Australia, June 2023. Preprint

1. Hedengren, J.D., Brower, D.V., Kidder, K., Hillman, Z., Data-Driven TLP Tendon Loads from Internal Hull Fiber-Optic Sensors, ASME 42nd International Conference on Ocean, Offshore and Arctic Engineering, OMAE2023/103309, Melbourne, Australia, June 2023. Preprint

2023

1. Hedengren, J.D., Brower, D.V., Kidder, K., Hillman, Z., Data-Driven TLP Tendon Loads from Internal Hull Fiber-Optic Sensors, ASME 42nd International Conference on Ocean, Offshore and Arctic Engineering, OMAE2023/103309, Melbourne, Australia, June 2023.

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 John D. Hedengren

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1. Wallace, J., Hill, D., Memmott, M., Hedengren, J., Modeling and Optimizing Molten Salt Thermal Storage for Nuclear Power, Transactions of the 2020 Winter ANS (American Nuclear Society) Meeting, November, 2020, Vol. 123, No. 1, pp. 144-145. Preprint | Article

1. Wallace, J., Hill, D., Memmott, M., Hedengren, J., Modeling and Optimizing Molten Salt Thermal Storage for Nuclear Power, Transactions of the 2020 Winter ANS (American Nuclear Society) Meeting, November, 2020, Vol. 123, No. 1, pp. 144-145. Article

1. Blackburn, L.D., Tuttle, J.F., Andersson, K., Hedengren, J.D., Powell, K.M., Dynamic Machine Learning-based Optimization Algorithm to Improve Boiler Efficiency, Journal of Process Control, Volume 120, December 2022, Pages 129-149, 2022, DOI: 10.1016/j.jprocont.2022.11.002 Article
2. Hill, D., Martin, A., Martin-Nelson, N., Granger, C., Memmott, M., Powell, K., Hedengren, J.D., Techno-economic sensitivity analysis for combined design and operation of a small modular reactor hybrid energy system, International Journal of Thermofluids, Volume 16, 100191, November 2022, DOI: 10.1016/j.ijft.2022.100191 Article (Open Access)
3. Ho, A., Hill, D., Hedengren J.D., Powell, K.M., A nuclear-hydrogen hybrid energy system with large-scale storage: A study in optimal dispatch and economic performance in a real-world market, Journal of Energy Storage, Volume 51, 104510, July 2022, DOI: 10.1016/j.est.2022.104510 Article

1. Gunnell, L., Manwaring, K., Lu, X., Reynolds, J., Vienna, J., Hedengren, J.D., Machine Learning with Gradient-based Optimization of Nuclear Waste Vitrification with Uncertainties and Constraints, Processes, 10(11), 2365, Nov 2022, DOI: 10.3390/pr10112365. Article (Open Access) | Documentation

1. Gunnell, L., Manwaring, K., Lu, X., Reynolds, J., Vienna, J., Hedengren, J.D., Machine Learning with Gradient-based Optimization of Nuclear Waste Vitrification with Uncertainties and Constraints, Processes, 10(11), 2365, Nov 2022, DOI: 10.3390/pr10112365. Article (Open Access)

1. Gunnell, L., Manwaring, K., Lu, X., Reynolds, J., Vienna, J., Hedengren, J.D., Machine Learning with Gradient-based Optimization of Nuclear Waste Vitrification with Uncertainties and Constraints, Processes, MDPI, 2022, accepted for publication.

 Professor

1. Gunnell, L., Manwaring, K., Lu, X., Reynolds, J., Vienna, J., Hedengren, J.D., Machine Learning with Gradient-based Optimization of Nuclear Waste

Vitrification with Uncertainties and Constraints, Processes, MDPI, 2022, accepted for publication.

1. Blackburn, L.D., Tuttle, J.F., Andersson, K., Hedengren, J.D., Powell, K.M., Dynamic Machine Learning-based Optimization Algorithm to Improve Boiler Efficiency, Journal of Process Control, 2022, accepted for publication.

Blackburn, L.D., Tuttle, J.F., Andersson, K., Hedengren, J.D., Powell, K.M., Dynamic Machine Learning-based Optimization Algorithm to Improve Boiler Efficiency, Journal of Process Control, 2022, accepted for publication.

1. Knotts, T., Hedengren, J.D., Babaei, M.R., Physics-Informed Deep Learning for Prediction of Thermophysical Properties: The Parachor Method for Surface Tension, AIChE Annual Meeting, Phoenix, AZ, Nov 13-18, 2022.
2. Yao, J., Gao, T., Hedengren, J.D., Powell, K., Two-Level Optimization Framework with Consideration of Economic Benefits and Long-Term Capacity Fading for Battery Energy Storage Systems, AIChE Annual Meeting, Phoenix, AZ, Nov 13-18, 2022.
3. Ho, A., Mohammadi, K., Memmott, M., Hedengren, J.D., Powell, K., Dynamic Modeling and Simulation of a Novel Nuclear-Hydrogen Hybrid Energy System with Large-Scale Storage in an Underground Salt Cavern, AIChE Annual Meeting, Phoenix, AZ, Nov 13-18, 2022.
4. Ho, A., Hill, D., Hedengren, J.D., Powell, K., An Optimal Dispatch and Economic Performance Study of a Nuclear-Hydrogen Hybrid Energy System with Large-Scale Storage in Underground Salt Cavern, AIChE Annual Meeting, Phoenix, AZ, Nov 13-18, 2022.
5. Ho, A., Mohammadi, K., Memmott, M., Hedengren, J.D., Powell, K., Dynamic Modeling and Simulation of a Novel Nuclear-Hydrogen Hybrid Energy System with Large-Scale Storage in an Underground Salt Cavern, AIChE Annual Meeting, Phoenix, AZ, Nov 13-18, 2022.

1. Hill, D., Martin, A., Martin-Nelson, N., Granger, C., Memmott, M., Powell, K., Hedengren, J.D., Techno-economic sensitivity analysis for combined design and operation of a small modular reactor hybrid energy system, International Journal of Thermofluids, Volume 16, 100191, November 2022. Article (Open Access)

1. Hill, D., Martin, A., Martin-Nelson, N., Granger, C., Memmott, M., Powell, K., Hedengren, J.D., Techno-economic sensitivity analysis for combined design and operation of a small modular reactor hybrid energy system, International Journal of Thermofluids, Volume 16, 100191, November 2022. Article (Open Access

1. Hill, D., Martin, A., Martin-Nelson, N., Granger, C., Memmott, M., Powell, K., Hedengren, J.D., Techno-economic sensitivity analysis for combined design and operation of a small modular reactor hybrid energy system, International Journal of Thermofluids, 2022. Article

1. Hill, D., Martin, A., Martin-Nelson, N., Granger, C., Memmott, M., Powell, K., Hedengren, J.D., Techno-economic sensitivity analysis for combined design and operation of a small modular reactor hybrid energy system, International Journal of Thermofluids, accepted for publication, 2022.

1. Serbezov, A., Zakova, K., Visioli, A., Rossiter, J.A., Douglas, B., Hedengren, J.D., Open access resources to support the first course in feedback, dynamics and control, Advanced in Control Education (ACE2022), Hamburg, Germany, 24-27 July 2022. Preprint

Dr. John Hedengren is a Professor at Brigham Young University in the Chemical Engineering Department. He leads the BYU Process Research and Intelligent Systems Modeling (PRISM) group with a current focus on structured machine learning for optimization of energy systems, unmanned aircraft, and drilling. Prior to BYU he worked in industry for 7 years on nonlinear estimation and predictive control for polymers. His work includes the APMonitor Optimization Suite with a recent extension to the Python GEKKO language. He led the development of the Arduino-based Temperature Control Lab that is currently used by 70 universities for process control education. His 85 publications span topics of data science, machine learning, smart grid optimization, unmanned aerial systems, and predictive control.

 Current CV

Professor, Brigham Young University

Dr. John Hedengren is a Professor at Brigham Young University in the Chemical Engineering Department. He leads the BYU Process Research and Intelligent Systems Modeling (PRISM) group with a current focus on structured machine learning for optimization of energy systems, unmanned aircraft, and drilling. Prior to BYU he worked in industry for 7 years on nonlinear estimation and predictive control for polymers. His work includes the APMonitor Optimization Suite with a recent extension to the Python GEKKO language. He led the development of the Arduino-based Temperature Control Lab that is currently used by 70 universities for process control education. His 60 publications span topics of data science, machine learning, smart grid optimization, unmanned aerial systems, and predictive control.

Dr. John Hedengren is an Associate Professor at Brigham Young University in the Chemical Engineering Department. He leads the BYU Process Research and Intelligent Systems Modeling (PRISM) group with a current focus on structured machine learning for optimization of energy systems, unmanned aircraft, and drilling. Prior to BYU he worked in industry for 7 years on nonlinear estimation and predictive control for polymers. His work includes the APMonitor Optimization Suite with a recent extension to the Python GEKKO language. He led the development of the Arduino-based Temperature Control Lab that is currently used by 70 universities for process control education. His 60 publications span topics of data science, machine learning, smart grid optimization, unmanned aerial systems, and predictive control.

His professional service includes IEEE CSS Chair of Control Education, adjunct professor at the University of Utah, member of the AIChE CAST Committee, and Communications Chair for the American Automatic Control Council (A2C2). In 2005, he received a Ph.D. (Ch.E.) from the University of Texas at Austin for contributions to control and estimation of large-scale dynamic systems. He served as a Society of Petroleum Engineers (SPE) Distinguished Lecturer for 2018-2019, visiting 22 local sections to deliver a presentation on "Drilling Automation and Downhole Monitoring with Physics-based Models". He completed a sabbatical in 2020 to collaboratively develop combined physics-based and machine learned methods for optimization and automation.

1. Rossiter, J.A., Visioli, A., Serbezov, A., Hedengren, J.D., Douglas, B., Zakova, K., Open access resources to support learning of control engineering, 2022 European Control Conference (ECC), London, UK, July, 11-14, 2022. Article

2022

1. Ho, A., Hill, D., Hedengren J.D., Powell, K.M., A nuclear-hydrogen hybrid energy system with large-scale storage: A study in optimal dispatch and economic performance in a real-world market, Journal of Energy Storage, March 2022.

1. Granger, C., Martin, A., Hill, D., Wallace, J., Memmott, M., Hedengren, J.D., Optimal Dispatch with MPC Using Lab Scale Arduino Hardware, Process Modeling and Simulation IV, 2022 AIChE Spring Meeting, San Antonio, TX, April 2022.

 ORCID 0000-0002-5535-5277

 Current CV

 Google Developer

1. Berrett, B.E., Vernon, C.A., Beckstrand, H., Pollei, M., Markert, K., Hedengren, J.D., Franke, K.W., Large-Scale Reality Modeling of a University Campus Using Combined UAV and Terrestrial Photogrammetry for Historical Preservation and Practice Use, Drones, Special Issue on Geoinformatics for the Preservation and Valorization of Cultural Heritage, Nov 17, 2021, 5(4), 136. DOI: 10.3390/drones5040136. Article

1. Berrett, B.E., Vernon, C.A., Beckstrand, H., Pollei, M., Markert, K., Hedengren, J.D., Franke, K.W., Large-Scale Reality Modeling of a University Campus Using Combined UAV and Terrestrial Photogrammetry for Historical Preservation and Practice Use, submitted to MDPI Drones, Nov 17, 2021, 5(4), 136. DOI: 10.3390/drones5040136. Article

1. Berrett, B.E., Vernon, C.A., Beckstrand, H., Pollei, M., Markert, K., Hedengren, J.D., Franke, K.W., Large-Scale Reality Modeling of a University Campus Using Combined UAV and Terrestrial Photogrammetry for Historical Preservation and Practice Use, submitted to MDPI Drones, Oct 1, 2021, under review.

2022

1. Rossiter, J.A., Visioli, A., Serbezov, A., Hedengren, J.D., Douglas, B., Zakova, K., Open access resources to support learning of control engineering, 2022 European Control Conference (ECC), London, UK, July, 11-14, 2022.

1. Ho, A., Mohammadi, K., Hedengren J.D., Memmott, M., Powell, K.M., Dynamic simulation of a novel nuclear hybrid energy system with large-scale hydrogen storage in an underground salt cavern, International Journal of Hydrogen Energy, 29 July 2021. Article

1. Franke, K.W., Rollins, K.M., Ledezma, C., Hedengren, J.D., Wolfe, D. Ruggles, S., Bender, C., Reimschiissel, B., Reconnaissance of Two Liquefaction Sites using Small Unmanned Aerial Vehicles and Structure from Motion Computer Vision Following the April 1, 2014 Chile Earthquake, Journal of Geotechnical and Geoenvironmental Engineering, Volume 143, Issue 5, May 2017, doi:10.1061/(ASCE)GT.1943-5606.0001647. Article

 Current CV

1. Gates, N.S., Hill, D.C., Billings, B.W., Powell, K.M., Hedengren, J.D., Benchmarks for Grid Energy Management with Python Gekko, 60th Conference on Decision and Control (CDC), Austin, TX, USA, December 13-15, 2021. Preprint | Benchmarks

1. Gates, N.S., Hill, D.C., Billings, B.W., Powell, K.M., Hedengren, J.D., Benchmarks for Grid Energy Management with Python Gekko, 60th Conference on Decision and Control (CDC), Austin, TX, USA, December 13-15, 2020. Preprint | Benchmarks

2021

Gates, N.S., Hill, D.C., Billings, B.W., Powell, K.M., Hedengren, J.D., Benchmarks for Grid Energy Management with Python Gekko, 60th Conference on Decision and Control (CDC), Austin, TX, USA, December 13-15, 2020. Preprint | Benchmarks

1. Ho, A., Mohammadi, K., Hedengren J.D., Memmott, M., Powell, K.M., Dynamic simulation of a novel nuclear hybrid energy system with large-scale hydrogen storage in an underground salt cavern, International Journal of Hydrogen Energy, 29 July 2021. Article

1. Ho, A., Mohammadi, K., Hedengren J.D., Memmott, M., Powell, K.M., Dynamic simulation of a novel nuclear hybrid energy system with large-scale hydrogen storage in an underground salt cavern, International Journal of Hydrogen Energy, accepted for publication.

