Temperature control of a pilot plant reactor system using a genetic algorithm model‐based control approach

Abstract

The work described in this paper aims at exploring the use of an artificial intelligence technique, i.e. genetic algorithm (GA), for designing an optimal model-based controller to regulate the temperature of a reactor. GA is utilized to identify the best control action for the system by creating possible solutions and thereby to propose the correct control action to the reactor system. This value is then used as the set point for the closed loop control system of the heat exchanger. A continuous stirred tank reactor is chosen as a case study, where the controller is then tested with multiple set-point tracking and changes in its parameters. The GA model-based control (GAMBC) is then implemented experimentally to control the reactor temperature of a pilot plant, where an irreversible exothermic chemical reaction is simulated by using the calculated steam flow rate. The dynamic behavior of the pilot plant reactor during the online control studies is highlighted, and comparison with the conventional tuned proportional integral derivative (PID) is presented. It is found that both controllers are able to control the process with comparable performance.  2007 Curtin Figure 2. Monitoring and control software (left) and genetic algorithm model-based controller (right) developed for the PARS-EX reactor. This figure is available in colour online at www.apjChemEng.com.

Advances in Machine Learning for Monitoring, Control, and Optimization of Temperature of Reactors

“…This controller was compared to an error-based adaptive controller, with the results demonstrating the genetic-ANFIS controller's robust performance in maintaining temperature control in the biodiesel microwave reactor during nonlinear real-time scenarios, ultimately improving reactor efficiency and performance. The challenge of regulating reactor temperature with minimal overshoot and effective handling of load disturbances in a pilot plant continuous stirred tank reactor scenario is addressed by Wahab et al in their work [56]. To tackle this problem, they utilize a GA to design an optimal model-based controller, demonstrating the GA's effectiveness in achieving these objectives.…”

“…To tackle this problem, they utilize a GA to design an optimal model-based controller, demonstrating the GA's effectiveness in achieving these objectives. Investigating the challenge of reactor temperature regulation, Khairi Abdul Wahab et al [57] utilize a GA to develop an optimal model-based controller. Their research demonstrates that the mathematical model controller evolved through GA effectively achieves the regulation of reactor temperature with minimal overshoot and the capability to reject load disturbances, highlighting the method's applicability in controlling pilot plant chemical reactors.…”