Optimization of a Biomass-Based Power and Fresh Water-Generation System by Machine Learning Using Thermoeconomic Assessment

Fatemeh Parnian Gharamaleki, Shayan Sharafi Laleh, Nima Ghasemzadeh (), Saeed Soltani () and Marc A. Rosen
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Fatemeh Parnian Gharamaleki: Faculty of Chemical and Petroleum Engineering, University of Tabriz, Tabriz 16471, Iran
Shayan Sharafi Laleh: Faculty of Mechanical Engineering, University of Tabriz, Tabriz 16471, Iran
Nima Ghasemzadeh: Faculty of Mechanical Engineering, University of Tabriz, Tabriz 16471, Iran
Saeed Soltani: Faculty of Engineering and Natural Sciences, Antalya Bilim University, 07190 Antalya, Turkey
Marc A. Rosen: Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, ON L1G 0C5, Canada

Sustainability, 2024, vol. 16, issue 20, 1-24

Abstract: Biomass is a viable and accessible source of energy that can help address the problem of energy shortages in rural and remote areas. Another important issue for societies today is the lack of clean water, especially in places with high populations and low rainfall. To address both of these concerns, a sustainable biomass-fueled power cycle integrated with a double-stage reverse osmosis water-desalination unit has been designed. The double-stage reverse osmosis system is provided by the 20% of generated power from the bottoming cycles and this allocation can be altered based on the needs for freshwater or power. This system is assessed from energy, exergy, thermoeconomic, and environmental perspectives, and two distinct multi-objective optimization scenarios are applied featuring various objective functions. The considered parameters for this assessment are gas turbine inlet temperature, compressor’s pressure ratio, and cold end temperature differences in heat exchangers 2 and 3. In the first optimization scenario, considering the pollution index, the total unit cost of exergy products, and exergy efficiency as objective functions, the optimal values are, respectively, identified as 0.7644 kg/kWh, 32.7 USD/GJ, and 44%. Conversely, in the second optimization scenario, featuring the emission index, total unit cost of exergy products, and output net power as objective functions, the optimal values are 0.7684 kg/kWh, 27.82 USD/GJ, and 2615.9 kW.

Keywords: biomass gasification; reverse osmosis; thermoeconomic; grey wolf optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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