2021

1. Ho, A., Mohammadi, K., Hedengren J.D., Memmott, M., Powell, K.M., Dynamic simulation of a novel nuclear hybrid energy system with large-scale hydrogen storage in an underground salt cavern,

International Journal of Hydrogen Energy, accepted for publication.

1. Moura Oliveira, P.B., Hedengren, J.D., Pires, E.J.S., Swarm-Based design of Proportional Integral and Derivative Controllers using a Compromise Cost Function: An Arduino Temperature Laboratory Case Study, Special Issue: Algorithms for PID Controller, Algorithms, 13(12), 315, DOI: 10.3390/a13120315, 2020. Article

1. Franke, K.W., Rollins, K.M. Ledezma, C., Hedengren, J.D., Wolfe, D. Ruggles, S., Bender, C., Reimschiissel, B., Reconnaissance of Two Liquefaction Sites using Small Unmanned Aerial Vehicles and Structure from Motion Computer Vision Following the April 1, 2014 Chile Earthquake, Journal of Geotechnical and Geoenvironmental Engineering, Volume 143, Issue 5, May 2017, doi:10.1061/(ASCE)GT.1943-5606.0001647. Article

 NSF Biographical Sketch

 Current CV

Dr. John Hedengren is an Associate Professor at Brigham Young University in the Chemical Engineering Department. He leads the BYU Process Research and Intelligent Systems Modeling (PRISM) group with a current focus on structured machine learning for optimization of energy systems, unmanned aircraft, and drilling. Prior to BYU he worked in industry for 7 years on nonlinear estimation and predictive control for polymers. His work includes the APMonitor Optimization Suite with a recent extension to the Python GEKKO language. He led the development of the Arduino-based Temperature Control Lab that is currently used by 70 universities for process control education. His 60 publications span topics of oil production, drilling automation, smart grid optimization, unmanned aerial systems, and nonlinear predictive control.

1. Moura Oliveira, P., Hedengren, J., Rossiter, J.A., Introducing Digital Controllers to Undergraduate Students Using the TCLab Arduino Kit, 21st IFAC World Congress, Berlin, Germany, Volume 53, Issue 2, July 12-17, 2020, pp. 17524-17529. Article

His professional service includes an appointment as an adjunct professor at the University of Utah, and member of the AIChE CAST Executive Committee (Webinar Editor). In 2005, he received a Ph.D. (Ch.E.) from the University of Texas at Austin for contributions to control and estimation of large-scale dynamic systems. He served as a Society of Petroleum Engineers (SPE) Distinguished Lecturer for 2018-2019, visiting 22 local sections to deliver a presentation on "Drilling Automation and Downhole Monitoring with Physics-based Models". He completed a sabbatical in 2020 to collaboratively develop combined physics-based and machine learned methods for optimization and automation.

His work includes the APMonitor Optimization Suite with a recent extension to the Python GEKKO language. He led the development of the Arduino-based Temperature Control Lab that is currently used by 70 universities for process control education. His 60 publications span topics of oil production, drilling automation, smart grid optimization, unmanned aerial systems, and nonlinear predictive control. His professional service includes an appointment as an adjunct professor at the University of Utah, and member of the AIChE CAST Executive Committee (Webinar Editor). In 2005, he received a Ph.D. (Ch.E.) from the University of Texas at Austin for contributions to control and estimation of large-scale dynamic systems. He served as a Society of Petroleum Engineers (SPE) Distinguished Lecturer for 2018-2019, visiting 22 local sections to deliver a presentation on "Drilling Automation and Downhole Monitoring with Physics-based Models". He completed a sabbatical in 2020 to collaboratively develop combined physics-based and machine learned methods for optimization and automation.

Dr. John Hedengren is an Associate Professor at Brigham Young University in the Chemical Engineering Department. He leads the BYU Process Research and Intelligent Systems Modeling (PRISM) group with a current focus on structured machine learning for optimization of energy systems, unmanned aircraft, and drilling. Prior to BYU he worked in industry for 7 years on nonlinear estimation and predictive control for polymers. His work includes the APMonitor Optimization Suite with a recent extension to the Python GEKKO language. He led the development of the Arduino-based Temperature Control Lab that is currently used by 70 universities for process control education. His 60 publications span topics of oil production, drilling automation, smart grid optimization, unmanned aerial systems, and nonlinear predictive control. His professional service includes an appointment as an adjunct professor at the University of Utah, and member of the AIChE CAST Executive Committee (Webinar Editor). In 2005, he received a Ph.D. (Ch.E.) from the University of Texas at Austin for contributions to control and estimation of large-scale dynamic systems. He served as a Society of Petroleum Engineers (SPE) Distinguished Lecturer for 2018-2019, visiting 22 local sections to deliver a presentation on "Drilling Automation and Downhole Monitoring with Physics-based Models". He completed a sabbatical in 2020 to collaboratively develop combined physics-based and machine learned methods for optimization and automation.

1. Wallace, J., Hill, D., Memmott, M., Hedengren, J., Modeling and Optimizing Molten Salt Thermal Storage for Nuclear Power, Transactions of the 2020 Winter ANS (American Nuclear Society) Meeting, November, 2020, Vol. 123, No. 1, pp. 144-145. Article

Prof. Hedengren has consulting experience with Apache, ENI Petroleum, HESS, SABIC Ibn Zahr, TOTAL, and other companies on automation solutions. He worked full-time for 5 years with ExxonMobil supporting advanced control and optimization solutions. His industrial control experience with PLC and DCS systems includes OPTO22, Honeywell TPS/TDC3000, Experion system, OPC, and Modbus. His area of expertise is in process dynamics, control, and optimization with applications in fiber optic monitoring, automation of oil and gas processes, unmanned aerial systems, systems biology, and grid-scale energy systems. In chemicals manufacturing, he has extensive experience in automation and modeling of the production of polymers such as polyethylene, polypropylene, butyl rubber, and polystyrene as well as specialty chemicals (polyalphaolefins). Automation software that he developed has been applied in over 100 industrial applications world-wide in refineries, chemical plants, and offshore oil platforms.

 Current CV

1. Hedengren, J.D., Teaching Dynamics and Control with Arduino-based TCLab, Invited Presentation at MathWorks Special Session, 59th Conference on Decision and Control, Jeju Island, Republic of Korea, December 14-18, 2020. Presentation

1. Moura Oliveira, P.B.; Hedengren, J.D.; Pires, Swarm-Based design of Proportional Integral and Derivative Controllers using a Compromise Cost Function: An Arduino Temperature Laboratory Case Study, Special Issue: Algorithms for PID Controller, Algorithms, 13(12), 315, DOI: 10.3390/a13120315, 2020. Article

1. # de Moura Oliveira, P.B.; Hedengren, J.D.; Pires, Swarm-Based design of Proportional Integral and Derivative Controllers using a Compromise Cost Function: An Arduino Temperature Laboratory Case Study, Special Issue: Algorithms for PID Controller, Algorithms, 13(12), 315, DOI: 10.3390/a13120315, 2020. Article

1. Moura Oliveira, P., Hedengren, J.D., Solteiro Pires, E.J., Swarm-Based design of Proportional Integral and Derivative Controllers using a Compromise Cost Function: An Arduino Temperature Laboratory Case Study, Special Issue: Algorithms for PID Controller, Algorithms, 2020.

1. Moura Oliveira, P., Hedengren, J.D., Solteiro Pires, E.J., Swarm-Based design of PID Controllers using an Aggregated Cost Function: A TCLab Arduino Case Study, Special Issue: Algorithms for PID Controller, Algorithms, 2020.

Dr. John Hedengren is an Associate Professor at Brigham Young University in the Chemical Engineering Department. He leads the BYU Process Research and Intelligent Systems Modeling (PRISM) group with a current focus on structured machine learning for optimization of energy systems, unmanned aircraft, and drilling. Prior to BYU he worked in industry for 7 years on nonlinear estimation and predictive control for polymers. His work includes the APMonitor Optimization Suite with a recent extension to the Python GEKKO language. He led the development of the Arduino-based Temperature Control Lab that is currently used by 70 universities for process control education. His 60 publications span topics of oil production, drilling automation, smart grid optimization, unmanned aerial systems, and nonlinear predictive control. His professional service includes an appointment as an adjunct professor at the University of Utah, Section EiC for the Processes Journal, and member of the AIChE CAST Executive Committee (Webinar Editor). In 2005, he received a Ph.D. (Ch.E.) from the University of Texas at Austin for contributions to control and estimation of large-scale dynamic systems. He served as a Society of Petroleum Engineers (SPE) Distinguished Lecturer for 2018-2019, visiting 22 local sections to deliver a presentation on "Drilling Automation and Downhole Monitoring with Physics-based Models". He completed a sabbatical in 2020 to collaboratively develop combined physics-based and machine learned methods for optimization and automation.

1. Park, J., Price, C., Pixton, D., Aghito, M., Nybø, R., Bjørkevoll, K., Hedengren, J.D., Model predictive control and estimation of managed pressure drilling using a real-time high fidelity flow model, ISA Transactions, 105, pp. 256-268, Oct 2020, doi: 10.1016/j.isatra.2020.05.035. Preprint | Article

1. Moura Oliveira, P., Hedengren, J., Boaventura, J., Bridging Theory to Practice: Feedforward and Cascade Control with TCLab Arduino Kit, 14th International Conference on Automatic Control and Soft Computing (CONTROLO), Bragança, Portugal, July 2020. Proceedings

1. Hedengren, J.D., Kantor, J., Computer Programming and Process Control Take-Home Lab, Computer Aids for Chemical Engineering (CACHE) News, Summer 2020. Article

1. Arce, S., Vernon, C.A., Hammond, J., Newell, V., Janson, J., Franke, K.W., Hedengren, J.D. Automated 3D Reconstruction Using Optimized View-Planning Algorithms for Iterative Development of Structure-from-Motion Models, Remote Sensing, 2020, 12, 2169. Article

1. Hammond, J.E., Vernon, C.A., Okeson, T.J., Barrett, B.J., Arce, S., Newell, V., Janson, J., Franke, K.W., Hedengren, J.D. Survey of 8 UAV Set-Covering Algorithms for Terrain Photogrammetry, Remote Sensing, 2020, 12, 2285. Article

1. Park, J., Price, C., Pixton, D., Aghito, M., Nybø, R., Bjørkevoll, K., Hedengren, J.D., Model predictive control and estimation of managed pressure drilling using a real-time high fidelity flow model, ISA Transactions, 2020. Preprint | Article

1. Park, J., Price, C., Pixton, D., Aghito, M., Nybø, R., Bjørkevoll, K., Hedengren, J.D., Model predictive control and estimation of managed pressure drilling using a real-time high fidelity flow model, ISA Transactions, 2020. Article

1. Park, J., Price, C., Pixton, D., Aghito, M., Nybø, R., Bjørkevoll, K., Hedengren, J.D., Model predictive control and estimation of managed pressure drilling using a real-time high fidelity flow model,

ISA Transactions, 2020. Article

1. Moura Oliveira, P., Hedengren, J., Boaventura, J., Bridging Theory to Practice: Feedforward and Cascade Control with TCLab Arduino Kit, 14th International Conference on Automatic Control and Soft Computing, Bragança, Portugal, July 2020.

1. Moura Oliveira, P., Hedengren, J., Rossiter, J.A., Introducing Digital Controllers to Undergraduate Students Using the TCLab Arduino Kit, 21st IFAC World Congress, Berlin, Germany, July 12-17, 2020.

 Current CV

1. Park, J., Martin, R.A., Kelly, J.D., Hedengren, J.D., Benchmark Temperature Microcontroller for Process Dynamics and Control, Computers & Chemical Engineering, Special Issue in Honor of Thomas F. Edgar, 135, 6 April 2020, doi: 10.1016/j.compchemeng.2020.106736. Preprint | Article

1. Park, J., Martin, R.A., Kelly, J.D., Hedengren, J.D., Benchmark Temperature Microcontroller for Process Dynamics and Control, Computers & Chemical Engineering, Special Issue in Honor of Tom Edgar's 75th birthday, 2020, Accepted for Publication. Preprint | Article

2020

1. Park, J., Martin, R.A., Kelly, J.D., Hedengren, J.D., Benchmark Temperature Microcontroller for Process Dynamics and Control, Computers & Chemical Engineering, Special Issue in Honor of Tom Edgar's 75th birthday, 2020, Accepted for Publication. Preprint

Dr. John Hedengren is an Associate Professor at Brigham Young University in the Chemical Engineering Department. He leads the BYU Process Research and Intelligent Systems Modeling (PRISM) group with a current focus on structured machine learning for optimization of energy systems, unmanned aircraft, and drilling. Prior to BYU he worked in industry for 7 years on nonlinear estimation and predictive control for polymers. His work includes the APMonitor Optimization Suite with a recent extension to the Python GEKKO language. He led the development of the Arduino-based Temperature Control Lab that is currently used by 40 universities for process control education. His 47 publications span topics of oil production, drilling automation, smart grid optimization, unmanned aerial systems, and nonlinear predictive control. His professional service includes an appointment as an adjunct professor at the University of Utah, Section EiC for the Processes Journal, and member of the AIChE CAST Executive Committee (Webinar Editor). In 2005, he received a Ph.D. (Ch.E.) from the University of Texas at Austin for contributions to control and estimation of large-scale dynamic systems. He served as a Society of Petroleum Engineers (SPE) Distinguished Lecturer for 2018-2019, visiting 22 local sections to deliver a presentation on "Drilling Automation and Downhole Monitoring with Physics-based Models". In 2020, he is on sabbatical to develop combined physics-based and machine learned methods for optimization and automation.

Dr. John Hedengren is an Associate Professor at Brigham Young University in the Chemical Engineering Department. He leads the BYU Process Research and Intelligent Systems Modeling (PRISM) group with a current focus on structured machine learning for optimization of energy systems, unmanned aircraft, and drilling. Prior to BYU he worked in industry for 7 years on nonlinear estimation and predictive control for polymers. His work includes the APMonitor Optimization Suite with a recent extension to the Python GEKKO language. He led the development of the Arduino-based Temperature Control Lab that is currently used by 40 universities for process control education. His 47 publications span topics of oil production, drilling automation, smart grid optimization, unmanned aerial systems, and nonlinear predictive control. His professional service includes an appointment as an adjunct professor at the University of Utah, Section EiC for the Processes Journal, and member of the AIChE CAST Executive Committee (Webinar Editor). In 2005, he received a Ph.D. (Ch.E.) from the University of Texas at Austin for contributions to control and estimation of large-scale dynamic systems. He served as a Society of Petroleum Engineers (SPE) Distinguished Lecturer for 2018-2019, visiting 22 local sections to deliver a presentation on "Drilling Automation and Downhole Monitoring with Physics-based Models".

