John Hedengren | Brigham Young University (original) (raw)
Papers by John Hedengren
Optimization and Engineering, 2015
Measurement technology is advancing in the oil and gas industry. Factors such as wireless transmi... more Measurement technology is advancing in the oil and gas industry. Factors such as wireless transmitters, reduced cost of measurement technology, and increased regulations that require active monitoring tend to increase the number of available measurements. There is a clear opportunity to distill the recent flood of measurements into relevant and actionable information. Common methods to do this include a filtered bias update, Implicit Dynamic Feedback, Kalman Filtering, and Moving Horizon Estimation. The purpose of these techniques is to validate measurements and align imperfect mathematical models to the actual process. Additionally, they can determine a best-estimate of the current state of the process and any potential disturbances. These methods allow potential improvements in earlier detection of disturbances, process equipment faults, and improved state estimates for optimization and control.
Algorithms
Simple and easy to use methods are of great practical demand in the design of Proportional, Integ... more Simple and easy to use methods are of great practical demand in the design of Proportional, Integral, and Derivative (PID) controllers. Controller design criteria are to achieve a good set-point tracking and disturbance rejection with minimal actuator variation. Achieving satisfactory trade-offs between these performance criteria is not easily accomplished with classical tuning methods. A particle swarm optimization technique is proposed to design PID controllers. The design method minimizes a compromise cost function based on both the integral absolute error and control signal total variation criteria. The proposed technique is tested on an Arduino-based Temperature Control Laboratory (TCLab) and compared with the Grey Wolf Optimization algorithm. Both TCLab simulation and physical data show that satisfactory trade-offs between the performance and control effort are enabled with the proposed technique.
Processes
This Special Issue (SI) of Processes, "Combined Scheduling and Control," includes approaches to f... more This Special Issue (SI) of Processes, "Combined Scheduling and Control," includes approaches to formulating combined objective functions, multi-scale approaches to integration, mixed discrete and continuous formulations, estimation of uncertain control and scheduling states, mixed integer and nonlinear programming advances, benchmark development, comparison of centralized and decentralized methods, and software that facilitates the creation of new applications and long-term sustainment of benefits. Contributions acknowledge strengths, weaknesses, and potential further advancements, along with a demonstration of improvement over current industrial best-practice. Advanced optimization algorithms and increased computational resources are opening new possibilities to integrate control and scheduling. Some of the most popular advanced control methods today were conceptualized decades ago. Over a time span of 30 years, computers have increased in speed by about 17,000 times and algorithms such as integer programming have a speedup of approximately 150,000 times on some benchmark problems. With the combined hardware and software improvements, benchmark problems can now be solved 2.5 billion times faster; i.e., applications that formerly required 120 years to solve are now completed in 5 s [1]. New computing architectures and algorithms advance the frontier of solving larger scale and more complex integrated problems. Recent work demonstrates economic and operational incentives for merging scheduling and control. The accepted publications cover a range of topics and methods for combining control and scheduling. There were many submissions to the special issue, and about 50% were accepted for publication. The seven that were accepted have novel approaches, summary surveys, and illustrative examples that validate the methods and motivate further investigation. The articles are summarized below. Lefebvre, D. Dynamical Scheduling and Robust Control in Uncertain Environments with Petri Nets for DESs [2]. This paper is about the incremental computation of control sequences for discrete event systems in uncertain environments through implementation of timed Petri nets. The robustness of the resulting trajectory is also evaluated according to risk probability. A sufficient condition is provided to compute robust trajectories. The proposed results are applicable to a large class of discrete event systems, in particular in the domains of flexible manufacturing. Joglekar, G. Using Simulation for Scheduling and Rescheduling of Batch Processes [3]. This paper uses a BATCHES simulation model to accurately represent the complex recipes and operating rules typically encountered in batch process manufacturing. By using the advanced capabilities of the simulator (such as modeling assignment decisions, coordination logic, and plant operation rules), very reliable and verifiable schedules can be generated for the underlying process. Scheduling methodologies for a one-segment recipe and a rescheduling methodology for day-today decisions are presented. Gupta, D.; Maravelias, C. A General State-Space Formulation for Online Scheduling [4]. This paper presents a generalized state-space model formulation particularly motivated by an online scheduling perspective, which allows for the modeling of (1) task-delays and unit breakdowns; (2) fractional delays and unit downtimes when using a discrete-time grid; (3) variable batch-sizes; (4) robust scheduling through the use of conservative yield estimates and processing times;
Processes
A novel formulation for combined scheduling and control of multi-product, continuous chemical pro... more A novel formulation for combined scheduling and control of multi-product, continuous chemical processes is introduced in which nonlinear model predictive control (NMPC) and noncyclic continuous-time scheduling are efficiently combined. A decomposition into nonlinear programming (NLP) dynamic optimization problems and mixed-integer linear programming (MILP) problems, without iterative alternation, allows for computationally light solution. An iterative method is introduced to determine the number of production slots for a noncyclic schedule during a prediction horizon. A filter method is introduced to reduce the number of MILP problems required. The formulation's closed-loop performance with both process disturbances and updated market conditions is demonstrated through multiple scenarios on a benchmark continuously stirred tank reactor (CSTR) application with fluctuations in market demand and price for multiple products. Economic performance surpasses cyclic scheduling in all scenarios presented. Computational performance is sufficiently light to enable online operation in a dual-loop feedback structure.
