In the rapidly evolving energy sector, the intersection of artificial intelligence (AI) and biogas production is emerging as a critical frontier, promising to revolutionize risk management and operational efficiency. A groundbreaking systematic and bibliometric review, published in the Waste Management Bulletin (translated to English as “Bulletin of Waste Management”), sheds light on the transformative potential of AI-driven approaches in biogas production within wastewater treatment plants (WWTPs). Led by Mohamed Abourida from the School of Chemistry and Chemical Engineering at the University of Surrey, this comprehensive study synthesizes the latest advancements and trends in this burgeoning field.
The review, which systematically analyzed 109 studies from seven academic databases, identified five key thematic clusters: biogas process safety, IoT integration and renewable energy, optimization and supply-chain resilience, AI-driven decision-support frameworks, and advanced machine-learning techniques. “The marked increase in publications since 2020 reflects a significant shift from conceptual modeling toward applied digital risk solutions,” notes Abourida. This trend underscores the growing recognition of AI’s potential to enhance safety, operational efficiency, and regulatory compliance in biogas facilities.
Europe and China are leading the charge in this research, although the study highlights the need for more collaborative networks and standardized methodologies. “Methodological heterogeneity persists, and full-scale validation of AI models in operational WWTP-based biogas plants remains limited,” Abourida explains. Most studies currently rely on laboratory experiments, simulations, or pilot-scale data, indicating a gap between theoretical advancements and practical implementation.
The commercial implications for the energy sector are substantial. AI-driven risk management can optimize biogas production processes, reduce operational costs, and enhance safety protocols. By integrating AI with Internet of Things (IoT) technologies, energy companies can achieve greater supply-chain resilience and operational efficiency. “AI-driven decision-support frameworks are particularly promising, as they can provide real-time insights and predictive analytics to inform critical decisions,” Abourida adds.
However, the path to widespread adoption is not without challenges. The study identifies several constraints, including publication bias, database coverage limitations, English-language restrictions, inconsistent performance metrics, and limited access to long-term Supervisory Control and Data Acquisition (SCADA) Systems datasets. To overcome these hurdles, the review calls for rigorous multi-site validation, standardized evaluation indicators, integration of explainable AI, and alignment with plant-level risk-governance frameworks.
As the energy sector continues to evolve, the insights from this review could shape future developments in biogas production. By leveraging AI-driven methods, energy companies can not only improve safety and efficiency but also contribute to sustainability goals. The study serves as a clarion call for increased collaboration, standardized methodologies, and practical applications to unlock the full potential of AI in the biogas industry. With the findings published in the Waste Management Bulletin, the stage is set for a new era of innovation and progress in the energy sector.