1. Tubbs, B., Hedengren, J.D., Data-Driven Operations Management and Production Optimization, Operations: Case Studies and Best Practices, Canadian Institute Of Mining Metallurgy And Petroleum Conference Convention, CIM Convention, Montreal, Canada, 2019.

1. Simmons, C., Arment, J., Powell, K.M., Hedengren, J.D., Proactive Energy Optimization in Residential Buildings with Weather and Market Forecasts, Processes, MDPI, 7 (12), 929, doi: 10.3390/pr7120929, 2019. Abstract | Article (Open Access)

2020

1. Park, J., Lyman, J., Darby, M., Lima, L., Nelson, C., and Hedengren, J.D., Hybrid Machine Learning and Fundamental Modeling for Real-Time Optimization of a Fluidized Bed Roaster, 2020 Spring Meeting & 16th Global Congress on Process Safety, AIChE, Houston, TX, 29 March-2 April, 2020. Abstract

1. Oliveira, P.M., Hedengren, J.D., An APMonitor Temperature Lab PID Control Experiment for Undergraduate Students, 24th IEEE Conference on Emerging Technologies and Factory Automation (ETFA), Sep 10th - 13th, 2019, pp. 790-797, Zaragoza, Spain. Preprint | Paper

1. Oliveira, P.M., Hedengren, J.D., An APMonitor Temperature Lab PID Control Experiment for Undergraduate Students, 24th IEEE Conference on Emerging Technologies and Factory Automation (ETFA), Sep 10th - 13th, 2019, pp. 790-797, Zaragoza, Spain. Preprint Paper

1. Oliveira, P.M., Hedengren, J.D., An APMonitor Temperature Lab PID Control Experiment for Undergraduate Students, 24th IEEE Conference on Emerging Technologies and Factory Automation (ETFA), Sep 10th - 13th, 2019, pp. 790-797, Zaragoza, Spain. Preprint

1. Rossiter, J.A., Jones, B.L., Pope, S., Hedengren, J.D., Evaluation and Demonstration of Take Home Laboratory Kit, Invited Session: Demonstration and poster session, 12th IFAC Symposium on Advances in Control Education, July 7-9, 2019, 52 (9), pp. 56-61, Philadelphia, PA, USA. Preprint

 Current CV

1. Oliveira, P.M., Hedengren, J.D., An APMonitor Temperature Lab PID Control Experiment for Undergraduate Students, 24th IEEE Conference on Emerging Technologies and Factory Automation (ETFA), Sep 10th - 13th, 2019, Zaragoza, Spain. Preprint

1. Freeman, M., Vernon, C., Berrett, B., Hastings, N., Derricott, J., Pace, J., Horne, B., Hammond, J., Janson, J., Chiabrando, F., Hedengren, J.D., Franke, K., Sequential Earthquake Damage Assessment Incorporating Optimized sUAV Remote Sensing at Pescara del Tronto, Special Issue: Remote Sensing Applications for Earthquake and Tsunami Damage Assessment, Geosciences, 2019. Article (Open Access)

1. Freeman, M., Vernon, C., Berrett, B., Hastings, N., Derricott, J., Pace, J., Horne, B., Hammond, J., Janson, J., Chiabrando, F., Hedengren, J.D., Franke, K., Sequential Earthquake Damage Assessment Incorporating Optimized sUAV Remote Sensing at Pescara del Tronto, Special Issue: Remote Sensing Applications for Earthquake and Tsunami Damage Assessment, Geosciences, 2019.

1. Freeman, M., Vernon, C., Berrett, B., Hastings, N.,

Derricott, J., Pace, J., Horne, B., Hammond, J., Janson, J., Chiabrando, F., Hedengren, J.D., Franke, K., Sequential Earthquake Damage Assessment Incorporating Optimized sUAV Remote Sensing at Pescara del Tronto, Special Issue: Remote Sensing Applications for Earthquake and Tsunami Damage Assessment, Geosciences, 2019.

1. Rossiter, J.A., Jones, B.L., Pope, S., Hedengren, J.D., Evaluation and Demonstration of Take Home Laboratory Kit, Invited Session: Demonstration and poster session, 12th IFAC Symposium on Advances in Control Education, July 7-9, 2019, Philadelphia, PA, USA. Preprint

1. Derricott, J.C., Willis, J.B., Peterson, C.K., Franke, K.W., Hedengren, J.D., Disaster Reconnaissance Using Multiple Small Unmanned Aerial Vehicles, Mechanical Engineering Magazine, 141, 16, doi: 10.1115/1.2019-JUN5, 2019. Article

1. Parkinson, A., Balling, R., Hedengren, J.D., Optimization Methods for Engineering Design, Brigham Young University, Edition 1 (2013), Edition 2 (2018). Book
2. Derricott, J.C., Willis, J.B., Peterson, C.K., Franke, K.W., Hedengren, J.D., Disaster Reconnaissance Using Multiple Small Unmanned Aerial Vehicles, Mechanical Engineering Magazine, 141, 16, 2019.

Article

1. Okeson, T.J., Barrett, B.J., Arce, S., Vernon, C.A., Franke, K.W, Hedengren, J.D., Achieving Tiered Model Quality in 3D Structure from Motion Models Using a Multi-Scale View-Planning Algorithm for Automated Targeted Inspection, Sensors, MDPI, 19 (12), 2703, doi: 10.3390/s19122703, 2019. Article (Open Access)

1. Rossiter, J.A., Jones, B.L., Pope, S., Hedengren, J.D., Evaluation and Demonstration of Take Home Laboratory Kit, Invited Session: Demonstration and poster session, 12th IFAC Symposium on Advances in Control Education, July 7-9, 2019, Philadelphia, PA, USA.

1. Hedengren, J.D., Martin, R.A., Kantor, J.C., Reuel, N., Temperature Control Lab for Dynamics and Control, AIChE Annual Meeting, Orlando, FL, Nov 2019. Abstract

1. Pastusek, P., Payette, G., Shor, R., Cayeux, E., Aarsnes, U.J., Hedengren, J.D., Menand, S., Macpherson, J., Gandikota, R., Behounek, M., Harmer, R., Detournay, E., Illerhaus, R., Liu, Y., Creating Open Source Models, Test Cases, and Data for Oilfield Drilling Challenges, SPE/IADC Drilling Conference, The Hague, Netherlands, March 2019, SPE-194082-MS. Article

1. Pastusek, P., Payette, G., Shor, R., Cayeux, E., Aarsnes, U.J., Hedengren, J.D., Menand, S., Macpherson, J., Gandikota, R., Behounek, M., Harmer, R., Detournay, E., Illerhaus, R., Liu, Y., Creating Open Source Models, Test Cases, and Data for Oilfield Drilling Challenges, SPE/IADC Drilling Conference, The Hague, Netherlands, March 2019, SPE-194082-MS.

Dr. John Hedengren is an Associate Professor at Brigham Young University in the Chemical Engineering Department. He leads the BYU Process Research and Intelligent Systems Modeling (PRISM) group with a current focus on structured machine learning for optimization of energy systems, unmanned aircraft, and drilling. Prior to BYU he worked in industry for 7 years on nonlinear estimation and predictive control for polymers. His work includes the APMonitor Optimization Suite with a recent extension to the Python GEKKO language. He led the development of the Arduino-based Temperature Control Lab that is currently used by 40 universities for process control education. His 47 publications span topics of oil production, drilling automation, smart grid optimization, unmanned aerial systems, and nonlinear predictive control. His professional service includes an appointment as an adjunct professor at the University of Utah, Section EiC for the Processes Journal, and member of the AIChE CAST Executive Committee (Webinar Editor). In 2005, he received a Ph.D. (Ch.E.) from the University of Texas at Austin for contributions to control and estimation of large-scale dynamic systems. He currently serves as a Society of Petroleum Engineers (SPE) Distinguished Lecturer for 2018-2019, visiting 22 local sections to deliver a presentation on "Drilling Automation and Downhole Monitoring with Physics-based Models".

1. Rossiter, J.A., Jones, B.L., Pope, S., Hedengren, J.D., Evaluation and demonstration of take home laboratory kit, 12th IFAC Symposium on Advances in Control Education (ACE 2019), July 7-9, 2019, Philadelphia, PA.

1. Gates, N.S., Moore, K.R., Ning, A., Hedengren, J.D., Combined Trajectory, Propulsion and Battery Mass Optimization for Solar-Regenerative High-Altitude Long Endurance Unmanned Aircraft, AIAA Science and Technology Forum (SciTech), 2019. Preprint

1. Gates, N.S., Moore, K.R., Ning, A., Hedengren, J.D., Combined Trajectory, Propulsion and Battery Mass Optimization for Solar-Regenerative High-Altitude Long Endurance Unmanned Aircraft, AIAA Science and Technology Forum (SciTech), 2019.

1. Blackburn, L., Young, A., Rogers, P., Hedengren, J.D., Powell, K.M., Dynamic Optimization of a District Energy System with Storage Using a Novel Mixed-Integer Quadratic Programming Algorithm, Optimization and Engineering, 2019. Article

1. Blackburn, L., Young, A., Rogers, P., Hedengren, J.D., Powell, K.M., Dynamic Optimization of a District Energy System with Storage Using a Novel Mixed-Integer Quadratic Programming Algorithm, Optimization and Engineering, 2019, submitted.
2. Brower, D.V., Hedengren, J.D., Seaman, C., Wilson, J.C., A Post-Installable Fiber-Optic Sensor System for Leak Detection using Acoustic and Vibration Monitoring, ASME 38th International Conference on Ocean, Offshore and Arctic Engineering, OMAE2019/95558, Glasgow, Scotland, UK, June 2019, submitted.
3. Hedengren, J.D., Brower, D.V., Wilson J.C., High, G., Witherow, K., Distributed Fiber Optic Strain Monitoring During Deployment Of A Deepwater Subsea Umbilical, Symposium 4 Pipelines, Risers, and Subsea Systems, ASME 38th International Conference on Ocean, Offshore and Arctic Engineering, OMAE2019/95558, Glasgow, Scotland, UK, June 2019, submitted.

1. Tubbs, B., Hedengren, J.D., Data-Driven Operations Management and Production Optimization, Operations: Case Studies and Best Practices, Canadian Institute Of Mining Metallurgy And Petroleum Conference Convention, CIM Convention, Montréal, CA, 2019.

1. Martin, R.A., Gates, N., Ning, A., Hedengren, J.D., Dynamic Optimization of High-Altitude Solar Aircraft Trajectories Under Station-Keeping Constraints, Journal of Guidance, Control, and Dynamics, 2018, doi: 10.2514/1.G003737. Preprint | Article | Source Code

1. Martin, R.A., Gates, N., Ning, A., Hedengren, J.D., Dynamic Optimization of High-Altitude Solar Aircraft Trajectories Under Station-Keeping Constraints, Journal of Guidance, Control, and Dynamics, 2018, accepted. Preprint | Article | Source Code

1. Martin, R.A., Gates, N., Ning, A., Hedengren, J.D., Dynamic Optimization of High-Altitude Solar Aircraft Trajectories Under Station-Keeping Constraints, Journal of Guidance, Control, and Dynamics, 2018, accepted. Preprint | Article |Source Code

Dr. John Hedengren is an Associate Professor at Brigham Young University in the Chemical Engineering Department. He leads the BYU Process Research and Intelligent Systems Modeling (PRISM) group with a current focus on structured machine learning for optimization of energy systems, unmanned aircraft, and drilling. Prior to BYU he worked in industry for 7 years on nonlinear estimation and predictive control for polymers. His work includes the APMonitor Optimization Suite with a recent extension to the Python GEKKO language. He led the development of the Arduino-based Temperature Control Lab that is currently used by 24 universities for process control education. His 47 publications span topics of oil production, drilling automation, smart grid optimization, unmanned aerial systems, and nonlinear predictive control. His professional service includes an appointment as an adjunct professor at the University of Utah, Section EiC for the Processes Journal, and member of the AIChE CAST Executive Committee (Webinar Editor). In 2005, he received a Ph.D. (Ch.E.) from the University of Texas at Austin for contributions to control and estimation of large-scale dynamic systems. He currently serves as a Society of Petroleum Engineers (SPE) Distinguished Lecturer for 2018-2019, visiting 22 local sections to deliver a presentation on "Drilling Automation and Downhole Monitoring with Physics-based Models".

1. Hansen, B., Tolbert, B., Vernon, C., Hedengren, J.D., Model Predictive Automatic Control of Sucker Rod Pump System with Simulation Case Study, Computers & Chemical Engineering, 121, pp. 263-284, 2019. Preprint | Article | Source Code

2019

1. Hansen, B., Tolbert, B., Vernon, C., Hedengren, J.D., Model Predictive Automatic Control of Sucker Rod Pump System with Simulation Case Study, Computers & Chemical Engineering, 121, pp. 263-284, 2018. Preprint | Article | Source Code

2019

1. Park, J., Hansen, B., Gates, N., Darby, M., Hedengren, J.D., Use of Nonlinear and Machine Learning Techniques for Improved APC Modeling, AIChE Spring Meeting, New Orleans, LA, April 2019. Abstract
2. Park, J., Patterson, C., Kelly, J., Hedengren, J.D., Closed-Loop PID Re-Tuning in a Digital Twin By Re-Playing Past Setpoint and Load Disturbance Data, AIChE Spring Meeting, New Orleans, LA, April 2019. Abstract

• APMonitor and GEKKO Optimization Suite: Advanced Process Monitoring (APM) is a software platform to solve large-scale Differential and Algebraic Equation (DAE) models in simulation, process control, and optimization applications. Recipient of the 2014 AIChE David Himmelblau Award and the 2018 AIChE Computing Practice Award.

