Mixed-Integer Non-Linear Programming (MINLP) Multi-Period Multi-Objective Optimization of Advanced Power Plant through Gasification of Municipal Solid Waste (MSW)

Ahmad Syauqi, Widodo Wahyu Purwanto

Research output: Contribution to journalArticlepeer-review

Abstract

Multi-objective optimization is one of the most effective tools for the decision support system. This study aims to optimize the gasification of municipal solid waste (MSW) for advanced power plant. MSW gasifier is simulated using Aspen Plus v11 to produce syngas, to be fed into power generation technologies. Four power generation technologies are selected, solid oxide fuel cell, gas turbine, gas engine, and steam turbine. Mixed-integer non-linear programming (MINLP) multi-objective optimization is developed to provide an optimal solution for minimum levelized cost of electricity (LCOE) and minimum CO2eq emissions. The optimization is conducted with a ϵ-constraint method using GAMS through time periods of 2020-2050. Decision variables include gasifier temperature, steam to carbon ratio, and power generation technologies. The optimization result demonstrates that the lower steam to carbon ratio gives lower LCOE and higher CO2eq emissions, and temperature variation gives no significant impact on LCOE and as it increases, CO2eq emission is reduced. It demonstrates that a gas turbine is the best option for generating electricity from 2020 to 2040 and beyond 2040 SOFC is the best option.

Original languageEnglish
JournalChemical Product and Process Modeling
DOIs
Publication statusAccepted/In press - 2020

Keywords

  • advanced power plant
  • gasification
  • MINLP
  • multi-objective optimization
  • municipal solid waste

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