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Updated: Oct 22, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Development of a dynamic optimization framework for waste management systems.

Mohamed Abdallah1, Sadeque Hamdan2, Ahmad Shabib1

  • 1Department of Civil and Environmental Engineering, University of Sharjah, Sharjah, United Arab Emirates.

Methodsx
|August 26, 2021
PubMed
Summary

Optimizing waste-to-energy (WTE) systems in a hybrid strategy maximizes benefits. Mathematical modeling identifies the best integrated solid waste management (ISWM) systems for energy recovery, carbon footprint, and profitability.

Keywords:
Carbon FootprintEnergy recoveryFinancial feasibilityMathematical modellingOptimization

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Area of Science:

  • Environmental Engineering
  • Operations Research
  • Energy Systems

Background:

  • Municipal solid waste management faces challenges with traditional methods.
  • Waste-to-energy (WTE) technologies offer environmental and economic advantages.
  • Single WTE systems may not achieve optimal integrated solid waste management (ISWM) benefits.

Purpose of the Study:

  • To develop a systematic optimization framework for selecting hybrid WTE systems.
  • To identify the most beneficial set of ISWM systems for enhanced material and energy recovery.
  • To maximize benefits and minimize negative impacts in waste management.

Main Methods:

  • Mathematical modeling to create a systematic optimization framework.
  • Computation of energy recovery, carbon footprint, and financial profitability for WTE facilities.
  • Multi-objective mathematical programming solved using the weighted comprehensive criterion method (WCCM) and implemented in CPLEX using OPL.

Main Results:

  • The study presents a framework for optimizing hybrid WTE systems.
  • Quantified energy recovery, carbon footprint, and financial profitability for WTE facilities.
  • Demonstrated the application of WCCM for multi-objective optimization in ISWM.

Conclusions:

  • Hybrid WTE strategies are superior to single-system approaches for ISWM.
  • The developed optimization framework effectively identifies beneficial ISWM system combinations.
  • Mathematical modeling provides a robust method for optimizing waste management decisions.