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

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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.