The molecular frontier: extending the boundaries of process design

在设计新的化学制造工艺时，通常在优化产品或工艺的拓扑结构或操作条件之前选择所需的分子或材料(例如，溶剂或催化剂)。这种连续的决策过程可能会导致整个过程的表现不佳，因为所有这些因素都是内在联系的。例如，在制药生产过程中，什么是反应的最佳溶剂取决于反应器的温度和压力，但也取决于过程中接下来发生的事情。如果是另一种反应，最好找到一种对两种反应都相当有效的溶剂，以避免昂贵的额外处理步骤，如将一种溶剂换成另一种。通过同时做出决策，我们可以通过减少材料使用和提高能源效率来显著提高工艺的经济性，并减少其对环境的影响。这种方法被称为集成产品和过程设计。集成的产品和工艺设计需要三个要素：可以将材料和工艺中的变化与性能联系起来的预测模型；以数学方式捕捉这种复杂系统固有的权衡的优化公式；能够有效解决由此产生的设计问题的可靠算法。近年来，我们开发了预测模型，为设计开辟了新的可能性。因此，本提案的目的是为集成产品和工艺设计提出新的公式和算法，并将其应用于一系列设计问题。问题表达的关键挑战是确保创新的(但未知的)解决方案嵌入到优化问题中，以便它们能够被发现。要做到这一点，一种方法是允许分子或材料的结构成为决策过程的一部分，通过离散的决策来表示它们。另一种补充方法是开发能够识别最佳混合物的配方。通过混合已知的分子，人们可以调整这一过程的性能。我们将为这类问题提出通用的公式。我们还将解决分子/混合物和工艺的同时设计问题。在用于解决这些设计问题的优化算法中，主要问题是在合理的时间内可靠地找到最好的(全局)解决方案。这是困难的，因为集成产品和工艺设计问题的性质：它是非线性的和组合的，这意味着可能存在许多局部解。我们将为这类问题开发稳健的算法，处理可能遇到的不同类型的数学，如微分方程和/或离散变量。这些通用算法将适用于大类问题，因此将有助于解决其他优化问题。这项研究的结果将在我们近年来通过与工业合作伙伴和其他学术团体合作收集的一系列设计案例研究中得到实施和测试。正在进行的合作将确保我们的公式和方法被软件工具捕获，并适合于解决现实的设计问题。

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.