• Development of an optimization...

*Result*: Development of an optimization genetic algorithm method for estimating municipal solid waste composition.

Title:
Development of an optimization genetic algorithm method for estimating municipal solid waste composition.
Authors:
Banifateme M; Department of Energy System Engineering, Faculty of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran., Zaroorian P; Department of Energy System Engineering, Faculty of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran., Behbahaninia A; Department of Energy System Engineering, Faculty of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran. Electronic address: alibehbahaninia@kntu.ac.ir., Pignatta G; School of Built Environment, University of New South Wales (UNSW), Sydney, Australia; High-Performance Architecture (HPA) Research Cluster, University of New South Wales (UNSW Sydney), NSW 2052, Australia; City Futures Research Centre (CFRC), University of New South Wales (UNSW Sydney), NSW 2052, Australia.
Source:
Waste management (New York, N.Y.) [Waste Manag] 2026 Jan 30; Vol. 211, pp. 115310. Date of Electronic Publication: 2025 Dec 24.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Pergamon Press Country of Publication: United States NLM ID: 9884362 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2456 (Electronic) Linking ISSN: 0956053X NLM ISO Abbreviation: Waste Manag Subsets: MEDLINE
Imprint Name(s):
Original Publication: New York : Pergamon Press, c1989-
MeSH Terms:
Solid Waste*/analysis , Waste Management*/methods , Refuse Disposal*/methods , Algorithms*, Iran ; Genetic Algorithms
Contributed Indexing:
Keywords: Genetic algorithm inverse solution; Municipal solid waste analysis; Power plants; Resource recovery; Waste management optimization; Waste-to-energy
Substance Nomenclature:
0 (Solid Waste)
Entry Date(s):
Date Created: 20251225 Date Completed: 20260106 Latest Revision: 20260106
Update Code:
20260130
DOI:
10.1016/j.wasman.2025.115310
PMID:
41447964
Database:
MEDLINE

*Further Information*

*The accurate estimation of municipal solid waste composition is crucial for effective waste management and resource recovery. Conventional approaches rely on direct sampling, which is both time-consuming and costly. This study presents an accurate and stable Genetic algorithm-based inverse method for estimating municipal solid waste composition without sampling. The method estimates municipal solid waste composition from measurable parameters, including flue gas, working fluid, ash, and leachate, using a genetic algorithm for accurate and stable estimation without physical sampling. The method's accuracy and stability are validated through numerical simulation experiments involving five distinct municipal solid waste compositions. Data from direct problem simulations, perturbed by random errors, serve as inputs for the genetic algorithm-based inverse solution. Results indicate that the inverse solution is stable. Results indicate that the inverse solution is stable and accurately reproduces the average composition of the five municipal solid waste samples used in the direct method. The results reveal that the estimated composition of municipal solid waste closely matches actual values, demonstrating the feasibility of this genetic algorithm-based approach. The modified methodology is employed at the Aradkooh waste-to-energy power plant in Tehran, Iran. The findings from the Aradkooh power station indicate that the carbon, oxygen, hydrogen, sulfur, moisture, and ash content of municipal solid waste are 27.14, 33.29, 3.16, 0.3, 15.41, and 20.21 percent, respectively. The novelty of this study lies in stabilizing the inverse problem by increasing the number of equations. As a result, the solution achieves higher accuracy and lower estimation errors compared to previous studies.
(Copyright © 2025 Elsevier Ltd. All rights reserved.)*

*Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.*