The molecular frontier: extending the boundaries of process design

In designing new chemical manufacturing processes, the molecules or materials required (e.g., solvents or catalysts) are often chosen prior to optimising the topology or operating conditions of the product or process. This sequential decision-making process can lead to poor performance of the overall process because all these factors are intrinsically linked. For example, what is the best solvent for a reaction in a pharmaceutical manufacturing process depends on the temperature and pressure of the reactor, but also on what comes next in the process. If it is another reaction, it may be best to find a solvent which works reasonably well for both reactions, in order to avoid expensive additional processing steps such as swapping one solvent for another. By making decisions simultaneously, we can significantly improve the economics of a process and reduce its environmental impact through decreased material use and increased energy efficiency. Such an approach is referred to as integrated product and process design.

There are three elements needed for integrated product and process design: predictive models that can relate changes in the materials and in the process to performance; optimisation formulations that capture mathematically the trade-offs inherent in such complex systems; reliable algorithms that can solve the resulting design problems efficiently. In recent years, we have developed predictive models that have opened up new possibilities in design. The aim of this proposal is therefore to propose new formulations and algorithms for integrated product and process design and to apply them to a series of design problems.

The key challenge in problem formulation is to ensure that innovative (but unknown) solutions are embedded within the optimisation problem so that they can be uncovered. One way to do this is to allow the structure of the molecules or the materials to be part of the decision process by representing them through discrete decisions. Another complementary approach is to develop formulations that allow the identification of optimal mixtures. By mixing known molecules, one can tune the performance of the process. We will propose generic formulations for such problems. We will also tackle the simultaneous design of molecules/mixtures and processes.

In the optimisation algorithms used to solve these design problems, the main issue is to identify the very best (global) solution reliably and in a reasonable amount of time. This is difficult due to the nature of the integrated product and process design problem: it is nonlinear and combinatorial, which means that many local solutions may exist. We will develop robust algorithms for such problems, tackling the different types of mathematics that may be encountered, such as differential equations and/or discrete variables. These generic algorithms will be applicable to large classes of problems and will therefore be useful to solve other optimisation problems.

The findings of this research will be implemented and tested on a set of design case studies we have gathered in recent years through collaboration with industrial partners and other academic groups. Ongoing collaborations will ensure that our formulations and approaches are captured in software tools and suitable to tackle realistic design problems.

Two main types of users will benefit from the results of this research.

First, the chemical and allied industries (pharmaceutical, oil & gas, ...) will benefit from the ability to design processes that are better - improved economics, decreased environmental impact and safety risks, increased sustainability. This is because they will be able to treat the design problem as a whole, rather than to break it down into smaller problems. By considering at the same time what are the best molecules or materials to use in a process, together with what the best process is, many new options open up. This project will give them tools to pose these important questions, and to answer them reliably.

Second, any business or public body that uses nonlinear optimisation can benefit from this research. Nonlinear optimisation covers a very large set of applications, from process design, to the identification of optimal delivery plans for a disease therapy, to the design of networks, to resource allocation problems. We will develop more robust techniques to solving optimisation problems, with the potential to identify much better solutions than those can be found today. It is not possible to assess a priori how much better the solution might be, but experience has shown us that it can be orders of magnitude better. The applications of optimisation are technological, economic and environmental, and as a result, the novel techniques we will develop in this project can resonate across all of these impact categories.

The impact of the project will be realised through a diverse set of routes in order to be sure to reach a wide audience. This will include training of skilled researchers, dissemination in international refereed journals and at international conferences, collaborations with industry through internships and case studies, workshops and short courses accessible to the academic and industrial communities, and the development of prototype software.