Open AccessFuzzy logic control of a catalytic naphtha unit
Author(s) -
Duraid Fadhil Ahmed,
Intisar Hussain Khalaf
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1067/1/012147
Subject(s) - pid controller , setpoint , control theory (sociology) , fuzzy logic , temperature control , controller (irrigation) , catalytic reforming , fuzzy control system , naphtha , control engineering , computer science , mathematics , engineering , chemistry , control (management) , catalysis , biology , agronomy , biochemistry , artificial intelligence
This work examines the fuzzy logic-based control of a catalytic reformer. The control process requires development of an exact process model valid over a wide range of operating conditions. Two control methods, proportional integral derivative (PID) and Fuzzy-PID, were applied to determine the optimal operating conditions. The output temperature from the feedback fuzzy controller was sent to the PID controllers as a setpoint in the heating system loop, and two fuzzy logic systems, one in the forward path and one in the feedback path were developed to give setpoints for temperature control loops in the four reactors. A fuzzy logic-based optimal control scheme was established to increase the aromatics yield, subject to constraints on the inlet temperature of the reactors. A rigorous kinetic model was then developed by expressing the heat and mass balances under unsteady state conditions. This model was used to determine the temperature and concentration profiles of three main hydrocarbons, naphthenes, paraffins, and aromatics, across the four reactors. A reaction scheme involving 27 pseudo components connected using a network of 71 reactions for components in a wide range of carbon numbers was thus modelled. The simulation results for the model were compared with previous studies to validate the model, and good agreement was found between the reformate composition of the proposed model and the plant reformate composition and temperature. The simulation results also showed that the PID –Fuzzy controller is accurate but that it requires additional time to reach a steady state value.