Error‐triggered on‐line model identification for model‐based feedback control

In industry, it may be difficult in many applications to obtain a first‐principles model of the process, in which case a linear empirical model constructed using process data may be used in the design of a feedback controller. However, linear empirical models may not capture the nonlinear dynamics over a wide region of state‐space and may also perform poorly when significant plant variations and disturbances occur. In the present work, an error‐triggered on‐line model identification approach is introduced for closed‐loop systems under model‐based feedback control strategies. The linear models are re‐identified on‐line when significant prediction errors occur. A moving horizon error detector is used to quantify the model accuracy and to trigger the model re‐identification on‐line when necessary. The proposed approach is demonstrated through two chemical process examples using a model‐based feedback control strategy termed Lyapunov‐based economic model predictive control (LEMPC). The chemical process examples illustrate that the proposed error‐triggered on‐line model identification strategy can be used to obtain more accurate state predictions to improve process economics while maintaining closed‐loop stability of the process under LEMPC. © 2016 American Institute of Chemical Engineers AIChE J, 63: 949–966, 2017     

Liquid‐to‐solid ratio control as an advanced process control solution for continuous twin‐screw wet granulation

Assuring compliance of intermediate and final quality attributes in a continuous pharmaceutical manufacturing campaign is of utmost importance. Application of corrective actions might be required in real‐time. This work exemplifies the steps needed to identify a linear pulse transfer function for the dynamic behavior of the granule liquid‐to‐solid ratio (%w/w) at the end of the granulation unit of a commercial ConsiGma^TM‐25 production line. Near‐infrared spectroscopy was used to monitor the granule composition in‐line. The outcome for both the tracking and regulator problem using either conventional or model predictive control was implemented and evaluated. Dynamic setpoints were correctly followed and an RMSE of 0.25%w/w with respect to the setpoint was obtained when inducing artificial disturbances. Important practical challenges were also tackled. Examples are fouling, computational limitations, and the limited flexibility of the automation software. Applying the proposed advanced process control solution offers an answer to upstream material flow rate deviations. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2500–2514, 2018     

Integrated B2B‐NMPC control strategy for batch/semibatch crystallization processes

The uncertainty in crystallization kinetics is of major concern in manufacturing processes, which can result in deterioration of most model‐based control strategies. In this study, uncertainties in crystallization kinetic parameters were characterized by Bayesian probability distributions. An integrated B2B‐NMPC control strategy was proposed to first update the kinetic parameters from batch to batch using a multiway partial least‐squares (MPLS) model, which described the variances of kinetic parameters from that of process variables and batch‐end product qualities. The process model with updated kinetic parameters was then incorporated into an NMPC design, the extended prediction self‐adaptive control (EPSAC), for online control of the final product qualities. Promising performance of the proposed integrated strategy was demonstrated in a simulated semibatch pH‐shift reactive crystallization process to handle major crystallization kinetic uncertainties of L‐glutamic acid, wherein smoother and faster convergences than the conventional B2B control were observed when process dynamics were shifted among three scenarios of kinetic uncertainties. © 2017 American Institute of Chemical Engineers AIChE J, 2017     

Data‐driven model predictive quality control of batch processes

The problem of driving a batch process to a specified product quality using data‐driven model predictive control (MPC) is described. 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. The accuracy of this type of quality model, however, is sensitive to the prediction of the future batch behavior until batch termination. In this work, we handle this “missing data” problem by integrating 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 MPC framework. The key feature of this approach is that the causality and nonlinear relationships between the future inputs and outputs are accounted for in predicting the final quality and computing the manipulated input trajectory. The efficacy of the proposed predictive control design is illustrated via closed‐loop simulations of a nylon‐6,6 batch polymerization process with limited measurements. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2852–2861, 2013     

Dynamic real‐time optimization and control of a hybrid energy system

A proactive energy management strategy for a stand‐alone hybrid renewable energy system is presented. The study was motivated by the system built in Lambton College (Sarnia, Ontario, Canada) which includes photovoltaic arrays, wind turbine, battery, electrolyzers, hydrogen storage tanks, and fuel cells. The control architecture consists of two levels of hierarchy: (1) optimal predictive scheduling at the supervisory level and (2) local controllers for each of the system units. A “day‐ahead” approach is followed at the supervisory level and a bidirectional communication between the supervisory, proactive control, and the low‐level control layer is established. The proposed energy management strategy accounts for external (i.e., weather and demand) and internal disturbances. The efficacy of the proposed strategy is demonstrated through case studies. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2546–2556, 2014     

