Brandon Corbett - Academia.edu (original) (raw)

Papers by Brandon Corbett

Research paper thumbnail of Numerical simulation of the general rate model of chromatography using orthogonal collocation

Computers & Chemical Engineering

Research paper thumbnail of Determining appropriate input excitation for model identification of a continuous bio-process

Digital Chemical Engineering

Research paper thumbnail of Data Driven Modeling and Model Predictive Control of Bioreactor for Production of Monoclonal Antibodies

2022 American Control Conference (ACC)

Research paper thumbnail of Model predictive control using subspace model identification

Computers & Chemical Engineering, 2021

Abstract This paper addresses the problem of designing and implementing a data-driven model based... more Abstract This paper addresses the problem of designing and implementing a data-driven model based model predictive controller (MPC). In particular, we consider the problem where a subspace identification approach is utilized to determine a state-space model, while applying first-principles based knowledge in the model identification (denoted as the constrained subspace model). The incorporation of the first-principles based constraints in the subspace matrix Patel et al. (2020) often leads to a feed-through matrix being present. Such a model then is the best representation of the system dynamics, but does not lend itself readily to existing linear MPC formulations where the feed-through matrix is assumed to be zero. Thus, an existing linear MPC formulation is adapted to handle the feed through matrix. The superior performance of this MPC design, which can utilize the constrained subspace model, over existing approaches is demonstrated using a two tank chemical stirred tank reactor process.

Research paper thumbnail of Batch Process Modeling and Control: Background

Modeling and Control of Batch Processes, 2018

Batch processes are an indispensable constituent of chemical process industries and are universal... more Batch processes are an indispensable constituent of chemical process industries and are universally used for manufacturing of high-quality products. The preeminent reason for their popularity can be attributed to their flexibility to control different grades of products by changing the initial conditions and input trajectories. However, a batch process is characterized by absence of operation around equilibrium conditions resulting in highly nonlinear dynamics, which make the classical approaches (for continuous processes) not directly applicable. The present chapter details the existing approaches for modeling and control as they pertain to batch processes.

Research paper thumbnail of Accelerating Product Innovation at Dow through Multivariate Modeling (Poster)

Research paper thumbnail of Safe-Steering of Batch Processes

Modeling and Control of Batch Processes, 2018

This Chapter considers the problem of controlling batch processes to achieve a desired final prod... more This Chapter considers the problem of controlling batch processes to achieve a desired final product quality subject to input constraints and faults in the control actuators. Specifically, faults are considered that cannot be handled via robust control approaches, and preclude the ability to reach the desired end-point, necessitating fault-rectification. A safe-steering framework is developed to address the problem of determining how to utilize the functioning inputs during fault rectification in order to ensure that after fault-rectification, the desired product properties can be reached upon batch termination. To this end, first a novel reverse-time reachability region (we define the reverse-time reachability region as the set of states from where the desired end-point can be reached by batch termination) based MPC is formulated that reduces online computations, as well as provides a useful tool for handling faults. Next, a safe-steering framework is developed that utilizes the re...

Research paper thumbnail of Subspace based model identification for missing data

AIChE Journal, 2020

This paper addresses the problem of missing process data in data-driven dynamic modeling approach... more This paper addresses the problem of missing process data in data-driven dynamic modeling approaches. The key motivation is to avoid using imputation methods or deletion of key process information when identifying the model, and utilizing the rest of the information appropriately at the model building stage. To this end, a novel approach is developed that adapts nonlinear iterative partial least squares (NIPALS) algorithms from both partial least squares (PLS) and principle component analysis (PCA) for use in subspace identication. Note that the existing subspace identication approaches often utilize singular value decomposition (SVD) as part of the identication algorithm which is generally not robust to missing data. In contrast, the NIPALS algorithms used in this work leverage the inherent correlation structure of the identication matrices to minimize the impact of missing data values while generating an accurate system model. Furthermore, in computing the system matrices, the calculated scores from the latent variable methods are utilized as the states of the system. The ecacy of the proposed approach is shown via simulation of a nonlinear batch process example.

