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Authors: Pierre Hémono, Ahmed Nait Chabane, M’hammed Sahnoun, Martin Choux
“In the context of Industry 5.0, scheduling heterogeneous resources, such as humans and robots, has become increasingly critical. Task allocation must balance human comfort, ergonomics, and trust with productivity and responsiveness to customer demands. This review explores recent advances and prospects in the automatic generation of schedules and action plans, particularly Behavior Trees (BTs), to improve human–robot collaboration. We examine the application of artificial intelligence techniques to classical production management problems, such as Job Shop Scheduling Problem (JSSP) and Assembly Line Balancing Problems (ALBP), for autonomous task scheduling and robotic behavior design. This includes highlighting innovative scheduling approaches and the advantages of Behavior Trees over traditional models such as Hierarchical Task Networks (HTN) and Finite-State Machines (FSM). Behavior Trees offer a modular and reactive programming structure essential for executing complex tasks assigned to robots. The review also discusses human operators’ perception of robotic actions and identifies best practices for implementing collaborative solutions that prioritize both efficiency and safety.”
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Authors: Reshma Maharjan, Per-Arne Andersen & Lei Jiao | Department of ICT, University of Agder, Kristiansand, Norway
Included in the following conference series: International Conference on Engineering Applications of Neural Networks
This paper presents a novel hybrid approach that combines Tsetlin Machines (TMs) and Q-learning, referred to as QTM, to solve the Job Shop Scheduling Problem (JSSP). The proposed model integrates the pattern recognition capabilities of TMs with the decision-making strengths of Q-learning to optimize scheduling decisions. We implement a job shop scheduling (JSS) environment that handles complex scenarios with multiple jobs, operations, and machines while maintaining comprehensive state tracking. The QTM framework employs a novel reward function that balances makespan minimization with machine utilization, and utilizes an innovative action selection mechanism combining TM predictions with scheduling factors. We evaluate our approach using multiple benchmark datasets, including Taillard and Lawrence instances, as well as a real-world battery disassembly case study. Experimental results demonstrate that QTM consistently outperforms traditional dispatching rules like FIFO, MWKR, and SPT, achieving lower makespan and optimality gaps across instances. In the battery disassembly case study, QTM achieved a makespan of 581 with an optimality gap of 3.38%, significantly better than traditional heuristic-based methods. For Lawrence instances, QTM maintained an average optimality gap of 22.98%, while for Taillard instances, it achieved a 30.30% gap, showing particular strength in handling larger, more complex scheduling scenarios. While not matching state-of-the-art performance offered by far more complicated deep learning approaches, QTM offers a resource-efficient and transparent alternative that outperforms standard Q-learning, making it suitable for practical industrial applications with computational resource constraint.
Authors: Ludovica D’Annibale, Pier Giorgio Schiavi, Arcangelo Celeste, Sergio Brutti, Francesca Pagnanelli, Pietro Altimari
Department of chemistry, University La Sapienza of Rome, Italy
An innovative hydrometallurgical process is analysed, enabling the direct synthesis of graphene oxide and lithium-manganese rich cathode materials from the electrodic powder (“black mass”) of spent lithium-ion batteries. The black mass leaching is performed by the Hummers’ method, an established solution to produce graphene oxide from graphite. The proposed strategy is based on the the observation that the reagents H2SO4 and H2O2, which are conventionally used to perform the leaching of the black mass, are also used, along with
KMnO4 and NaNO3, in the implementation of the Hummers’ method. Accordingly, the Hummers’ method is here carried out to extract the cathode metals from the black mass (Li, Mn, Co, Ni). To valorise the manganese excess generated by the addition of KMnO4, co-precipitation from the residual solution obtained after graphene oxide recovery is carried out to synthesize the precursor of the lithium-manganese rich cathode materials, which is then used to produce the cathode material by solid-state reaction with LiOH. In this contribution, an experimental analysis of this resynthesis process is presented, allowing to evaluate the impact of the main process parameters on the precursor material synthesis. The proposed process includes the removal of impurities such as copper, iron, and aluminium, followed by the coprecipitation of a precursor with the composition Ni0.375Mn1.375Co0.25(OH)2.
