Neuro-genetic approach for optimization of the water flowrates distribution on a hydrogen sulphide cooling system
Efficient utilities usage and enhanced heat transfer are imperative in todays’ industrial and technological processes. Over these bases, a neuro-genetic procedure was proposed for optimization of the water flowrates distribution on a hydrogen sulphide gas coolers system. It relied on Genetic Algorithms, combined with an improved ɛ-NTU model for simulation of jacketed shell-and-tube heat exchangers. Artificial Neural Networks were furtherly applied to correlate the optimum water flowrates to predictive variables. The heat transfer incremental was estimated from 3695 to 10514 W, while reduction of the gas exit temperature was projected between 2.9 and 9.8 K. Calculated heat recovery averaged 12.44 %, varying from 3.90 to 22.16 %. The optimized water distribution scheme improved the system energy performance under a fixed network concept and unvaried overall feed water flowrate, thus effectively avoiding additional cost incurred if topology modification is applied. This research provided a technological solution to the studied problem, consisting on installation of automatic valves and programmable flow control-loops linked to a PLC.