Using big data and system-of-systems engineering to design efficient and resilient waste value chains

The World Bank has projected the generation of municipal waste to increase to 2.2 billion-tonnes/year by 2025. Poor waste management is a global problem that is particularly severe in developing countries and is further exacerbated by rapid population growth and industrialisation. This project aims to turn this global challenge on its head by treating the wastes as raw materials in a “waste value chain” that will create development opportunities for new industries that will support different activities in the value chain (e.g. converting wastes into valuable products), thus creating enviro-socio-economic benefits.

Waste management is a very complex problem that needs to be considered as a system of systems: from the household level to the municipal and national scales as well as considering the interactions between the different sectors that produce waste and consume products that can be made from waste. This approach is necessary to identify synergies between the different value chains and make the most effective use of municipal and industrial solid waste, closing the loop on many resources.

In this project, you will use big data and system-of-systems optimisation techniques to design efficient and resilient waste value chains. You will develop high-resolution maps of waste potentials, a comprehensive database of waste-processing technologies and build a detailed mathematical model to optimise waste value chains, considering the spatial distribution of resources and a long planning horizon (beyond 2050). The model will determine the combination of technologies, when to invest and where to locate them, and the operation of the network. You will consider a large number of “what-if” scenarios to explore how waste value chains could develop over time under different policy conditions, rates of technological development and so on. An opportunity cost analysis will be performed to quantify the importance of different resources and technologies to the overall system. You will also conduct a stochastic analysis/robust optimisation to ensure that the solutions are resilient with respect to uncertainties associated with the current and future resources and technologies.