Centralized and Decentralized Control System for Reactive Distillation Diphenyl Carbonate Process

Abstract

Reactive distillation (RD), a process-intensified technique, involves the integration of reaction and separation in a single unit. High non-linearities associated with the reactive distillation process constrict the control degrees of freedom and set the key challenge in the design of a robust control system. In this chapter, reactive distillation diphenyl carbonate (RD-DPC) design is optimized, and a decentralized as well as centralized feedback control configuration is applied to carry out the control studies. To execute the control scheme, a dynamic model of RD-DPC process is developed using Aspen Dynamic and interfaced with MATLAB Simulink for online control implementation. A comparative multi-loop feedback controller control performance study is done for different transfer function models obtained by using analytical- and optimization-based process identification techniques. The controller parameters obtained from the simple internal model control (SIMC) tuning relations for decentralized controller and Tanttu & Lieslehto (TL) tuning relations for centralized controller are applied to (i) the linear transfer function model and (ii) non-linear plant model. Set-point tracking, load rejection studies and robust stability analysis are carried out to compare the performance of different models and to investigate the controller performance of the non-linear model.

Design of a Decentralized PID Controller Using the Relative Gain Array Technique for a Coupled Flotation Process

“…For that reason, the resultant control operational stability must be checked. This is done according to the rule of the Niederlinski index as presented by [28,32]…”

“…Due to its simplicity and utility, the RGA analysis has been widely used to identify capable decentralized multi-loop control systems based on limited information, and steady-state gains. It is noted that [28] derived the controller settings for linear and non-linear models from the Internal Model Controller (IMC) and Tanttu & Lieslehto (TL) tuning relations, these parameters showed proper setpoint tracking and disturbance rejections. However, in some cases, there are high chances of encountering failures in real engineering systems, and they could cause instabilities in the system's operation [25].…”

“…Since the decentralized system involves interaction, it can be estimated by the Relative Gain Analysis (RGA) index and Niederlinski method by paring the manipulated and control variables for a stable system [2]. It is important to note that each element in the RGA matrix for a n×n system represents the ratio of the open-loop gain for a specific loop in the situation where all other loops are open, to the closed-loop gain for that loop when all other loops are closed [26][27][28][29].…”