• APMonitor and GEKKO Optimization Suite: Advanced Process Monitoring (APM) is a software platform to solve large-scale Differential and Algebraic Equation (DAE) models in simulation, process control, and optimization applications. Recipient of the 2014 AIChE David Himmelblau Award and the 2018 AIChE Computing Practice Award.

• APMonitor Software: Advanced Process Monitoring (APM) is a software platform to solve large-scale Differential and Algebraic Equation (DAE) models in simulation, process control, and optimization applications. Recipient of the 2014 AIChE David Himmelblau Award and the 2018 AIChE Computing Practice Award.
• Drilling Automation
• Energy System Optimization
• Model Predictive Control
• Unmanned Aerial Systems
• Machine Learning

 330L Engineering Building

Dr. John Hedengren is an Associate Professor at Brigham Young University in the Chemical Engineering Department. He leads the BYU Process Research and Intelligent Systems Modeling (PRISM) group with a current focus on structured machine learning for optimization of energy systems, unmanned aircraft, and drilling. Prior to BYU he worked in industry for 7 years on nonlinear estimation and predictive control for polymers. His work includes the APMonitor Optimization Suite with a recent extension to the Python GEKKO language. He led the development of the Arduino-based Temperature Control Lab that is currently used by 24 universities for process control education. His 47 publications span topics of oil production, drilling automation, smart grid optimization, unmanned aerial systems, and nonlinear predictive control. His professional service includes an appointment as an adjunct professor at the University of Utah, Associate Editor for the Processes Journal, and member of the AIChE CAST Executive Committee (Webinar Editor). In 2005, he received a Ph.D. (Ch.E.) from the University of Texas at Austin for contributions to control and estimation of large-scale dynamic systems. He currently serves as a Society of Petroleum Engineers (SPE) Distinguished Lecturer for 2018-2019, visiting 22 local sections to deliver a presentation on "Drilling Automation and Downhole Monitoring with Physics-based Models".

1. Hansen, B., Tolbert, B., Vernon, C., Hedengren, J.D., Model Predictive Automatic Control of Sucker Rod Pump System with Simulation Case Study, Computers & Chemical Engineering, 2018. Preprint | Article | Source Code

1. Hansen, B., Tolbert, B., Vernon, C., Hedengren, J.D., Model Predictive Automatic Control of Sucker Rod Pump System with Simulation Case Study, Computers & Chemical Engineering, 2018. Preprint Article

1. Hansen, B., Tolbert, B., Vernon, C., Hedengren, J.D., Model Predictive Automatic Control of Sucker Rod Pump System with Simulation Case Study, Computers & Chemical Engineering, 2018, accepted. Preprint

Dr. John Hedengren is an Associate Professor at Brigham Young University in the Chemical Engineering Department. He leads the BYU Process Research and Intelligent Systems Modeling (PRISM) group with a current focus on structured reinforcement learning for optimization of energy systems, unmanned aircraft, and drilling. Prior to BYU he worked in industry for 7 years on nonlinear estimation and predictive control for polymers. His work includes the APMonitor Optimization Suite with a recent extension to the Python GEKKO language. He led the development of the Arduino-based Temperature Control Lab that is currently used by 24 universities for process control education. His 47 publications span topics of oil production, drilling automation, smart grid optimization, unmanned aerial systems, and nonlinear predictive control. His professional service includes an appointment as an adjunct professor at the University of Utah, Associate Editor for the Processes Journal, and member of the AIChE CAST Executive Committee (Webinar Editor). In 2005, he received a Ph.D. (Ch.E.) from the University of Texas at Austin for contributions to control and estimation of large-scale dynamic systems. He currently serves as a Society of Petroleum Engineers (SPE) Distinguished Lecturer for 2018-2019, visiting 22 local sections to deliver a presentation on "Drilling Automation and Downhole Monitoring with Physics-based Models".

1. Martin, R.A., Gates, N., Ning, A., Hedengren, J.D., Dynamic Optimization of High-Altitude Solar Aircraft Trajectories Under Station-Keeping Constraints, Journal of Guidance, Control, and Dynamics, 2018, accepted. Preprint | Source Code

1. Martin, R.A., Heiner, B., Hedengren, J.D., Targeted 3D Modeling from UAV Imagery, Proc. SPIE 10645, Geospatial Informatics, Motion Imagery, and Network Analytics VIII, 106450D (27 April 2018); doi: 10.1117/12.2304436 Article

1. Hansen, B., Tolbert, B., Vernon, C., Hedengren, J.D., Model Predictive Automatic Control of Sucker Rod Pump System with Simulation Case Study, Computers & Chemical Engineering, 2018, accepted.
2. Beal, L.D.R., Hill, D., Martin, R.A., and Hedengren, J.D., GEKKO Optimization Suite, Processes, Volume 6, Number 8, 2018, doi: 10.3390/pr6080106. Article

1. Martin, R.A., Gates, N., Ning, A., Hedengren, J.D., Dynamic Optimization of High-Altitude Solar Aircraft Trajectories Under Station-Keeping Constraints, Journal of Guidance, Control, and Dynamics, 2018, accepted. Preprint

1. Martin, R.A., Gates, N., Ning, A., Hedengren, J.D., Dynamic Optimization of High-Altitude Solar Aircraft Trajectories Under Station-Keeping Constraints, Journal of Guidance, Control, and Dynamics, 2018, accepted.

1. Beal, L., Clark, J., Anderson, M., Warnick, S., Hedengren, J.D., Combined Scheduling and Control with Diurnal Constraints and Costs using a Discrete Time Formulation, FOCAPO / CPC 2017, Tucson, AZ, Jan 2017.
2. Udy, J., Blackburn, L., Hedengren, J.D., Darby, M., Reduced Order Modeling for Reservoir Injection Optimization and Forecasting, FOCAPO / CPC 2017, Tucson, AZ, Jan 2017. Preprint

1. Beal, L.D., Petersen, D., Grimsman, D., Warnick, S., Hedengren, J.D., Integrated Scheduling and Control in Discrete-time with Dynamic Parameters and Constraints, Computers & Chemical Engineering, 115, pp. 361-376, 2018. Article

1. Hansen, B., Model Predictive Automatic Control of Sucker Rod Pump System with Simulation Case Study, Computers & Chemical Engineering, 2018, accepted.

1. Safdarnejad, S.M., Hedengren, J.D., Powell, K.M., Performance Comparison of Low Temperature and Chemical Absorption Carbon Capture Processes in Response to Dynamic Electricity Demand and Price Profiles, Applied Energy, Volume 228, pp. 577-592, 2018, doi:10.1016/j.apenergy.2018.06.098. Article

1. Beal, L.D.R., Hill, D., Martin, R.A., and Hedengren, J. D., GEKKO Optimization Suite, Processes, Volume 6, Number 8, 2018, doi: 10.3390/pr6080106. Article

1. Blackburn, L., Hedengren, J.D., Powell, K.M., Real-time Optimization of Chillers with Thermal Energy Storage and Variable Electricity Rates, Smart City & Sustainable Communities, INFORMS 2018 Annual Meeting, Phoenix, AZ, USA, Nov. 4-7, 2018.

1. Safdarnejad, S.M., Hedengren, J.D., Powell, K.M., Performance Comparison of Low Temperature and Chemical Absorption Carbon Capture Processes in Response to Dynamic Electricity Demand and Price Profiles, Applied Energy, accepted, 2018.

Dr. John Hedengren is an Associate Professor at Brigham Young University in the Chemical Engineering Department. He leads the BYU Process Research and Intelligent Systems Modeling (PRISM) group with a current focus on structured reinforcement learning for optimization of energy systems, unmanned aircraft, and drilling. Prior to BYU he worked in industry for 7 years on nonlinear estimation and predictive control for polymers. His work includes the APMonitor Optimization Suite with a recent extension to the Python GEKKO language. He led the development of the Arduino-based Temperature Control Lab that is currently used by 24 universities for process control education. His 47 publications span topics of oil production, drilling automation, smart grid optimization, unmanned aerial systems, and nonlinear predictive control. He is a 2018-19 distinguished lecturer for the Society of Petroleum Engineers and serves as an associate editor for the Processes Journal, the IEEE Control Systems Society, and AIChE webinars. In 2005, he received a Ph.D. (Ch.E.) from the University of Texas at Austin for contributions to control and estimation of large-scale dynamic systems.

Prof. Hedengren has consulting experience with Apache, ENI Petroleum, HESS, SABIC Ibn Zahr, TOTAL, and other companies on automation solutions. He worked full-time for 5 years with ExxonMobil supporting advanced control and optimization solutions. His industrial control experience with PLC and DCS systems includes OPTO22, Honeywell TPS/TDC3000, Experion system, OPC, and Modbus. His area of expertise is in process dynamics, control, and optimization with applications in fiber optic monitoring, automation of oil and gas processes, unmanned aerial systems, systems biology, and grid-scale energy systems. In chemicals manufacturing, he has extensive experience in automation and modeling of the production of polymers such as polyethylene, polypropylene, butyl rubber, and polystyrene as well as specialty chemicals (polyalphaolefins). Automation software (APMonitor) that he developed has been applied in over 100 industrial applications world-wide in refineries, chemical plants, and offshore oil platforms.

1. Brower, D.V., Bentley, N.L., Hedengren, J.D., Kipp, R.M., Le, S.Q., Seaman, C., Tang, H.H., Wilson, J.C., Full-Scale Testing of a Friction-Based, Post-Installable, Fiber-Optic Strain Sensor for Subsea Monitoring Systems, Topic: 4-6 Innovative Technologies for Deepwater Low-Cost Production, Symposium 4 Pipelines, Risers, and Subsea Systems, ASME 37th International Conference on Ocean, Offshore and Arctic Engineering, OMAE2018/77117, Madrid, Spain, June 2018.

1. Blackburn, L., Hedengren, J.D., Powell, K., Real-time Optimization of Chillers with Thermal Energy Storage and Variable Electricity Rates, INFORMS Annual Meeting, Phoenix, AZ, Oct 2018.

1. Beal, L.D., Petersen, D., Grimsman, D., Warnick, S., Hedengren, J.D., Integrated Scheduling and Control in Discrete-time with Dynamic Parameters and Constraints, Computers & Chemical Engineering, 115, pp. 361-376, 2018. Article, Free Access Until July 2018

1. Beal, L.D., Petersen, D., Grimsman, D., Warnick, S., Hedengren, J.D., Integrated Scheduling and Control in Discrete-time with Dynamic Parameters and Constraints, Computers & Chemical Engineering, 2018. Article, Free Access Until July 2018

1. Beal, L.D., Petersen, D., Grimsman, D., Warnick, S., Hedengren, J.D., Integrated Scheduling and Control in Discrete-time with Dynamic Parameters and Constraints, Computers & Chemical Engineering, 2018. Article

1. Beal, L.D., Petersen, D., Grimsman, D., Warnick, S., Hedengren, J.D., Integrated Scheduling and Control in Discrete-time with Dynamic Parameters and Constraints, Computers & Chemical Engineering, Accepted for publication, 2018.

 Current CV

1. Brower, D.V., Seaman, C., Bentley, N.L., Tang, H.H., Kipp, R.M., Wilson, J.C., Le, S.Q., Hedengren, J.D., Full-Scale Testing of a Friction-Based, Post-Installable, Fiber-Optic Strain Sensor for Subsea Monitoring Systems, Topic: 4-6 Innovative Technologies for Deepwater Low-Cost Production, Symposium 4 Pipelines, Risers, and Subsea Systems, ASME 37th International Conference on Ocean, Offshore and Arctic Engineering, OMAE2018/77117, Madrid, Spain, June 2018.

Prof. Hedengren has consulting experience with Apache, ENI Petroleum, HESS, SABIC Ibn Zahr, TOTAL, and other companies on automation solutions. He worked full-time for 5 years with ExxonMobil supporting advanced control and optimization solutions. His industrial control experience with PLC and DCS systems includes OPTO22, Honeywell TPS/TDC3000, Experion system, OPC, and Modbus. His area of expertise is in process dynamics, control, and optimization with applications in fiber optic monitoring, automation of oil and gas processes, unmanned aerial systems, systems biology, and grid-scale energy systems. In chemicals manufacturing, he has extensive experience in automation and modeling of the production of polymers such as polyethylene, polypropylene, butyl rubber, and polystyrene as well as specialty chemicals (polyalphaolefins). Automation software (APMonitor) that he developed has been applied in over 100 industrial applications world-wide in refineries, chemical plants, and offshore oil platforms. He is a Distinguished Lecturer with the Society of Petroleum Engineers (SPE) starting in 2018.

1. Seaman, C., Brower, D.V., Hedengren, J.D., et al., Full-Scale Testing of a Friction-Based, Post-Installable, Fiber-Optic Strain Sensor for Subsea Monitoring Systems, Topic: 4-6 Innovative Technologies for Deepwater Low-Cost Production, Symposium 4 Pipelines, Risers, and Subsea Systems, ASME 37th International Conference on Ocean, Offshore and Arctic Engineering, OMAE2018/77117, Madrid, Spain, June 2018.
2. Hedengren, J.D., Brower, D.V., Wilson J.C., High, G., Witherow, K., New Flow Assurance System With High Speed Subsea Fiber Optic Monitoring Of Pressure And Temperature, Symposium 4 Pipelines, Risers, and Subsea Systems, ASME 37th International Conference on Ocean, Offshore and Arctic Engineering, OMAE2018/78079, Madrid, Spain, June 2018. Preprint

1. Hedengren, J.D., Beal, L., Special Issue: Combined Scheduling and Control, Processes, 6(3), 24, doi: 10.3390/pr6030024, 2018. Editorial and Special Issue

Prof. Hedengren has consulting experience with Apache, ENI Petroleum, HESS, SABIC Ibn Zahr, TOTAL, and other companies on automation solutions. He worked full-time for 5 years with ExxonMobil supporting advanced control and optimization solutions. His industrial control experience with PLC and DCS systems includes OPTO22, Honeywell TPS/TDC3000, Experion system, OPC, and Modbus. His area of expertise is in process dynamics, control, and optimization with applications in fiber optic monitoring, automation of oil and gas processes, unmanned aerial systems, systems biology, and grid-scale energy systems. In chemicals manufacturing, he has extensive experience in automation and modeling of the production of polymers such as polyethylene, polypropylene, butyl rubber, and polystyrene as well as specialty chemicals (polyalphaolefins). Automation software (APMonitor) that he developed has been applied in over 100 industrial applications world-wide in refineries, chemical plants, and offshore oil platforms. He is a Distinguished Lecturer with the Society of Petroleum Engineers (SPE) starting starting in 2018.

