Exergy-Economic-Environment Optimization of the Waste-to-Energy Power Plant Using Multi-Objective Particle-Swarm Optimization (MOPSO)

Abstract

This paper brings together the benefits of the results of exergy, exergoeconomic, exergoenvironmental analysis, and optimization for a waste-to-energy power plant. Initially, exergoeconomic balances for each stream were calculated. For validating the current simulations, the actual data of the Amsterdam waste-to-energy power plant in working conditions were examined. Moreover, the behaviors of the influential parameters on the objective functions were evaluated. In order to perform multi-objective optimization, the Multi-Objective Particle-Swarm Optimization algorithm is implemented. To obtain optimum operating conditions, 14 design parameters, and 3 objective functions are considered, while the total cost rate, total exergy efficiency of the cycle, and environmental impacts are the objective functions. Finally, the TOPSIS decision-making method determined optimum-operating conditions. The results of exergy analysis indicated that the most exergy destruction belonged to the incinerator unit at 66%. Instead, the pumps contributed the least in this field, (approximately 1%). Because of the optimization process, the total exergy efficiency of the power plant increased from 30.89% to 38.9% while the total cost rate was 5188.05 USD/hour. By comparison between the obtained results from the optimization procedure, introducing optimum working condition has caused an increase in exergy efficiency and reduced exergy destruction for components.

Multiobjective particles swarm optimization and multicriteria decision making of improved cumene production process including economic, environmental, and safety criteria

“…[ 22 ] Also, Esmaeilion et al used the MOPSO algorithm to obtain optimal operating conditions in a waste‐to‐energy power plant. [ 23 ]…”

Critical review of multigeneration system powered by geothermal energy resource from the energy, exergy, and economic point of views

“…100,101 According to Equation ( 7), the total exergy of geothermal systems, like other energy systems, can be divided into four general categories, which are: (1) kinetic exergy (Ex KN ), (2) physical exergy (Ex PH ), (3) potential exergy (Ex PT ) and (4) chemical exergy (Ex CH ). 102,103 Ex = Ex + Ex + Ex + Ex .…”

“…According to Equation (), the total exergy of geothermal systems, like other energy systems, can be divided into four general categories, which are: (1) kinetic exergy (Ex KN ), (2) physical exergy (Ex PH ), (3) potential exergy (Ex PT ) and (4) chemical exergy (Ex CH ) 102,103 $$$\mathrm{Ex}={\mathrm{Ex}}_{\mathrm{PH}}+{\mathrm{Ex}}_{\mathrm{KN}}+{\mathrm{Ex}}_{\mathrm{PT}}+{\mathrm{Ex}}_{\mathrm{CH}}.$$$…”

Assessment of a novel geothermal powered polygeneration system with zero liquid discharge

“…In the RO unit, the produced PW and seawater are directly related, as follows 17,45,46 : $$${\dot{m}}_{\mathrm{SW}}={\dot{m}}_{{\rm{B}}}+{\dot{m}}_{\mathrm{PW}},$$$ $$${\dot{m}}_{\mathrm{SW}}{X}_{\mathrm{SW}}={\dot{m}}_{{\rm{B}}}{X}_{{\rm{B}}}+{\dot{m}}_{\mathrm{PW}}{X}_{\mathrm{PW}},$$$ $$${\dot{m}}_{\mathrm{PW}}={RR}{\dot{m}}_{\mathrm{SW}}{. }$$$Here, subscripts SW, B, and PW denote seawater, brine, and PW, respectively.…”