SPE/IADC International Drilling Conference and Exhibition
The drilling industry has substantially improved performance based on knowledge from physics-base... more The drilling industry has substantially improved performance based on knowledge from physics-based, statistical, and empirical models of components and systems. However, most models and source code have been recreated multiple times, which requires significant effort and energy with little additional benefit or step-wise improvements. The authors propose that it is time to form a coalition of industry and academic leaders to support an open source effort for drilling, to encourage the reuse of continuously improving models and coding efforts. The vision for this guiding coalition is to 1) set up a repository for source code, data, benchmarks, and documentation, 2) encourage good coding practices, 3) review and comment on the models and data submitted, 4) test, use and improve the code, 5) propose and collect anonymized real data, 6) attract talent and support to the effort, and 7) mentor those getting started. Those interested to add their time and talent to the cause may publish th...
Geosciences
A sequence of large earthquakes in central Italy ranging in moment magnitudes (Mw) from 4.2 to 6.... more A sequence of large earthquakes in central Italy ranging in moment magnitudes (Mw) from 4.2 to 6.5 caused significant damage to many small towns in the area. After each earthquake in 2016 (24 August and 26 October), automated small unmanned aerial vehicles (sUAV) acquired valuable imagery data for post-hazard reconnaissance in the mountain village of Pescara del Tronto, and were applied to 3D reconstruction using Structure-from-Motion (SfM). In July 2018, the site was again monitored to obtain additional imagery data capturing changes since the last visit following the 30 October 2016 Earthquake. A genetic-based mission-planning algorithm that delivers optimal viewpoints and path planning was field tested and reduced the required photos for 3D reconstruction by 9.1%. The optimized 3D model provides a better understanding of the current conditions of the village, when compared to the nadir models, by containing fewer holes on angled surfaces, including an additional 17% surface area,...
Sensors
This study presents a novel multi-scale view-planning algorithm for automated targeted inspection... more This study presents a novel multi-scale view-planning algorithm for automated targeted inspection using unmanned aircraft systems (UAS). In industrial inspection, it is important to collect the most relevant data to keep processing demands, both human and computational, to a minimum. This study investigates the viability of automated targeted multi-scale image acquisition for Structure from Motion (SfM)-based infrastructure modeling. A traditional view-planning approach for SfM is extended to a multi-scale approach, planning for targeted regions of high, medium, and low priority. The unmanned aerial vehicle (UAV) can traverse the entire aerial space and facilitates collection of an optimized set of views, both close to and far away from areas of interest. The test case for field validation is the Tibble Fork Dam in Utah. Using the targeted multi-scale flight planning, a UAV automatically flies a tiered inspection using less than 25% of the number of photos needed to model the entire...
Processes
Performance of integrated production scheduling and advanced process control with disturbances is... more Performance of integrated production scheduling and advanced process control with disturbances is summarized and reviewed with four progressive stages of scheduling and control integration and responsiveness to disturbances: open-loop segregated scheduling and control, closed-loop segregated scheduling and control, open-loop scheduling with consideration of process dynamics, and closed-loop integrated scheduling and control responsive to process disturbances and market fluctuations. Progressive economic benefit from dynamic rescheduling and integrating scheduling and control is shown on a continuously stirred tank reactor (CSTR) benchmark application in closed-loop simulations over 24 h. A fixed horizon integrated scheduling and control formulation for multi-product, continuous chemical processes is utilized, in which nonlinear model predictive control (NMPC) and continuous-time scheduling are combined.
Optimization and Engineering
Processes
In order to support the development of early career researchers involved in chemical and biologic... more In order to support the development of early career researchers involved in chemical and biological process/systems engineering, Processes launched the second Travel Awards for Post-doctoral Fellows and Ph. [...]
