Optimization of a near-zero-emission energy system for the production of desalinated water and cooling using waste energy of fuel cells.

Journal: Chemosphere
PMID: 37244560

Abstract

In the present study, a biomass-based multi-purpose energy system that can generate power, desalinated water, hydrogen, and ammonia is presented. The gasification cycle, gas turbine, Rankine cycle, PEM electrolyzer, ammonia production cycle using the Haber-Bosch process, and MSF water desalination cycle are the primary subsystems of this power plant. On the suggested system, a thorough thermodynamic and thermoeconomic evaluation has been conducted. For the analysis, the system is first modeled and investigated from an energy point of view, after which it is similarly studied from an exergy point of view before the system is subjected to economic analysis (exergoeconomic analysis). The system is evaluated and modeled using artificial intelligence to aid in the system optimization process after energy, exergy, and economic modeling and analysis. The resulting model is then optimized using a genetic algorithm to maximize system efficiency and reduce system expenses. EES software does the first analysis. After that, it sends the data to MATLAB program for optimization and to see how operational factors affect thermodynamic performance and overall cost rate. To find the best solution with the maximum energy efficiency and lowest total cost, multi-objective optimization is used. In order to shorten computation time and speed up optimization, the artificial neural network acts as a middleman in the process. In order to identify the energy system's optimal point, the link between the objective function and the choice factors has been examined. The results show that increasing the flow of biomass enhances efficiency, output, and cost while raising the temperature of the gas turbine's input decreases cost while simultaneously boosting efficiency. Additionally, according to the system's optimization results, the power plant's cost and energy efficiency are 37% and 0.3950$/s, respectively, at the ideal point. The cycle's output is estimated at 18900 kW at this stage.

Authors

  • Jianbo Lu
    Guangxi Key Lab of Human-machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, 530001, China. Electronic address: lujianbo@nnnu.edu.cn.
  • Azher M Abed
    Air Conditioning and Refrigeration Techniques Engineering Department, Al-Mustaqbal University College, Babylon, 51001, Iraq. Electronic address: azhermuhson@uomus.edu.iq.
  • Kaushik Nag
    College of Engineering and Technology, American University of the Middle East, Kuwait.
  • Mohamed Fayed
    College of Engineering and Technology, American University of the Middle East, Kuwait. Electronic address: Mohamed.fayed@aum.edu.kw.
  • Ahmed Deifalla
    Full Professor Future University in Egypt, South Teseen, New Cairo, 11835, Egypt. Electronic address: ahmed.deifalla@fue.edu.eg.
  • Ahmed Al-Zahrani
    ‏Department of Mechanical and Materials Engineering, Faculty of Engineering, University of Jeddah, Jeddah, 21589, Saudi Arabia. Electronic address: aalzahrani@uj.edu.sa.
  • Nivin A Ghamry
    Cairo University, Fuculty of Computers and Artificial Intelligene, Giza, Egypt. Electronic address: nivin@fci-cu.edu.eg.
  • Ahmed M Galal
    Department of Mechanical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam Bin Abdulaziz University, Saudi Arabia; Production Engineering and Mechanical Design Department, Faculty of Engineering, Mansoura University, P.O 35516, Mansoura, Egypt. Electronic address: ahm.mohamed@psau.edu.sa.

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