Control of a continuous polybutadiene polymerization reactor train

An approach to controlling a continuous industrial polymerization reactor train for the production of stereoregular butadiene rubber based on the use of quality control laboratory data is presented. Empirical transfer function and disturbance models are identified directly from data collected from a designed experimental program carried out while the plant remained under operator control. The models are used to design minimum variance type control strategies for the monomer conversion and the Mooney viscosity of the final product. These controllers manipulate the flowrates of the Ziegler-Natta catalyst components and a chemical transfer agent. The plant testing program, the development of the control strategy, and results from testing the control strategy in the plant are all described.     

Simultaneous process synthesis and control design under uncertainty: A worst‐case performance approach

A new methodology that includes process synthesis and control structure decisions for the optimal process and control design of dynamic systems under uncertainty is presented. The method integrates dynamic flexibility and dynamic feasibility in a single optimization formulation, thus, reducing the costs to assess the optimal design. A robust stability test is also included in the proposed method to ensure that the optimal design is stable in the presence of magnitude‐bounded perturbations. Since disturbances are treated as stochastic time‐discrete unmeasured inputs, the optimal process synthesis and control design specified by this method remains feasible and stable in the presence of the most critical realizations in the disturbances. The proposed methodology has been applied to simultaneously design and control a system of CSTRs and a ternary distillation column. A study on the computational costs associated with this method is presented and compared to that required by a dynamic optimization‐based scheme. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2497–2514, 2013     

Dynamic Matrix Control of a Bubble‐Column Reactor for Microbial Synthesis Gas Fermentation

A spatiotemporal metabolic model of a representative syngas bubble‐column reactor was applied to design and evaluate dynamic matrix control (DMC) schemes for regulation of the desired by‐product ethanol and the undesired by‐product acetate. This model was used to develop linear step response models for controller design and also served as the process in closed‐loop simulations. A 2 × 2 DMC scheme with manipulation of the liquid and gas feed flows to the column provided a superior performance to proportional integral (PI) control due to slow process dynamics combining the multivariable and constrained nature of the control problem. Ethanol concentration control for large disturbances was further improved by adding the flow of a pure hydrogen stream as a third manipulated variable. The advantages of DMC for syngas bubble‐column reactor control are demonstrated and a design strategy for future industrial applications is provided.     

Integrated batch‐to‐batch and nonlinear model predictive control for polymorphic transformation in pharmaceutical crystallization

Polymorphism, a phenomenon in which a substance can have more than one crystal form, is a frequently encountered phenomenon in pharmaceutical compounds. Different polymorphs can have very different physical properties such as crystal shape, solubility, hardness, color, melting point, and chemical reactivity, so that it is important to ensure consistent production of the desired polymorph. In this study, an integrated batch‐to‐batch and nonlinear model predictive control (B2B‐NMPC) strategy based on a hybrid model is developed for the polymorphic transformation of L ‐glutamic acid from the metastable α‐form to the stable β‐form crystals. The hybrid model comprising of a nominal first‐principles model and a correction factor based on an updated PLS model is used to predict the process variables and final product quality. At each sampling instance during a batch, extended predictive self‐adaptive control (EPSAC) is employed as a NMPC technique to calculate the control action by using the current hybrid model as a predictor. At the end of the batch, the PLS model is updated by utilizing the measurements from the batch and the above procedure is repeated to obtain new control actions for the next batch. In a simulation study using a previously reported model for a polymorphic crystallization with experimentally determined parameters, the proposed B2B‐NMPC control strategy produces better performance, where it satisfies all the state constraints and produces faster and smoother convergence, than the standard batch‐to‐batch strategy. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

Safe‐steering of batch process systems

This work 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 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 reverse‐time reachability region based MPC in steering the state trajectory during fault rectification to enable (upon fault recovery) the achieving of the desired end point properties by batch termination. The proposed controller and safe‐steering framework are illustrated using a fed‐batch process example. © 2009 American Institute of Chemical Engineers AIChE J, 2009     

Raising the Decision‐Making Level to Improve the Enterprise‐Wide Production Flexibility

