Control Of CSTR Research Articles

A data-driven tracking control framework using physics-informed neural networks and deep reinforcement learning for dynamical systems

This paper addresses how physical knowledge can improve machine learning in process control. A data-driven tracking control framework using physics-informed neural networks (PINNs) and deep reinforcement learning (DRL) is proposed for dynamical systems, which is particularly important when iterations or repetitions of data collection experiments are limited or even not allowed. An indirect control approach is followed to accomplish this. First, a new process model is identified using PINNs trained from a model representing the experimental data collected from the plant. Then, the DRL-based control agent utilizes the identified PINN model to deliver an offline policy. We evaluate our framework for data-driven tracking control of nonisothermal CSTR, considering measurement noise and stochastic dynamics for closed-loop control. Our approach performed comparably to an NMPC without requiring a model to predict the process dynamics.

Discrete‐time contraction constrained nonlinear model predictive control using graph‐based geodesic computation

AbstractModern chemical processes need to operate around time‐varying operating conditions to optimize plant economy, in response to dynamic supply chains (e.g., time‐varying specifications of product and energy costs). As such, the process control system needs to handle a wide range of operating conditions whilst optimizing system performance and ensuring stability during transitions. This article presents a reference‐flexible nonlinear model predictive control approach using contraction based constraints. Firstly, a contraction condition that ensures convergence to any feasible state trajectories or setpoints is constructed. This condition is then imposed as a constraint on the optimization problem for model predictive control with a general (typically economic) cost function, utilizing Riemannian weighted graphs and shortest path techniques. The result is a reference flexible and fast optimal controller that can trade‐off between the rate of target trajectory convergence and economic benefit (away from the desired process objective). The proposed approach is illustrated by a simulation study on a CSTR control problem.

Continuous-Time Surrogate Models for Data-Driven Dynamic Optimization

This work addresses the control optimization of time-varying systems without the full discretization of the underlying high-fidelity models and derives optimal control trajectories using surrogate modeling and data-driven optimization. Time-varying systems are ubiquitous in the chemical process industry and their systematic control is essential for ensuring each system to be operated at the desired settings. To this end, we postulate nonlinear continuous-time control action trajectories using time-varying surrogate models and derive the parameters of these functional forms using data-driven optimization. Data-driven optimization allows us to collect data from the high-fidelity model without pursuing any discretization and fine-tune candidate control trajectories based on the retrieved input-output information from the nonlinear system. We test exponential and polynomial surrogate forms for the control trajectories and explore various data-driven optimization strategies (local vs. global and sample-based vs. model-based) to test the consistency of each approach for controlling dynamic systems. The applicability of our approach is demonstrated on a motivating example and a CSTR control case study with favorable results.

Optimal design of FOPID Controller for the control of CSTR by using a novel hybrid metaheuristic algorithm

Optimal design of FOPID Controller for the control of CSTR by using a novel hybrid metaheuristic algorithm

Application of Cholette’s model in non-ideal mixing CSTR: a simulation study on dynamic behavior

Abstract Non-ideal mixing phenomena are widely found in industrial chemical reactors. In this work we derived the bifurcation formulas for a non-adiabatic CSTR with an irreversible exothermic first order reaction with the non-ideal mixing effect. This is investigated via dynamic behavior simulations based on Chollete’s model. The results show that the non-ideal mixing parameter n (the fraction of the feed entering the perfect mixing zone) determines the variation between six classified regions and dominates the dynamic behavior patterns in the steady-state response diagram. On the other hand, the phase portraits of examples verify the formulas derived in this work. We note that the non-ideal mixing effect has significant importance in CSTR design and control steps. For example, in the safe operating region for an ideal mixing CSTR, non-linear dynamics are obtained by the system under non-ideal mixing conditions (n ≠ 1). The present study has significance and help for chemical reactor design and CSTR control.

WITHDRAWN: Tabu search based optimization of PID parameters for temperature control of non-isothermal CSTR

WITHDRAWN: Tabu search based optimization of PID parameters for temperature control of non-isothermal CSTR

Modelling for Temperature Non-Isothermal Continuous Stirred Tank Reactor Using Fuzzy Logic

As we study chemical engineering and in process engineering, we come across the importance of a Continuous Stirred Tank Reactor, CSTR. Many types of controllers have been applied on the CSTR process to control the reactor temperature. In this research paper, an analysis of the response of the conventional Proportional-Integral-Derivative, PID controller and multi types of Fuzzy Logic controller for temperature control of CSTR and to design a control system of a non-isothermal temperature control of a CSTR in order to produce the most theoretically stable response curve. A mathematical model of a CSTR using the most general operating condition was developed through a set of differential equations which were later then converted into S-function using MATLAB (Matrix Laboratory) editor. After developing the S-function of the defined CSTR model was used in SIMULINK (a graphical programming environment from MATLAB), model by using a path called User-Defined functions which allows us to apply a M.file system that was created prior to the simulation as for this project we used a system block of the S-function or S-function builder.

