AI-Driven Optimization of Waste-derived Biofuel Production Processes

Thesis Abstract

The escalating global demand for sustainable energy sources necessitates the optimization of biofuel production processes derived from waste materials, which presents significant challenges due to process inefficiencies and variability in feedstock properties. This study aims to develop an artificial intelligence (AI)-driven framework to enhance the efficiency and yield of waste-derived biofuel production processes through advanced process modeling and real-time optimization. The specific objectives include (1) identifying key process variables influencing biofuel yield from municipal solid waste; (2) developing predictive models using machine learning algorithms to forecast process outcomes based on feedstock and operational parameters; (3) designing an optimization algorithm to determine optimal operational settings; and (4) validating the proposed AI framework through simulation and experimental data. The research adopts a mixed-methods approach, integrating quantitative modeling with qualitative insights. The study population comprises operational data from three biorefinery plants specializing in waste-to-biofuel conversion, with a total dataset of 1,200 operational cycles collected over a two-year period. The primary data collection instruments include process sensors for parameters such as temperature, pressure, moisture content, and feedstock composition, complemented by laboratory analyses of biofuel yield and quality using gas chromatography-mass spectrometry (GC-MS) and calorimetry. To ensure data validity and reliability, calibration protocols and repeated measurements were employed, and data preprocessing involved normalization and outlier detection using Z-score methods. The analytical methodology hinges on the application of supervised machine learning techniques, specifically random forest and support vector regression, to develop accurate predictive models of biofuel yields. Sensitivity analysis identified the most influential variables. These models inform the development of a multi-objective optimization algorithm based on genetic algorithms (GA), designed to maximize biofuel yield while minimizing processing time and operational costs. Model performance was assessed through cross-validation, with metrics such as R-squared, root mean square error (RMSE), and mean absolute error (MAE). The theoretical underpinning of the research aligns with the Theory of Planned Behavior (TPB), explaining operational staff’s engagement with process adjustments guided by AI recommendations, and the Technology Acceptance Model (TAM), facilitating stakeholder acceptance of AI-based decision support systems. The anticipated findings include robust predictive models that accurately forecast biofuel yields under varying operational conditions, and an effective optimization framework capable of recommending conditions that improve process efficiency by at least 15% compared to baseline operations. The integration of AI techniques is expected to reduce process variability and enhance feedstock utilization rates. The study will also reveal critical process variables, providing insights into process control and feedstock preprocessing strategies. This research contributes to the existing body of knowledge by demonstrating the viability of machine learning and evolutionary algorithms in optimizing waste-to-biofuel conversion processes, offering a scalable and adaptable decision support system for industrial applications. It advances understanding of how AI can address environmental and economic challenges associated with renewable energy production from waste streams. In conclusion, the study underscores the transformative potential of AI in renewable energy manufacturing, recommending the adoption of AI-enabled process optimization frameworks to biofuel producers globally. It suggests further research into integrating real-time sensor data with adaptive machine learning models and exploring the scalability of such systems across different waste feedstocks and conversion technologies. Policy implications include supporting investments in AI infrastructure within the renewable energy sector and fostering training programs for operational staff to effectively utilize AI-driven tools for sustainable biofuel production.