(401e) Transforming the Circular Economy with the Value Web Model – a Multi-Objective Spatio-Temporal MILP Model for Planning, Design & Operation of Integrated Circular Value Chains

Authors: 
Samsatli, S., University of Bath
Chemical engineering deals with designing systems that can convert low-value resources to high-value products at a sufficiently large scale to be profitable. As well as designing reactors and unit operations, this also involves determining how and where to source raw materials, where to locate processing facilities, how to deliver products to customers and how to manage inventory levels to satisfy demands at all times. Coordinating all of these activities and designing the network are known as supply chain management. Nearly all current supply chain models are linear and the ones that are closed-loop are quite limited in terms of dealing with problems in the circular economy. More efficient, flexible and resilient supply chains can result from integrating supply chains from different sectors thus forming a value web.

The Value Web Model (VWM) is a multi-objective spatio-temporal optimisation model, based on mixed integer linear programming, that can simultaneously determine the design and operation of any integrated multi-vector energy networks. It has been developed to answer variants of the following questions: “What is the most effective way, in terms of cost, value/profit and/or emissions, of designing and operating the integrated multi-vector energy networks that utilise a variety of primary energy sources to deliver different energy services, such as heat, electricity and mobility, given the availability of primary resources and the levels of demands and their distribution across space and time? When to invest in technologies, where to locate them; what resources should be used, where, when and how to convert them to the energy services required; how to transport the resources and manage storage inventory?”

The many different possible pathways from all possible primary energy sources to all energy services via any number of energy vectors form a complex web of interactions between all of the elements of the system, including circular pathways. As value is being added at each transformation from primary resource to energy service, the whole system can be viewed as a value web. The system needs to be robust with respect to uncertainties such as the availability of primary energy sources, levels and timings of demands, costs and prices, policy changes and other technological uncertainties (e.g. appearance of a disruptive technology). The VWM can be applied to a wide variety of systems such as integrated energy systems, water networks, chemicals and pharmaceutical supply chains. In this presentation the focus will be on the application of VWM for strategic design and tactical operation of integrated energy value chains with various energy carriers such as hydrogen, electricity, syngas, methanol, biofuels/bioenergy and so on.

References:

[1] S. Samsatli, N.J. Samsatli (2018). A multi-objective MILP model for the design and operation of future integrated multi-vector energy networks capturing detailed spatio-temporal dependencies. Applied Energy. DOI: 10.1016/j.apenergy.2017.09.055.

[2] S. Samsatli, I. Staffell, N.J. Samsatli (2016). Optimal design and operation of integrated wind-hydrogen-electricity networks for decarbonising the domestic transport sector in Great Britain. Int. J. of Hydrogen Energy, 41, 447-475.

[3] S. Samsatli, N.J. Samsatli (2015). A general spatio-temporal model of energy systems with a detailed account of transport and storage. Computers and Chemical Engineering, 80, 155-176.

[4] S.M. Jarvis, S. Samsatli (2018). Technologies and infrastructures underpinning future CO2 value chains: a comprehensive review and comparative analysis. Renewable & Sustainable Energy Reviews, 85, 46-68.

[5] S. Samsatli, N.J. Samsatli, N. Shah (2015). BVCM: a comprehensive and flexible toolkit for whole-system biomass value chain analysis and optimisation - mathematical formulation. Applied Energy, 147, pp. 131-160.

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