ECS Meeting Abstracts
Authors: Pier Giorgio Schiavi, Ludovica D’Annibale, Andrea Giacomo Marrani, Francesca Pagnanelli and Pietro Altimari
ECS Meeting Abstracts
Authors: Ludovica D’Annibale, Pier Giorgio Schiavi, Arcangelo Celeste, Francesca Pagnanelli, Sergio Brutti and Pietro Altimari
Electrolyte recovery from spent Li-ion batteries remains a significant challenge in the current recycling process. Li-ion battery waste streams containing electrolyte residues are classified as hazardous waste and entail a financial and workplace safety burden for the recycling industry. Recent studies show the potential use of supercritical CO2 extraction for the recovery of electrolyte solvents. In this study, the extraction behavior of electrolyte solvents from Li-ion battery black mass using supercritical CO2 process under pressures of 100 and 140 bar at 40°C was investigated. The extraction yield of dimethyl carbonate, ethyl methyl carbonate, and diethyl carbonate exceeded 99 % at both pressures. Ethylene carbonate, biphenyl, and propylene carbonate were successfully extracted with an extraction yield exceeding 95 % using 140 bar and 40°C. The extraction rates of biphenyl, ethylene carbonate and propylene carbonate at 140 bar and 40°C in the linear extraction regime of the extraction curve were determined to be 0.18 mg/g CO2, 1.9 mg/g CO2 and 0.4 mg/g CO2, respectively. The research demonstrates that supercritical CO₂ processing is a highly promising method not only for recycling electrolytes but also for mitigating the hazardous risks associated with battery waste.
Download the publication from the “Journal of CO2 Utilization”, Elsevier
The recycling of polyvinylidene fluoride (PVDF) from spent lithium-ion battery black mass was investigated using supercritical carbon dioxide (SCCO2) combined with dimethyl sulfoxide (DMSO) as a co-solvent. Experiments were conducted at 70 °C and 80 bar for 15 min, varying the DMSO volume. Thermogravimetric analyses revealed that utilizing 4 mL of DMSO enabled the cumulative recovery of 55.6 wt% of PVDF. Thermogravimetric analysis confirmed a significant enhancement in PVDF extraction compared to atmospheric pressure (1 atm), where minimal PVDF was removed even after extended periods of solvent mixed black mass up to 18 days at room temperature and 24 h at 70 °C. Scanning electron microscopy revealed particle size reduction from approximately 93 µm to 43 µm and decreased agglomeration in treated samples, demonstrating improved particle homogeneity due to binder removal. Fourier-transform infrared spectroscopy and X-ray diffraction analyses confirmed that the chemical structure and crystalline phases of recovered PVDF remained intact. Despite its significance, PVDF recycling has not yet been established at an industrial scale. This study demonstrates the potential of the SCCO2–DMSO system as a rapid, sustainable, and scalable approach for the efficient recovery of PVDF from industrial lithium-ion battery waste.
Download the publication from the “Separation and Purification Technology”, Elsevier
Ethylene carbonate is, among other applications, used in Li-ion batteries as an electrolyte solvent to dissociate Li-salt. Supercritical CO2 extraction is a promising method for the recycling of electrolyte solvents from spent batteries. To design an extraction process, knowledge of the solute solubility is essential. In this work, the solubility of ethylene carbonate at different pressure (80–160 bar) and temperature (40 °C, and 60 °C) conditions is studied. It is shown that the solubility of ethylene carbonate increased with pressure at both temperatures, ranging from 0.24 to 8.35 g/kg CO2. The retrieved solubility data were fitted using the Chrastil model, and the average equilibrium association number was determined to be 4.46 and 4.02 at 40 °C and 60 °C, respectively. Scanning electron microscopy, Fourier-transform infrared spectroscopy, and X-ray diffraction analysis of the collected ethylene carbonate indicated that the crystal morphology and structure remained unchanged. A proof-of-principle experiment showed that EC can be successfully extracted from Li-ion battery waste at 140 bar and 40 °C.
Download the publication from the MDPI Batteries website
This study investigates how common pretreatments for recovering black mass from end-of-life (EoL) electric vehicle (EV) lithium-ion batteries (LIBs) influence graphene oxide (GO) synthesis. Black mass was obtained through (i) industrial-scale carbothermal reduction of whole EV battery packs, (ii) industrial-scale mechanical processing, and (iii) lab-scale mechanochemical treatment via reactive ball milling. Characterizations assessed the impact of these pretreatments, along with conventional acid leaching, on graphite properties such as interlayer spacing, oxidation degree, and defectivity—key factors for potential anode reuse. The mechanochemically treated sample achieved an outstanding GO yield of 92 %, whereas other black masses reached up to 30 %. GO yields were further analysed using the Hummers’ method after acid leaching for metal removal. This approach enhanced yields, reaching 96 % for the mechanochemically treated sample and up to 46 % for the others. The improvements were attributed to reduced reagent consumption and the partial exfoliation and oxidation of graphite during leaching. Additionally, lithium intercalation/deintercalation during battery cycling increased GO yield compared to commercial pristine graphite. These findings highlight mechanochemical pretreatment as a promising strategy to integrate high-yield GO production into LIB recycling workflows.