1. Hedengren, J.D., Beal, L., Special Issue: Combined Scheduling and Control, Processes, 6(3), 24, doi: 10.3390/pr6030024, 2018. Editorial and Special Issue (7 articles)

1. Hedengren, J.D., Beal, L., Special Issue: Combined Scheduling and Control, Processes, 6(3), 24, doi: 10.3390/pr6030024, 2018. Article and Special Issue (7 articles)

1. Hedengren, J.D., Beal, L., Special Issue: Combined Scheduling and Control, Processes, 6(3), 24, doi: 10.3390/pr6030024, 2018. Article

2018

1. Hedengren, J.D., Brower, D.V., Wilson J.C., High, G., Witherow, K., New Flow Assurance System With High Speed Subsea Fiber Optic Monitoring Of Pressure And Temperature, ASME 37th International Conference on Ocean, Offshore and Arctic Engineering, OMAE2018/78079, Madrid, Spain, June 2018. Preprint

1. Safdarnejad, S.M., Hedengren, J.D., Baxter, L.L, Dynamic Optimization of a Hybrid System of Energy-Storing Cryogenic Carbon Capture and a Baseline Power Generation Unit, Applied Energy, 172 (15), 66–79, June 2016, doi:10.1016/j.apenergy.2016.03.074. Article

1. Martin, R.A., Heiner, B., Hedengren, J.D., Targeted 3D Modeling from UAV Imagery, SPIE Defense + Security Symposium, Geospatial Informatics, and Motion Imagery Analytics VIII, 15 - 19 April 2018, Orlando, Florida.

1. Beal, L.D., Petersen D., Pila G., Davis, B., Warnick, S., and Hedengren, J.D., Economic Benefit from Progressive Integration of Scheduling and Control for Continuous Chemical Processes, Processes, 5(4), 84, doi:10.3390/pr5040084, 2017. Article (Open Access)
2. Petersen, D., Beal, L.D., Prestwich D., Warnick, S., and Hedengren, J. D., Combined Noncyclic Scheduling and Advanced Control for Continuous Chemical Processes, Processes, 2017, Processes, 5(4), 83, doi:10.3390/pr5040083, 2017. Article (Open Access)

2018

Martin, R.A., Heiner, B., Hedengren, J.D., Targeted 3D Modeling from UAV Imagery, SPIE Defense + Security Symposium, Geospatial Informatics, and Motion Imagery Analytics VIII, 15 - 19 April 2018, Orlando, Florida.

1. Eaton, A., Safdarnejad, S.M., Hedengren, J.D., Moffat, K., Hubbell, C., Brower, D.V., Brower, A.D., Post-Installed Fiber Optic Pressure Sensors on Subsea Production Risers for Severe Slugging Control, ASME 34th International Conference on Ocean, Offshore and Arctic Engineering, OMAE2015-42196, St. John's, Newfoundland, Canada, June 2015. Preprint

1. Asgharzadeh Shishavan, R., Brower, D.V., Hedengren, J.D., Brower, A.D., New Advances in Post-Installed Subsea Monitoring Systems for Structural and Flow Assurance Evaluation, ASME 33rd International Conference on Ocean, Offshore and Arctic Engineering, OMAE2014-24300, San Francisco, CA, June 2014. Presentation and Preprint

1. Brower, D., Hedengren, J.D., Asgharzadeh Shishavan, R., and Brower, A., Advanced Deepwater Monitoring System, ASME 32st International Conference on Ocean, Offshore and Arctic Engineering, OMAE2013-10920, Nantes, France, June 2013, ISBN: 978-0-7918-5531-7. Preprint, Presentation

1. Mojica, J.L., Petersen, D.J., Hansen, B., Powell, K.M., Hedengren, J.D., Optimal Combined Long-Term Facility Design and Short-Term Operational Strategy for CHP Capacity Investments, Energy, Vol 118, 1, pp. 97–115, January 2017, DOI: 10.1016/j.energy.2016.12.009. Article

1. Beal, L.D., Petersen D., Pila G., Davis, B., Warnick, S., and Hedengren, J.D., Economic Benefit from Progressive Integration of Scheduling and Control for Continuous Chemical Processes, Processes, 2017, accepted for publication.
2. Petersen, D., Beal, L.D., Prestwich D., Warnick, S., and Hedengren, J. D., Combined Noncyclic Scheduling and Advanced Control for Continuous Chemical Processes, Processes, 2017, accepted for publication.

1. Martin, R.A., Blackburn, L., Pulsipher, J., Franke, K., Hedengren, J.D., Potential Benefits of Combining Anomaly Detection with View Planning for UAV Infrastructure Modeling, Remote Sensing, 9(5), 434, 2017, doi:10.3390/rs9050434. Article (Open Access)

1. Safdarnejad, S.M., Strahl, W., Hedengren, J.D., Baxter, L.L., Comparison in Dynamic Response of Energy-Storing Cryogenic and Chemical Absorption Carbon Capture Systems to Electricity Demand, AIChE Annual Meeting, Minneapolis, MN, Nov 2017. Presentation Abstract

1. Safdarnejad, S.M., Strahl, W., Hedengren, J.D., Baxter, L.L., Comparison in Dynamic Response of Energy-Storing Cryogenic and Chemical Absorption Carbon Capture Systems to Electricity Demand, AIChE Annual Meeting, Minneapolis, MN, Nov 2017. Presentation [[https://aiche.confex.com/aiche/2017/meetingapp.cgi/Paper/494085|Abstract]

1. Taysom, S., Hedengren, J.D., Sorensen, C., A Comparison of Model Predictive Control and PID Temperature Control in Friction Stir Welding, Journal of Manufacturing Processes, 29, pp. 232-241, 2017, doi: 10.1016/j.jmapro.2017.07.015. Article

1. Incorporating Dynamic Simulation into Chemical Engineering Curricula, Hedengren, J.D., Badgwell, T.A., Grover, M., Braatz, R., ASEE Summer School for New Chemical Engineering Faculty, Raleigh, North Carolina, July 2017. Abstract

1. Incorporating Dynamic Simulation into Chemical Engineering Curricula, Hedengren, J.D., Badgwell, T.A., Grover, M., ASEE Summer School for New Chemical Engineering Faculty, Raleigh, North Carolina, July 2017. Abstract

1. Taysom, S., Hedengren, J.D., Sorensen, C., A Comparison of Model Predictive Control and PID Temperature Control in Friction Stir Welding, Journal of Manufacturing Processes, 2017, accepted.

1. Beal, L., Park, J., Petersen, D., Warnick, S., Hedengren, J.D., Combined Model Predictive Control and Scheduling with Dominant Time Constant Compensation, Computers & Chemical Engineering, 104, pp. 271-282, 2017, doi: 10.1016/j.compchemeng.2017.04.024 Article

• Beal, L., Park, J., Petersen, D., Warnick, S., Hedengren, J.D., Combined Model Predictive Control and Scheduling with Dominant Time Constant Compensation, Computers & Chemical Engineering, 104, pp. 271-282, 2017, doi: 10.1016/j.compchemeng.2017.04.024 Article

• Assistant Professor (2011-16) / Associate Professor (2016-present), Brigham Young University, Provo, UT

1. Udy, J., Hansen, B., Maddux, S., Peterson, D., Heilner, S., Stevens, K., Lignell, D., Hedengren, J.D., Review of Field Development Optimization of Waterflooding, EOR, and Well Placement Focusing on History Matching and Optimization Algorithms, Processes, 5(3), 34, 2017, doi:10.3390/pr5030034. Article (Open Access)

1. Mojica, J.L., Petersen, D.J., Hansen, B., Powell, K.M., Hedengren, J.D., Optimal Combined Long-Term Facility Design and Short-Term Operational Strategy for CHP Capacity Investments, Energy, Vol 118, 1 January 2017, pp. 97–115. Article

1. Udy, J., Hansen, B., Maddux, S., Peterson, D., Heilner, S., Stevens, K., Lignell, D., Hedengren, J.D., Review of Field Development Optimization of Waterflooding, EOR, and Well Placement Focusing on History Matching and Optimization Algorithms, Processes, in review, 2017.

1. Beal, L., Park, J., Petersen, D., Warnick, S., Hedengren, J.D., Combined Model Predictive Control and Scheduling with Dominant Time Constant Compensation, 2017, doi: 10.1016/j.compchemeng.2017.04.024. Preprint | Article (Free Access until July 17)

 Associate Professor
 350E Clyde Building

John Hedengren is Associate Professor at Brigham Young University in the Chemical Engineering Department, leading the PRISM (Process Research and Intelligent System Modeling) group. Prof. Hedengren teaches courses on computational methods, process dynamics and control, optimization, dynamic optimization, and fundamentals of chemical engineering. He is a chemical engineer by training with a B.S. and M.S. degree from Brigham Young University, and a Ph.D. from the University of Texas at Austin.

Prof. Hedengren has consulting experience with Apache, ENI Petroleum, HESS, SABIC Ibn Zahr, TOTAL, and other companies on automation solutions. He worked full-time for 5 years with ExxonMobil supporting advanced control and optimization solutions. His industrial control experience with PLC and DCS systems includes OPTO22, Honeywell TPS/TDC3000, Experion system, OPC, and Modbus. His area of expertise is in process dynamics, control, and optimization with applications in fiber optic monitoring, automation of oil and gas processes, unmanned aerial systems, systems biology, and grid-scale energy systems. In chemicals manufacturing, he has extensive experience in automation and modeling of the production of polymers such as polyethylene, polypropylene, butyl rubber, and polystyrene as well as specialty chemicals (polyalphaolefins). Automation software (APMonitor) that he developed has been applied in over 100 industrial applications world-wide in refineries, chemical plants, and offshore oil platforms.

John Hedengren is Associate Professor at Brigham Young University in the Chemical Engineering Department, leading the PRISM (Process Research and Intelligent System Modeling) group. Prof. Hedengren teaches courses on computational methods, process dynamics and control, optimization, dynamic optimization, and fundamentals of chemical engineering. He is a chemical engineer by training with a B.S. and M.S. degree from Brigham Young University, and a Ph.D. from the University of Texas at Austin.

Associate Professor at Brigham Young University in the Chemical Engineering Department, leading the PRISM (Process Research and Intelligent System Modeling) group. Prof. Hedengren teaches courses on computational methods, process dynamics and control, optimization, dynamic optimization, and fundamentals of chemical engineering. He is a chemical engineer by training with a B.S. and M.S. degree from Brigham Young University, and a Ph.D. from the University of Texas at Austin.

Prof. Hedengren has consulting experience with Apache, ENI Petroleum, HESS, SABIC Ibn Zahr, TOTAL, and other companies on automation solutions. He worked full-time for 5 years with ExxonMobil supporting advanced control and optimization solutions. His industrial control experience with PLC and DCS systems includes OPTO22, Honeywell TPS/TDC3000, Experion system, OPC, and Modbus. His area of expertise is in process dynamics, control, and optimization with applications in fiber optic monitoring, automation of oil and gas processes, unmanned aerial systems, systems biology, and grid-scale energy systems. In chemicals manufacturing, he has extensive experience in automation and modeling of the production of polymers such as polyethylene, polypropylene, butyl rubber, and polystyrene as well as specialty chemicals (polyalphaolefins). Automation software (APMonitor) that he developed has been applied in over 100 industrial applications world-wide in refineries, chemical plants, and off-shore oil platforms.

 Provo, UT 84602 

• Model Predictive Control

Training and Experience

I am an Associate Professor at Brigham Young University in the Chemical Engineering Department and lead the PRISM (Process Research and Intelligent System Modeling) group. I teach courses on computational methods, process dynamics and control, optimization, dynamic optimization, and fundamentals of chemical engineering. I am a chemical engineer by training with a B.S. and M.S. degree from Brigham Young University, and a Ph.D. from the University of Texas at Austin.

I consulted for Apache, ENI Petroleum, HESS, SABIC Ibn Zahr, and TOTAL on automation solutions and then full-time for 5 years with ExxonMobil supporting advanced control and optimization solutions. I have experience with industrial control PLC and DCS systems including Honeywell TPS/TDC3000, Experion system, OPC, and Modbus. My area of expertise is in process dynamics, control, and optimization with applications in fiber optic monitoring, automation of oil and gas processes, unmanned aerial systems, systems biology, and grid-scale energy systems. In chemicals manufacturing, I have extensive experience in automation and modeling of the production of polymers such as polyethylene, polypropylene, butyl rubber, and polystyrene as well as specialty chemicals (polyalphaolefins). Automation software (APMonitor) that I developed has been applied in over 100 industrial applications world-wide in refineries, chemical plants, and off-shore oil platforms.

I am an Associate Professor at Brigham Young University in the Chemical Engineering Department and lead the PRISM (Process Research and Intelligent System Modeling) group (http://apm.byu.edu/prism). I am a chemical engineer by training with a B.S. and M.S. degree from Brigham Young University, and a Ph.D. from the University of Texas at Austin. I consulted for Apache, ENI Petroleum, HESS, SABIC Ibn Zahr, and TOTAL on automation solutions and then full-time for 5 years with ExxonMobil supporting advanced control and optimization solutions. I have experience with industrial control PLC and DCS systems including Honeywell TPS/TDC3000, Experion system, OPC, and Modbus. My area of expertise is in process dynamics, control, and optimization with applications in fiber optic monitoring, automation of oil and gas processes, unmanned aerial systems, systems biology, and grid-scale energy systems. In chemicals manufacturing, I have extensive experience in automation and modeling of the production of polymers such as polyethylene, polypropylene, butyl rubber, and polystyrene as well as specialty chemicals (polyalphaolefins). Automation software (APMonitor) that I developed has been applied in over 100 industrial applications world-wide in refineries, chemical plants, and off-shore oil platforms. I teach courses on computational methods, process dynamics and control, optimization, dynamic optimization, and fundamentals of chemical engineering.

