(88f) Improved Characterization of Membrane Transport Properties through Advanced Data Analytics
AIChE Annual Meeting
2022
2022 Annual Meeting
Topical Conference: Applications of Data Science to Molecules and Materials
Applications of Data Science to High Throughput Experimentation
Monday, November 14, 2022 - 9:15am to 9:30am
Design of Experiments (DoE) methods enable optimization of computational and physical experiments that maximize the information gain and minimize time and resource costs. Classical âblack-boxâ DoE approaches (a.k.a. factorial, response surface), which decide the best design by the input-output relationship, do not (directly) incorporate membrane science knowledge; in contrast, model-based DoE (MBDoE) leverages high-fidelity models constructed from underlying physical principles that describe the experimental system. [6] The information collected from experiments can be applied to discriminate between scientific hypotheses, posed as mathematical models, and to improve the precision of parameter estimation. The emergence of techniques within MBDoE has great potential in the design of instruments and experimental conditions to better characterize the performance of separation devices as a function of solute concentration and in complex feed streams. However, to date, their application to problems in membrane science remains limited.
We recently proposed the Diafiltration Apparatus for high-Throughput Analysis (DATA), which enables a 10-times reduction in the time, realized with fewer experiments necessary to characterize membrane transport properties. [7] In follow-up work, we mathematically quantified these improvements in the form of information gain and further refine the static experimental conditions needed in DATA to characterize membrane transport properties. [8] In this talk, we apply the Fisher information matrix (FIM) analyses and MBDOE to further improve DATA. We highlight two non-ideal phenomena, namely âlagâ and âoverflowâ, which occur when changing the operating pressure of the system. Guided by the tools of data science, we show that modeling these phenomena can leverage the additional data within the start-up process to elucidate the underlying physics, improve the parameter precision, and brings insights to design a time-varying applied pressure in DATA. A time correction for permeate product collected is also introduced to improve the model predictions. Moreover, our framework, which integrates data analytics and instrumentation design can be applied to investigate concentration-dependent membrane performance to further accelerate the development of materials. For example, we apply the improved DATA to explore the dependency of membrane transport parameters on feed conditions of a surface-charged membrane by ranking candidate models using information criteria.
References
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- Ouimet, J. A., Liu, X., Brown, D. J., Eugene, E. A., Popps, T., Muetzel, Z. W., ... & Phillip, W. A. (2022). DATA: Diafiltration Apparatus for high-Throughput Analysis. Journal of Membrane Science, 641, 119743.
- Liu, X., Wang, J., Ouimet, J. A., Phillip, W., Dowling, A. (2022) Membrane characterization with model-based design of experiments. In 14th International Symposium on Process Systems Engineering.