Optimal 6E design of an integrated solar energy-driven polygeneration and CO2 capture system: A machine learning approach

• Published in: Thermal science and engineering progress Vol. 38; p. 101669
• Main Authors: Khani, Nastaran, Khoshgoftar Manesh, Mohammad H., Onishi, Viviani C.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2023
• Subjects: 6E Analyses; Dynamic analysis; Humidification-Dehumidification (HDH); Multi-objective Optimization; Sensitivity Analysis; Solar Energy; 6E Analyses; Solar Energy; Sensitivity Analysis; Multi-objective Optimization; Humidification-Dehumidification (HDH); Dynamic analysis
• ISSN: 2451-9049; 2451-9049
• DOI: 10.1016/j.tsep.2023.101669

•Multi-objective optimization of new solar-assisted polygeneration and CO2 capture system.•Machine learning-based multi-objective optimization approach combining GP and ANN.•6E analyses used to enhance thermodynamic, economic and environmental performances.•Sensitivity analysis applied to identify most influential variables and set objective functions.•Optimization approach significantly reduces overall costs and environmental impacts. Renewable energy-driven decentralized polygeneration systems herald great potential in tackling climate change issues and promoting sustainable development. In this light, this study introduces a new machine learning-based multi-objective optimization approach of an integrated solar energy-driven polygeneration and CO2 capture system for meeting a greenhouse’s power, freshwater, and CO2 demands. The integrated solar-assisted polygeneration system comprises a 486-kW gas turbine, two steam turbines, two organic Rankine cycles, a humidification-dehumidification desalination unit to recover waste heat while producing freshwater, and a post-combustion CO2 capture unit. The proposed system is mathematically modelled and evaluated via a dynamic simulation approach implemented in MATLAB software. Moreover, sensitivity analysis is conducted to identify the most influential decision variables on the system performance. The machine learning-based multi-objective optimization strategy combines Genetic Programming (GP) and Artificial Neural Networks (ANN) to minimize total costs, environmental impacts, and economic and environmental emergy rates whilst maximizing the system exergy efficiency and freshwater production. Finally, the system performance is further investigated through comprehensive Energy, Exergy, Exergoeconomic, Exergoenvironmental, Emergoeconomic, and Emergoenvironmental (6E) analyses. The three-objective optimization of the integrated system reduces total costs, environmental impacts, and monthly environmental emergy rate by 11.4%, 34.31% and 6.38%, respectively. Furthermore, reductions up to 56.81%, 50.19% and 77.07%, respectively, are obtained for the previous indicators by the four-objective optimization model. Hence, the proposed multi-objective optimization methodology represents a valuable tool for decision-makers in implementing more cost-effective and environment-friendly solar-assisted integrated polygeneration and CO2 capture systems.