Processes
This paper introduces GEKKO as an optimization suite for Python. GEKKO specializes in dynamic opt... more This paper introduces GEKKO as an optimization suite for Python. GEKKO specializes in dynamic optimization problems for mixed-integer, nonlinear, and differential algebraic equations (DAE) problems. By blending the approaches of typical algebraic modeling languages (AML) and optimal control packages, GEKKO greatly facilitates the development and application of tools such as nonlinear model predicative control (NMPC), real-time optimization (RTO), moving horizon estimation (MHE), and dynamic simulation. GEKKO is an object-oriented Python library that offers model construction, analysis tools, and visualization of simulation and optimization. In a single package, GEKKO provides model reduction, an object-oriented library for data reconciliation/model predictive control, and integrated problem construction/solution/visualization. This paper introduces the GEKKO Optimization Suite, presents GEKKO’s approach and unique place among AMLs and optimal control packages, and cites several exam...
SPE Bergen One Day Seminar
Physics-based hydraulic models are essential for proceeding to a high level of automation in dril... more Physics-based hydraulic models are essential for proceeding to a high level of automation in drilling. Mathematical models can facilitate process understanding and problem detection, and determine appropriate actions in case of mismatch between model and data. Furthermore, calculations may replace measurements where and when the latter are not available, as normally occurs during connections or when instruments or signal transmissions fail. However, advanced hydraulic models rely on a large set of inputs, such as pipe and wellbore geometry, various tuning parameters and fluid properties. The models are therefore time-consuming and difficult to configure in the field, where third-party experts may be needed at each well, to properly initiate the automation system and adjust it during the drilling process. Although the methods described in this paper are relevant to any critical drilling operation, they are applied to Managed Pressure Drilling (MPD) as a widely deployed example of dri...
Computers Chemical Engineering, Apr 1, 2008
In situ adaptive tabulation (ISAT) is applied to store and retrieve solutions of nonlinear model ... more In situ adaptive tabulation (ISAT) is applied to store and retrieve solutions of nonlinear model predictive control (NMPC) problems. ISAT controls approximation error by adaptively building the database of NMPC solutions with piecewise linear local approximations. Unlike initial state or constraint parameterized constrained quadratic programming (QP) solutions, ISAT approximates NMPC solutions within a specified tolerance, thereby easing the dimensionality difficulties
Optimization and Engineering, 2015
Measurement technology is advancing in the oil and gas industry. Factors such as wireless transmi... more Measurement technology is advancing in the oil and gas industry. Factors such as wireless transmitters, reduced cost of measurement technology, and increased regulations that require active monitoring tend to increase the number of available measurements. There is a clear opportunity to distill the recent flood of measurements into relevant and actionable information. Common methods to do this include a filtered bias update, Implicit Dynamic Feedback, Kalman Filtering, and Moving Horizon Estimation. The purpose of these techniques is to validate measurements and align imperfect mathematical models to the actual process. Additionally, they can determine a best-estimate of the current state of the process and any potential disturbances. These methods allow potential improvements in earlier detection of disturbances, process equipment faults, and improved state estimates for optimization and control.
Algorithms
Simple and easy to use methods are of great practical demand in the design of Proportional, Integ... more Simple and easy to use methods are of great practical demand in the design of Proportional, Integral, and Derivative (PID) controllers. Controller design criteria are to achieve a good set-point tracking and disturbance rejection with minimal actuator variation. Achieving satisfactory trade-offs between these performance criteria is not easily accomplished with classical tuning methods. A particle swarm optimization technique is proposed to design PID controllers. The design method minimizes a compromise cost function based on both the integral absolute error and control signal total variation criteria. The proposed technique is tested on an Arduino-based Temperature Control Laboratory (TCLab) and compared with the Grey Wolf Optimization algorithm. Both TCLab simulation and physical data show that satisfactory trade-offs between the performance and control effort are enabled with the proposed technique.
