Stochastic optimisation of organic waste-to-resource value chain.

Abstract

Organic fraction municipal solid waste (OFMSW) has a high potential for energy and value-added product recovery due to its carbon- and nutrient-rich composition; however, traditional value chains have treated OFMSW as an undesired by-product. This study focuses on value chain optimisation to assist the transition to resource recovery value chains. To achieve this, this work combined two stage stochastic mathematical optimisation with geographical spatial analysis and time series waste generation analysis. Existing infrastructure in England, including anaerobic digestion plants and road transportation networks, were included in the model. To account for uncertainty in waste generation, multiple scenarios and their associated probabilities were developed based on environmental variables. The optimisation problem was solved to further advance the understanding of economically optimal waste-to-resource value chains under waste generation variability. The pertinent decision variables included sizing, technology selection, waste flows and location of thermochemical treatment sites. The model highlights the potential reduction in system profitability as a result of different operating constraints, such as minimum plant operating capacity factors and landfill taxation. The latter was shown to have the largest impact on profitability as overconservative systems designs were implemented to hedge against the waste variability. Such computer-aided models offer opportunities to overcome the challenges posed by waste generation variability and waste to resource value chain transformation.