Energy, exergy, and exergy-economic optimization of a multigeneration system driven by geothermal primary heat source using multi-objective genetic algorithm (MOGA) - Ekariansyah, Andi S.; Muwonge, Martin; Saefuttamam, M. Rifqi; Dikaimana, Yophie; Nasruddin (Energy (Oxford), 2025)
A modified multigeneration system (MGS) using geothermal heat to provide products of cooling, heating, power generation, hydrogen, and fresh water through seawater desalination, has been proposed and analyzed. It uses liquefied natural gas (LNG) as a heat sink during the process and enhanced by incorporating a thermoelectric generation (TEG) module and reverse osmosis (RO) desalination unit. The modified system has been evaluated in terms of thermal efficiency, exergy efficiency, and the sum unit cost of products (SUCP) to assess its thermal, exergy, and exergy-economic performances. These three objective functions were then optimized using the Multi-objective Genetic Algorithm (MOGA) based on selected decision variables from the Engineering Equation Solver (EES) code connected with the MATLAB module and the TOPSIS method to obtain their most optimal values. The system could generate a heating capacity of 3577 kW, cooling capacity of 505.9 kW, output power of 282.5 kW, hydrogen production of 2.969 kg/h, and distillate water of 145.8 m3/hr. The most optimal values for thermal efficiency, exergy efficiency, and SUCP are 60.08 %, 18.65 %, and 159.389 $/GJ, respectively. With the TEG and RO desalination unit, the MGS resulted in a decrease in both thermal and exergy efficiency but with an increase in the SUCP. •Geothermal heat as the energy source and LNG as heat sink drive a multigeneration system (MGS) providing cooling, heating, and hydrogen products.•Introduction of a thermoelectric generation (TEG) module and reverse osmosis (RO) desalination unit changes the systems into modified MGS.•Those additional systems into the MGS affect the performances of thermal efficiency, exergy efficiency, and the sum unit cost of products (SUCP).•MOGA in MATLAB optimizes system performance using EES decision variables, reducing thermal/exergy inefficiencies while increasing SUCP.