Additive manufacturing has enabled the production of more advanced reactor geometries, resulting in the potential for significantly larger and more complex design spaces. Identifying and optimising promising configurations within broader design spaces presents a significant challenge for existing human-centric design approaches. As such, existing parameterisations of coiled-tube reactor geometries are low-dimensional with expensive optimisation limiting more complex solutions. Given algorithmic improvements and the onset of additive manufacturing, we propose two novel coiled-tube parameterisations enabling the variation of cross-section and coil path, resulting in a series of high dimensional, complex optimisation problems. To ensure tractable, non-local optimisation where gradients are not available, we apply multi-fidelity Bayesian optimisation. Our approach characterises multiple continuous fidelities and is coupled with parameterised meshing and simulation, enabling lower quality, but faster simulations to be exploited throughout optimisation. Through maximising the plug-flow performance, we identify key characteristics of optimal reactor designs, and extrapolate these to produce two novel geometries that we 3D print and experimentally validate. By demonstrating the design, optimisation, and manufacture of highly parameterised reactors, we seek to establish a framework for the next-generation of reactors, demonstrating that intelligent design coupled with new manufacturing processes can significantly improve the performance and sustainability of future chemical processes.

翻译：增材制造使更先进反应器几何结构的制造成为可能，从而催生了显著更大、更复杂的设计空间。在更广泛的设计空间中识别和优化有前景的构型，对现有以人为中心的设计方法构成了重大挑战。因此，现有盘管式反应器几何结构的参数化方案维度较低，且昂贵的优化过程限制了更复杂解决方案的探索。考虑到算法改进和增材制造的兴起，我们提出了两种新型盘管式反应器参数化方法，能够实现截面形状和盘管路径的变化，从而产生一系列高维、复杂的优化问题。为确保在梯度不可用的情况下进行可处理的非局部优化，我们采用了多保真贝叶斯优化方法。该方法能够表征多个连续保真度，并与参数化网格划分和仿真相结合，使优化过程中能够利用质量较低但速度更快的仿真。通过最大化平推流性能，我们识别了最优反应器设计的关键特征，并将其外推得到两种新型几何结构，进而通过3D打印和实验进行了验证。通过展示高度参数化反应器的设计、优化和制造过程，我们力图建立下一代反应器的设计框架，证明智能设计与新型制造工艺相结合能够显著提升未来化学过程的性能和可持续性。