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

Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1939
Author(s):  
Maria João Regufe ◽  
Vinicius V. Santana ◽  
Alexandre F. P. Ferreira ◽  
Ana M. Ribeiro ◽  
José M. Loureiro ◽  
...  

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.

2021 ◽  
Author(s):  
Harini Narayanan ◽  
M. Nicolas Cruz Bournazou ◽  
Gonzalo Guillen-Gosalbez ◽  
Alessandro Butte

Mathematical models used for the representation of (bio)-chemical processes can be grouped into two broad paradigms: white-box or mechanistic models, completely based on knowledge or black-box data-driven models based on patterns observed in data. However, in the past two-decade, hybrid modeling that explores the synergy between the two paradigms has emerged as a pragmatic compromise. The data-driven part of these have been largely based on conventional machine learning algorithm (e.g., artificial neural network, support vector regression), which prevents interpretability of the finally learnt model by the domain-experts. In this work we present a novel hybrid modeling framework, the Functional-Hybrid model, that uses the ranked domain-specific functional beliefs together with symbolic regression to develop dynamic models. We demonstrate the successful implementation of these hybrid models for four benchmark systems and a microbial fermentation reactor, all of which are systems of (bio)chemical relevance. We also demonstrate that compared to a similar implementation with the conventional ANN, the performance of Functional-Hybrid model is at least two times better in interpolation and extrapolation. Additionally, the proposed framework can learn the dynamics in 50% lower number of experiments. This improved performance can be attributed to the structure imposed by the functional transformations introduced in the Functional-Hybrid model.


2017 ◽  
Vol 23 (2) ◽  
pp. 218-230 ◽  
Author(s):  
Xiaoying Zhu ◽  
Renbi Bai

Background: Bioactive compounds from various natural sources have been attracting more and more attention, owing to their broad diversity of functionalities and availabilities. However, many of the bioactive compounds often exist at an extremely low concentration in a mixture so that massive harvesting is needed to obtain sufficient amounts for their practical usage. Thus, effective fractionation or separation technologies are essential for the screening and production of the bioactive compound products. The applicatons of conventional processes such as extraction, distillation and lyophilisation, etc. may be tedious, have high energy consumption or cause denature or degradation of the bioactive compounds. Membrane separation processes operate at ambient temperature, without the need for heating and therefore with less energy consumption. The “cold” separation technology also prevents the possible degradation of the bioactive compounds. The separation process is mainly physical and both fractions (permeate and retentate) of the membrane processes may be recovered. Thus, using membrane separation technology is a promising approach to concentrate and separate bioactive compounds. Methods: A comprehensive survey of membrane operations used for the separation of bioactive compounds is conducted. The available and established membrane separation processes are introduced and reviewed. Results: The most frequently used membrane processes are the pressure driven ones, including microfiltration (MF), ultrafiltration (UF) and nanofiltration (NF). They are applied either individually as a single sieve or in combination as an integrated membrane array to meet the different requirements in the separation of bioactive compounds. Other new membrane processes with multiple functions have also been developed and employed for the separation or fractionation of bioactive compounds. The hybrid electrodialysis (ED)-UF membrane process, for example has been used to provide a solution for the separation of biomolecules with similar molecular weights but different surface electrical properties. In contrast, the affinity membrane technology is shown to have the advantages of increasing the separation efficiency at low operational pressures through selectively adsorbing bioactive compounds during the filtration process. Conclusion: Individual membranes or membrane arrays are effectively used to separate bioactive compounds or achieve multiple fractionation of them with different molecule weights or sizes. Pressure driven membrane processes are highly efficient and widely used. Membrane fouling, especially irreversible organic and biological fouling, is the inevitable problem. Multifunctional membranes and affinity membranes provide the possibility of effectively separating bioactive compounds that are similar in sizes but different in other physical and chemical properties. Surface modification methods are of great potential to increase membrane separation efficiency as well as reduce the problem of membrane fouling. Developing membranes and optimizing the operational parameters specifically for the applications of separation of various bioactive compounds should be taken as an important part of ongoing or future membrane research in this field.


2021 ◽  
Vol 767 ◽  
pp. 144346
Author(s):  
Xiang Li ◽  
Shuting Shen ◽  
Yuye Xu ◽  
Ting Guo ◽  
Hongliang Dai ◽  
...  

2017 ◽  
Vol 41 (15) ◽  
pp. 7177-7185 ◽  
Author(s):  
Deying Mu ◽  
Yuanlong Liu ◽  
Ruhong Li ◽  
Quanxin Ma ◽  
Changsong Dai

A highly-selective electrolyte recovery method-transcritical CO2 extraction—was presented which combined the extraction and separation processes together.


Author(s):  
Malcolm Stein ◽  
Chien-Fan Chen ◽  
Matthew Mullings ◽  
David Jaime ◽  
Audrey Zaleski ◽  
...  