Research paper thumbnail of Narrowly Dispersed, Degradable, and Scalable Poly(oligoethylene glycol methacrylate)-Based Nanogels via Thermal Self-Assembly

Industrial & Engineering Chemistry Research, 2018

Covalently cross-linked and hydrolytically degradable poly(oligoethylene glycol methacrylate) (PO... more Covalently cross-linked and hydrolytically degradable poly(oligoethylene glycol methacrylate) (POEGMA)-based nanogels are fabricated using an all-aqueous self-assembly approach. The nanogels are composed of hydrazide-(POH) and aldehyde-functionalized (POA) POEGMA precursor polymers that exhibit lower critical solution temperature (LCST) behavior in aqueous media and form a covalent, yet degradable, hydrazone linkage upon mixing. By systematically

Research paper thumbnail of Subspace Identification-Based Modeling and Control of Batch Particulate Processes

Industrial & Engineering Chemistry Research, 2017

This paper addresses the problem of subspace based model identication and predictive control of p... more This paper addresses the problem of subspace based model identication and predictive control of particulate process subject to uncertainty and time varying parameters. To this end, subspace identication techniques are rst adapted to handle the batch nature of the data. A linear model predictive controller (MPC) is next formulated to enable achieving a particle size distribution with desired characteristics subject to both manipulated input and product quality constraints. The proposed approach is implemented on a seeded batch crystallizer process and compared with an open loop policy as well as a traditional trajectory tracking policy using classical control. The proposed MPC is shown to achieve superior performance and the ability to respect tighter product quality constraints as well as robustness to uncertainty.

Research paper thumbnail of Handling multi-rate and missing data in variable duration economic model predictive control of batch processes

AIChE Journal, 2017

In the present work we consider the problem of variable duration economic model predictive contro... more In the present work we consider the problem of variable duration economic model predictive control (EMPC) of batch processes subject to multi-rate and missing data. To this end, we first generalize a recently developed subspace-based model identification approach for batch processes to handle multi-rate and missing data by utilizing the incremental singular value decomposition technique. Exploiting the fact that the proposed identification approach is capable of handling inconsistent batch lengths, the resulting dynamic model is integrated into a tiered EMPC formulation that optimizes process economics (including batch duration). Simulation case studies involving application to the energy intensive electric arc furnace process demonstrate the efficacy of the proposed approach compared to a traditional trajectory tracking approach subject to limited availability of process measurements, missing data, measurement noise and constraints.

Research paper thumbnail of Data-driven quality control of batch processes via subspace identification

2016 American Control Conference (ACC), 2016

In this work we present a novel, data-driven, quality modeling and control approach for batch pro... more In this work we present a novel, data-driven, quality modeling and control approach for batch processes. Specifically, we adapt subspace identification methods for use with batch data to identify a state-space model from available process measurements and input moves. We demonstrate that the resulting LTI, dynamic, state-space model is able to describe the transient behavior of finite duration batch processes. Next, we relate the terminal quality to the terminal value of the identified states. Finally, we apply the resulting model in a shrinking-horizon, model predictive control scheme to directly control terminal product quality. The theoretical properties of the proposed approach are studied and compared to state-of-the-art latent variable control approaches. The efficacy of the proposed approach is demonstrated through a simulation study of a batch polymethyl methacrylate (PMMA) polymerization reactor. Results for both disturbance rejection and set-point changes (that is, new quality grades) are demonstrated.

Research paper thumbnail of 382586 Quality Control of Penicillin Production Based on Multiple Data-Driven Models

Research paper thumbnail of Model predictive quality control of batch processes

2012 American Control Conference (ACC), 2012

This work addresses the problem of driving a batch process to a specified product quality using m... more This work addresses the problem of driving a batch process to a specified product quality using model predictive control (MPC) with data-driven models. To address the problem of unavailability of online quality measurements, an inferential quality model, which relates the process conditions over the entire batch duration to the final quality, is required for this problem. At a given sampling instant, the accuracy of this type of quality model, however, is sensitive to the prediction of the future (unknown) batch behavior. That is, errors in the predicted future data are propagated to the quality prediction, adding uncertainty to any control action based on the predicted quality. To address this “missing data” problem, we integrate a previously developed data-driven modeling methodology, which combines multiple local linear models with an appropriate weighting function to describe nonlinearities, with the inferential model in a predictive control framework. The key benefit of this approach is that the causality and nonlinear relationship between the future inputs and outputs are accounted for in predicting the final quality, resulting in more effective control action. The efficacy of the proposed predictive control design is illustrated via closed-loop simulations of an industrially relevant nylon-6,6 batch polymerization process.

