Steam consumption minimization using genetic algorithm optimization method: an industrial case study

Abstract

ABSTRACT Condensate stabilization is a process where hydrocarbon condensate recovered from natural gas reservoirs is processed to meet the required storage, transportation, and export specifications. The process involves stabilizing of hydrocarbon liquid by separation of light hydrocarbon such as methane from the heavier hydrocarbon constituents such as propane. An industrial scale back-up condensate stabilization unit was simulated using Aspen HYSYS software and validated with the plant data. The separation process consumes significant amount of energy in form of steam. The objectives of the paper are to find the minimum steam consumption of the process and conduct sensitivity and exergy analyses on the process. The minimum steam consumption was found using genetic algorithm optimization method for both winter and summer conditions. The optimization was carried out using MATLAB software coupled with Aspen HYSYS software. The optimization involves six design variables and four constraints, such that realistic results are achieved. The results of the optimization show that savings in steam consumption is 34% as compared to the baseline process while maintaining the desired specifications. The effect of natural gas feed temperature has been investigated. The results show that steam consumption is reduced by 46% when the natural gas feed temperature changes from 17.7 to 32.7°C. Exergy analysis shows that exergy destruction of the optimized process is 37% less than the baseline process. Abbreviations BCSU: Back-up condensate stabilization unit; GA: Genetic algorithm; MEG: Monoethylene glycol; RVP: Reid Vapor Pressure; LPG: Liquefied petroleum gas; : Heat rate; : Saturated steam mass flow rate; : Latent heat; HP: High pressure; LP: Low pressure; LHV: Low heating value; : Exergy destruction; : Entropy generation; Equality constraint; : Inequality constraint; : Design variables.