Associate Professor, Brigham Young University

1. Beal, L., Park, J., Petersen, D., Warnick, S., Hedengren, J.D., Combined Model Predictive Control and Scheduling with Dominant Time Constant Compensation, 2017, doi: 10.1016/j.compchemeng.2017.04.024. Preprint

1. Martin, R.A., Blackburn, L., Pulsipher, J., Franke, K., Hedengren, J.D., Potential Benefits of Combining Anomaly Detection with View Planning for UAV Infrastructure Modeling, 9(5), 434, 2017, doi:10.3390/rs9050434. Article (Open Access)

1. Martin, R.A., Rojas, I., Franke, K.W., Hedengren, J.D., Evolutionary View Planning for Optimized UAV Terrain Modeling in a Simulated Environment, Remote Sensing, 8(1), 26, 2016, DOI:10.3390/rs8010026. Article (Open Access)

 Current CV

1. Martin, R.A., Blackburn, L., Pulsipher, J., Franke, K., Hedengren, J.D., Potential Benefits of Combining Anomaly Detection with View Planning for UAV Infrastructure Modeling, accepted, 2017. Preprint

1. Udy, J., Maddux, S., Peterson, D., Heilner, S., Stevens, K., Lignell, D., Hedengren, J.D., Review of Injection Optimization for Enhanced Oil Recovery, Journal of Petroleum Science and Engineering, in review, 2017.

1. Martin, R.A., Blackburn, L., Pulsipher, J., Franke, K., Hedengren, J.D., Potential Benefits of Combining Anomaly Detection

with View Planning for UAV Infrastructure Modeling, accepted, 2017. Preprint

1. Beal, L., Park, J., Petersen, D., Warnick, S., Hedengren, J.D., Combined Model Predictive Control and Scheduling with Dominant Time Constant Compensation, 2017, doi: 10.1016/j.compchemeng.2017.04.024.

1. Aghito, M., Bjørkevoll, K.S., Nybø, R., Eaton, A., Hedengren, J.D., Automatic Model Calibration for Drilling Automation, SPE Bergen One Day Seminar, Bergen, Norway, 5 April 2017. Article

1. Park, J., Webber, T.R., Asgharzadeh Shishavan, R., Hedengren, J., Improved Bottomhole Pressure Control with Wired Drillpipe and Physics-Based Models, SPE-184610-MS, SPE/IADC Drilling Conference and Exhibition, The Hague, The Netherlands, 14-16 March 2017. Paper | Presentation

1. Park, J., Webber, T.R., Asgharzadeh Shishavan, R., Hedengren, J., Improved Bottomhole Pressure Control with Wired Drillpipe and Physics-Based Models, SPE-184610-MS, SPE/IADC Drilling Conference and Exhibition, The Hague, The Netherlands, 14-16 March 2017. Abstract | Paper | Presentation

1. Park, J., Webber, T.R., Asgharzadeh Shishavan, R., Hedengren, J., Improved Bottomhole Pressure Control with Wired Drillpipe and Physics-Based Models, SPE-184610-MS, SPE/IADC Drilling Conference and Exhibition, The Hague, The Netherlands, 14-16 March 2017. Abstract | [[https://www.onepetro.org/conference-paper/SPE-184610-MS

|Paper]] | Presentation

1. Park, J., Webber, T.R., Asgharzadeh Shishavan, R., Hedengren, J., Improved Bottomhole Pressure Control with Wired Drillpipe and Physics-Based Models, SPE-184610-MS, SPE/IADC Drilling Conference and Exhibition, The Hague, The Netherlands, 14-16 March 2017. Abstract | Presentation

• APMonitor Software: Advanced Process Monitoring (APM) is a software platform to solve large-scale Differential and Algebraic Equation (DAE) models in simulation, process control, and optimization applications. Recipient of the 2014 AIChE David Himmelblau Award.

1. Beal, L., Park, J., Petersen, D., Warnick, S., Hedengren, J.D., Combined Model Predictive Control and Scheduling Optimization to Exploit Dynamic Energy and Product Pricing, submitted, 2017.

1. Park, J., Webber, T.R., Asgharzadeh Shishavan, R., Hedengren, J., Improved Bottomhole Pressure Control with Wired Drillpipe and Physics-Based Models, SPE-184610-MS, SPE/IADC Drilling Conference and Exhibition, The Hague, The Netherlands, 14-16 March 2017. Abstract | Paper | Presentation

1. Park, J., Webber, T.R., Asgharzadeh Shishavan, R., Hedengren, J., Improved Bottomhole Pressure Control with Wired Drillpipe and Physics-Based Models, SPE-184610-MS, SPE/IADC Drilling Conference and Exhibition, The Hague, The Netherlands, 14-16 March 2017. Abstract Paper Presentation

1. Hedengren, J. D., Eaton, A. N., Overview of Estimation Methods for Industrial Dynamic Systems, Optimization and Engineering, Springer, Vol 18 (1), 2017, pp. 155-178, DOI: 10.1007/s11081-015-9295-9. Preprint, Article

1. Franke, K.W., Rollins, K.M. Ledezma, C., Hedengren, J.D., Wolfe, D. Ruggles, S., Bender, C., Reimschiissel, B., Reconnaissance of Two Liquefaction Sites using Small Unmanned Aerial Vehicles and Structure from Motion Computer Vision Following the April 1, 2014 Chile Earthquake, Journal of Geotechnical and Geoenvironmental Engineering, 2016, doi:10.1061/(ASCE)GT.1943-5606.0001647. Article

1. Aghito, M., Bjørkevoll, K.S., Nybø, R., Eaton, A., Hedengren, J.D., Automatic Model Calibration for Drilling Automation, SPE Bergen One Day Seminar, Bergen, Norway, 5 April 2017. Abstract

1. Aghito, M., Bjørkevoll, K.S., Nybø, R., Eaton, A., Hedengren, J.D., Automatic Model Calibration for Drilling Automation, SPE Bergen One Day Seminar, Bergen, Norway, 5 April 2017. Preprint

1. Okeson, T., Barrett, B., Blackburn, L., Hedengren, J.D., Franke, K., Optimized Infrastructure Monitoring: 3D Modeling in Complex Environments, Center for Unmanned Aircraft Systems (C-UAS), Atlanta, GA, 8 Feb 2017. Presentation

1. Martin, R.A., Blackburn, L., Pulsipher, J., Franke, K., Hedengren, J.D., Quantifying Potential Benefits of Multi-Scale UAV Monitoring of Long Linear Infrastructure, submitted, 2017. Preprint

 Google Scholar

1. Taysom, S., Hedengren, J.D., Sorensen, C., A Comparison of Model Predictive Control and PID Temperature Control in Friction Stir Welding, 2017, submitted.

1. Mojica, J.L., Petersen, D.J., Hansen, B., Powell, K.M., Hedengren, J.D., Optimal Combined Long-Term Facility Design and Short-Term Operational Strategy for CHP Capacity Investments, Energy, Vol 118, 1 January 2017, pp. 97–115. Article

1. Mojica, J.L., Petersen, D.J., Hansen, B., Powell, K.M., Hedengren, J.D., Optimal Combined Long-Term Facility Design and Short-Term Operational Strategy for CHP Capacity Investments, Energy, accepted, Vol. 118, 1 January 2017, pp. 97–115. Article

1. Mojica, J.L., Petersen, D.J., Hansen, B., Powell, K.M., Hedengren, J.D., Optimal Combined Long-Term Facility Design and Short-Term Operational Strategy for CHP Capacity Investments, Energy, accepted, Vol. 118, 1 January 2017, pp. 97–115. https://authors.elsevier.com/a/1UEQ7_8CgI-dD6

2017

1. Mojica, J.L., Petersen, D.J., Hansen, B., Powell, K.M., Hedengren, J.D., Optimal Combined Long-Term Facility Design and Short-Term Operational Strategy for CHP Capacity Investments, Energy, accepted, 2017.
2. Eaton, A.N., Beal, L., Thorpe, S., Hubbell, C., Hedengren, J.D., Nybø, R., Aghito, M., Real Time Model Identification Using Multi-Fidelity Models in Managed Pressure Drilling, Computers and Chemical Engineering, Vol 97, 2 February 2017, pp. 76–84, doi:10.1016/j.compchemeng.2016.11.008. Article

 Current CV

1. Martin, R.A., Blackburn, L., Pulsipher, J., Franke, K., Hedengren, J.D., Quantifying Potential Benefits of Multi-Scale UAV Monitoring of Long Linear Infrastructure, ISPRS Journal of Photogrammetry and Remote Sensing, submitted, 2016.

1. Franke, K.W., Rollins, K.M. Ledezma, C., Hedengren, J.D., Wolfe, D. Ruggles, S., Bender, C., Reimschiissel, B., Reconnaissance of Two Liquefaction Sites using Small Unmanned Aerial Vehicles and Structure from Motion Computer Vision Following the April 1, 2014 Chile Earthquake, Journal of Geotechnical and Geoenvironmental Engineering, 2016, doi:10.1061/(ASCE)GT.1943-5606.0001647. Article

1. Udy, J., Blackburn, L., Hedengren, J.D., Darby, M., Reduced Order Modeling for Reservoir Injection Optimization and Forecasting, FOCAPO / CPC 2017, Tuscon, AZ, Jan 2017. Preprint

1. Eaton, A.N., Beal, L., Thorpe, S., Hubbell, C., Hedengren, J.D., Nybø, R., Aghito, M., Real Time Model Identification Using Multi-Fidelity Models in Managed Pressure Drilling, Computers and Chemical Engineering, 2016, doi:10.1016/j.compchemeng.2016.11.008. Article

1. Ruggles, S., Clark, J., Franke, K.W., Wolfe, D., Reimschiissel, B., Martin, R.A., Okeson, T.J., Hedengren, J.D., Comparison of SfM Computer Vision Point Clouds of a Landslide Derived from Multiple Small UAV Platforms and Sensors to a TLS based Model, Journal of Unmanned Vehicle Systems, 2016, doi:10.1139/juvs-2015-0043. Preprint

1. Eaton, A.N., Beal, L., Thorpe, S., Hubbell, C., Hedengren, J.D., Nybø, R., Aghito, M., Real Time Model Identification Using Multi-Fidelity Models in Managed Pressure Drilling, Computers and Chemical Engineering, accepted for publication, 2016.

1. Beal, L., Clark, J., Anderson, M., Warnick, S., Hedengren, J.D., Combined Scheduling and Control with Diurnal Constraints and Costs using a Discrete Time Formulation, FOCAPO / CPC 2017, Tuscon, AZ, Jan 2017.
2. Udy, J., Blackburn, L., Hedengren, J.D., Darby, M., Reduced Order Modeling for Reservoir Injection Optimization and Forecasting, FOCAPO / CPC 2017, Tuscon, AZ, Jan 2017.

1. Franke, K., Nguyen, T., Shao, L., Bender, C., Wolfe, D., Hedengren, J.D., Reimschiissel, B., The Use of Unmanned Aerial Vehicles (UAVs) and Structure from Motion (SfM) to Measure Volume Change at a Deep Dynamic Compaction Site, Geotechnical Frontiers, March 12-15, 2017, Orlando, Florida.

1. Udy, J., Blackburn, L., Hedengren, J.D., Darby, M., Reduced Order Modeling for Reservoir Injection Optimization and Forecasting, FOCAPO / CPC 2017, Tuscon, AZ, in review, Jan 2017.

1. Udy, J., Blackburn, L., Hedengren, J.D., Darby, M., Constrained Estimation of Higher Order Data Driven Reservoir Models, American Control Conference, Seattle, WA, in review, May 2017.

1. Eaton, A., Beal, L., Thorpe, S.D., Janis, E.H., Hubbell, C., Hedengren, J.D., Nybø, R., Aghito, M., Bjørkevoll, K., El Boubsi, R., Braaksma, J., and van Og, G., Ensemble Model Predictive Control for Robust Automated Managed Pressure Drilling, SPE Drilling & Completion, SPE-174969-MS, in review, 2016.

1. Franke, K.W., Rollins, K.M. Ledezma, C., Hedengren, J.D., Wolfe, D. Ruggles, S., Bender, C., Reimschiissel, B., Reconnaissance of Two Liquefaction Sites using Small Unmanned Aerial Vehicles and Structure from Motion Computer Vision Following the April 1, 2014 Chile Earthquake, Journal of Geotechnical and Geoenvironmental Engineering, accepted for publication.

2017

1. Park, J., Webber, T.R., Asgharzadeh Shishavan, R., Hedengren, J., Improved Bottomhole Pressure Control with Wired Drillpipe and Physics-Based Models, SPE-184610-MS, SPE/IADC Drilling Conference and Exhibition, The Hague, The Netherlands, 14-16 March 2017. Abstract

1. Hallac, B., Keyvanloo, K., Hedengren, J.D., Hecker, W.C., Argyle, M.D., An Optimized Simulation Model for Iron-Based Fischer-Tropsch Catalyst Design: Transfer Limitations as Functions of Operating and Design Conditions, Chemical Engineering Journal, Volume 263, 1 March 2015, Pages 268–279, ISSN 1385-8947, doi: 10.1016/j.cej.2014.10.108. Article

1. Ruggles, S., Clark, J., Franke, K.W., Wolfe, D., Reimschiissel, B., Martin, R.A., Okeson, T.J., Hedengren, J.D., Comparison of SfM Computer Vision Point Clouds of a Landslide Derived from Multiple Small UAV Platforms and Sensors to a TLS based Model, Journal of Unmanned Vehicle Systems, 2016, doi:10.1139/juvs-2015-0043.Preprint

1. Powell, K.M., Kim, J.S., Kapoor, K., Mojica, J.L., Hedengren, J.D., and Edgar, T.F., Thermal Energy Storage to Minimize Cost and Improve Efficiency of a Polygeneration District Energy System in a Real-time Electricity Market, Energy, 113, 52–63, 2016, doi:10.1016/j.energy.2016.07.009. Article

1. Powell, K.M., Kim, J.S., Kapoor, K., Mojica, J.L., Hedengren, J.D., and Edgar, T.F., Thermal Energy Storage and Improve Efficiency of a Polygeneration Distributed Energy System in a Real-Time Electricity Market, Energy, 113, 52–63, 2016, doi:10.1016/j.energy.2016.07.009. Article

1. Taysom, S., Hedengren, J.D., Sorensen, C., Dynamic Modeling of Friction Stir Welding for Model Predictive Control, Journal of Manufacturing Processes, 23, 165-174, 2016, doi:10.1016/j.jmapro.2016.06.004. Article

1. Taysom, S., Hedengren, J.D., Sorensen, C., Dynamic Modeling of Friction Stir Welding for Model Predictive Control, Journal of Manufacturing Processes, 23, 165-174, 2016, doi:10.1016/j.jmapro.2016.06.004. |Article

1. Taysom, S., Hedengren, J.D., Sorensen, C., Dynamic Modeling of Friction Stir Welding for Model Predictive Control, Journal of Manufacturing Processes, 23, 165-174, 2016, doi:10.1016/j.jmapro.2016.06.004. |http://www.sciencedirect.com/science/article/pii/S1526612516300603?via%3Dihub?

