An Integrated Framework of Artificial Intelligence and Genetic Algorithm for Optimization of Shell and Tube Heat Exchanger under Uncertainty

Abstract

To reduce energy consumption, an energy-efficient process is vital. Heat exchanger is one of the most abundant used equipment in the process industry. The shell and tube heat exchanger (STHE) is widely used in chemical, petroleum, and other process industries. In the literature, a lot of work has been done on the design and optimization of the STHE using different optimization methods. Although no one is focusing on the optimization of shell and tube heat exchangers under uncertain process conditions. The current work developed an Integrated Framework of Artificial Intelligence and Genetic Algorithm for STHE to predict the optimum inlet stream mass flow rates in the presence of uncertainty in process conditions. Using optimized industrial data, the STHE model was regenerated in Aspen EDR. The COM server was used to build the interface between Aspen HYSYS and MATLAB. The data set was generated by inserting the variation of ±1, ±2, ±3, ±4, and ±5 in the crude oil composition as well as in the inlet temperature and pressure of cold crude and hot kerosene oil. The optimum mass flow rate for each variation was determined using a single objective genetic algorithm. A total of 400 samples were generated, 70% were used for the training of feed-forward neural network and the remaining samples were equally divided for the validation and testing of the model. The proposed artificial neural network (ANN) model has a correlation coefficient of 0.999. The high accuracy and robustness of the ANN model, make it suitable for real-time industrial application, to reduce energy consumption