Di Xu

An intensified solvent extraction process using an oscillatory flow packed column (OFPC) was developed for sequential recovery of Mn, Co, and Ni from lithium-ion battery (LIB) leachate streams. Hydrodynamic feasibility was first evaluated to establish the operating window of the OFPC, followed by single-factor experiments and response surface methodology (RSM) to systematically investigate the effects of pH, phase ratio, and residence time on extraction performance. Under optimal conditions, extraction efficiencies exceeding 99% were achieved for Mn, Co, and Ni with high separation factors, confirming efficient sequential metal partitioning in a continuous intensified system. Experimental results showed good agreement with model predictions, validating the robustness of the RSM-based optimization strategy. A Waste Reduction Algorithm-based environmental assessment was further conducted to benchmark the OFPC process against a conventional mixer–settler configuration. Owing to enhanced mass-transfer efficiency and reduced energy demand, the OFPC process achieved a 37.6% reduction in total potential environmental impact (PEI) output rate and a 50.8% decrease in PEI generation rate. Overall, the coupling of oscillatory hydrodynamics with packed column intensification enables high-efficiency sequential recovery of critical metals while significantly reducing environmental burdens, providing a robust and sustainable alternative for LIB recycling.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Effective in situ H₂S removal is essential for the utilization of small, remote natural gas wells, where centralized treatment is often unfeasible. This study presents an integrated environmental–economic assessment of two such processes, LO-CAT^® and triazine-based absorption, using a scenario-based framework. Environmental impacts were assessed via the Waste Reduction Algorithm (WAR), considering both Potential Environmental Impact (PEI) generation and output across eight categories, while economic performance was analyzed based on equipment, chemical, energy, environmental treatment, and labor costs. Results show that the triazine-based process offers superior environmental performance due to lower toxic emissions, whereas LO-CAT^® demonstrates better economic viability at higher gas flow rates and H₂S concentrations. An integrated assessment combining monetized environmental impacts with economic costs reveals that the triazine-based process becomes competitive only if environmental impacts are priced above specific thresholds. This study contributes a practical evaluation framework and scenario-based dataset that support sustainable process selection for decentralized sour gas treatment applications.

Enhancing the sustainability of wastewater treatment plants (WWTPs) is crucial due to their manifold benefits, which encompass environmental preservation, cost reduction, and resource and energy conservation. The achievement of these advantages relies on the careful choice and implementation of retrofit technologies to upgrade WWTPs. However, this decision-making process is intricate, given the trade-offs between the objectives and the inherent decision uncertainties. To address these complexities, this work presents an innovative weighted multi-objective optimization (MOO) framework tailored for WWTP enhancement amid uncertain conditions. This framework comprises two phases. The first phase involves basic definition and information collection through a case-specific assessment, while the second phase includes model formulation and solver optimization, which serves as a generic tool for the weighted MOO problem. In the model formulation, a combined weighting approach that integrates expert opinions and statistical insights is introduced to assign significance to each objective. The solver optimization employs a projection-based algorithm to identify the optimal technology configuration that achieves a satisfactory and balanced improvement across multiple sustainable objectives. By applying this framework to a case plant for retrofit technology selection, the comprehensive sustainability performance, the targeting of discharged pollution, the operational cost, and the GHG emissions improved by 46.7% to 68.3%.

Battery technologies offer promising solutions for renewable energy storage. However, selecting the most suitable battery requires proper investigation. This study introduces a multi-criteria decision-making framework for assessing batteries based on various criteria and uncertain data, by using a combined objective weighting method and an uncertainty-preserved complex proportional assessment (UP-COPRAS). The proposed weighting method ensures objectivity and fairness in the weighting result by integrating interval entropy and a gray relational coefficient-supported decision-making trial and evaluation laboratory to capture variation and correlation degrees among the criteria. After incorporating interval numbers with a compensatory ranking method, the UP-COPRAS prioritizes batteries in a simple yet rigorous way using uncertain evaluation data. To test the feasibility of the framework, an illustrative case was employed to assess four battery alternatives using a five-dimensional criteria system. Through results comparison, two mathematical contributions are confirmed. First, the combined objective weighting method uses the variation and correlation features of numerical data to determine criteria weights, which prevents subjective manipulation and eliminates bias in statistical analysis. Second, the UP-COPRAS preserves uncertainties throughout the evaluation, resulting in a rational decision output by eliminating interference in the original data.