E M A N N J. Proc. Cont. Vol. 5, No 1, pp. 19-27, 1995 Copyright © 1995 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0959-1524/95 10.00 + 0.00 Model predictive control of an industrial packed bed reactor using neural networks Kwaku O. Temengfl* Phillip D. Schnelle* and Thomas J. McAvoy* *E. L du Pont de Nemours Co. Inc, Wilmington, Delaware, USA ~Department of Chemical Engineering, University of Maryland, College Park, Maryland, USA This paper discusses an industrial application of a multivaria ble nonli near feedforward /feedback model predictive control where the model is given by a dynamic neural network. A multi-pass packed bed reactor temperature pro ne is modelled via recurrent neural networks using the backpropagatio n through time traini ng algorithm. Th is model is then use d in conjunction with an optimizer to build a nonlinear model predictiv e control ler. Results show that, compared with conventional control schemes, the neural network model based controller can achieve tighter temperature control for disturbance rejection Keywords: model predictive control; packed bed reactor; neural networks In the chemical process industry, the drive to reduce operating costs and develop new markets has frequently emphasized improvements in product quality, better use of energy resources and reduced environmental emis- sions. These objectives, in turn, have placed stringent requirements on the available process control systems. These control systems must usually cope with multi- variable process interactions, constraints on manipu- lated and controlled variables, as well as time delays and other problematic dynamic characteristics. Model predictive control (MPC) algorithms have been recognized as effective tools for handling some of the difficult control problems in industry 1,2. MPC schemes derive some of their industrial appeal from their ability to handle input and output constraints, time delays, non-minimum phase behaviour and multi- variable systems. Two popular variations of the model predi ctive control algorithm are dynamic matrix control (DMC) 3, and model algorithmic control (MAC) 4. The underlying strategy of MPC algorithms is to use a model to predict the future output trajectory of the process and compute a controller action to minimize the difference between the predicted trajectory and a user- specified trajectory. Despite the success enjoyed by MPCs in industry, there are some pro cesse s which pose a challenge for the standard, linear model-based algorithms. For example, batch and semi-batch processes are carried out over a wide dynamic range; hence, the concept of operation around a steady state becomes invalid. Also, there are some continuous processes which undergo frequent transitions to permit the manufacture of several grades tAuthor to whom correspondence should be addressed of a basic product. Such processes oper ate at severa l steady state levels and may experience start-ups and shutdowns on a daily basis, making the use of linear models impractical. Lastly, there are some chemical processes (e.g. some polymer reactors) which are so severely nonlinear that small to moderate perturbations around the steady state can render a linear model-based controller inadequate or even unstable 5. Thus, there is an incentive to develop extensions of MPC to tackle nonlinear systems. The goal of this paper is to demonstrate the nonlinear model predictive control of a spent acid recovery converter (a packed bed reactor), where the model of the process is given by a dynamic neural network. We call this scheme neural mo del predictive control. We use nonlinear MPC because of the nonlinear and highly interactive natu re of the pro cess. Furthermore, the lack of some key measurements rendered the construction of a reliable first principles model virtually impossible. The paper is organized as follows: first we offer reviews of MPC algorithms and nonlinear system iden- tification methods, including neural networks and their application to MPCs. We then describe the process and the control problem, as well as the system identification of the pr oc ess . We devote the remainder of the paper to the control structure, implementation details and discussion of results. Model predictive control Linear Jbrmulation The essential elements of the various predictive control schemes are: (1) at sampling time k, use an appropriate
