Abstract
Renewable energy and energy efficiency are increasingly recognised as crucial for creating new economic opportunities and mitigating environmental impacts. Anaerobic digestion (AD) transforms organic materials into a clean, renewable energy source. Co-digestion of various organic wastes and energy crops addresses the disadvantages of single-substrate digestion, increasing production flexibility yet adding process complexity and sensitivity. This study employs a two-pronged approach to optimise biogas production while considering global warming potential: a nonlinear programming (NLP) model for dynamic system economic optimisation with a model predictive control (MPC) strategy for precise temperature regulation within the digester. The NLP model integrates a combined heat and power (CHP) system to leverage dynamic electricity, heat, and gas prices, accounting for physical and economic parameters such as biomethane potential, chemical oxygen demand, and substrate density. A cardinal temperature and pH model ensures accurate depiction of substrate degradation and gas production rates under varying conditions. The MPC scheme, formulated as a system of differential-algebraic equations, offers fine-grained temperature control, capturing real-world complexities like heating/cooling delays, ambient conditions, and multiple feed components with different optimal digestion temperatures. Results demonstrate that this integrated model optimises the interaction between electricity production, biogas generation, and CHP operation for real-time multi-objective optimisation of profit, global warming potential and temperature control. A case study validates the model’s capability for guiding decision-making in biogas facilities, emphasising strategic feedstock management and precise temperature control. Overall, this integrated approach advances the modelling and control of anaerobic co-digestion systems, enhancing both efficiency and profitability in biogas production.