AI-driven optimization and IoT-enabled monitoring in adaptive automated biogas production systems

The rising global demand for renewable energy and sustainable waste management has intensified research into biogas generation from organic kitchen waste, which is rich in carbohydrates, proteins, and lipids. However, variations in feedstock composition, pH instability, and inconsistent substrate ratios often limit anaerobic digestion (AD) efficiency and methane yield. This study introduces a novel AI- and IoT-integrated adaptive biogas production framework that intelligently monitors, predicts, and optimizes process parameters in real time.

Experimental findings reveal that maintaining an optimal feedstock mixture of 45–50% organic matter and 55–60% water at a pH range of 6.8–7.5 significantly enhances methane yield and digestion stability. The incorporation of AI-driven surrogate models—such as artificial neural networks, regression algorithms, and reinforcement learning—enables dynamic control of feedstock ratios, organic loading rates, and temperature conditions. Concurrently, IoT-enabled sensors provide continuous real-time data on pH, temperature, volatile solids, and microbial activity, which are fed back into the AI system for adaptive learning and self-optimization.

The results indicate that AI-guided optimization, enhanced by IoT-based sensing, significantly increases methane concentration, process stability, and calorific value compared to conventional manual operations. The system exhibits self-adaptive learning capabilities, allowing it to automatically adjust to variations in feedstock composition, which ensures reliable and scalable performance across domestic, industrial, and agricultural applications. The integration of AI and IoT in this study represents a novel advancement in automated, intelligent biogas systems—enhancing renewable energy efficiency, promoting sustainable waste valorization, reducing greenhouse gas emissions, and supporting the transition toward a circular and smart energy economy.