1. Ruggles, S., Clark, J., Franke, K.W., Wolfe, D., Reimschiissel, B., Martin, R.A., Okeson, T.J., Hedengren, J.D., Comparison of SfM Computer Vision Point Clouds of a Landslide Derived from Multiple Small UAV Platforms and Sensors to a TLS based Model, Journal of Unmanned Vehicle Systems, 2016. Preprint

1. Ruggles, S., Clark, J., Franke, K.W., Wolfe, D., Reimschiissel, B., Martin, R.A., Okeson, T.J., Hedengren, J.D., Comparison of SfM Computer Vision Point Clouds of a landslide derived from Multiple Small UAV Platforms and Sensors compared to a TLS based Model, Journal of Unmanned Vehicle Systems, 2016. Preprint Δ

1. Franke, K., Nguyen, T., Shao, L., Bender, C., Wolfe, D., Hedengren, J.D., Reimschiissel, B., The Use of Unmanned Aerial Vehicles (UAVs) and Structure from Motion (SfM) to Measure Volume Change at a Deep Dynamic Compaction Site, Geotechnical Frontiers, March 12-15, 2017, Orlando, Florida, in review.
2. Franke, K.W., Rollins, K.M. Ledezma, C., Hedengren, J.D., Wolfe, D. Ruggles, S., Bender, C., Reimschiissel, B., Journal of Geotechnical and Geoenvironmental Engineering, Reconnaissance of Two Liquefaction Sites using Small Unmanned Aerial Vehicles and Structure from Motion Computer Vision Following the April 1, 2014 Chile Earthquake, in review.

1. Taysom, S., Hedengren, J.D., Sorensen, C., Dynamic Modeling of Friction Stir Welding for Model Predictive Control, Journal of Manufacturing Processes, 23, 165-174, 2016, doi:10.1016/j.jmapro.2016.06.004.

1. Taysom, S., Hedengren, J.D., Sorensen, C., Dynamic Modeling of Friction Stir Welding for Model Predictive Control, Journal of Manufacturing Processes, 2016, accepted for publication.
2. Powell, K.M., Kim, J.S., Kapoor, K., Mojica, J.L., Hedengren, J.D., and Edgar, T.F., Thermal Energy Storage and Improve Efficiency of a Polygeneration Distributed Energy System in a Real-Time Electricity Market, Energy, 2016, accepted for publication.
3. Ruggles, S., Clark, J., Franke, K.W., Wolfe, D., Reimschiissel, B., Martin, R.A., Okeson, T.J., Hedengren, J.D., Comparison of SfM Computer Vision Point Clouds of a landslide derived from Multiple Small UAV Platforms and Sensors compared to a TLS based Model, Journal of Unmanned Vehicle Systems, 2016, accepted for publication.

1. Franke, K.W., Rollins, K.M. Ledezma, C., Hedengren, J.D., Wolfe, D. Ruggles, S., Bender, C., Reimschiissel, B., Journal of Geotechnical and Geoenvironmental Engineering, Reconnaissance of Two Liquefaction Sites using Small Unmanned Aerial Vehicles and Structure from Motion Computer Vision Following the April 1, 2014 Chile Earthquake, in review, 2016.

1. Franke, K., Nguyen, T., Shao, L., Bender, C., Wolfe, D., Hedengren, J.D., Reimschiissel, B., The Use of Unmanned Aerial Vehicles (UAVs) and Structure from Motion (SfM) to Measure Volume Change at a Deep Dynamic Compaction Site, Geotechnical Frontiers, March 12-15, 2017, Orlando, Florida.

1. Hedengren, J.D., Asgharzadeh Shishavan, R., Powell, K.M., Edgar, T.F., Nonlinear Modeling, Estimation and Predictive Control in APMonitor, Computers and Chemical Engineering, Volume 70, pg. 133–148, 2014, DOI: 10.1016/j.compchemeng.2014.04.013. Preprint Article

1. Park, J., Webber, T.R., Asgharzadeh Shishavan, R., Hedengren, J.D., Improved Bottomhole Pressure Control with Wired Drillpipe and Physics-Based Models, SPE/IADC Drilling Conference and Exhibition, 14-16 March 2017, The Hague, The Netherlands, in review.

1. Udy, J., Maddux, S., Peterson, D., Heilner, S., Stevens, K., Lignell, D., Hedengren, J.D., Review of Injection Optimization for Enhanced Oil Recovery, Journal of Petroleum Science and Engineering, in review, 2016.

1. Udy, J., Maddux, S., Peterson, D., Heilner, S., Stevens, K.,

Lignell, D., Hedengren, J.D., Review of Injection Optimization for Enhanced Oil Recovery, Journal of Petroleum Science and Engineering, in review, 2016.

1. Ruggles, S., Clark, J., Franke, K.W., Wolfe, D., Hedengren, J.D., Martin, R.A., Reimschiissel, B., Comparison of SfM Computer Vision Point Clouds of a Landslide Developed from Multiple Small UAV Platforms and Sensors, Journal of Unmanned Vehicle Systems, in review, 2016.

1. Mojica, J.L., Petersen, D.J., Hansen, B., Powell, K.M., Hedengren, J.D., Optimal Combined Long-Term Facility Design and Short-Term Operational Strategy for CHP Capacity Investments, Energy, in review, 2016.

1. Powell, K.M., Kim, J.S., Kapoor, K., Mojica, J.L., Hedengren, J.D., and Edgar, T.F., Thermal Energy Storage and Improve Efficiency of a Polygeneration Distributed Energy System in a Real-Time Electricity Market, Energy, in review, 2016.
2. Eaton, A.N., Beal, L., Thorpe, S., Hubbell, C., Hedengren, J.D., Nybø, R., Aghito, M., Real Time Model Identification Using Multi-Fidelity Models in Managed Pressure Drilling, Computers and Chemical Engineering, in review, 2016.
3. Grimsman, D., Warnick, S., Beal, L., Hedengren, J.D., Feedback Linearization of a Continuous Stirred-Tank Reactor for the Integration of Scheduling and Control, 55th IEEE Conference on Decision and Control (CDC), ARIA Resort & Casino, Las Vegas, NV, in review, 2016.

1. Taysom, S., Hedengren, J.D., Sorensen, C., Dynamic Modeling of Friction Stir Welding for Model Predictive Control, Journal of Manufacturing Processes, accepted for publication.

Pending

Taysom, S., Hedengren, J.D., Sorensen, C., Dynamic Modeling of Friction Stir Welding for Model Predictive Control, Journal of Manufacturing Processes, accepted for publication.

1. Eaton, A.N., Park, J., Thorpe, S., Webber, T., Safdarnejad, S.M., Hedengren, J.D., High-Speed Data and High-Fidelity Models: Opportunities and Challenges in Well Manufacturing, AIChE Spring Meeting, Houston, TX, April 2016. Presentation Abstract
2. Safdarnejad, S.M., Richards, J., Griffiths, J., Hedengren, J.D., Baxter, L.L., Increased Stability of a Power Grid by Energy Storage of Cryogenic Carbon Capture, AIChE Spring Meeting, Houston, TX, April 2016. Presentation Abstract
3. Nikbakhsh, S., Hedengren, J.D., Darby, M., Udy, J., Constrained Model Identification Using Open-Equation Nonlinear Optimization, AIChE Spring Meeting, Houston, TX, April 2016. Presentation Abstract

1. Safdarnejad, S.M., Hedengren, J.D., Baxter, L.L, Dynamic Optimization of a Hybrid System of Energy-Storing Cryogenic Carbon Capture and a Baseline Power Generation Unit Applied Energy, Applied Energy Journal, 172 (15), 66–79, June 2016, doi:10.1016/j.apenergy.2016.03.074. Article

1. Safdarnejad, S.M., Hedengren, J.D., Baxter, L.L, Dynamic Optimization of a Hybrid System of Energy-Storing Cryogenic Carbon Capture and a Baseline Power Generation Unit Applied Energy, Applied Energy Journal, 172 (15), 66–79, June 2016,

doi:10.1016/j.apenergy.2016.03.074. Article

1. Powell, K. M., Eaton, A. N., Hedengren, J. D., Edgar, T. F., A Continuous Formulation for Logical Decisions in Differential Algebraic Systems using Mathematical Programs of Complementarity Constraints, Processes, 2016, 4(1), 7; doi:10.3390/pr4010007. Article

1. Safdarnejad, S.M., Hedengren, J.D., Baxter, L.L, Dynamic Optimization of a Hybrid System of Energy-Storing Cryogenic Carbon Capture and a Baseline Power Generation Unit Applied Energy, Applied Energy Journal, 2016, accepted.

1. Powell, K. M., Eaton, A. N., Hedengren, J. D., Edgar, T. F., A Continuous Formulation for Logical Decisions in Differential Algebraic Systems using Mathematical Programs of Complementarity Constraints, Processes, 2016, accepted.

1. Safdarnejad, S. M., Gallacher, J. R., Hedengren, J. D., Dynamic Parameter Estimation and Optimization for Batch Distillation, Computers & Chemical Engineering, Vol. 86, pp. 18–32, 2016, DOI: 10.1016/j.compchemeng.2015.12.001.Preprint, Article
2. Martin, R.A., Rojas, I., Franke, K.W., Hedengren, J.D., Evolutionary View Planning for Optimized UAV Terrain Modeling in a Simulated Environment, Remote Sensing, 8(1), 26, 2016, DOI:10.3390/rs8010026. Article

1. Martin, R.M., Hall, A., Brinton, C., Franke, K., and Hedengren, J.D., Privacy Aware Mission Planning and Video Masking for UAV Systems, UMS-01, Unmanned Systems: Mission Management and Planning Technologies, Jan 4, 2016, AIAA Infotech at Aerospace, AIAA Science and Technology Forum and Exposition 2016, San Diego, California, USA, 4-8 Jan 2016, DOI: 10.2514/6.2016-0250. Article

1. Martin, R.M., Hall, A., Brinton, C., Franke, K., and Hedengren, J.D., Privacy Aware Mission Planning and Video Masking for UAV Systems, UMS-01, Unmanned Systems: Mission Management and Planning Technologies, Jan 4, 2016, AIAA Infotech at Aerospace, AIAA Science and Technology Forum and Exposition 2016, San Diego, California, USA, 4-8 Jan 2016. Article

1. Martin, R.A., Rojas, I., Franke, K.W., Hedengren, J.D., Evolutionary View Planning for Optimized UAV Terrain Modeling in a Simulated Environment, Remote Sensing, 8(1), 26, doi:10.3390/rs8010026, 2016. Article

 Current CV

1. Martin, R.A., Rojas, I., Franke, K.W., Hedengren, J.D., Evolutionary View Planning for Optimized UAV Terrain Modeling in a Simulated Environment, Remote Sensing, Accepted for Publication, 2016.

1. Sun, L., Hedengren, J. D., and Beard, R. W., Optimal Trajectory Generation using Model Predictive Control for Aerially Towed Cable Systems, Journal of Guidance, Control, and Dynamics, Vol. 37, Issue 2, pp. 525-539, 2014. Preprint, Article

1. Safdarnejad, S. M., Gallacher, J. R., Hedengren, J. D., Dynamic Parameter Estimation and Optimization for Batch Distillation, Computers & Chemical Engineering, 2016, accepted for publication, DOI: 10.1016/j.compchemeng.2015.12.001. Preprint, Article

2016

1. Safdarnejad, S. M., Gallacher, J. R., Hedengren, J. D., Dynamic Parameter Estimation and Optimization for Batch Distillation, Computers & Chemical Engineering, 2016, accepted for publication, DOI: 10.1016/j.compchemeng.2015.12.001. Preprint Article

1. Safdarnejad, S. M., Gallacher, J. R., Hedengren, J. D., Dynamic Parameter Estimation and Optimization for Batch Distillation, Computers & Chemical Engineering, 2016, accepted for publication, DOI: 10.1016/j.compchemeng.2015.12.001. Preprint

Upcoming

1. Safdarnejad, S. M., Gallacher, J. R., Hedengren, J. D., Dynamic Parameter Estimation and Optimization for Batch Distillation, Computers & Chemical Engineering, 2016, accepted for publication. Preprint

1. Sun, L., Castagno, J., Hedengren, J. D., and Beard, R. W., Parameter Estimation for Towed Cable Systems Using Moving Horizon Estimation, IEEE Transactions on Aerospace and Electronic Systems, Vol. 51, No. 2, April 2015. Preprint, Article

1. Eaton, A.N., Beal, L., Janis, E., Hubbell, C., Hedengren, J.D., Nybø, R., Aghito, M., Bjørkevoll, K., Addressing Control Challenges of Discontinuous Processes with Multi-Fidelity Model Predictive Control, Modeling, Control and Optimization of Energy Systems II, AIChE Annual Meeting, Salt Lake City, UT, Nov 2015. Abstract, Presentation

1. Park, J., Safdarnejad, M., Asgharzadeh Shishavan, R., Hedengren, J.D., Rastegar, R., Snell, A., Nonlinear Model Predictive Control of Managed Pressure Drilling Based on Hammerstein-Wiener Piecewise Linear Models, AIChE Annual Meeting, Salt Lake City, UT, Nov 2015. Abstract, Presentation

1. Safdarnejad, M., Hedengren, J.D., Baxter, L.B., Reduction in Cycling of the Boilers By Using Large-Scale Energy Storage of Cryogenic Carbon Capture, AIChE Annual Meeting, Salt Lake City, UT, Nov 2015. Abstract, Presentation