Processes
This Special Issue (SI) of Processes, "Combined Scheduling and Control," includes approaches to f... more This Special Issue (SI) of Processes, "Combined Scheduling and Control," includes approaches to formulating combined objective functions, multi-scale approaches to integration, mixed discrete and continuous formulations, estimation of uncertain control and scheduling states, mixed integer and nonlinear programming advances, benchmark development, comparison of centralized and decentralized methods, and software that facilitates the creation of new applications and long-term sustainment of benefits. Contributions acknowledge strengths, weaknesses, and potential further advancements, along with a demonstration of improvement over current industrial best-practice. Advanced optimization algorithms and increased computational resources are opening new possibilities to integrate control and scheduling. Some of the most popular advanced control methods today were conceptualized decades ago. Over a time span of 30 years, computers have increased in speed by about 17,000 times and algorithms such as integer programming have a speedup of approximately 150,000 times on some benchmark problems. With the combined hardware and software improvements, benchmark problems can now be solved 2.5 billion times faster; i.e., applications that formerly required 120 years to solve are now completed in 5 s [1]. New computing architectures and algorithms advance the frontier of solving larger scale and more complex integrated problems. Recent work demonstrates economic and operational incentives for merging scheduling and control. The accepted publications cover a range of topics and methods for combining control and scheduling. There were many submissions to the special issue, and about 50% were accepted for publication. The seven that were accepted have novel approaches, summary surveys, and illustrative examples that validate the methods and motivate further investigation. The articles are summarized below. Lefebvre, D. Dynamical Scheduling and Robust Control in Uncertain Environments with Petri Nets for DESs [2]. This paper is about the incremental computation of control sequences for discrete event systems in uncertain environments through implementation of timed Petri nets. The robustness of the resulting trajectory is also evaluated according to risk probability. A sufficient condition is provided to compute robust trajectories. The proposed results are applicable to a large class of discrete event systems, in particular in the domains of flexible manufacturing. Joglekar, G. Using Simulation for Scheduling and Rescheduling of Batch Processes [3]. This paper uses a BATCHES simulation model to accurately represent the complex recipes and operating rules typically encountered in batch process manufacturing. By using the advanced capabilities of the simulator (such as modeling assignment decisions, coordination logic, and plant operation rules), very reliable and verifiable schedules can be generated for the underlying process. Scheduling methodologies for a one-segment recipe and a rescheduling methodology for day-today decisions are presented. Gupta, D.; Maravelias, C. A General State-Space Formulation for Online Scheduling [4]. This paper presents a generalized state-space model formulation particularly motivated by an online scheduling perspective, which allows for the modeling of (1) task-delays and unit breakdowns; (2) fractional delays and unit downtimes when using a discrete-time grid; (3) variable batch-sizes; (4) robust scheduling through the use of conservative yield estimates and processing times;
Processes
A novel formulation for combined scheduling and control of multi-product, continuous chemical pro... more A novel formulation for combined scheduling and control of multi-product, continuous chemical processes is introduced in which nonlinear model predictive control (NMPC) and noncyclic continuous-time scheduling are efficiently combined. A decomposition into nonlinear programming (NLP) dynamic optimization problems and mixed-integer linear programming (MILP) problems, without iterative alternation, allows for computationally light solution. An iterative method is introduced to determine the number of production slots for a noncyclic schedule during a prediction horizon. A filter method is introduced to reduce the number of MILP problems required. The formulation's closed-loop performance with both process disturbances and updated market conditions is demonstrated through multiple scenarios on a benchmark continuously stirred tank reactor (CSTR) application with fluctuations in market demand and price for multiple products. Economic performance surpasses cyclic scheduling in all scenarios presented. Computational performance is sufficiently light to enable online operation in a dual-loop feedback structure.
SPE/IADC International Drilling Conference and Exhibition
The drilling industry has substantially improved performance based on knowledge from physics-base... more The drilling industry has substantially improved performance based on knowledge from physics-based, statistical, and empirical models of components and systems. However, most models and source code have been recreated multiple times, which requires significant effort and energy with little additional benefit or step-wise improvements. The authors propose that it is time to form a coalition of industry and academic leaders to support an open source effort for drilling, to encourage the reuse of continuously improving models and coding efforts. The vision for this guiding coalition is to 1) set up a repository for source code, data, benchmarks, and documentation, 2) encourage good coding practices, 3) review and comment on the models and data submitted, 4) test, use and improve the code, 5) propose and collect anonymized real data, 6) attract talent and support to the effort, and 7) mentor those getting started. Those interested to add their time and talent to the cause may publish th...
Geosciences
A sequence of large earthquakes in central Italy ranging in moment magnitudes (Mw) from 4.2 to 6.... more A sequence of large earthquakes in central Italy ranging in moment magnitudes (Mw) from 4.2 to 6.5 caused significant damage to many small towns in the area. After each earthquake in 2016 (24 August and 26 October), automated small unmanned aerial vehicles (sUAV) acquired valuable imagery data for post-hazard reconnaissance in the mountain village of Pescara del Tronto, and were applied to 3D reconstruction using Structure-from-Motion (SfM). In July 2018, the site was again monitored to obtain additional imagery data capturing changes since the last visit following the 30 October 2016 Earthquake. A genetic-based mission-planning algorithm that delivers optimal viewpoints and path planning was field tested and reduced the required photos for 3D reconstruction by 9.1%. The optimized 3D model provides a better understanding of the current conditions of the village, when compared to the nadir models, by containing fewer holes on angled surfaces, including an additional 17% surface area,...
