A novel disassembly process of end-of-life lithium-ion batteries enhanced by online sensing and machine learning techniques

Direct recycling of spent Li-ion batteries: Challenges and opportunities toward practical applications

With the exponential expansion of electric vehicles (EVs), the disposal of Li-ion batteries (LIBs) is poised to increase significantly in the coming years. Effective recycling of these batteries is essential to address environmental concerns and tap into their economic value. Direct recycling has recently emerged as a promising solution at the laboratory level, offering significant environmental benefits and economic viability compared to pyrometallurgical and hydrometallurgical recycling methods. However, its commercialization has not been realized in the terms of financial feasibility. This perspective provides a comprehensive analysis of the obstacles that impede the practical implementation of direct recycling, ranging from disassembling, sorting, and separation to technological limitations. Furthermore, potential solutions are suggested to tackle these challenges in the short term. The need for long-term, collaborative endeavors among manufacturers, battery producers, and recycling companies is outlined to advance fully automated recycling of spent LIBs. Lastly, a smart direct recycling framework is proposed to achieve the full life cycle sustainability of LIBs.

Processing Optimization of Shear Thickening Fluid Assisted Micro-Ultrasonic Machining Method for Hemispherical Mold Based on Integrated CatBoost-GA Model

Micro-electro-mechanical systems (MEMS) hemispherical resonant gyroscopes are used in a wide range of applications in defense technology, electronics, aerospace, etc. The surface roughness of the silicon micro-hemisphere concave molds (CMs) inside the MEMS hemispherical resonant gyroscope is the main factor affecting the performance of the gyroscope. Therefore, a new method for reducing the surface roughness of the micro-CM needs to be developed. Micro-ultrasonic machining (MUM) has proven to be an excellent method for machining micro-CMs; shear thickening fluids (STFs) have also been used in the ultra-precision polishing field due to their perfect processing performance. Ultimately, an STF-MUM polishing method that combines STF with MUM is proposed to improve the surface roughness of the micro-CM. In order to achieve the excellent processing performance of the new technology, a Categorical Boosting (CatBoost)-genetic algorithm (GA) optimization model was developed to optimize the processing parameters. The results of optimizing the processing parameters via the CatBoost-GA model were verified by five groups of independent repeated experiments. The maximum absolute error of CatBoost-GA is 7.21%, the average absolute error is 4.69%, and the minimum surface roughness is reduced by 28.72% compared to the minimum value of the experimental results without optimization.

Machine-Learning-Assisted Prediction of Maximum Metal Recovery from Spent Zinc–Manganese Batteries

Spent zinc–manganese batteries contain heavy toxic metals that pose a serious threat to the environment. Recovering these metals is vital not only for industrial use but also for saving the environment. Recycling metal from spent batteries is a complex task. In this study, machine-learning-based predictive models are developed for predicting metal recovery from spent zinc–manganese batteries by studying the energy substrates concentration, pH control of bioleaching media, incubating temperature and pulp density. The main objective of this study is to make a detailed comparison among five machine learning models, namely, linear regression, random forest regression, AdaBoost regression, gradient boosting regression and XG boost regression. All the machine learning models are tuned for optimal hyperparameters. The results from each of the machine learning models are compared using several statistical metrics such as R2, mean squared error (MSE), mean absolute error (MAE), maximum error and median error. The XG Boost regression model is observed to be the most effective among the tested algorithms.