GALINI: Global ALgorithms for mixed-Integer Nonlinear optimisation of Industrial systems

Awarded by
Engineering & Physical Sciences Research Council

This fellowship develops 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 aim to 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?

GALINI develops 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.

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