Design of Deaerator Storage Tank Level Control System at Industrial Steam Power Plant with Comparison of Neural Network (NN) and Extreme Learning Machine (ELM) Method

Abstract

This paper has proposed a prototype of a control system in deaerator storage tank level at Industrial Steam Power Plant based on artificial intelligence (AI). There are two kind methods of AI which are implemented in this research, first is Back Propagation Neural Network (BP-NN) and the second is Extreme Learning Machine (ELM). The proposed method is aimed to improve the performance of an existing Proportional Integral (PI) control method. The input variables are error level and load condition. The output variables are control valve percentage and indicator value. From the experiment, the result proved that ELM is fast superior to BP-NN according to the time of training process and error tolerance. Prototype-based on ELM is also working properly with an error tolerance of 0.15 %.