A Hybrid Modeling Framework for Membrane Separation Processes: Application to Lithium-Ion Recovery from Batteries

Maria João Regufe,Alexandre F P Ferreira,Vinicius V Santana,Idelfonso B R Nogueira,Ana M Ribeiro,José M Loureiro

doi:10.3390/pr9111939

Abstract

This study proposed a hybrid modeling framework for membrane separation processes where lithium from batteries is recovered. This is a pertinent problem nowadays as lithium batteries are popularized in hybrid and electric vehicles. The hybrid model is based on an artificial intelligence (AI) structure to model the mass transfer resistance of several experimental separations found in the literature. It is also based on a phenomenological model to represent the transient system regime. An optimization framework was designed to perform the AI model training and simultaneously solve the Ordinary Differential Equation (ODE) system representing the phenomenological model. The results demonstrate that the hybrid model can better represent the experimental validation sets than the phenomenological model alone. This strategy opens doors for further investigations of this system.

Highlights

Application to Lithium-Ion RecoveryPhenomenological modeling is an essential step for systems design, development, and optimization
Phenomenological modeling is done by applying conservation laws, capable of representing the known phenomenology through one or a system of differential equations
Hybrid ral network (ODE-artificial neural network (ANN)) model is presented. This novel strategy is compared to a pheThe phenomenology involved in a liquid membrane separation comprises the aqueous nomenological model of this system to understand better the methodology proposed here

Summary

Introduction

Application to Lithium-Ion RecoveryPhenomenological modeling is an essential step for systems design, development, and optimization. The modeling of first principles is usually associated with system parameters, such as mass transfer coefficient, reaction rate, etc. These parameters are generally embedded into a constant that needs to be estimated through experimental data. The way these parameters are added in the conservation laws is usually associated with simplifications that allow the model to be identifiable. This is a limiting factor that will be reflected in the model precision and reliability

Methods

Results

Conclusion