Fuzzy model predictive control of nonlinear pH process

A new fuzzy model-based predictive control scheme was developed to control a nonlinear pH process. The control scheme is based on the Takagi-Sugeno type fuzzy model of the process being controlled. In the present fuzzy model predictive control method, the process model maintains a linear representation of the conclusion parts of fuzzy rules. Therefore, it has a significant advantage over other types of models in the sense that nonlinear processes can be handled effectively by embedding the linear characteristic. The fuzzy model of the pH process to be controlled was constructed and used in the predictive control algorithm. Results of computer simulations and experiments demonstrated the effectiveness of the present control method.     

Neural network-based predictive control for multivariable processes

In this paper, the systematic derivations of setting up a nonlinear model predictive control based on the neural network are presented. This extends our previous work (Chen, 1998) into a multivariable system to explore the characteristics of the design. There are two stages for the development of nonlinear neural network predictive control: a neural network model and a control design. In the neural network model design, a parallel multiple-input, single-output neural network autoregressive with a model of exogenous inputs (NNARX) is proposed for multistep ahead predictions. In control design, the controller with extended control horizon is developed. The Levenberg-Marquardt algorithm is applied to training the NNARX model. Also, the sequential quadratic programming is used to search for the optimal manipulated inputs. The gradient of the objective function and constraints that require computation of Jacobian matrices are completely derived for optimization calculation. To demonstrate the control ability of MIMO cases, the proposed method is applied through two nonlinear simulation problems.     

Model predictive control of a fixed-bed reactor with nonlinear quality inference

A generic model predictive control framework has been proposed for a fixed-bed reactor with exothermic reaction. The proposed framework can conduct nonlinear inferential control of a product concentration together with linear multivariable control of bed temperatures. In addition, the framework can accommodate the multi-rate sampling and analysis delay caused by the product measurement. Performance of the proposed technique has been demonstrated with a non-adiabatic fixed bed reactor model producing maleic anhydride under various operating scenarios.     

On improving accuracy of computationally efficient nonlinear predictive control based on neural models

For nonlinear processes the classical model predictive control (MPC) algorithm, in which a linear model is used, usually does not give satisfactory closed-loop performance. In such nonlinear cases a suboptimal MPC strategy is typically used in which the nonlinear model is successively linearised on-line for the current operating point and, thanks to linearisation, the control policy is calculated from a quadratic programming problem. Although the suboptimal MPC algorithm frequently gives good results, for some nonlinear processes it would be beneficial to further improve control accuracy. This paper details a computationally efficient nonlinear MPC algorithm in which a neural model is linearised on-line along the predicted trajectory in an iterative way. The algorithm needs solving on-line only a series of quadratic programming problems. Advantages of the discussed algorithm are demonstrated in the control system of a high-purity ethylene–ethane distillation column for which the classical linear MPC algorithm does not work and the classical suboptimal MPC algorithm is slow. It is shown that the discussed algorithm can give practically the same control accuracy as the algorithm with on-line nonlinear optimisation and, at the same time, the algorithm is significantly less computationally demanding.     

Considerations on nonlinear model predictive control techniques

The nonlinear model predictive control (NMPC) is an on-line application based on nonlinear convolution models. It is an appealing control methodology, but it is difficult to implement and its solution is not so performing since it unavoidably means to solve a usually large-scale, constrained, and multidimensional optimization. To increase the difficulty, this optimization problem is subject to computationally heavy differential and algebraic constraints constituting the same convolution model and the least squares nature of the objective function easily leads to narrow valleys and multimodality issues.Beyond a short review of the state-of-the-art, the paper is aimed at highlighting the possibility to exploit at best the intrinsic features of the specific system one is going to control using the NMPC. The idea is to give the NMPC the possibility to automatically select the best combination of algorithms (differential solvers and optimizers) in accordance with the specific problem to be solved. From this perspective, the NMPC could be easily extended to many scientific fields traditionally far from process systems and computer-aided process engineering and the user has not to worry about which specific differential solvers and optimizers are needed to solve his/her problem.     

