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A Machine Learning-Driven Sustainability Assessment of Geothermal Turbine Systems: The Novel PRODSI Framework

Authors

  • Swasti Singhal Amity Institute of Information Technology, Amity University, Uttar Pradesh, India
  • Laxmi Ahuja Amity Institute of Information Technology, Amity University, Uttar Pradesh, India
  • Himanshu Monga JLN Government Engineering College, Himachal Pradesh, India
Volume: 15 | Issue: 4 | Pages: 25280-25289 | August 2025 | https://doi.org/10.48084/etasr.11609

Received: 19 April 2025 | Revised: 6 May 2025, 10 May 2025, and 18 May 2025 | Accepted: 25 May 2025 | Online: 27 June 2025


Corresponding author: Swasti Singhal

Abstract

Geothermal energy represents a crucial component of sustainable energy strategies due to its consistent availability and minimal emissions. However, the comprehensive assessment of sustainability for geothermal turbine systems remains challenging, primarily due to complex interactions among environmental, social, and economic dimensions. This study develops and applies an advanced Product Sustainability Index (PRODSI) framework, uniquely combining expert-driven fuzzy Analytic Hierarchy Process (fuzzy-AHP), data-driven Entropy methods, and Convolutional Neural Networks (CNN) for robust validation. Utilizing a detailed dataset derived from the System Advisor Model (SAM) Discounted Cash Flow (DCF), IPSEpro off-design performance models, Energy Information Administration (EIA) consumption data, and extensive supplementary tables, sustainability indicators were normalized and weighted systematically. The results indicate significant variation in sustainability scores across evaluated geothermal turbines, notably identifying the 5 MW turboexpander as the most balanced and sustainable choice, with a PRODSI score of 7.08, compared to 2.7 for the 1 MW turbine and 5.32 for the 20 MW steam turbine. This study contributes by integrating subjective expert insights with objective data analysis and validating this integration through CNN-driven machine learning, establishing a novel standard for sustainability assessments of renewable energy systems. The PRODSI framework offers a transparent, validated, and scalable tool for decision-making in geothermal sustainability. It provides actionable guidance for investors, developers, and policymakers, facilitating optimized technology selection and resource allocation. Additionally, it establishes a foundation for real-time decision support and broader applications in renewable energy.

Keywords:

sustainability, geothermal turbines, PRODSI, fuzzy-AHP, entropy method, ML, CNN, renewable energy

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[1]
Singhal, S., Ahuja, L. and Monga, H. 2025. A Machine Learning-Driven Sustainability Assessment of Geothermal Turbine Systems: The Novel PRODSI Framework. Engineering, Technology & Applied Science Research. 15, 4 (Aug. 2025), 25280–25289. DOI:https://doi.org/10.48084/etasr.11609.

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