Research paper thumbnail of Model-based intensification of CHO cell cultures: one-step strategy from fed-batch to perfusion

ABSTRACTThere is a growing interest in continuous processing of the biopharmaceutical industry. H... more ABSTRACTThere is a growing interest in continuous processing of the biopharmaceutical industry. However, the technology transfer from traditional batch-based processes is considered a challenge as protocol and tools still remain to be established for their usage at the manufacturing scale. Here, we present a model-based approach to design optimized perfusion cultures of CHO cells using only the knowledge captured during small-scale fed-batch experiments. The novelty of the proposed model lies in the simplicity of its structure. Thanks to the introduction of a new catch-all variable representing a bulk of by-products secreted by the cells during their cultivation, the model was able to successfully predict cellular behavior under different operating modes without changes in its formalism. To our knowledge, this is the first experimentally validated model capable, with a single set of parameters, to capture culture dynamic under different operating modes and at different scales.

Research paper thumbnail of Artificial Neural Network-Based Model Predictive Control Using Correlated Data

Industrial & Engineering Chemistry Research, 2022

Research paper thumbnail of Modeling and Control of Batch Processes

Advances in Industrial Control, 2019

Research paper thumbnail of Development of a high fidelity and subspace identification model of a hydrogen plant startup dynamics

2017 American Control Conference (ACC), 2017

In this work, the problem of determining a data-driven model of a hydrogen production unit is add... more In this work, the problem of determining a data-driven model of a hydrogen production unit is addressed. The framework is applied to a high fidelity simulation model developed in this work. To this end, first a high fidelity model of the entire plant is developed in Honeywell's UniSim Design, capable of simulating the startup and shutdown phase, with appropriate adaptation of the plant standard operating procedure (SOP). Several startups are simulated to generate training data for identification of a data-driven model. Then an LTI data-driven model of the process using subspace identification based methods is determined and validated against new simulated startup. Simulation results demonstrate the prediction capabilities of the identified model.

Research paper thumbnail of Quality control of variable duration batch processes via subspace identification

2017 American Control Conference (ACC), 2017

Batch process reactors are often used for products where quality is of paramount importance. To t... more Batch process reactors are often used for products where quality is of paramount importance. To this end, this work addresses the problem of direct, data-driven, quality control for batch processes. Specifically, previous results using subspace identification for modeling dynamic evolution and making quality predictions are extended with two key novel contributions: first, a method is proposed to account for mid-batch ingredient additions in both the modeling and control stages. Second, a novel model predictive control scheme is proposed that includes batch duration as a decision variable. The efficacy of the proposed modeling and control approaches are demonstrated using a simulation study of a polymethyl methacrylate (PMMA) reactor. Closed loop simulation results show that the proposed controller is able to reject disturbances in feed stock and drive the number average molecular weight, weight average molecular weight, and conversion to their respective set-points. Specifically, m...

Research paper thumbnail of Subspace Based Model Identification for an Industrial Bioreactor: Handling Infrequent Sampling Using Missing Data Algorithms

Processes, 2020

This manuscript addresses the problem of modeling an industrial (Sartorius) bioreactor using proc... more This manuscript addresses the problem of modeling an industrial (Sartorius) bioreactor using process data. In the context of the Sartorius Bioreactor, it is important to appropriately address the problem of dealing with a large number of variables, which are not always measured or are measured at different sampling rates, without taking recourse to simpler interpolation- or imputation-based approaches. To this end, a dynamic model for the Sartorius Bioreactor is developed via appropriately adapting a recently presented subspace model identification technique, which in turn uses nonlinear iterative partial least squares (NIPALS) algorithms to gracefully handle the missing data. The other key contribution is evaluating the ability of the identification approach to provide insight into the process by computing interpretable variables such as metabolite rates. The results demonstrate the ability of the proposed approach to model data from the Sartorius Bioreactor.

