PREDICTING FLOW RATE AND LEVEL OF CUSTOM TANK USING PARTICLE FILTER

One of the basic elements that any plant process facility consists of is control tank system. Custom tank is one type of these control tank systems. Proper controlling of this element will help the facility to work smoothly and it will increase the reliability of the whole system. This project is looking into predicting the state of variables that completely represent the dynamics the custom tank (height of fluid or output flow rate). This prediction can be used in controlling the custom tank (predictive control). The project involve MATLAB SIMULINK simulation program for the custom tank along with different prediction models. The obtained results showed that introducing Multilayer Perceptron (MLP) Neural Network architecture improve the prediction significantly where different algorithms, Recursive Kalman Filter (RKF) and Extended Kalman Filter (EKF) have been used simultaneously to estimate fluid height and output flow. It further shows that introducing cantered finite difference with EKF (particle filter) improve the performance of the network. The report consists of an introduction, problem statement, objectives, literature review and methodology used to solve the problem. It further looks into the obtained results with consistent discussion.