Sensors
This study presents a novel multi-scale view-planning algorithm for automated targeted inspection... more This study presents a novel multi-scale view-planning algorithm for automated targeted inspection using unmanned aircraft systems (UAS). In industrial inspection, it is important to collect the most relevant data to keep processing demands, both human and computational, to a minimum. This study investigates the viability of automated targeted multi-scale image acquisition for Structure from Motion (SfM)-based infrastructure modeling. A traditional view-planning approach for SfM is extended to a multi-scale approach, planning for targeted regions of high, medium, and low priority. The unmanned aerial vehicle (UAV) can traverse the entire aerial space and facilitates collection of an optimized set of views, both close to and far away from areas of interest. The test case for field validation is the Tibble Fork Dam in Utah. Using the targeted multi-scale flight planning, a UAV automatically flies a tiered inspection using less than 25% of the number of photos needed to model the entire...
Processes
Performance of integrated production scheduling and advanced process control with disturbances is... more Performance of integrated production scheduling and advanced process control with disturbances is summarized and reviewed with four progressive stages of scheduling and control integration and responsiveness to disturbances: open-loop segregated scheduling and control, closed-loop segregated scheduling and control, open-loop scheduling with consideration of process dynamics, and closed-loop integrated scheduling and control responsive to process disturbances and market fluctuations. Progressive economic benefit from dynamic rescheduling and integrating scheduling and control is shown on a continuously stirred tank reactor (CSTR) benchmark application in closed-loop simulations over 24 h. A fixed horizon integrated scheduling and control formulation for multi-product, continuous chemical processes is utilized, in which nonlinear model predictive control (NMPC) and continuous-time scheduling are combined.
Optimization and Engineering
Processes
In order to support the development of early career researchers involved in chemical and biologic... more In order to support the development of early career researchers involved in chemical and biological process/systems engineering, Processes launched the second Travel Awards for Post-doctoral Fellows and Ph. [...]
Processes
This paper introduces GEKKO as an optimization suite for Python. GEKKO specializes in dynamic opt... more This paper introduces GEKKO as an optimization suite for Python. GEKKO specializes in dynamic optimization problems for mixed-integer, nonlinear, and differential algebraic equations (DAE) problems. By blending the approaches of typical algebraic modeling languages (AML) and optimal control packages, GEKKO greatly facilitates the development and application of tools such as nonlinear model predicative control (NMPC), real-time optimization (RTO), moving horizon estimation (MHE), and dynamic simulation. GEKKO is an object-oriented Python library that offers model construction, analysis tools, and visualization of simulation and optimization. In a single package, GEKKO provides model reduction, an object-oriented library for data reconciliation/model predictive control, and integrated problem construction/solution/visualization. This paper introduces the GEKKO Optimization Suite, presents GEKKO’s approach and unique place among AMLs and optimal control packages, and cites several exam...
SPE Bergen One Day Seminar
Physics-based hydraulic models are essential for proceeding to a high level of automation in dril... more Physics-based hydraulic models are essential for proceeding to a high level of automation in drilling. Mathematical models can facilitate process understanding and problem detection, and determine appropriate actions in case of mismatch between model and data. Furthermore, calculations may replace measurements where and when the latter are not available, as normally occurs during connections or when instruments or signal transmissions fail. However, advanced hydraulic models rely on a large set of inputs, such as pipe and wellbore geometry, various tuning parameters and fluid properties. The models are therefore time-consuming and difficult to configure in the field, where third-party experts may be needed at each well, to properly initiate the automation system and adjust it during the drilling process. Although the methods described in this paper are relevant to any critical drilling operation, they are applied to Managed Pressure Drilling (MPD) as a widely deployed example of dri...
Computers Chemical Engineering, Apr 1, 2008
In situ adaptive tabulation (ISAT) is applied to store and retrieve solutions of nonlinear model ... more In situ adaptive tabulation (ISAT) is applied to store and retrieve solutions of nonlinear model predictive control (NMPC) problems. ISAT controls approximation error by adaptively building the database of NMPC solutions with piecewise linear local approximations. Unlike initial state or constraint parameterized constrained quadratic programming (QP) solutions, ISAT approximates NMPC solutions within a specified tolerance, thereby easing the dimensionality difficulties