Research paper thumbnail of Numerical simulation of the general rate model of chromatography using orthogonal collocation

Computers & Chemical Engineering

Research paper thumbnail of Determining appropriate input excitation for model identification of a continuous bio-process

Digital Chemical Engineering

Research paper thumbnail of Data Driven Modeling and Model Predictive Control of Bioreactor for Production of Monoclonal Antibodies

2022 American Control Conference (ACC)

Research paper thumbnail of Model predictive control using subspace model identification

Computers & Chemical Engineering, 2021

Abstract This paper addresses the problem of designing and implementing a data-driven model based... more Abstract This paper addresses the problem of designing and implementing a data-driven model based model predictive controller (MPC). In particular, we consider the problem where a subspace identification approach is utilized to determine a state-space model, while applying first-principles based knowledge in the model identification (denoted as the constrained subspace model). The incorporation of the first-principles based constraints in the subspace matrix Patel et al. (2020) often leads to a feed-through matrix being present. Such a model then is the best representation of the system dynamics, but does not lend itself readily to existing linear MPC formulations where the feed-through matrix is assumed to be zero. Thus, an existing linear MPC formulation is adapted to handle the feed through matrix. The superior performance of this MPC design, which can utilize the constrained subspace model, over existing approaches is demonstrated using a two tank chemical stirred tank reactor process.

Research paper thumbnail of Batch Process Modeling and Control: Background

Modeling and Control of Batch Processes, 2018

Batch processes are an indispensable constituent of chemical process industries and are universal... more Batch processes are an indispensable constituent of chemical process industries and are universally used for manufacturing of high-quality products. The preeminent reason for their popularity can be attributed to their flexibility to control different grades of products by changing the initial conditions and input trajectories. However, a batch process is characterized by absence of operation around equilibrium conditions resulting in highly nonlinear dynamics, which make the classical approaches (for continuous processes) not directly applicable. The present chapter details the existing approaches for modeling and control as they pertain to batch processes.

Research paper thumbnail of Accelerating Product Innovation at Dow through Multivariate Modeling (Poster)

Research paper thumbnail of Safe-Steering of Batch Processes

Modeling and Control of Batch Processes, 2018

This Chapter considers the problem of controlling batch processes to achieve a desired final prod... more This Chapter considers the problem of controlling batch processes to achieve a desired final product quality subject to input constraints and faults in the control actuators. Specifically, faults are considered that cannot be handled via robust control approaches, and preclude the ability to reach the desired end-point, necessitating fault-rectification. A safe-steering framework is developed to address the problem of determining how to utilize the functioning inputs during fault rectification in order to ensure that after fault-rectification, the desired product properties can be reached upon batch termination. To this end, first a novel reverse-time reachability region (we define the reverse-time reachability region as the set of states from where the desired end-point can be reached by batch termination) based MPC is formulated that reduces online computations, as well as provides a useful tool for handling faults. Next, a safe-steering framework is developed that utilizes the re...

Research paper thumbnail of Subspace based model identification for missing data

AIChE Journal, 2020

This paper addresses the problem of missing process data in data-driven dynamic modeling approach... more This paper addresses the problem of missing process data in data-driven dynamic modeling approaches. The key motivation is to avoid using imputation methods or deletion of key process information when identifying the model, and utilizing the rest of the information appropriately at the model building stage. To this end, a novel approach is developed that adapts nonlinear iterative partial least squares (NIPALS) algorithms from both partial least squares (PLS) and principle component analysis (PCA) for use in subspace identication. Note that the existing subspace identication approaches often utilize singular value decomposition (SVD) as part of the identication algorithm which is generally not robust to missing data. In contrast, the NIPALS algorithms used in this work leverage the inherent correlation structure of the identication matrices to minimize the impact of missing data values while generating an accurate system model. Furthermore, in computing the system matrices, the calculated scores from the latent variable methods are utilized as the states of the system. The ecacy of the proposed approach is shown via simulation of a nonlinear batch process example.

