Machine learning for design of chemical engineering unit operations - a microevaporator, leading to a 3D structured multiphase absorber

用于化学工程单元操作设计的机器学习 - 微蒸发器，形成 3D 结构多相吸收器

• 批准号: 466504162
• 负责人: Professor Dr.-Ing. Roland Dittmeyer, since 4/2022
• 金额: --
• 依托单位: Department of Chemical Engineering
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/466504162?language=en
• 关键词: Machine; learning; design; chemical; engineering

Machine learning (ML) has evolved at an incredible pace over the past years, and for various reasons the applications have been focused most heavily in particular domains, including natural language processing, computer vision, and some of the natural sciences. We plan to use ML to combine data about unit operations in chemical engineering (e.g. micro-evaporators or multiphase reactors) generated using simulations and experiments, to not only predict device characteristics, but to additionally design and suggest device improvements, along with explanations of why the ML model suggested them. We conducted an initial proof-of-concept study, in which we showed that machine learning models, in particular convolutional neural networks (CNNs) are capable of predicting properties of flow devices. Building on top of our preliminary study, we plan to develop generative ML models to design microevaporators and 3D structured multiphase absorbers, leveraging the capabilities of novel machine learning tools and state-of-the-art simulations. While this task sounds highly application specific, it will in turn enable us to improve existing, widely applicable machine learning methods. Focus along these lines will be on uncertainty quantification and active learning, as well as on scientific interpretation and of generative models.The first objective of our project is the predicting novel flow channel structures using machine learning methods, including the development of integrated active learning workflows using uncertainty aware 2D convolutional neural networks, as well as the evaluation of ML based device design based on virtual high throughput screening, generative models and genetic algorithms for exploration and optimization. The second objective is the design novel microfluidic evaporators consisting of arrangements of flow channels into arrays that produce high quality saturated vapour using ML model predictions. Here, we plan to develop ML models to bridge the gap between highly accurate direct numerical simulations and cost effective simulations of multiple-channel microstructured evaporators. The third objective is the exploration of completely novel 3D geometries for complex multiphase flow systems using ML based design. We intend to develop methods for the training of generative ML models on heterogeneous datasets from simulation and experiment, for the incorporation of heuristics for printability and finally for the metal 3D printing and experimental evaluation of ML predictions.

机器学习（ML）在过去几年中以令人难以置信的速度发展，由于各种原因，应用程序主要集中在特定领域，包括自然语言处理，计算机视觉和一些自然科学。我们计划使用ML来结合联合收割机关于使用模拟和实验生成的化学工程单元操作（例如微蒸发器或多相反应器）的数据，不仅预测设备特性，而且还设计和建议设备改进，沿着解释ML模型为什么建议它们。我们进行了初步的概念验证研究，在研究中，我们证明了机器学习模型，特别是卷积神经网络（CNN）能够预测流量设备的特性。在我们初步研究的基础上，我们计划开发生成ML模型来设计微蒸发器和3D结构多相吸收器，利用新型机器学习工具和最先进的模拟功能。虽然这项任务听起来非常特定于应用程序，但它将使我们能够改进现有的、广泛适用的机器学习方法。重点沿着这些路线将是不确定性量化和主动学习，以及科学解释和生成模型。我们项目的第一个目标是使用机器学习方法预测新的流道结构，包括使用不确定性感知2D卷积神经网络开发集成主动学习工作流程，以及基于虚拟高通量筛选、生成模型和用于探索和优化的遗传算法的基于ML的器件设计的评估。第二个目标是设计新颖的微流体蒸发器，其由流动通道排列成阵列组成，使用ML模型预测产生高质量的饱和蒸气。在这里，我们计划开发ML模型，以弥合高精度直接数值模拟和多通道微结构蒸发器的成本效益模拟之间的差距。第三个目标是使用基于ML的设计探索复杂多相流系统的全新3D几何形状。我们打算开发方法，用于在来自模拟和实验的异构数据集上训练生成ML模型，用于纳入可打印性的几何学，最后用于金属3D打印和ML预测的实验评估。