Particle size plays an important role in the electrochemical performance of cathodes for lithium-ion (Li-ion) batteries. High energy planetary ball milling of LiNi1/3Mn1/3Co1/3O2 (NMC) cathode materials was investigated as a route to reduce the particle size and improve the electrochemical performance. The effect of ball milling times, milling speeds, and composition on the structure and properties of NMC cathodes was determined. X-ray diffraction analysis showed that ball milling decreased primary particle (crystallite) size by up to 29%, and the crystallite size was correlated with the milling time and milling speed. Using relatively mild milling conditions that provided an intermediate crystallite size, cathodes with higher capacities, improved rate capabilities, and improved capacity retention were obtained within 14 μm-thick electrode configurations. High milling speeds and long milling times not only resulted in smaller crystallite sizes but also lowered electrochemical performance. Beyond reduction in crystallite size, ball milling was found to increase the interfacial charge transfer resistance, lower the electrical conductivity, and produce aggregates that influenced performance. Computations support that electrolyte diffusivity within the cathode and film thickness play a significant role in the electrode performance. This study shows that cathodes with improved performance are obtained through use of mild ball milling conditions and appropriately designed electrodes that optimize the multiple transport phenomena involved in electrochemical charge storage materials.


2020 ◽  
Vol 15 (1) ◽  
pp. 122-132 ◽  
Author(s):  
Carolina Conde-Mejía ◽  
Arturo Jiménez-Gutiérrez

AbstractAfter the biomass pretreatment and fermentation processes, the purification step constitutes a major task in bioethanol production processes. The use of membranes provides an interesting choice to achieve high-purity bioethanol. Membrane separation processes are generally characterized by low energy requirements, but a high capital investment. Some major design aspects for membrane processes and their application to the ethanol dehydration problem are addressed in this work. The analysis includes pervaporation and vapor permeation methods, and considers using two types of membranes, A-type zeolite and amorphous silica membrane. The results identify the best combination of membrane separation method and type of membrane needed for bioethanol purification.


Author(s):  
Ning He ◽  
Cheng Qian ◽  
Lile He

Abstract As an important energy storage device, lithium-ion batteries have vast applications in daily production and life. Therefore, the remaining useful life prediction of such batteries is of great significance, which can maintain the efficacy and reliability of the system powered by lithium-ion batteries. For predicting remaining useful life of lithium-ion batteries accurately, an adaptive hybrid battery model and an improved particle filter are developed. Firstly, the adaptive hybrid model is constructed, which is a combination of empirical model and long-short term memory neural network model such that it could characterize battery capacity degradation trend more effectively. In addition, the adaptive adjustment of the parameters for hybrid model is realized via optimization technique. Then, the beetle antennae search based particle filter is applied to update the battery states offline constructed by the proposed adaptive hybrid model, which can improve the estimation accuracy. Finally, remaining useful life short-term prediction is realized online based on long short-term memory neural network rolling prediction combined historical capacity with online measurements and latest offline states and model parameters. The battery data set published by NASA is used to verify the effectiveness of proposed strategy. The experimental results indicate that the proposed adaptive hybrid model can well represent the battery degradation characteristics, and have a higher accuracy compared with other models. The short-term remaining useful life prediction results have good performance with the errors of 1 cycle, 3 cycles, and 1 cycle, above results indicate proposed scheme has a good performance on short-term remaining useful life prediction.


2021 ◽  
Author(s):  
Anna Maria De Girolamo ◽  
Youssef Brouziyne ◽  
Lahcen Benaabidate ◽  
Aziz Aboubdillah ◽  
Ali El Bilali ◽  
...  

<p>The non-perennial streams and rivers are predominant in the Mediterranean region and play an important ecological role in the ecosystem diversity in this region. This class of streams is particularly vulnerable to climate change effects that are expected to amplify further under most climatic projections. Understanding the potential response of the hydrologic regime attributes to climatic stress helps in planning better conservation and management strategies. Bouregreg watershed (BW) in Morocco, is a strategic watershed for the region with a developed non-perennial stream network, and with typical assets and challenges of most Mediterranean watersheds. In this study, a hybrid modeling approach, based on the Soil and Water Assessment Tool (SWAT) model and Indicator of Hydrologic Alteration (IHA) program, was used to simulate the response of BW's stream network to climate change during the period: 2035-2050. Downscaled daily climate data from the global circulation model CNRM-CM5 were used to force the hybrid modeling framework over the study area. Results showed that, under the changing climate, the magnitude of the alteration will be different across the stream network; however, almost the entire flow regime attributes will be affected. Under the RCP8.5 scenario, the average number of zero-flow days will rise up from 3 to 17.5 days per year in some streams, the timing of the maximum flow was calculated to occur earlier by 17 days than in baseline, and the timing of the minimal flow should occur later by 170 days in some streams. The used modeling approach in this study contributed in identifying the most vulnerable streams in the BW to climate change for potential prioritization in conservation plans.</p>


Sign in / Sign up

Export Citation Format

Share Document