Robust discrete-time set-based adaptive predictive control for nonlinear systems

The problem of robust adaptive predictive control for a class of discrete-time nonlinear systems is considered. First, a parameter estimation technique, based on an uncertainty set estimation, is formulated. This technique is able to provide robust performance for nonlinear systems subject to exogenous variables. Second, an adaptive MPC is developed to use the uncertainty estimation in a framework of min–max robust control. A Lipschitz-based approach, which provides a conservative approximation for the min–max problem, is used to solve the control problem, retaining the computational complexity of nominal MPC formulations and the robustness of the min–max approach. Finally, the set-based estimation algorithm and the robust predictive controller are successfully applied in two case studies. The first one is the control of anonisothermal CSTR governed by the van de Vusse reaction. Concentration and temperature regulation is considered with the simultaneous estimation of the frequency (or pre-exponential) factors of the Arrhenius equation. In the second example, a biomedical model for chemotherapy control is simulated using control actions provided by the proposed algorithm. The methods for estimation and control were tested using different disturbances scenarios.

MULTI-LOOP CONTROL DESIGN IN MULTIVARIABLE (2X2) CONTINUOUS STIRRED TANK REACTOR

With this study, the design and tuning of multi-loop for multivariable (2x2) CSTR will be made in order to achieve optimum CSTR control performance. This study used Bequette model reactor and MATLAB software and is expected to be able to cope with disturbances in the reactor so that the reactor system is able to stabilize quickly despite the distractions. In this study, the design will be made using multi-loop approach, along with PI controller as the next step. Then, BLT and auto-tune tuning method will be used in PI controller and given disturbances to both of tuning method. The controller performances are then compared. Results of the study are then analyzed for discussions and conclusions. Results from this study have shown that in terms of disturbance rejection, BLT is better than auto-tune based on comparison between both of controller performances. For IAE for the case of temperature, BLT is 30% better than auto-tune, but it is almost the same for the case of concentration. For settling time for the case of concentration, BLT is 30% better than auto-tune, and for the case of temperature, BLT is 18% better than auto-tune. For rise time for the case of concentration and temperature, BLT is 30% better than auto-tune.

Partial inventory control of the CSTR via reaction-dependent generalized inventories

Motivated by considering structurally dynamical properties of reaction systems, we develop an approach to deal with the nonlinear control of an unstable CSTR. More precisely, this paper shows that the CSTR dynamics can be split into reaction variant and reaction invariant dynamics modes through the use of linear combinations of system variables (called inventories). Since the reaction invariant dynamics mode is exponentially stable, the control design for an exponential stabilization of the reactor is simply solved together with (partial) inventory control on the basis of controlling the reaction variant dynamics mode. An extension of the approach to multiple chemical reaction systems is briefly outlined. Simulation results for a first order chemical reaction with multiplicity behavior are given to illustrate the theoretical development.

NonLinear Discrete-Time Gain-Scheduling Control for Affine Nonlinear Polynomial Systems

This paper is devoted to trajectory tracking control for nonlinear discrete-time polynomial systems. The proposed approach makes full use of a feedback linearization technique combined with gain-scheduling approach to create a tracking control structure. The synthesized controller is based mainly on the reverse trajectory method which allows estimating domains of attraction around the operating points. A smooth transition from the actual operating conditions to the desired ones is ensured via an accurate geometrical approach. The effectiveness of the control schema is demonstrated through its simulation to a CSTR control problem.

Sensitivity compensating nonlinear control: Exact model based approach

A novel sensitivity compensating nonlinear control (SCNC) approach is proposed within generic model control (GMC) framework for processes exhibiting input sensitivity. The proposed approach consists of defining a new process, control law and set point such that the determined control action drives the original process to its desired set point. External reset feedback (ERF), used to compensate for input saturation, is extended to higher relative degree systems as extended ERF (EERF), and is incorporated in the context of SCNC approach. The proposed control algorithms are evaluated by application to an open-loop unstable CSTR control problem and a multi-product semi-batch polymerization reactor temperature control problem. The present study illustrates the versatility of the proposed SCGMC schemes compared to the basic GMC schemes in terms of output tracking and smoother input profiles. SCNC can be extended to other nonlinear model based controllers where the control law can be expressed analytically.