Bilinear model predictive control of grade change operations in paper production plants

In this work the bilinear model predictive control method is applied to control the grade change operations in paper production plants. Because of the high nonlinearity of the grade change processes, control of the grade change operations has been performed manually by experienced engineers in the plants. In some cases the bilinear model can be very effective to represent nonlinear processes. In this study a bilinear model for paper plants is identified first. It is found that the bilinear model tracks the plant without significant discrepancy. Based on the multivariable bilinear plant model the optimal input variables are computed using the one-step ahead prediction method. Even for frequent changes in paper grades the bilinear model predictive control scheme exhibits good control performance.     

Model predictive control for the reactant concentration control of a reactor

The reactant concentration control of a reactor using Model Predictive Control (MPC) is presented in this paper. Two major difficulties in the control of reactant concentration are that the measurement of concentration is not available for the control point of view and it is not possible to control the concentration without considering the reactor temperature. Therefore, MIMO control techniques and state and parameter estimation are needed. One of the MIMO control techniques widely studied recently is MPC. The basic concept of MPC is that it computes a control trajectory for a whole horizon time minimising a cost function of a plant subject to a dynamic plant model and an end point constraint. However, only the initial value of controls is then applied. Feedback is incorporated by using the measurements/estimates to reconstruct the calculation for the next time step. Since MPC is a model based controller, it requires the measurement of the states of an appropriate process model. However, in most industrial processes, the state variables are not all measurable. Therefore, an extended Kalman filter (EKF), one of estimation techniques, is also utilised to estimate unknown/uncertain parameters of the system. Simulation results have demonstrated that without the reactor temperature constraint, the MPC with EKF can control the reactant concentration at a desired set point but the reactor temperator is raised over a maximum allowable value. On the other hand, when the maximun allowable value is added as a constraint, the MPC with EKF can control the reactant concentration at the desired set point with less drastic control action and within the reactor temperature constraint. This shows that the MPC with EKF is applicable to control the reactant concentration of chemical reactors.     

Application of generalized predictive control to baker's yeast production

Generalized Predictive Control (GPC) was applied in the production of baker's yeast. The bioreactor was modeled with the autoregressive integrated moving average exogenous (ARIMAX) parametric difference equation model.A 2 L bioreactor with a cooling jacket was used for collecting input-output data. In order to measure pH, temperature, and dissolved oxygen in the bioreactor growth medium, suitable sensors were placed in the bioreactor. Medium temperature and the heat of the immersed heater were selected as output and manipulated variable, respectively. Square wave and a pseudo-random binary sequence (PRBS) signal were used as disturbance. Model parameters were calculated by using the recursive least square parameter estimation method. Bioreactor temperature was controlled theoretically using the GPC algorithm. The control performance was investigated by giving positive and negative step responses to the set point. The GPC algorithm holds the bioreactor temperature succesfully at the optimal set point. Optimum values of the maximum costing horizon ( N 2 ), control horizon ( N U ), and control weighting ( u ) were found to be 10, 1, and 0.005, respectively.     

Simulation-based learning of cost-to-go for control of nonlinear processes

In this paper, we present a simulation-based dynamic programming method that learns the ‘cost-to-go’ function in an iterative manner. The method is intended to combat two important drawbacks of the conventional Model Predictive Control (MPC) formulation, which are the potentially exorbitant online computational requirement and the inability to consider the future interplay between uncertainty and estimation in the optimal control calculation. We use a nonlinear Van de Vusse reactor to investigate the efficacy of the proposed approach and identify further research issues. This paper is dedicated to Professor Hyun-Ku Rhee on the occasion of his retirement from Seoul National University.     

A study on the adaptive predictive control method for multivariable bilinear processes

A predictive control method for multivariable bilinear processes is derived based on ARMA model. To identify bilinear process models, we use simple equation error method extended to multivariable system. We can obtain the adaptive predictive controller for multivariable bilinear processes by incorporation of the identification algorithm. Offset compensator is provided to correct for the effects of unmeasured disturbances and model inaccuracies. A filter with a singled parameter is used to correct for the effects of an incorrect model. Results of simulation on multivariable bilinear processes show that the proposed control method has satisfactory performance.     

