Comparison of Mathematical-based and ANN-based Models of a Coupled Industrial Tank System

Abstract

Liquid tank systems play important role in industrial application such as in food processing, beverage, dairy, filtration, effluent treatment, pharmaceutical industry, water purification system, industrial chemical processing and spray coating. Recently, classical PID controller based on the linear model is widely used in controlling industrial liquid level control application. However aggressive performance requirements may not be achievable with this controller due to the presence of nonlinear dynamics inherent in the liquid control system and system parameter variations caused by for example corrosive build-up in liquid level systems creates variation of cross section areas of the tank and discharge orifice. In order to allow advanced controller which can deal with system nonlinearities, a nonlinear model of the tank system is required. This paper describes a comparison study of mathematical-based and artificial neural network-based model of a coupled industrial tank system including its nonlinearity. First, a non-linear mathematical model is developed and its parameters are identified using extended Kalman filter (EKF) based on the experimental data. Next, a multi layer feedforward neural network trained by using backpropagation learning algorithm is used to develop the system model based on the experimental data. A series of validation test is carried out to evaluate the effectiveness of the both models. The results confirm that ANN-based model is more suitable model of the lab-scale industrial tank than EKF-based system.