Time delay integrating systems: a challenge for process control industries. A practical solution

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Abstract

Temperature control of processes that involve the heating and cooling of a closed batch reactor can be a real problem for conventional proportional-integral-derivative (PID) based loop controllers. This paper describes the application of a new industrial advanced process controller. This controller is designed to handle integrating type processes with long dead times and long time constants. The results demonstrate that reactors that could previously only be operated manually can be easily automated using an adaptive model predictive control technology. The barrier to automation of the reactor batch controls can be now removed resulting in tremendous improvements in batch consistency, batch cycle times, and productivity.

Introduction

Control of processes involving the heating and cooling of a closed batch reactor are a real problem for conventional proportional-integral-derivative (PID) based loop controllers, due to the reduced stability margins typical for these applications. These processes exhibit long dead times and time constants and have an integrating response due to the circulation of the heating or cooling medium through coils within the reactor or jackets on the outside of it.

The advanced controller described in this paper has the ability to model and control marginally stable processes with long and time varying delays. This controller exhibits the ability to incorporate and model the effect of known and unknown disturbances. The field application results presented are demonstrating that reactors which could previously only be operated manually can now automated using model predictive control technology. The barrier to automation of the reactor batch controls is removed resulting in improvements in batch consistency, batch cycle times and productivity.

A number of industrial applications of advanced control methods are reported for batch processes. The limitations of these schemes are fundamentally connected with the application. These schemes lack the generality required to solve batch reactor industrial control in an unified manner. Schemes previously proposed for this application include conventional feedback control with feed forward compensation (Berber, 1996), gain scheduling or multiple models (Krishnan & Kosanovich, 1997; Jutan & Uppal, 1984), generic internal model control (Morari & Zafiriou, 1989) and adaptive regulators (Cabassud, Le Lann, Chamayou, & Casamatta, 1989; Chen & Peng, 1998). The typical batch process variables evolve over a wide range therefore time linear invariant models tend to fail in describing completely the process dynamics. Few authors are looking into these challenges from the perspective of predictive control (Garcia, 1984; Koncar, Koubaa, Legrand, Bruniaux, & Vasseur, 1997; Lee, Chin, Lee, & Lee, 2000; Nagy & Agachi, 1997). Some authors (Jarupintusophon, Lelann, Cabassud, & Casamatta, 1994; Fileti & Pereira, 1997) are reporting applied adaptive control techniques. However, a potential problem of their approach can arise from the identification scheme adopted (e.g. the use of ARMAX models limits the generality of the approach). Also, in the case of grey box models used, as in (Jarupintusophon et al., 1994), an intimate knowledge of the plant is required.

The paper content is split in six sections. After the introduction containing achievements to date in the second section the theory behind dynamic modelling and control is addressed. Section 3 describes the process to be controlled. In Section 4 the results concerning the controlled process are provided. Other successful applications are encompassed in Section 5 followed by conclusions in Section 6.

Section snippets

The adaptive predictive control strategy

Based on an original theoretical development by Dumont and Zervos (1986); Zervos (1988) the controller was first developed for self regulating systems. This controller was credited by various users with features like reduced effort required to obtain accurate process models, inclusion of adaptive feed forward compensation and ability to cope with severe changes in the process etc.

These features together with a recognized need in process control industry made the authors of this paper consider a

A batch reactor processes

The chemical batch reactor in this application is used to produce various polyester compounds. The process involves combining the reagents and then applying heat to the mixture in order to control the reactions and resulting products. A specific temperature profile sequence for the batch reaction must be followed to ensure that the exothermic reactions occur in a controlled fashion and that the resulting products will have consistent properties. An additional requirement is that the reaction

Application results

The advanced model based controller was implemented on the reactor temperature control loop. The controller parameters were estimated from the observed system response from a previous batch and an approximate model of the system was developed in the controller using 15 Laguerre filters. There was some concern that a single model of the system may not be valid for the entire batch sequence because the composition and viscosity of the polyester in the reactor changes substantially during the

Other successful applications

The model based predictive controller has been implemented on many other industrial processes involving significant time delays. Control of pulp bleaching to achieve a desired brightness following a 40 min reaction time in a retention tower is among them. In this example, the mill had designed a Smith Predictor compensated PID controller and the model predictive controller was installed to compare performance. The results of this application demonstrated a 48% reduction in pulp brightness

Conclusions

An advanced model based predictive controller MBPC developed for use on processes with an integrating response exhibiting long dead time and time constants has been successfully applied to the temperature control of a batch reactor. The controller was easy to apply and configure. It has achieved very good control performance on a reactor that could not be controller in a satisfactory manner using PID controls implemented in the plant DCS.

The automatic control of the reactor temperature now

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