Model-based control strategies for a chemical batch reactor with exothermic reactions

Batch reactor control provides a very challenging problem for the process control engineer. This is because a characteristic of its dynamic behavior shows a high nonlinearity. Since applicability of the batch reactor is quite limited to the effectiveness of an applied control strategy, the use of advanced control techniques is often beneficial. This work presents the implementation and comparison of two advanced nonlinear control strategies, model predictive control (MPC) and generic model control (GMC), for controlling the temperature of a batch reactor involving a complex exothermic reaction scheme. An extended Kalman filter is incorporated in both controllers as an on-line estimator. Simulation studies demonstrate that the performance of the MPC is slightly better than that of the GMC control in nominal case. For model mismatch cases, the MPC still gives better control performance than the GMC does in the presence of plant/model mismatch in reaction rate and heat transfer coefficient.     

Methods for performance monitoring and diagnosis of multivariable model-based control systems

Methods for performance monitoring and diagnosis of multivariable closed loop systems have been proposed aiming at application to model predictive control systems for industrial processes. For performance monitoring, the well-established traditional statistical process control method is empolyed. To meet the underlying premise that the observed variable is univariate and statistically independent, a temporal and spatial decorrelation procedure for process variables has been suggested. For diagnosis of control performance deterioration, a method to estimate the model-error and disturbance signal has been devised. This method enables us to identify the cause of performance deterioration among the controller, process, and disturbance. The proposed methods were evaluated through numerical examples.     

NEURAL NETWORK-BASED PREDICTIVE CONTROL FOR MULTIVARIABLE PROCESSES

In this paper, the systematic derivations of setting up a nonlinear model predictive control based on the neural network are presented. This extends our previous work (Chen, 1998) into a multivariable system to explore the characteristics of the design. There are two stages for the development of nonlinear neural network predictive control: a neural network model and a control design. In the neural network model design, a parallel multiple-input, single-output neural network autoregressive with a model of exogenous inputs (NNARX) is proposed for multistep ahead predictions. In control design, the controller with extended control horizon is developed. The Levenberg-Marquardt algorithm is applied to training the NNARX model. Also, the sequential quadratic programming is used to search for the optimal manipulated inputs. The gradient of the objective function and constraints that require computation of Jacobian matrices are completely derived for optimization calculation. To demonstrate the control ability of MIMO cases, the proposed method is applied through two nonlinear simulation problems.     

A Monte-Carlo based model approximation technique for linear model predictive control of nonlinear systems

In this paper we present a model approximation technique based on N-step-ahead affine representations obtained via Monte-Carlo integrations. The approach enables simultaneous linearization and model order reduction of nonlinear systems in the original state space thus allowing the application of linear MPC algorithms to nonlinear systems. The methodology is detailed through its application to benchmark model examples.     

Nonlinear model based control of two-product reactive distillation column

Nonlinear feedback control scheme for reactive distillation column has been proposed. The proposed control scheme is derived in the framework of Nonlinear Internal Model Control. The product compositions and liquid and vapor flow rates in sections of the reactive distillation column are estimated from selected tray temperature measurements by an observer. The control scheme is applied to an example reactive distillation column in which two products are produced in a single column and the reversible reaction A+B=C+D occurs. The relative volatilities are favorable for reactive distillation so that the reactants are intermediate boilers between the light product C and the heavy product D. Ideal physical properties, kinetics, and vapor-liquid equilibrium are also assumed. It is shown that the proposed control scheme keeps tight product composition control.     

