May 24, 2024
Shoaib Khanmohammadi

Shoaib Khanmohammadi

Academic rank: Associate professor
Address: Department of Mechanical Engineering, Kermanshah University of Technology, Kermanshah, Iran
Education: Ph.D in Mechanical Engineering
Phone: 0833-8305001
Faculty: Faculty of Engineering


A comprehensive approach for optimizing a biomass assisted geothermal power plant with freshwater production: Techno-economic and environmental evaluation
Type Article
Biomass Desalination Environmental evaluation Geothermal power plant Optimization
Researchers Parisa Heidarnejad، Hadi GEnceli، Mustafa Asker، Shoaib Khanmohammadi


In the current research, a comprehensive thermodynamic study of an innovative biomass-geothermal power plant combined with a desalination system is presented and analyzed. The suggested system is a geothermal based cascaded steam/organic Rankine cycle benefiting from municipal solid waste combustion in order to enhance its performance. Besides, the exhaust gasses of municipal solid waste combustion are utilized as the primary energy source for driving a multi-effect desalination subsystem which converts the seawater into low salinity water. A comprehensive approach including energy and exergy analyses along with thermoeconomic evaluation is applied to investigate the viability of the plant. First of all, validation of the presented model has been tested by means of comparing the results with published data, through which a good agreement has achieved. The energy and exergy efficiencies can be reached to 13.9% and 19.4% respectively while the total product cost rate of the system is estimated to be 285.3 $/h. Moreover, environmental analysis is conducted in terms of estimation of CO2 and NOx emissions to address the environmental benefits of utilizing municipal solid waste combustion instead of coal for improving the performance of the geothermal power plant. Results indicate that municipal solid waste utilization saves 8,092 tonnes of CO2 emission and 36 tonnes of NOx emission annually in relative to coal utilization. Finally, a three-objective optimization is performed regarding exergy efficiency, total product cost rate, and CO2 emission rate as objective functions through applying the Genetic Algorithm in order to figure out the optimum performance of the system and the Pareto frontier is extracted. The results of optimization indicate that in the optimum case, exergy efficiency of 20.72%, total product cost rate of 306.1 $/h and CO2 emission rate of 1967.7 tonnes/y are achievable.