Large gaps persist between the environmental impact of state-of-the-art solid waste treatment technologies and the net-zero decarbonization goals outlined in the Paris Agreement. This results in a critical opportunity to improve waste management systems by advancing circular economy objectives for material recovery whilst avoiding burden shifting.
Waste management systems are multi-domain, multi-energy and multi-product (“Waste-to-X”): they include logistics, storage units, chemical conversion processes of waste materials into valuable resources such as energy, fuels, chemicals, or recycled materials, and all types of related processing for these commodities. The integration of waste treatment technologies via superstructure optimization has been extensively studied to improve resource valorization, but the drawback of integrated mathematical models is the lack of flexibility for refined process design. The evaluation of site-specific Waste-to-X systems critically requires the inclusion of dynamic models, with the capability to include any type of complexity such as the modeling of heterogeneous flows to track all impurities and compounds across a system, as well as the implementation of control strategies for the operations of the units. The analysis of such systems should handle input data uncertainties and provide transparent performance indicators computation to increase stakeholders’ confidence in the results.
In this contribution, we present specific tool requirements to assess waste management systems, and introduce a corresponding design of a simulation and optimization framework. The tool (“platform”) relies on OpenModelica for waste treatment process modeling and simulation, and on Python interfaces for data management, process and system co-optimization, and post-processing workflows to provide key performance indicators. We show how the platform structure allows to account simultaneously for the three pillars of sustainability (economic, environmental and social) for decision-making in an industrial R&D context. We provide insights on modeling conventions and emphasize their importance when establishing the Life-Cycle Inventory, for instance when defining heterogeneous streams (which can be environmental emissions, exchanged commodities or waste streams). We present how the Modelica-Python interface is built, showing how input data and modeling documentation are handled systematically to increase transparent evaluation of Waste-to-X systems.
We illustrate platform features (“workflows”) such as pinch analysis for process heat integration, Life-Cycle Impact Assessment (LCIA), and cost breakdown analysis, on municipal solid waste treatment systems including sorting technologies, storage tanks, waste incineration, and post-combustion carbon capture.