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Sewage sludge valorization into fuel: Process development, AI-driven optimization, and sustainable process selection using multi-variate path analysis

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  • Ayub, Yousaf
  • Moktadir, Md Abdul
  • Shi, Tao
  • Ren, Jingzheng

Abstract

This study evaluates various pathways for sewage sludge (SS) valorization using multivariate path analysis. The primary process, Supercritical Water Gasification (SCWG), was integrated with six sub-processes to create three distinct treatment methods for SS, optimized through the Non-dominated Sorting Genetic Algorithm II (NSGA-II). A sustainability analysis was conducted for all three processes, focusing on energy, exergy, economy, environment, and safety (4E, 1S). The findings revealed energy efficiencies ranging from 19 % to 32 %, exergy efficiencies between 19 % and 20 %, and an economic internal rate of return (IRR) of 3.2 %–10.9 % at full operational efficiency. Environmental performance scores ranged from 11.34 to 11.44 mPt, while safety index scores varied from 367 to 523. Comparative assessments indicated that Process 3 (CH3OH, CHP, CO2 production) is the most sustainable, with a Shannon entropy-based sustainability index (SI) of 0.972 for the base case, compared to 0.785 and 0.863 for Processes 1 and 2, respectively. In the optimized scenario, Process 3 maintained an SI of 0.901. The study findings also recommend actionable policy implications, including financial incentives, a legal framework, tax relief, and AI integration to promote the adoption of sustainable SS valorization.

Suggested Citation

  • Ayub, Yousaf & Moktadir, Md Abdul & Shi, Tao & Ren, Jingzheng, 2025. "Sewage sludge valorization into fuel: Process development, AI-driven optimization, and sustainable process selection using multi-variate path analysis," Energy, Elsevier, vol. 332(C).
    • Handle: RePEc:eee:energy:v:332:y:2025:i:c:s0360544225023758
    DOI: 10.1016/j.energy.2025.136733
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    References listed on IDEAS

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