Adaptive Control of Pyrolysis Reactor's Temperature Modes Based on Fuzzy Logic and Metaheuristic Optimization

Abstract

This article introduces and validates an advanced approach to adaptive automatic control of pyrolysis reactors, aimed at optimizing the utilization of agricultural plastics. By integrating fuzzy logic with enhanced metaheuristic optimization technique, the proposed method achieves superior performance in regulating reactor temperature modes. A key feature of this approach is the incorporation of the reactor's current loading level as an additional variable in the antecedents of the adaptive fuzzy controller's rule base. This dynamic adaptation is further enhanced through the application of the hybrid improved gray wolf optimization algorithm, which optimizes the rule base parameters to increase the robustness and accuracy of control. To assess the effectiveness of this methodology, a fuzzy control system was designed for a specific pyrolysis reactor. Simulation results confirm the approach's superiority over traditional controllers in response speed, accuracy and robustness in various operating conditions. In particular, at a reactor loading level of 100% at startup of the installation and subsequent transition to steady-state operation, the application of the proposed approach made it possible to reduce the transient process duration by 2.26 times and decrease in overshoot by 48.4% compared to existing systems. This, in turn, will lead to a reduction in molecular weight fluctuations and an improvement in the quality of the resulting fuel fractions, thereby significantly enhancing the general energy efficiency and economic performance of agricultural enterprises utilizing pyrolysis technologies.