When market demand significantly changes, as in the ongoing worldwide economic crisis, many production plants are forced to operate far from nominal conditions. In this case, the current plant‐wide optimization of production sites is a myopic approach that could lead to plant inefficiencies and unconventional operation issues, thus, resulting in ineffective prevention of economic losses. A way to tackle low‐demand conditions is to raise the decision‐making process from the plant‐wide (or business‐wide) level to the enterprise‐wide (or corporate) level by assigning a Boolean variable to each production site so as to manage their on/off status. By doing so, certain additional (social) constraints may become relevant. The case of operating industrial gases supply chains is considered. © 2012 American Institute of Chemical Engineers AIChE J, 59: 1588–1598, 2013     

A data‐driven rolling‐horizon online scheduling model for diesel production of a real‐world refinery

A rolling‐horizon optimal control strategy is developed to solve the online scheduling problem for a real‐world refinery diesel production based on a data‐driven model. A mixed‐integer nonlinear programming (MINLP) scheduling model considering the implementation of nonlinear blending quality relations and quantity conservation principles is developed. The data variations which drive the MINLP model come from different sources of certain and uncertain events. The scheduling time horizon is divided into equivalent discrete time intervals, which describe regular production and continuous time intervals which represent the beginning and ending time of expected and unexpected events that are not restricted to the boundaries of discrete time intervals. This rolling‐horizon optimal control strategy ensures the dimension of the diesel online scheduling model can be accepted in industry use. LINGO is selected to be the solution software. Finally, the daily diesel scheduling scheme of one entire month for a real‐world refinery is effectively solved. © 2012 American Institute of Chemical Engineers AIChE J, 59: 1160–1174, 2013     

A thermal‐flywheel approach to distributed temperature control in microchannel reactors

Microchannel reactors are a promising route for monetizing distributed natural gas resources. However, intensification and miniaturization represent a significant challenge for reactor control. Focusing on autothermal methane‐steam reforming reactors, a novel microchannel reactor temperature control strategy based on confining a layer of phase‐change material (PCM) between the reactor plates is introduced. Melting‐solidification cycles, which occur with latent heat exchange at constant temperature, allow the PCM layer to act as an energy storage buffer—a “thermal flywheel”—constituting a distributed controller that mitigates temperature excursions caused by fluctuations in feedstock quality. A novel stochastic optimization algorithm for selecting the PCM layer thickness (i.e., distributed controller “tuning”) is introduced. Furthermore, a hierarchical control structure, whereby the PCM layer is complemented by a supervisory controller that addresses persistent disturbances, is proposed. The proposed concepts are illustrated in a comprehensive case study using a detailed two‐dimensional reactor model. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2051–2061, 2013     

Flow chemistry—Microreaction technology comes of age

Over the past two decades, microreaction technology has matured from early devices and concepts to encompass a wide range of commercial equipment and applications. This evolution has been aided by the confluence of microreactor development and adoption of continuous flow technology in organic chemistry. This Perspective summarizes the current state‐of‐the art with focus on enabling technologies for reaction and separation equipment. Automation and optimization are highlighted as promising applications of microreactor technology. The move towards continuous processing in pharmaceutical manufacturing underscores increasing industrial interest in the technology. As an example, end‐to‐end fabrication of pharmaceuticals in a compact reconfigurable system illustrates the development of on‐demand manufacturing units based on microreactors. The final section provides an outlook for the technology, including implementation challenges and integration with computational tools. AIChE J, 2017 © 2016 American Institute of Chemical Engineers AIChE J, 63: 858–869, 2017     

Neuro‐Fuzzy Control of a Continuous Cooled MSMPR Crystallizer

This work deals with the design and application of a neuro‐fuzzy controller of the magma density of the fine crystals for the stabilisation of the crystal size distribution (CSD) of the product from an MSMPR (Mixed Suspension, Mixed Product Removal) continuous crystallizer. The cooling crystallization of potassium sulphate from aqueous solutions in a pilot‐scale stirred vessel was investigated. A control strategy, based on the elimination of fines by using a combined sedimentation‐dissolution device, is presented. The control scheme was successfully applied to a pilot‐scale draft‐tube (DT) crystallizer and yielded promising results. The proposed process control system allowed the reduction of the fines fraction by more than 30 %, while maintaining operation stability and short transient responses.     

