An integrated optimisation platform for sustainable resource and infrastructure planning

It is crucial for sustainable planning to consider broad environmental and social dimensions and systemic implications of new infrastructure to build more resilient societies, reduce poverty, improve humanwellbeing, mitigate climate change and address other global change processes. This article presents resilience.io,2 a platform to evaluate new infrastructure projects by assessing their design and effectiveness in meeting growing resource demands, simulated using Agent-Based Modelling due to socio-economic population changes. We then use Mixed-Integer Linear Programming to optimise a multi-objective function to find cost-optimal solutions, inclusive of environmental metrics such as greenhouse gas emissions. The solutions in space and time provide planning guidance for conventional and novel technology selection, changes in network topology, system costs, and can incorporate any material, waste, energy, labour or emissions flow. As an application, a use case is provided for the Water, Sanitation and Hygiene (WASH) sector for a four million people city-region in Ghana. © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Software/data availability The resilience.io WASH model was built by Charalampos P. Triantafyllidis, Xiaonan Wang, Koen H. van Dam, Kamal Kuriyan and Nilay Shah from Imperial College London with data and technical support by Rembrandt Koppelaar, Zoltan Kis and Hannes Kunz from the Institute of Integrated Economic Research. The platform is Engineering, Department of Chem [email protected] (C.P. Trian perial.ac.uk (N. Shah). Ltd. This is an open access article u designed for open-source future release by using only open-source software. A beta-version of the platform is available upon request from the authors including the data-set to reproduce the presented use case. The platform is of approximately 800MB in size on a Windows-based machine. Further details concerning the hardware/software used can be found in Table 3. ical Engineering, Imperial College London, Exhibition Road, London SW7 2AZ, UK. tafyllidis), [email protected] (R.H.E.M. Koppelaar), [email protected] (X. Wang), nder the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). C.P. Triantafyllidis et al. / Environmental Modelling & Software 101 (2018) 146e168 147