Optimal Control of Reactor Concentration and Temperature in Sulfuric Acid Production Process

Abstract

The current laws are making rules stricter to lessen the impact of the chemical industry on the environment. In this study, it is defined at how a sulfuric acid plant operates through modeling, simulating, and optimizing its processes. The model, which was used, covers various parts like reactors, heat exchangers, mixers, and absorption columns. However, it was mainly focusing on the reactor part for this document. Some parameters were adjusted based on actual plant data, while others were estimated using standard methods. The results from this modeling match up well with in real plants modelling. Additionally, the controller was proposed using a Fuzzy logic system specifically designed for sulfuric acid plants. This system involves identifying inputs, outputs, and setting membership functions for different conditions from regular operations to emergencies in a sulfuric acid plant. In addition, after the fuzzy is designed the PI controller is added as fuzzy-PI controller for getting better optimality in the whole process in the reactor. This document mainly possesses modelling and control of concentration and temperature inside the reactor. Both parameters are tasted in both side of the reactor, which is inlet and outlet part. The fuzzy-PI controller is used for optimizing the concentration and creating stability in temperature inside the reactor.in addition to the controller the servo and regulator techniques are applied to the system for better performance. The whole simulation is tasted in MATLAB Simulink. After the simulation, the validation of the controller is analyzed and the regulated fuzzy-PI controller have better performance that PI controller and servo based fuzzy-PI controller. This all validation and performance are checked by showing the dynamic performance parameter described in chapter 5 in table 5.1. from this figure error in PI controller from temperature is 1.127% and in fuzzy PI controller the error is 0.24 and the overshoot is reduced from 0.168% to 0.0254% in PI and fuzzy PI controller respectively. Settling time in concentration was reduced from 6.539 to 1.248 in PI and fuzzy PI controller. From this it is concluded that fuzzy PI controller have better performance than PI controller for this system.