Optimization-based control of a reactive simulated moving bed process for glucose isomerization

In this paper, we investigate the continuous production of high-fructose corn syrup in a reactive simulated moving bed (RSMB) process which combines a quasi-continuous chromatographic separation with the enzymatic biochemical conversion of glucose to fructose. Such an integration of reaction and separation in one unit operation is advantageous for the equilibrium limited glucose isomerization. However, it complicates process design and process control. The continuous operating parameters and the discrete distribution of the columns over the different zones of the RSMB process are determined using a rigorous model-based optimization strategy. In order to maintain the product purity in the presence of disturbances while injecting a minimal additional amount of eluent, a nonlinear model predictive controller was developed which can deal with the complex hybrid (continuous/discrete) dynamics of the RSMB plant and takes hard process constraints (e.g. the maximal allowable pressure drop) into account. The efficiency of the control concept is proven in experimental studies using a 6-column RSMB plant of pharmaceutical scale.     

Integrating data‐based modeling and nonlinear control tools for batch process control

A data‐based multimodel approach is developed in this work for modeling batch systems in which multiple local linear models are identified using latent variable regression and combined using an appropriate weighting function that arises from fuzzy c‐means clustering. The resulting model is used to generate empirical reverse‐time reachability regions (RTRRs) (defined as the set of states from where the data‐based model can be driven inside a desired end‐point neighborhood of the system), which are subsequently incorporated in a predictive control design. Simulation results of a fed‐batch reactor system under proportional‐integral (PI) control and the proposed RTRR‐based design demonstrate the superior performance of the RTRR‐based design in both a fault‐free and faulty environment. The data‐based modeling methodology is then applied on a nylon‐6,6 batch polymerization process to design a trajectory tracking predictive controller. Closed‐loop simulation results illustrate the superior tracking performance of the proposed predictive controller over PI control. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Nonlinear model predictive control for the polymorphic transformation of L‐glutamic acid crystals

Polymorphism, a phenomenon where a substance can have more than one crystal forms, has recently become a major interest to the food, speciality chemical, and pharmaceutical industries. The different physical properties for polymorphs such as solubility, morphology, and dissolution rate may jeopardize operability or product quality, resulting in significant effort in controlling crystallization processes to ensure consistent production of the desired polymorph. Here, a nonlinear model predictive control (NMPC) strategy is developed for the polymorphic transformation of L ‐glutamic acid from the metastable α‐form to the stable β‐form crystals. The robustness of the proposed NMPC strategy to parameter perturbations is compared with temperature control (T‐control), concentration control (C‐control), and quadratic matrix control with successive linearization (SL‐QDMC). Simulation studies show that T‐control is the least robust, whereas C‐control performs very robustly but long batch times may be required. SL‐QDMC performs rather poorly even when there is no plant‐model mismatch due to the high process nonlinearity, rendering successive linearization inaccurate. The NMPC strategy shows good overall robustness for two different control objectives, which were both within 7% of their optimal values, while satisfying all constraints on manipulated and state variables within the specified batch time. © 2009 American Institute of Chemical Engineers AIChE J, 2009     

Transition from Batch to Continuous Operation in Bio‐Reactors: A Model Predictive Control Approach and Application

This work considers the problem of determining the transition of ethanol‐producing bio‐reactors from batch to continuous operation and subsequent control subject to constraints and performance considerations. To this end, a Lyapunov‐based non‐linear model predictive controller is utilized that stabilizes the bio‐reactor under continuous mode of operation. The key idea in the predictive controller is the formulation of appropriate stability constraints that allow an explicit characterization of the set of initial conditions from where feasibility of the optimization problem and hence closed‐loop stability is guaranteed. Additional constraints are incorporated in the predictive control design to expand on the set of initial conditions that can be stabilized by control designs that only require the value of the Lyapunov function to decay. Then, the explicit characterization of the set of stabilizable initial conditions is used in determining the appropriate time for which the reactor must be run in batch mode. Specifically, the predictive control approach is utilized in determining the appropriate batch length that achieves stabilizable values of the state variables at the end of the batch. Application of the proposed method to the ethanol production process using Zymomonas mobilis as the ethanol producing micro‐organism demonstrates the effectiveness of the proposed model predictive control strategy in stabilizing the bio‐reactor.