Research paper thumbnail of Narrowly Dispersed, Degradable, and Scalable Poly(oligoethylene glycol methacrylate)-Based Nanogels via Thermal Self-Assembly

Industrial & Engineering Chemistry Research, 2018

Covalently cross-linked and hydrolytically degradable poly(oligoethylene glycol methacrylate) (PO... more Covalently cross-linked and hydrolytically degradable poly(oligoethylene glycol methacrylate) (POEGMA)-based nanogels are fabricated using an all-aqueous self-assembly approach. The nanogels are composed of hydrazide-(POH) and aldehyde-functionalized (POA) POEGMA precursor polymers that exhibit lower critical solution temperature (LCST) behavior in aqueous media and form a covalent, yet degradable, hydrazone linkage upon mixing. By systematically

Research paper thumbnail of Subspace Identification-Based Modeling and Control of Batch Particulate Processes

Industrial & Engineering Chemistry Research, 2017

This paper addresses the problem of subspace based model identication and predictive control of p... more This paper addresses the problem of subspace based model identication and predictive control of particulate process subject to uncertainty and time varying parameters. To this end, subspace identication techniques are rst adapted to handle the batch nature of the data. A linear model predictive controller (MPC) is next formulated to enable achieving a particle size distribution with desired characteristics subject to both manipulated input and product quality constraints. The proposed approach is implemented on a seeded batch crystallizer process and compared with an open loop policy as well as a traditional trajectory tracking policy using classical control. The proposed MPC is shown to achieve superior performance and the ability to respect tighter product quality constraints as well as robustness to uncertainty.

Research paper thumbnail of Handling multi-rate and missing data in variable duration economic model predictive control of batch processes

AIChE Journal, 2017

In the present work we consider the problem of variable duration economic model predictive contro... more In the present work we consider the problem of variable duration economic model predictive control (EMPC) of batch processes subject to multi-rate and missing data. To this end, we first generalize a recently developed subspace-based model identification approach for batch processes to handle multi-rate and missing data by utilizing the incremental singular value decomposition technique. Exploiting the fact that the proposed identification approach is capable of handling inconsistent batch lengths, the resulting dynamic model is integrated into a tiered EMPC formulation that optimizes process economics (including batch duration). Simulation case studies involving application to the energy intensive electric arc furnace process demonstrate the efficacy of the proposed approach compared to a traditional trajectory tracking approach subject to limited availability of process measurements, missing data, measurement noise and constraints.

Research paper thumbnail of Data-driven quality control of batch processes via subspace identification

2016 American Control Conference (ACC), 2016

In this work we present a novel, data-driven, quality modeling and control approach for batch pro... more In this work we present a novel, data-driven, quality modeling and control approach for batch processes. Specifically, we adapt subspace identification methods for use with batch data to identify a state-space model from available process measurements and input moves. We demonstrate that the resulting LTI, dynamic, state-space model is able to describe the transient behavior of finite duration batch processes. Next, we relate the terminal quality to the terminal value of the identified states. Finally, we apply the resulting model in a shrinking-horizon, model predictive control scheme to directly control terminal product quality. The theoretical properties of the proposed approach are studied and compared to state-of-the-art latent variable control approaches. The efficacy of the proposed approach is demonstrated through a simulation study of a batch polymethyl methacrylate (PMMA) polymerization reactor. Results for both disturbance rejection and set-point changes (that is, new quality grades) are demonstrated.

Research paper thumbnail of 382586 Quality Control of Penicillin Production Based on Multiple Data-Driven Models

Research paper thumbnail of Model predictive quality control of batch processes

2012 American Control Conference (ACC), 2012

This work addresses the problem of driving a batch process to a specified product quality using m... more This work addresses the problem of driving a batch process to a specified product quality using model predictive control (MPC) with data-driven models. To address the problem of unavailability of online quality measurements, an inferential quality model, which relates the process conditions over the entire batch duration to the final quality, is required for this problem. At a given sampling instant, the accuracy of this type of quality model, however, is sensitive to the prediction of the future (unknown) batch behavior. That is, errors in the predicted future data are propagated to the quality prediction, adding uncertainty to any control action based on the predicted quality. To address this “missing data” problem, we integrate a previously developed data-driven modeling methodology, which combines multiple local linear models with an appropriate weighting function to describe nonlinearities, with the inferential model in a predictive control framework. The key benefit of this approach is that the causality and nonlinear relationship between the future inputs and outputs are accounted for in predicting the final quality, resulting in more effective control action. The efficacy of the proposed predictive control design is illustrated via closed-loop simulations of an industrially relevant nylon-6,6 batch polymerization process.

