Control and monitoring system optimalization of combustion in furnace boiler prototype at industrial steam power plant with comparison of Neural Network (NN) and Extreme Learning Machine (ELM) method

Abstract

This paper is presenting a design and research studies in industrial steam power plant system called “control and monitoring system to optimize the combustion process in the furnace boiler prototype with comparison of neural network and extreme learning machine”. Comparison between Neural Network and ELM (Extreme Learning Machine) methods will be used to this combustion control system and will be implemented in a prototype with microcontroller. This prototype is using the value of temperature sensor and value of smoke sensor in the furnace as parameter of heat to control the flow of air and fuel oil. The temperature sensor in this research is type K Thermocouple. The smoke sensor is MQ sensor. This prototype also used fan and pump oil as an actuator. Fans are used to supply the oxygen and pump is used to supply the fuel oil. From the experimental result, this prototype shows the optimization of combustion system using ELM (Extreme Learning Machine) method can work well compared with NN (Neural Network) method. ELM Control System has a very good response and it can work well (RMSE = 6,32456E-05). So, if the system is applied in the industrial steam power plant, it can improve the performance of combustion control systems and able to save the fuel.