Prototype control and monitoring system safety device from leakage ammonia at marine loading arm with comparison of Neural Network (NN) and Extreme Learning Machine (ELM) method

This paper presents design and research studies in marine loading arm plant system. Artificial Neural Network (NN) and ELM (Extreme Learning Machine) methods are used and compared in this valve control system by implement it in a prototype using microcontroller. This prototype use value of temperature sensor and value of ammonia gas sensor in the furnace as parameter of heat to control the flow of air and valve of safety device. The temperature sensor used in this research is the type of DHT11. The ammonia gas sensor is MQ sensor. This prototype also uses fan and servo as the actuator. Fans are used to supply the oxygen and servo is used to control the valve of ammonia. From the experimental result, the data shows that the optimization of safety device system using ELM method works better compared with NN. The control system has a very good response and it can work well (percentage of error is less than 0.4%). Hence, if the system is applied in the marine loading arm plant, it could improve the performance of safety device control systems and save the leakage of ammonia gas.