Open AccessArtificial Neural Network Control of a Multiple Effect Evaporators via Simulation
Author(s) -
Duraid Fadhil Ahmed,
Zainab A. 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/1094/1/012004
Subject(s) - artificial neural network , overshoot (microwave communication) , control theory (sociology) , pid controller , model predictive control , offset (computer science) , evaporator , computer science , controller (irrigation) , temperature control , control engineering , control (management) , engineering , artificial intelligence , mechanical engineering , telecommunications , agronomy , heat exchanger , biology , programming language
This research studies the dynamic model and control of multiple effect evaporators of tomato solutions by implementing three control strategies: PID, neural model reference, and neural model predictive controllers. The evaporator’s control is crucial to maintain the product specifications at different operation conditions at minimum operating cost. The model reference control and model predictive control has been designed and evaluated. The simulation results showed that the neural predictive controller is more suitable, has lower overshoot, less offset value, and less integral absolute error.