Details
Problem context
When water companies propose to invest in infrastructure, both its design and the timing of its activation must be considered. Is a particular proposed infrastructure option resilient in light of plausible future climate change scenarios?
As society tries to decrease emissions and increase resilience, there is an increasing demand to justify the prioritisation and sequencing of assets when investing in infrastructure systems. In multi-sector systems, with interdependencies and competition between economic sectors, the complexity of the problem increases.
Adaptive planning approaches are progressively being recognised as the state of the art in selecting and prioritising infrastructure investments over time. Such adaptive planning exercises are referred to as ‘real options’ analysis, ‘adaptive policy pathways’ planning, or simply as an effort to identify appropriately flexible interventions.
Problem statement
Adaptive planning aims to incorporate information as it becomes available to adjust upcoming actions. In adaptive infrastructure system capacity expansion optimisation modelling, decisions made initially must consider their ability to accommodate later possible decisions in the planning horizon, including cases where supply and/or demand develops differently than what was initially predicted. Adaptive optimisation recognises the fact that present decisions impact a system’s ability to adapt to future needs, and therefore that flexibility in activating, delaying, and replacing engineering projects should be considered in infrastructure investment planning.
Proposed research
This doctorate will study these methods, propose one for water resource systems, and implement it on a water company case. The proposed project aims to develop and use an adaptive infrastructure investment model for the East England multi-sector water resource system. It will trial and compare a suite of adaptive optimisation model formulations (methods) such that an appropriate use of adaptive planning can be found for multi-sector water systems. A sensitivity analysis will help determine how different formulations of the adaptive planning problem affect results. Formulations will vary by simulation parameterisation, objectives, decision variables, constraints, and the algorithmic structure of the adaptive search (optimisation) process. The case-study benefits from an existing simulation model, which means the PhD student will be able to focus their investigations on the adaptive planning research, rather than on data gathering and model building.
Relevant background research
The following papers are relevant to this project and give a good indication of the kind of work that will be carried out: https://www.sciencedirect.com/science/article/pii/S0309170821002669
https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2017WR021803
https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020WR028117
https://www.sciencedirect.com/science/article/pii/S1364815219307133
Admissions Qualifications:
Applicants should have a first class degree in engineering or applied mathematics. Water engineering background is not needed. Computer programming skills and interest required.
Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact. We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.
We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).
Funding Notes
PhD is funded jointly by University of Manchester and Anglian Water. The funding program is for UK students only and its for 4 years.
