Heat exchangers play a very vivacious role in almost all the fields of science and technology today. There are many other types of heat transfers available, but due to their numerous benefits, shell and tube heat exchangers are most frequently utilized. Due to its changing steady state gain and time constants with the process fluid, control of the hot water output temp in a shell and tube heat exchanger is difficult. This paper discusses about the Bacterial Foraging Optimization (BFO) algorithm-Deep Neural Network based Internal Model Controller is proposed to control the hot water outlet temperature of shell and tube heat exchanger system. In terms of characteristics like settling time, rising time, overshoot, integral absolute error (IAE), and integral square error (ISE), the performance of the suggested controller method is contrasted with that of a traditional PID controller.

Heat exchanger, shell and tube, controller, neural network and optimization.