Refuse-derived fuel-3 production simulation using network flow modeling: Predicting the uncertainty in quality standards

Junaid Tahir, Zhigang Tian, Rafiq Ahmad*, Pablo Martinez Rodriguez

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Municipal solid waste management requires intelligent and integrated decision-making to achieve sustainable waste treatment processes. In particular, a waste treatment system that transforms municipal solid wastes into a commodity called refuse-derived fuel (RDF) is being researched as a promising waste to energy solution. This waste processing faces limitations in maintaining consistent production and quality control standards of RDF. In this context, a network flow modeling technique is used to design a stochastic discrete-event simulation model for the production in a general material recovery facility (MRF) to evaluate its performance. The developed model supports revisions in the strategic, tactical, and operational decision levels and is integrated with varied uncertainties like probability distributions of in-feed waste compositions, moisture content, and calorific value of individual waste components, affecting the energy performance of a MRF. The model provides improvements to operating conditions and enables prediction for quality standards of RDF, enabling the waste management authority to meet their outlined quality specification for the final product. The validation of the model is conducted in a way, where the quality measures of the final product collected from an MRF are compared with the estimated values those from the simulation. The comparison reveals that precision in results from the developed model, in all performed tests is consistent with actual observed results, inferring the developed simulation model as a viable tool for estimating quality measures for RDF. The foundations of the model are based of assumptions like emphasis on general representation but not physical properties of MRF, only selected sets of uncertainties are studied, and variations in the operating conditions can affect estimated quality of RDF.
Original languageEnglish
Article number128168
Number of pages18
JournalFuel
Volume345
Early online date11 Apr 2023
DOIs
Publication statusPublished - 1 Aug 2023

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