Multi-objective nonlinear model predictive control through switching cost functions and its applications to chemical processes

This paper proposes a switching multi-objective model predictive control (MOMPC) algorithm for constrained nonlinear continuous-time process systems. Different cost functions to be minimized inMPC are switched to satisfy different performance criteria imposed at different sampling times. In order to ensure recursive feasibility of the switching MOMPC and stability of the resulted closed-loop system, the dual-mode control method is used to design the switching MOMPC controller. In this method, a local control law with some free-parameters is constructed using the control Lyapunov function technique to enlarge the terminal state set of MOMPC. The correction termis computed if the states are out of the terminal set and the free-parameters of the local control laware computed if the states are in the terminal set. The recursive feasibility of the MOMPC and stability of the resulted closed-loop system are established in the presence of constraints and arbitrary switches between cost functions. Finally, implementation of the switching MOMPC controller is demonstrated with a chemical process example for the continuous stirred tank reactor.     

Dynamic optimization oriented modeling and nonlinear model predictive control of the wet limestone FGD system

Nonlinear model predictive control (NMPC) scheme is an effective method of multi-objective optimization control in complex industrial systems. In this paper, a NMPC scheme for the wet limestone flue gas desulphurization (WFGD) system is proposed which provides a more flexible framework of optimal control and decision-making compared with PID scheme. At first, a mathematical model of the FGD process is deduced which is suitable for NMPC structure. To equipoise the model's accuracy and conciseness, the wet limestone FGD system is separated into several modules. Based on the conservation laws, a model with reasonable simplification is developed to describe dynamics of different modules for the purpose of controller design. Then, by addressing economic objectives directly into the NMPC scheme, the NMPC controller can minimize economic cost and track the set-point simultaneously. The accuracy of model is validated by the field data of a 1000 MW thermal power plant in Henan Province, China. The simulation results show that the NMPC strategy improves the economic performance and ensures the emission requirement at the same time. In the meantime, the control scheme satisfies the multiobjective control requirements under complex operation conditions (e.g., boiler load fluctuation and set point variation). The mathematical model and NMPC structure provides the basic work for the future development of advanced optimized control algorithms in the wet limestone FGD systems.     

Robust Integrated Design of processes with terminal penalty model predictive controllers

In this work, a novel methodology for the Integrated Design (ID) of processes with linear Model Predictive Control (MPC) is addressed, providing simultaneously the plant dimensions, the control system parameters and a steady state working point. The MPC chosen operates over infinite horizon in order to guarantee stability and it is implemented with a terminal penalty. The ID methodology considers norm based indexes for controllability, as well as robust performance conditions by using a multi-model approach. Mathematically, the ID is stated as a multiobjective nonlinear constrained optimization problem, tackled in different ways. Particularly, objective functions include investment, operating costs, and dynamical indexes based on the weighted sum of some norms of different closed loop transfer functions of the system. The paper illustrates the application of the proposed methodology with the ID of the activated sludge process of a wastewater treatment plant (WWTP).     

Integration of scheduling and control for batch processes using multi‐parametric model predictive control

Integration of scheduling and control results in Mixed Integer Nonlinear Programming (MINLP) which is computationally expensive. The online implementation of integrated scheduling and control requires repetitively solving the resulting MINLP at each time interval. (Zhuge and Ierapetritou, Ind Eng Chem Res. 2012;51:8550–8565) To address the online computation burden, we incorporare multi‐parametric Model Predictive Control (mp‐MPC) in the integration of scheduling and control. The proposed methodology involves the development of an integrated model using continuous‐time event‐point formulation for the scheduling level and the derived constraints from explicit MPC for the control level. Results of case studies of batch processes prove that the proposed approach guarantees efficient computation and thus facilitates the online implementation. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3169–3183, 2014     

Modeling and control of the melt index in HDPE process

We investigate the model for an industrial isothermal HDPE slurry reactor. The model, consisting of several nonlinear equations, can be linearized to give sets of linear time invariant state space model. The effectiveness of the linearized model is verified by the numerical simulations. A simple model predictive control scheme is constructed based on the linear state space model. The value of the melt index is obtained from the values of the manipulated and controlled variables generated from the control scheme. The control performance can be evaluated from the comparison between the computed melt index values and measured melt index values. The control scheme shows good tracking performance in the numerical simulations. We believe that the model developed in the present study can be effectively used to predict process variables as well as to control the melt index.