Parallelising Mixed-Integer Optimisation: Energy Efficiency Applications

Awarded by
Engineering & Physical Sciences Research Council


Mathematical models for optimal decisions often require both nonlinear and discrete components. These mixed-integer nonlinear programs (MINLP) form an important class of optimisation problems of pressing societal need. For example, MINLP is necessary for optimising the energy use of large industrial plants, for integrating renewable sources into energy networks, for biological and biomedical design, and for countless other applications. The first MINLP algorithms and software were designed by application engineers. While these efforts initially proved very useful, scientists, engineers, and practitioners have realised that a transformational shift in technology will be required for MINLP to achieve its full potential.

Deterministic global optimisation of mixed integer nonlinear programs (MINLP) may effectively design energy efficient networks, but current MINLP technology for this problem class is limited by nonconvex nonlinear heat transfer functions and the many isomorphic possibilities of routing sreams to heat exchangers.


We were very happy to have Radu Baltean-Lugojan, Georgia Kouyialis, and Dr Dimitris Letsios collaborating on this project.


We discovered that we can solve (or at least rigorously approximate) several important classes of energy efficiency problems quickly. In particular, we considered the applications of (i) designing effective heat recovery networks and (ii) blending and intermediate storage in petrochemical processes.

During the grant itself, we’ve presented this work to academic experts in both computer science and process systems engineering:

Where do we go from here?

We’re really excited about the outcomes and we’re looking to share the results more broadly. I presented the EPSRC First Grant results to the process systems engineering community during my keynote at the 2018 European Symposium for Computer-Aided Process Engineering.

For the computer science community, we have offered a useful path into energy efficiency applications by making all case studies freely-available online. We’ve made it possible to contribute to designing heat recovery networks with solely a background in computer science or mathematical optimisation.

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