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.