Blar i NTNU Open på forfatter "Bradford, Eric"
-
Cautious MPC-based control with Machine Learning
Langåker, Helge-Andre (Master thesis, 2018)Using Gaussian processes as a nonparametric regression model together with model predictive control has the recent years showed promising results by utilizing the expected uncertainty that follows the GP. By utilizing the ... -
Distributed learning for wind farm optimization with Gaussian processes
Andersson, Leif Erik; Bradford, Eric; Imsland, Lars Struen (Journal article; Peer reviewed, 2020) -
Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes
Bradford, Eric; Schweidtmann, Artur M.; Zhang, Dongda; Jing, Keju; del Rio-Chanona, Ehecatl Antonio (Journal article; Peer reviewed, 2018)Dynamic modeling is an important tool to gain better understanding of complex bioprocesses and to determine optimal operating conditions for process control. Currently, two modeling methodologies have been applied to ... -
Economic Stochastic Model Predictive Control Using the Unscented Kalman Filter
Bradford, Eric; Imsland, Lars Struen (Journal article; Peer reviewed, 2018)Economic model predictive control is a popular method to maximize the efficiency of a dynamic system. Often, however, uncertainties are present, which can lead to lower performance and constraint violations. In this paper, ... -
Economic stochastic nonlinear model predictive control of a semi-batch polymerization reaction
Bradford, Eric; Imsland, Lars Struen (Journal article; Peer reviewed, 2019)Batch processes are ubiquitous in the chemical industry and difficult to control, such that nonlinear model predictive control is one of the few promising control techniques. Many chemical process models however are affected ... -
Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm
Bradford, Eric; Schweidtmann, Artur M.; Lapkin, Alexei A. (Journal article; Peer reviewed, 2018)Many engineering problems require the optimization of expensive, black-box functions involving multiple conflicting criteria, such that commonly used methods like multiobjective genetic algorithms are inadequate. To tackle ... -
Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives
Schweidtmann, Artur M.; Clayton, Adam D; Holmes, Nicholas; Bradford, Eric; Richard A, Bourne; Lapkin, AA (Journal article; Peer reviewed, 2018)Automated development of chemical processes requires access to sophisticated algorithms for multi-objective optimization, since single-objective optimization fails to identify the trade-offs between conflicting performance ... -
Modifier-Adaptation Schemes Employing Gaussian Processes and Trust Regions for Real-Time Optimization
del Rio Chanona, Ehecatl Antonio; Alves Graciano, José Eduardo; Bradford, Eric; Chachuat, Benoit (Journal article; Peer reviewed, 2019)This paper investigates modifier-adaptation schemes based on Gaussian processes to handle plant-model mismatch in real-time optimization of uncertain processes. Building upon the recent work by Ferreira et al. [European ... -
Nonlinear model predictive control with explicit back-offs for Gaussian process state space models
Bradford, Eric; Imsland, Lars Struen; del Rio Chanona, Ehecatl Antonio (Chapter, 2019)Nonlinear model predictive control (NMPC) is an efficient control approach for multivariate nonlinear dynamic systems with process constraints. NMPC does however require a plant model to be available. A powerful tool to ... -
Output feedback stochastic nonlinear model predictive control for batch processes
Bradford, Eric; Imsland, Lars Struen (Journal article; Peer reviewed, 2019)Batch processes play a vital role in the chemical industry, but are difficult to control due to highly nonlinear behaviour and unsteady state operation. Nonlinear model predictive control (NMPC) is therefore one of the few ... -
Output feedback stochastic nonlinear model predictive control of a polymerization batch process
Bradford, Eric; Reble, Marcus; Imsland, Lars Struen (Chapter; Peer reviewed, 2019)Nonlinear model predictive control (NMPC) is one of the few methods that can handle multivariate nonlinear control problems while accounting for process constraints. Many dynamic models are however affected by significant ... -
Reinforcement Learning for Batch Bioprocess Optimization
Petsagkourakis, Panagiotis; Sandoval, Ilya Orson; Bradford, Eric; Zhang, Dongda; del Rio-Chanona, Ehecatl Antonio (Peer reviewed; Journal article, 2020)Bioprocesses have received a lot of attention to produce clean and sustainable alternatives to fossil-based materials. However, they are generally difficult to optimize due to their unsteady-state operation modes and ... -
Reinforcement Learning for Batch-to-Batch Bioprocess Optimisation
Petsagkourakis, Panagiotis; Sandoval, Ilya Orson; Bradford, Eric; Zhang, Dongda; del Rio-Chanona, Ehecatl Antonio (Chapter, 2019)Bioprocesses have received great attention from the scientific community as an alternative to fossil-based products by microorganisms-synthesised counterparts. However, bioprocesses are generally operated at unsteady-state ... -
Review of advanced physical and data‐driven models for dynamic bioprocess simulation: Case study of algae–bacteria consortium wastewater treatment
del Rio-Chanona, Ehecatl Antonio; Cong, Xiaoying; Bradford, Eric; Zhang, Dongda; Jing, Keju (Peer reviewed; Journal article, 2019)Microorganism production and remediation processes are of critical importance to the next generation of sustainable industries. Undertaking mathematical treatment of dynamic biosystems operating at any spatial or temporal ... -
Stochastic data-driven model predictive control using gaussian processes
Bradford, Eric; Imsland, Lars Struen; Zhang, Dongda; Chanona del Rio, Ehecatl Antonio (Peer reviewed; Journal article, 2020)Nonlinear model predictive control (NMPC) is one of the few control methods that can handle multivariable nonlinear control systems with constraints. Gaussian processes (GPs) present a powerful tool to identify the required ... -
Stochastic NMPC of Batch Processes Using Parameterized Control Policies
Bradford, Eric; Imsland, Lars Struen (Journal article; Peer reviewed, 2018)Nonlinear model predictive control (NMPC) is an effective method for optimal operation of batch processes. Most dynamic models however contain significant uncertainties. It is therefore important to take these uncertainties ... -
Stochastic nonlinear model predictive control for chemical batch processes
Bradford, Eric (Doctoral theses at NTNU;2020:132, Doctoral thesis, 2020)The chemical industry is a vital part of the world economy transforming raw materials into crucial intermediary products. Batch processes are common in many sectors of the chemical industry, which are gaining in importance ... -
Stochastic nonlinear model predictive control of a batch fermentation process
Bradford, Eric; Imsland, Lars Struen (Journal article; Peer reviewed, 2019)Nonlinear model predictive control (NMPC) is an attractive control approach to regulate batch processes reliant on an accurate dynamic model. Most dynamic models however are affected by significant uncertainties, which may ... -
Stochastic Nonlinear Model Predictive Control Using Gaussian Processes
Bradford, Eric; Imsland, Lars Struen (Chapter, 2018)Model predictive control is a popular control approach for multivariable systems with important process constraints. The presence of significant stochastic uncertainties can however lead to closed-loop performance and ... -
Stochastic Nonlinear Model Predictive Control with State Estimation by Incorporation of the Unscented Kalman Filter
Bradford, Eric; Imsland, Lars Struen (Journal article, 2017)Nonlinear model predictive control has become a popular approach to deal with highly nonlinear and unsteady state systems, the performance of which can however deteriorate due to unaccounted uncertainties. Model predictive ...