1. Brower, D., Hedengren, J.D., Asgharzadeh Shishavan, R., and Brower, A., Advanced Deepwater Monitoring System, ASME 32st International Conference on Ocean, Offshore and Arctic Engineering, OMAE2013/10920, Nantes, France, June 2013, ISBN: 978-0-7918-5531-7. Preprint, Presentation
2. Jacobsen, L. T. and Hedengren, J. D., Model Predictive Control with a Rigorous Model of a Solid Oxide Fuel Cell, American Control Conference (ACC), Washington, DC, pp. 3747–3752, 2013. Preprint, Presentation
3. Kelly, J. D. and Hedengren, J. D., A Steady-State Detection (SSD) Algorithm to Detect Non-Stationary Drifts in Processes, Journal of Process Control, 23, 3, pp. 326–331, March 2013. Preprint, Article

1. Hedengren, J. D., Eaton, A. N., Overview of Estimation Methods for Industrial Dynamic Systems, Special Issue on Optimization in the Oil and Gas Industry, Optimization and Engineering, Springer, 2015, DOI: 10.1007/s11081-015-9295-9. Preprint, Article

1. Hedengren, J. D., Eaton, A. N., Overview of Estimation Methods for Industrial Dynamic Systems, Special Issue on Optimization in the Oil and Gas Industry, Optimization and Engineering, Springer, 2015, DOI: 10.1007/s11081-015-9295-9. Preprint Article

1. Hedengren, J. D., Eaton, A. N., Overview of Estimation Methods for Industrial Dynamic Systems, Special Issue on Optimization in the Oil and Gas Industry, Optimization and Engineering, Springer, 2016, DOI: 10.1007/s11081-015-9295-9. Preprint

1. Palmer, L.M., Franke, K.W., Martin, R.A., Sines, B.E., Rollins, K.M., and Hedengren, J.D., The Application and Accuracy of Structure from Motion Computer Vision Models with Full-Scale Geotechnical Field Tests. Proceedings, 2015 International Foundation Congress and Equipment Expo, Paper 301, ASCE, IFCEE 2015, pp. 2432-2441, doi: 10.1061/9780784479087.225, Reston, VA, 2015. Preprint

1. Eaton, A. N., Hedengren, J. D., Overview of Estimation Methods for Industrial Dynamic Systems, Special Issue on Optimization in the Oil and Gas Industry, Optimization and Engineering, Springer, 2016, accepted. Preprint

1. Asgharzadeh Shishavan, R., Hubbell, C., Perez, H.D., Hedengren, J.D., Pixton, D.S., and Pink, A.P., Multivariate Control for Managed Pressure Drilling Systems Using High Speed Telemetry, SPE Journal, SPE-170962, Published Online 7 Oct 2015, DOI: 10.2118/170962-PA. Article

1. Asgharzadeh Shishavan, R., Hubbell, C., Perez, H.D., Hedengren, J.D., Pixton, D.S., and Pink, A.P., Multivariate Control for Managed Pressure Drilling Systems Using High Speed Telemetry, SPE Journal, SPE-170962, 7 Oct 2015, DOI: 10.2118/170962-PA. Article

 Current CV

1. Safdarnejad, S.M., Kennington, L., Baxter, L., Hedengren, J.D., Investigating the Impact of Cryogenic Carbon Capture on Power Plant Performance, American Control Conference, pp. 5016-5021, Chicago, IL, July 2015, DOI: 10.1109/ACC.2015.7172120. Preprint

1. Safdarnejad, S.M., Hedengren, J.D., Baxter, L.L., Dynamic Optimization of the Hybrid System of a Baseline Power Generation Unit and Cryogenic Carbon Capture, Western Section of the Combustion Institute, Fall 2015 Meeting, Provo, UT, October 5-6, 2015.

1. Safdarnejad, S.M., Hedengren, J.D., Lewis, N.R., Haseltine, E., Initialization Strategies for Optimization of Dynamic Systems, Computers and Chemical Engineering, 2015, Vol. 78, pp. 39-50, DOI: 10.1016/j.compchemeng.2015.04.016. Article

1. Lewis, N.R., Hedengren, J.D., Haseltine, E.L., Hybrid Dynamic Optimization Methods for Systems Biology with Efficient Sensitivities, Special Issue on Algorithms and Applications in Dynamic Optimization, Processes, 2015, 3(3), 701-729; doi:10.3390/pr3030701. Article

1. Sugiura, J., Samuel, R., Oppelt, J., Ostermeyer, G.P., Hedengren, J.D., and Pastusek, P., Drilling Modeling and Simulation: Current State and Future Goals, SPE IADC Drilling Conference and Exhibition, SPE-173045, 17-19 March 2015, UK, London. Article

1. Sugiura, J., Samuel, R., Oppelt, J., Ostermeyer, G.P., Hedengren, J.D., and Pastusek, P., Drilling Modeling and Simulation: Current State and Future Goals, SPE IADC Drilling Conference and Exhibition, SPE-173045, 17-19 March 2015, UK, London. JPT Article Δ OnePetro Article

1. Lewis, N.R., Hedengren, J.D., Haseltine, E.L., Hybrid Dynamic Optimization Methods for Systems Biology with Efficient Sensitivities, Special Issue on Algorithms and Applications in Dynamic Optimization, Processes, 2015, accepted.

1. Lewis, N.R., Hedengren, J.D., Haseltine, E.L., Hybrid Dynamic Optimization Methods for Systems Biology with Efficient Sensitivities, Processes, Special Issue on Algorithms and Applications in Dynamic Optimization, 2015, accepted.

2016

1. Martin, R.M., Hall, A., Brinton, C., Franke, K., and Hedengren, J.D., Privacy Aware Mission Planning and Video Masking for UAV Systems, UMS-01, Unmanned Systems: Mission Management and Planning Technologies, Jan 4, 2016, AIAA Infotech at Aerospace, AIAA Science and Technology Forum and Exposition 2016, San Diego, California, USA, 4-8 Jan 2016.

1. Safdarnejad, M., Gallacher, J., Hedengren, J.D., Baxter, L.B., A New Framework for Dynamic Parameter Estimation and Optimization of Batch Distillation Columns, AIChE Annual Meeting, Salt Lake City, UT, Nov 2015. Abstract

1. Eaton, A.N., Beal, L., Janis, E., Hubbell, C., Hedengren, J.D., Nybø, R., Aghito, M., Bjørkevoll, K., Addressing Control Challenges of Discontinuous Processes with Multi-Fidelity Model Predictive Control, Modeling, Control and Optimization of Energy Systems II, AIChE Annual Meeting, Salt Lake City, UT, Nov 2015. Abstract

1. Eaton, A.S., Beal, L., Janis, E., Hubbell, C., Hedengren, J.D., Nybø, R., Aghito, M., Bjørkevoll, K., Addressing Control Challenges of Discontinuous Processes with Multi-Fidelity Model Predictive Control, Modeling, Control and Optimization of Energy Systems II, AIChE Annual Meeting, Salt Lake City, UT, Nov 2015. Abstract

1. Eaton, A., Beal, L., Thorpe, S.D., Janis, E.H., Hubbell, C., Hedengren, J.D., Nybø, R., Aghito, M., Bjørkevoll, K., El Boubsi, R., Braaksma, J., and van Og, G., Ensemble Model Predictive Control for Robust Automated Managed Pressure Drilling, SPE Annual Technical Conference and Exhibition (ATCE), SPE-174969-MS, Houston, TX: 28-30 Sept 2015.

Upcoming

1. Martin, R.A., Rojas, I., Lund, C., Reimschiissel, B., Farrell, R., Franke, K., Hedengren, J.D., Optimized Terrain Surveillance with UAV Flight Path Planning, Journal of Computing in Civil Engineering, submitted, 2015.
2. Eaton, A. N., Hedengren, J. D., Overview of Estimation Methods for Industrial Dynamic Systems, Special Issue on Optimization in the Oil and Gas Industry, Optimization and Engineering, Springer, 2015, submitted. Preprint

2015

1. Safdarnejad, S.M., Hedengren, J.D., Lewis, N.R., Haseltine, E., Initialization Strategies for Optimization of Dynamic Systems, Computers and Chemical Engineering, Vol. 78, pp. 39-50, DOI: 10.1016/j.compchemeng.2015.04.016. Article
2. Safdarnejad, S.M., Hedengren, J.D., Baxter, L.L, Plant-level Dynamic Optimization of Cryogenic Carbon Capture with Conventional and Renewable Power Sources, Applied Energy Journal, Vol. 149, pp. 354-366, 2015, DOI: 10.1016/j.apenergy.2015.03.100 Article

1. Safdarnejad, S.M., Hedengren, J.D., Lewis, N.R., Haseltine, E., Initialization Strategies for Optimization of Dynamic Systems, Computers and Chemical Engineering, Vol. 78, pp. 39-50, DOI: 10.1016/j.compchemeng.2015.04.016. Article

1. Park, J., Safdarnejad, M., Asgharzadeh Shishavan, R., Hedengren, J.D., Rastegar, R., Snell, A., Nonlinear Model Predictive Control of Managed Pressure Drilling Based on Hammerstein-Wiener Piecewise Linear Models, AIChE Annual Meeting, Salt Lake City, UT, Nov 2015. Abstract
2. Safdarnejad, M., Hedengren, J.D., Baxter, L.B., Reduction in Cycling of the Boilers By Using Large-Scale Energy Storage of Cryogenic Carbon Capture, AIChE Annual Meeting, Salt Lake City, UT, Nov 2015. Abstract

1. Eaton, A., Beal, L., Thorpe, S.D., Janis, E.H., Hubbell, C., Hedengren, J.D., Nybø, R., Aghito, M., El Boubsi, R., Braaksma, J., and van Og, G., Ensemble Model Predictive Control for Robust Automated Managed Pressure Drilling, SPE Annual Technical Conference and Exhibition (ATCE), SPE-174969-MS, Houston, TX: 28-30 Sept 2015.

1. Safdarnejad, S.M., Hedengren, J.D., Baxter, L.L, Plant-level Dynamic Optimization of Cryogenic Carbon Capture with Conventional and Renewable Power Sources, Applied Energy Journal, Vol. 149, pp. 354-366, 2015, DOI: 10.1016/j.apenergy.2015.03.100 Article

1. Asgharzadeh Shishavan, R., Hubbell, C., Perez, H.D., Hedengren, J.D., Pixton, D.S., and Pink, A.P., Multivariate Control for Managed Pressure Drilling Systems Using High Speed Telemetry, SPE Journal, SPE-170962, accepted for publication.

1. Hallac, B., Kayvanloo, K., Hedengren, J.D., Hecker, W.C., Argyle, M.D., An Optimized Simulation Model for Iron-Based Fischer-Tropsch Catalyst Design: Transfer Limitations as Functions of Operating and Design Conditions, Chemical Engineering Journal, Volume 263, 1 March 2015, Pages 268–279, ISSN 1385-8947, doi: 10.1016/j.cej.2014.10.108. Article

1. Safdarnejad, S.M., Hedengren, J.D., Lewis, N.R., Haseltine, E., Initialization Strategies for Optimization of Dynamic Systems, Computers and Chemical Engineering, Vol. 78, pp. 39-50, DOI: 10.1016/j.compchemeng.2015.04.016. Article (Free Download until July 5, 2015)

1. Safdarnejad, S.M., Hedengren, J.D., Lewis, N.R., Haseltine, E., Initialization Strategies for Optimization of Dynamic Systems, Computers and Chemical Engineering, Vol. 78, pp. 39-50, DOI: 10.1016/j.compchemeng.2015.04.016. Article (Free Download until July 5, 2015

1. Eaton, A., Hedengren, J.D., Nybø, R., Aghito, M., El Boubsi, R., Braaksma, J., and van Og, G., Model Redundancy for Data Validation in Automated Managed Pressure Drilling, SPE Annual Technical Conference and Exhibition (ATCE), SPE-174969-MS, Houston, TX: 28-30 Sept 2015.

1. Eaton, A., Hedengren, J.D., Nybø, R., Aghito, M., El Boubsi, R., Braaksma, J., and van Og, G., Model Redundancy for Data Validation in Automated Managed Pressure Drilling, SPE Annual Technical Conference and Exhibition (ATCE), Houston, TX: 28-30 Sept 2015.

1. Asgharzadeh Shishavan, R., Hubbell, C., Perez, H.D., Hedengren, J.D., and Pixton, D.S., Combined Rate of Penetration and Pressure Regulation for Drilling Optimization Using High Speed Telemetry, SPE Drilling & Completion Journal, SPE-170275-PA, 30 (1), pp. 17-26, 5 March 2015. Article

1. Safdarnejad, S.M., Hedengren, J.D., Lewis, N.R., Haseltine, E., Initialization Strategies for Optimization of Dynamic Systems, Computers and Chemical Engineering, DOI: 10.1016/j.compchemeng.2015.04.016. Article

1. Safdarnejad, S.M., Hedengren, J.D., Lewis, N.R., Haseltine, E., Initialization Strategies for Optimization of Dynamic Systems, Computers and Chemical Engineering, DOI: 10.1016/j.compchemeng.2015.04.016, accepted.

1. Safdarnejad, S.M., Hedengren, J.D., Baxter, L.L, Plant-level Dynamic Optimization of Cryogenic Carbon Capture with Conventional and Renewable Power Sources, Applied Energy Journal, Vol. 149, pp. 354-366, 2015, DOI: 10.1016/j.apenergy.2015.03.100 Article (Free Download until June 4, 2015)
2. Safdarnejad, S.M., Hedengren, J.D., Lewis, N.R., Haseltine, E., Initialization Strategies for Optimization of Dynamic Systems, Computers and Chemical Engineering, accepted.

1. Safdarnejad, S.M., Hedengren, J.D., Lewis, N.R., Haseltine, E., Initialization Strategies for Optimization of Dynamic Systems, Computers and Chemical Engineering, submitted.

1. Safdarnejad, S.M., Hedengren, J.D., Baxter, L.L, Plant-level Dynamic Optimization of Cryogenic Carbon Capture with Conventional and Renewable Power Sources, Applied Energy Journal, accepted, DOI: 10.1016/j.apenergy.2015.03.100