Optimisation problems are abundant both in lab and industrial chemistry, where the determination of ideal reaction conditions poses particular challenges. This work contributes to the extant research on self-optimisation by developing a modular, autonomous platform that performs multi-variate and multi-objective optimisations in real-time, thereby generating cost and time savings. The platform consists of a fully-automated microreactor setup that is equipped with real-time reaction monitoring (through inline FT-IR spectroscopy) and a self-optimisation procedure. To demonstrate its flexibility (which extends to industrial production settings), the performances (in terms of identifying optimal reaction conditions) of two common optimisation strategies, modified simplex algorithm and model-free design of experiments, are subsequently compared. Besides enabling model-free autonomous optimisation, this novel system also permits the simultaneous collection of kinetic data to gain further insights into the involved chemical processes. In a second step, the system is enhanced to become capable of providing real-time responses to disturbances to the chemical process. Thus, this work assists researchers and production engineers alike in selecting the most suitable strategy for a given optimisation scenario, while also counteracting potential malfunctions in chemical production processes.

中文翻译：

---

使用Nelder–Mead在微反应器系统中进行有机合成的自优化过程和实时优化以及实验设计

优化问题在实验室和工业化学中都存在，这对确定理想的反应条件提出了特殊的挑战。这项工作通过开发一个模块化的，自主的平台来实时进行多变量和多目标优化，从而为自优化进行了广泛的研究，从而节省了成本和时间。该平台由一个全自动微反应器设置组成，该设置配有实时反应监控（通过在线FT-IR光谱法）和自优化程序。为了证明其灵活性（扩展到工业生产设置），随后比较了两种常见的优化策略（改进的单纯形算法和无模型实验设计）的性能（在确定最佳反应条件方面）。除了实现无模型的自主优化之外，这种新颖的系统还允许同时收集动力学数据，以进一步了解所涉及的化学过程。在第二步中，系统被增强以能够对化学过程的干扰提供实时响应。因此，这项工作可帮助研究人员和生产工程师为给定的优化方案选择最合适的策略，同时还能消除化学生产过程中的潜在故障。该系统得到了增强，使其能够提供对化学过程干扰的实时响应。因此，这项工作可帮助研究人员和生产工程师为给定的优化方案选择最合适的策略，同时还能消除化学生产过程中的潜在故障。该系统得到了增强，使其能够提供对化学过程干扰的实时响应。因此，这项工作可帮助研究人员和生产工程师为给定的优化方案选择最合适的策略，同时还能消除化学生产过程中的潜在故障。