Dynamic simulation and control of auto-refrigerated CSTR and tubular reactor for bulk styrene polymerization

Dynamic simulation and control of a two-stage continuous bulk styrene polymerization process is developed to predict the performance of auto-refrigerated CSTR and tubular reactors. The tubular reactor is subdivided into three temperature-control jacket zones. In this paper temperature control of auto-refrigerated continuous stirred tank reactor and tubular reactor are carried out, simultaneously. Two strategies are proposed for the control of tubular reactor. At the first strategy the controlled variable is jacket temperature and in the second strategy the controlled variable is the reactor temperature at the exit of each section. The set points for polymer grade transition are obtained using optimization of reactors temperatures via genetic algorithm (GA). Simulation results show that both of the control strategies are successful but second strategy has better performance in the control of polymer properties in the presence of disturbance and model mismatch.

Optimized Fuzzy Control by Particle Swarm Optimization Technique for Control of CSTR

Optimized Fuzzy Control by Particle Swarm Optimization Technique for Control of CSTR

Identification of a Typical CSTR Using Optimal Focused Time Lagged Recurrent Neural Network Model with Gamma Memory Filter

A focused time lagged recurrent neural network (FTLR NN) with gamma memory filter is designed to learn the subtle complex dynamics of a typical CSTR process. Continuous stirred tank reactor exhibits complex nonlinear operations where reaction is exothermic. It is noticed from literature review that process control of CSTR using neuro-fuzzy systems was attempted by many, but optimal neural network model for identification of CSTR process is not yet available. As CSTR process includes temporal relationship in the input-output mappings, time lagged recurrent neural network is particularly used for identification purpose. The standard back propagation algorithm with momentum term has been proposed in this model. The various parameters like number of processing elements, number of hidden layers, training and testing percentage, learning rule and transfer function in hidden and output layer are investigated on the basis of performance measures like MSE, NMSE, and correlation coefficient on testing data set. Finally effects of different norms are tested along with variation in gamma memory filter. It is demonstrated that dynamic NN model has a remarkable system identification capability for the problems considered in this paper. Thus FTLR NN with gamma memory filter can be used to learn underlying highly nonlinear dynamics of the system, which is a major contribution of this paper.

Robust Adaptive Backstepping Control Based on High-Gain Feedback and Its Application to a CSTR Control

In this paper, we propose a robust adaptive control for uncertain nonlinear systems via backstepping procedure based on high gain feedback strategy. It is shown that one can design a stable adaptive control system provided that the uncertain nonlinearities can be evaluated by an unknown constant and a known nonlinear function which give upper bounds of uncertainties. The effectiveness of the proposed methods is demonstrated by a simulation for a CSTR model.

Multiple Model Approach for CSTR Control

A method for combining multiple local models to describe a nonlinear system. The local model weights are based on the linear interpolation of the current operating point from the closest local model operating points defined as the filtered value of the current process input. This local modeling method can be used to describe systems with changing system gains and dynamics, as well as input multiplicity. A case study for a benchmark CSTR reactor is presented. Local linear models are used to synthesize IMC based PID controllers.

A computationally attractive nonlinear predictive control scheme with guaranteed stability for stable systems

Abstract We introduce in this paper a nonlinear model predictive control scheme for open-loop stable systems subject to input and state constraints. Closed-loop stability is guaranteed by an appropriate choice of the finite prediction horizon, independent of the specification of the desired control performance. In addition, this control scheme is likely to allow ‘real time’ implementation, because of its computational attractiveness. The theoretical results are demonstrated and discussed with a CSTR control application.

A Quasi-Infinite Horizon Nonlinear Predictive Control Scheme for Stable Systems: Application to a CSTR

Abstract We introduce in this paper a nonlinear model predictive control scheme for stable systems with input constraints. Closed-loop stability is guaranteed by an appropriate choice of the finite prediction horizon, independent of the specification of the desired control performance. In addition, this control scheme is likely to allow “real time” implementation, because of its computational attractiveness. The theoretical results will be demonstrated and discussed with a CSTR control application.

APPLICATION OF FUZZY ADAPTIVE CONTROLLER IN NONLINEAR PROCESS CONTROL

Abstract In general, physical processes are usually nonlinear and control system design based on the linearization technique cannot control the process well for a wide range of operation. Use of the variable transformation method may not always solve the problem. In this paper, a fuzzy adaptive controller is proposed to control the nonlinear process. The CSTR control problem has also been considered. The results are compared with the method of nonlinear model predictive control (NMPC) with constrained and unconstrained control variables. A fuzzy model-following control system scheme is also proposed. The results show that the proposed controller is a feasible control structure for a nonlinear or parameter-variations process control.