Optimization of a Dairy Plant's Waste-to-Energy System via Anaerobic Digestion and Biogas Utilization

Thesis Abstract

This study addresses the low-cost, sustainable valorisation of dairy processing waste through optimization of a Waste-to-Energy (WTE) system based on anaerobic digestion (AD) and biogas utilization within a mid-sized dairy plant located in the temperate region of the European Union. The problem arises from suboptimal organic waste management, fluctuating biogas yields, and underutilization of renewable energy potential, leading to excessive disposal costs, greenhouse gas emissions, and missed opportunities for energy self-sufficiency. The aim is to design, implement, and evaluate an integrated AD-based WTE system that maximizes biogas production, energy recovery, and nutrient recycling while maintaining product quality and regulatory compliance. Specific objectives include (i) characterising the dairy’s organic waste streams (milk processing residues, whey, and cleaning-in-place effluents) in terms of chemical oxygen demand (COD), volatile solids (VS), and biodegradable fraction; (ii) developing a pilot-scale AD process model to optimise hydraulic retention time (HRT), organic loading rate (OLR), and temperature regime (mesophilic vs. thermophilic) using batch and continuous experiments; (iii) evaluating the integration of biogas upgrading and combined heat and power (CHP) utilization strategies, including potential for grid export and on-site heat recovery; (iv) performing techno-economic and life cycle assessments to quantify net energy balance, greenhouse gas reductions, payback period, and sensitivity to feedstock variability; and (v) proposing a scalable implementation roadmap aligned with industrial safety and regulatory standards. The methodology adopts a mixed-methods design combining experimental, modelling, and econometric approaches. The population consists of dairy process streams and operational data from three production lines, with a purposive sample of 12 composite waste samples for biochemical methane potential (BMP) assays and 6 months of plant-wide operational data. Data collection instruments include BMP assay kits, multi-parameter water quality probes, gas chromatographs for methane and CO2 profiling, online biogas flow meters, calorimeters for energy content, and structured interviews with process engineers. Validity and reliability are ensured through calibration against standard reference materials, triplicate BMP measurements, instrument inter-calibration, and triangulation across experimental and operational datasets. Data analysis employs ANOVA and regression analysis to quantify relationships between feedstock characteristics, AD performance (biogas yield, methane percentage, VS removal), and system energy output. A process-based AD model is developed and calibrated using Python-based optimization routines and validated against pilot-scale results, incorporating a Bayesian updating mechanism to account for feedstock variability. The conceptual framework draws on the Theory of Planned Behavior to explore operator practices affecting AD performance, the Nutrient Balancing Theory to optimize substrate co-digestion, and the Second Law of Thermodynamics to assess energy efficiency limits. The study anticipates key findings including (i) quantified drivers of biogas yield across different dairy waste streams and optimal co-digestion strategies; (ii) identified HRT-OLR-temperature combinations that maximize methane productivity while maintaining digestate quality suitable for fertiliser use; (iii) a viable path for biogas upgrading and CHP integration achieving net energy-positive operation with reduced fossil energy inputs; and (iv) robust economic indicators demonstrating favorable payback periods under moderate feedstock variability. The anticipated contribution to knowledge includes a holistic, data-driven framework for optimizing dairy WTE systems that integrates process engineering, energy economics, and sustainability assessment, with transferable insights for dairy processors in similar climates. The study concludes that a strategically designed AD-based WTE system can substantially reduce waste disposal costs, lower lifecycle greenhouse gas emissions, and enhance energy autonomy without compromising product quality or safety. Recommendations emphasize pilot-to-full-scale deployment timelines, contingencies for feedstock fluctuations, an incremental retrofitting plan for CHP and biogas upgrading, and policy alignment to support renewable energy incentives and nutrient recycling regimes.