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Projects: Projects for Investigator
Reference Number EP/P016871/1
Title GALINI: Global ALgorithms for mixed-Integer Nonlinear optimisation of Industrial systems
Status Completed
Energy Categories Energy Efficiency(Industry) 100%;
Research Types Basic and strategic applied research 100%
Science and Technology Fields PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics) 100%
UKERC Cross Cutting Characterisation Not Cross-cutting 50%;
Systems Analysis related to energy R&D (Other Systems Analysis) 50%;
Principal Investigator Dr R Misener
No email address given
Imperial College London
Award Type Standard
Funding Source EPSRC
Start Date 01 September 2017
End Date 31 August 2022
Duration 60 months
Total Grant Value £984,063
Industrial Sectors Information Technologies; Manufacturing
Region London
Programme NC : Engineering
Investigators Principal Investigator Dr R Misener , Computing, Imperial College London (100.000%)
  Industrial Collaborator Project Contact , Sandia National Laboratories, USA (0.000%)
Project Contact , Process Systems Enterprise Limited (0.000%)
Project Contact , IBM, USA (0.000%)
Web Site
Abstract At the 2015 Paris climate conference, 195 countries agreed that global greenhouse gases should peak as soon as possible and that countries should thereafter rapidly reduce their emissions. The process industries must therefore reduce their energy consumption and increase efficiency while maintaining consumer services. Next generation decision-making software at the interface of engineering, computer science, and mathematics is critical for these efficient systems of the future. Already, state-of-the-art computational packages are routine in the process industries; practically every major company uses simulation and optimisation to model production in different modes including: continuous, batch, and semi-continuous production systems. But more efficient industrial systems require simultaneously considering many tightly integrated subsystems which exponentially increase complexity and necessitate many temporal/spatial scales; the resulting decision making problems may not be solvable with current techniques. Increasing efficiency may also jeopardise safety: the process integration required for efficiency implies interchanging heat between processes and may damage safety precautions by transferring disturbances across a plant.During this fellowship, we propose to develop GALINI, new decision-making software constructing and deploying next generation process optimisation tools dealing with combinatorial complexity, disparate temporal/spatial scales, and safety considerations. The GALINI project proposes step-changes in optimisation algorithms that are immediately applicable to efficiency challenges in process systems engineering (PSE): safely operating batch reactors, retrofitting heat-exchanger networks, intermediate blending, and integrating planning and scheduling. We will freely release our software on open-source platform Pyomo and build an international user community.The primary GALINI research aim is to develop optimisation software that pushes the boundary of computational tractability for PSE energy efficiency applications. Effective optimisation software in the process industries answers: How can we best achieve a definite engineering objective? Given constraints such as an existing plant layout or a contractual obligation to produce specific products, the software supports novel engineering by quantitatively comparing the implications of different options and identifying the best decision. GALINI is particularly interested in design: How should we build new facilities or modify existing ones to achieve our design goals with maximum efficiency?The state-of-the-art in decision making for the process industries is represented by commercial modelling software such as AspenTech and gPROMS. Practically every major company in the process industries uses these software tools since the outputs of the simulation or optimisation can be implemented with minimal day-to-day operational disruption and savings can be realised with a payback time as short as 6-12 months. GALINI will develop deterministic global optimisation software for mixed-integer nonlinear programs, a type of optimisation problem highly relevant to energy efficiency and process systems engineering. Energy efficiency instances may exhibit the mathematical property of nonconvexity, i.e. have many locally optimal solutions; global optimisation mathematically guarantees the best process engineering solution. GALINI proposes transformational shifts in algorithms that creatively reimagine the core divide-and-conquer algorithm typically applied to this type of optimisation problem. Our approach is to freely release GALINI to users including those in the process industries, publicise the software, demonstrate its utility, and build a user community that will feed back into software development.
Publications (none)
Final Report (none)
Added to Database 19/02/18