Stochastic optimisation of organic waste-to-resource value chain

Area of Science:

• Environmental Science
• Waste Management
• Operations Research

Background:

• Organic fraction municipal solid waste (OFMSW) is rich in carbon and nutrients, offering potential for energy and product recovery.
• Traditional waste management often overlooks OFMSW's value, treating it as a by-product.
• Transitioning to resource recovery value chains requires optimizing OFMSW management.

Purpose of the Study:

• To optimize value chains for organic fraction municipal solid waste (OFMSW) to facilitate resource recovery.
• To develop a model for economically optimal waste-to-resource value chains considering waste generation variability.
• To assess the impact of operational constraints on system profitability.

Main Methods:

• Combined two-stage stochastic mathematical optimization with geographical spatial and time series waste generation analysis.
• Incorporated existing infrastructure in England, including anaerobic digestion plants and road networks.
• Developed multiple waste generation scenarios with probabilities based on environmental variables to address uncertainty.

Main Results:

• Identified key decision variables including sizing, technology selection, waste flows, and location of thermochemical treatment sites.
• Quantified the reduction in system profitability due to operating constraints like minimum plant capacity factors and landfill taxation.
• Landfill taxation demonstrated the most significant impact on profitability, leading to overconservative system designs.

Conclusions:

• Computer-aided models can effectively address challenges posed by waste generation variability in waste-to-resource transformations.
• Optimized value chains are crucial for maximizing the economic potential of OFMSW.
• Understanding the impact of constraints is vital for designing resilient and profitable waste recovery systems.