Multi-objective Genetic Algorithm Optimization of Natural Gas Pressure Drop Station Heaters Using the Entropy Generation Minimization Method

In recent years, with the continuous growth of natural gas consumption in Iran, the number of pressure drop stations has increased significantly. In throttling valves of these stations, the temperature drop due to the Joule-Thomson effect causes the gas to hydrate, freeze the valves, and block the transmission path. Hence, about 14,000 indirect-fired water-bath heaters have a duty for preheating high-pressure gas before entering them. Unfortunately, the 30% average efficiency of indirectly fired water-bath heaters wastes nearly one billion cubic meters of processed natural gas every year, equivalent to a 400 MW power plant capacity. In this article, intending to optimize, indirect-fired water-bath heaters were modeled thermodynamically and thermo-economically, and three objective functions including thermal efficiency, entropy generation number, and wasted cost number are defined and the mathematical model was proposed in two scenarios. Then the model was solved based on the multi-objective genetic algorithm, using the entropy generation minimization method, and the Pareto optimal fronts of the scenarios were determined. The model implementation results with a deviation of less than ±10% compared to the results of a real sample indicate its acceptable performance. Based on the techno-economic justified results, it is possible to improve the efficiency of indirectly fired water-bath heaters between 48 and 55% depending on the gas volume flow rate. The relations, curves, and dimensionless groups obtained, can be used as a reference for the optimal design of indirect-fired water-bath heaters.