Research paper thumbnail of Model-based intensification of CHO cell cultures: one-step strategy from fed-batch to perfusion

ABSTRACTThere is a growing interest in continuous processing of the biopharmaceutical industry. H... more ABSTRACTThere is a growing interest in continuous processing of the biopharmaceutical industry. However, the technology transfer from traditional batch-based processes is considered a challenge as protocol and tools still remain to be established for their usage at the manufacturing scale. Here, we present a model-based approach to design optimized perfusion cultures of CHO cells using only the knowledge captured during small-scale fed-batch experiments. The novelty of the proposed model lies in the simplicity of its structure. Thanks to the introduction of a new catch-all variable representing a bulk of by-products secreted by the cells during their cultivation, the model was able to successfully predict cellular behavior under different operating modes without changes in its formalism. To our knowledge, this is the first experimentally validated model capable, with a single set of parameters, to capture culture dynamic under different operating modes and at different scales.

Research paper thumbnail of Artificial Neural Network-Based Model Predictive Control Using Correlated Data

Industrial & Engineering Chemistry Research, 2022

Research paper thumbnail of Modeling and Control of Batch Processes

Advances in Industrial Control, 2019

Research paper thumbnail of Development of a high fidelity and subspace identification model of a hydrogen plant startup dynamics

2017 American Control Conference (ACC), 2017

In this work, the problem of determining a data-driven model of a hydrogen production unit is add... more In this work, the problem of determining a data-driven model of a hydrogen production unit is addressed. The framework is applied to a high fidelity simulation model developed in this work. To this end, first a high fidelity model of the entire plant is developed in Honeywell's UniSim Design, capable of simulating the startup and shutdown phase, with appropriate adaptation of the plant standard operating procedure (SOP). Several startups are simulated to generate training data for identification of a data-driven model. Then an LTI data-driven model of the process using subspace identification based methods is determined and validated against new simulated startup. Simulation results demonstrate the prediction capabilities of the identified model.

Research paper thumbnail of Quality control of variable duration batch processes via subspace identification

2017 American Control Conference (ACC), 2017

Batch process reactors are often used for products where quality is of paramount importance. To t... more Batch process reactors are often used for products where quality is of paramount importance. To this end, this work addresses the problem of direct, data-driven, quality control for batch processes. Specifically, previous results using subspace identification for modeling dynamic evolution and making quality predictions are extended with two key novel contributions: first, a method is proposed to account for mid-batch ingredient additions in both the modeling and control stages. Second, a novel model predictive control scheme is proposed that includes batch duration as a decision variable. The efficacy of the proposed modeling and control approaches are demonstrated using a simulation study of a polymethyl methacrylate (PMMA) reactor. Closed loop simulation results show that the proposed controller is able to reject disturbances in feed stock and drive the number average molecular weight, weight average molecular weight, and conversion to their respective set-points. Specifically, m...

Research paper thumbnail of Subspace Based Model Identification for an Industrial Bioreactor: Handling Infrequent Sampling Using Missing Data Algorithms

Processes, 2020

This manuscript addresses the problem of modeling an industrial (Sartorius) bioreactor using proc... more This manuscript addresses the problem of modeling an industrial (Sartorius) bioreactor using process data. In the context of the Sartorius Bioreactor, it is important to appropriately address the problem of dealing with a large number of variables, which are not always measured or are measured at different sampling rates, without taking recourse to simpler interpolation- or imputation-based approaches. To this end, a dynamic model for the Sartorius Bioreactor is developed via appropriately adapting a recently presented subspace model identification technique, which in turn uses nonlinear iterative partial least squares (NIPALS) algorithms to gracefully handle the missing data. The other key contribution is evaluating the ability of the identification approach to provide insight into the process by computing interpretable variables such as metabolite rates. The results demonstrate the ability of the proposed approach to model data from the Sartorius Bioreactor.