(341n) Simultaneous Optimization of Membrane Design, Process Design, and Process Operation
We propose a novel methodology that simultaneously performs the optimization of membrane synthesis protocol, process design, and process operation [3-5]. The method builds on our developed modeling approach [3-5] and our developed optimization method .
A mechanistic model mapping the membrane synthesis protocol to its process performance has not been available in the previous literature. We develop a hybrid mechanistic/data-driven membrane model [3-5]. The model uses an extensive experimental data set for novel ion separation membranes synthesized with a layer-by-layer (LbL) fabrication methodology. It combines artificial neural networks with physical mass transport models. The hybrid model predicts ion retention and water flux values based on membrane synthesis protocols [3,4]. This membrane model is integrated into superstructure process models including cost correlations .
The optimization of superstructure process models with artificial neural networks embedded results in large-scale mixed-integer nonlinear problems that are difficult to solve to global optimality. Recently, we found that a reduced-space formulation is favorable for optimization problems with artificial neural networks embedded  and process flowsheeting [7,8]. We solve the proposed optimization problems to guaranteed global optimality using our open-source solver MAiNGO  and machine-learning modeling library MeLOn .
A multi-objective optimization approach reveals the inherent trade-off between the conflicting objectives minimal annual operation costs and minimal permeate impurity. The presented method enables the tailoring of membrane fabrication to specific separation tasks. The results outperform conventional design approaches where membranes are selected among a set of commercial membranes. In future work, the methodology has the potential to accelerate the development of new membrane materials through active learning.
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 Schweidtmann, A. M., Netze, L., & Mitsos, A. (2020). MeLOn - Machine Learning Models for Optimization. https://git.rwth-aachen.de/avt.svt/public/MeLOn