Long-term evaluation of biogas and electricity production in a wastewater treatment plant reveals key performance dynamics.
This study assessed the long-term energy self-sufficiency and operational dynamics of a full-scale wastewater treatment plant over the period 2015–2023, with particular emphasis on biogas-driven energy recovery and time-dependent process interactions. The relationship between biogas production and electricity and heat generation was evaluated alongside the influence of different sludge streams on system performance using cross-correlation analysis. The results demonstrated a high level of energy recovery, with biogas-derived electricity covering, on average, 60% of the plant’s demand and reaching a maximum of 74% annually. A very strong correlation was observed between annual biogas production and electricity generation (r = 0.94), confirming the direct energetic coupling of both processes. Monthly analyses further indicated strong consistency between biogas yield and both electricity and heat production (r = 0.55–0.91 and r = 0.86, respectively). Cross-correlation analysis identified Thickened Waste Activated Sludge and Primary Sludge as important process drivers, with statistically significant delayed effects at 10–20 days. In contrast, recirculation-related streams exhibited negligible influence on system dynamics. Statistical analysis revealed that most heavy metals, including Cd, Cr, Ni, and Hg, exhibited high variability (Coefficient Variability > 40%), which can directly impact the stability of methane production. These results indicate that wastewater treatment plants’ energy performance is governed by delayed process responses linked to sludge residence time, highlighting the need for predictive models incorporating at least two weeks of historical operational data. In addition, physicochemical analysis of sewage sludge confirmed generally stable nutrient content, despite variability in biological parameters and heavy metal concentrations. Overall, the study demonstrates that integrating long-term operational datasets with time-lag analysis provides valuable insights for optimizing energy recovery and supporting circular economy strategies in wastewater treatment plants.
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Halecki et al. (2026) studied this question.
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