Abstract:
Fouling of feed/effluent heat exchanger in Hydrotreating unit have a significant impact on the operation efficiency. Hydrotreater fouling presents loss of heat transfer, enlargement of energy consumption, increasing in maintenance cost and throughput limitation. Thus, to solve these problems, heat exchangers are cleaned during shutdown. In this research, an artificial neural network has been used to develop the fouling model of feed/effluent heat exchanger in Hydrotreating unit. Developed algorithm is employed to determine the optimal operation and cleaning interval of the feed/effluent heat exchanger surfaces. The artificial neural network is widely applied in modeling and optimization of chemical process especially nonlinear systems. The main advantage of the use of artificial neural network is obtaining a highly accurate mathematical model of the system without the detail of the system. The process modeling applications use the artificial neural network to approximate the relationship between the input and output variables.
