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<p>Energy audits in wastewater treatment plants (WWTPs), conducted under ISO 50002 and aligned with ISO 50001, are essential to improve sustainability in a highly energy-intensive sector. However, the nonlinearity of biological processes, operational variability, and the limited number of conventional indicators hinder the accurate identification and interpretation of energy inefficiencies. This study proposes SEA-WWTPs, a hybrid quantitative methodology combining deterministic diagnostics with machine learning models trained on event-level features derived from monitored energy performance indicators (EnPIs), to detect and classify energy-inefficiency events and rank likely root causes to support audit-oriented corrective decision-making. A case study in a leachate wastewater treatment plant (WWTP-L) in Portugal analyzed 484,810 1-min records collected over approximately three years. A total of 28,608 inefficiency occurrences (aggregated events) were detected; 96.25% were persistent, indicating predominance of structural and electromechanical causes. The random forest model achieved a Macro-F1 of 0.716 and a Top-2 accuracy of 0.788 using equipment-grouped GroupKFold validation. Overall, the proposed methodology enhances energy audits by integrating data-driven decision support, ensuring traceability, audit-oriented interpretability, and compatibility with ISO standards, thereby supporting the digitalization of sustainable energy management in WWTPs and contributing to the sustainable development goals (SDGs).</p>

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Hybrid deterministic and machine learning approach for smart energy audits in wastewater treatment plants

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月17日

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  • Francisco Esteves,
  • José C. Cardoso,
  • Sérgio Leitão,
  • E. J. Solteiro Pires

摘要

Energy audits in wastewater treatment plants (WWTPs), conducted under ISO 50002 and aligned with ISO 50001, are essential to improve sustainability in a highly energy-intensive sector. However, the nonlinearity of biological processes, operational variability, and the limited number of conventional indicators hinder the accurate identification and interpretation of energy inefficiencies. This study proposes SEA-WWTPs, a hybrid quantitative methodology combining deterministic diagnostics with machine learning models trained on event-level features derived from monitored energy performance indicators (EnPIs), to detect and classify energy-inefficiency events and rank likely root causes to support audit-oriented corrective decision-making. A case study in a leachate wastewater treatment plant (WWTP-L) in Portugal analyzed 484,810 1-min records collected over approximately three years. A total of 28,608 inefficiency occurrences (aggregated events) were detected; 96.25% were persistent, indicating predominance of structural and electromechanical causes. The random forest model achieved a Macro-F1 of 0.716 and a Top-2 accuracy of 0.788 using equipment-grouped GroupKFold validation. Overall, the proposed methodology enhances energy audits by integrating data-driven decision support, ensuring traceability, audit-oriented interpretability, and compatibility with ISO standards, thereby supporting the digitalization of sustainable energy management in WWTPs and contributing to the sustainable development goals (SDGs).