Optimization of sludge-derived fuel formulation based on co-combustion behaviors and artificial neural network modeling, with economic assessment

Zhaowang Gan, Qiang Guo, Taishan Liu, Guangchao Ding and Songgeng Li

Energy, 2025, vol. 334, issue C

Abstract: Sludge-derived fuel (SDF), formed by drying, mixing and extruding sludge with high-calorific value auxiliary fuels, address the challenges of high moisture content and low calorific value of sludge incineration individually, with potential to mitigate environmental issues and facilitate energy recovery. This study investigates the co-combustion behavior, formula optimization, and economic assessment of SDF. Thermogravimetric (TG) experiments on municipal sludge (MS), textile sludge (TS) and rubber char (RC) reveal that co-combustion of MS and TS lowers ignition and burnout temperatures, while adding RC and TS enhances the comprehensive combustion index (CCI) of the ternary blend. Incorporating up to 30 % RC improves synergy and reduces apparent activation energy (Ea). A high-precision artificial neural network (ANN) model was developed based on experimental data to predict the optimal SDF formulation under constraints that maximize the CCI. The model achieved R2, MAE, and RMSE values of 0.9984, 0.7419, and 0.9851, respectively. The recommended formulation with MS/TS/RC = 50.1:39:10.9. Economic assessment showed a net present value of 2.19 million USD and an internal rate of return of 13.78 %. This work provides insights for the rapid and scientific optimization of SDF formulations.

Keywords: Thermogravimetric analysis; Sludge; Interaction effects; ANN-based prediction; Economic assessment (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225033924
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:334:y:2025:i:c:s0360544225033924

DOI: 10.1016/j.energy.2025.137750

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().