Journal of water process engineering最新文献

{"title":"Green and efficient recovery of phosphorus as Vivianite via anaerobic fluidized bed reactor (AFBR) from aquaculture wastewater","authors":"Hongjin Ji ,&nbsp;Yitong Sun ,&nbsp;Danhui Liang ,&nbsp;Jifei Chang ,&nbsp;Xiaoming Yang ,&nbsp;Xin Wang ,&nbsp;Nan Li","doi":"10.1016/j.jwpe.2025.108718","DOIUrl":"10.1016/j.jwpe.2025.108718","url":null,"abstract":"<div><div>The expansion of aquaculture has led to high-density farming wastewater rich in Total Phosphorus (TP) and Chemical Oxygen Demand (COD), causing severe eutrophication. In response to the low phosphorus recovery efficiency observed in conventional fish aquaculture wastewater (AWW) systems, a novel phosphorus recovery process was developed by integrating an anaerobic fluidized bed reactor (AFBR) with the formation of vivianite (Fe₃(PO₄)₂·8H₂O) as the precipitate product. The long-term laboratory-scale trials demonstrated the performance of the AFBR in treating AWW. Optimization of the iron source to FeCl₃ enabled the AFBR to achieve a vivianite recovery efficiency of 77 %, along with the phosphorus removal of 98 % and the COD removal efficiency of 99 %. Parameter optimization experiments revealed that extending the hydraulic retention time (HRT) to 24 h and increasing the upward flow velocity to 12 m·h⁻¹ improved phosphorus recovery efficiency by 11 % and 5.8 %, respectively. Based on the Life Cycle Assessment (LCA) and cost analysis, the FeCl₃-enhanced AFBR process producing vivianite offers better environmental performance and a net profit of 0.54 USD per ton of AWW treated.This study presents an efficient, eco-friendly, and cost-effective method for phosphorus recovery from the AWW, offering new insights into the high-value conversion of aquaculture effluents.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"78 ","pages":"Article 108718"},"PeriodicalIF":6.7,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-informed feature engineering with fuzzy symbolic regression for predicting settling velocity in water treatment","authors":"Adriano Bressane ,&nbsp;Daniel H.R. Toda ,&nbsp;Rogerio G. Negri ,&nbsp;Jorge K.S. Formiga ,&nbsp;Abayomi O. Bankole ,&nbsp;Afolashade R. Bankole ,&nbsp;Soroosh Sharifi ,&nbsp;Rodrigo Moruzzi","doi":"10.1016/j.jwpe.2025.108749","DOIUrl":"10.1016/j.jwpe.2025.108749","url":null,"abstract":"<div><div>Predicting the settling velocity of fractal aggregates remains a challenge in water treatment, as classical models like Stokes' Law oversimplify the influence of non-sphericity, porosity, and complex morphology. Empirical and fractal-based models lack generalizability, while most machine learning models operate as black boxes, providing limited physical insight. This study proposes a Physics-Informed Machine Learning Fuzzy Symbolic Regression (PIML-SR) framework enhanced with fuzzy preprocessing to derive interpretable and physically consistent equations for settling velocity prediction. A dataset of <em>Al</em>-kaolinite flocs was obtained using high-speed imaging in a sedimentation column. Morphological parameters and physics-based descriptors, such as drag force and Reynolds number, were incorporated through fuzzy preprocessing, which converts normalized features into smooth membership functions to handle regime transitions and measurement uncertainty, combined with fuzzy symbolic regression. The PIML-SR model demonstrated excellent accuracy (R² &gt; 0.99, MAE ≈ 0.015 μm/s) and robustness to up to 10 % Gaussian noise. In contrast, a baseline symbolic model (R² ≈ 0.56, MAE ≈ 556.6 μm/s) and a purely data-driven artificial neural network (R² ≈ 0.63, MAE ≈ 518.3 μm/s), both trained solely on morphological features, along with a Physics-Informed Neural Network (R² ≈ −1.93, MAE ≈ 1794.9 μm/s), all exhibited limited or poor accuracy, underscoring the critical importance of integrating physical knowledge, as achieved by the proposed fuzzy symbolic regression approach, for attaining high-fidelity, generalizable, and interpretable predictions. This represents the first application of a fuzzy-enhanced PIML-SR framework for sedimentation, providing an interpretable, physically grounded, and noise-resilient approach for optimizing sedimentation processes in water treatment.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"78 ","pages":"Article 108749"},"PeriodicalIF":6.7,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Active site on boron‑nitrogen co-doping lignin-based carbon nanotube-coated nZVI for enhanced hexavalent chromium removal by adsorption-redox behavior from groundwater","authors":"Canghai Guan ,&nbsp;Long Guo ,&nbsp;Hongyu Ren ,&nbsp;Kunteng Jia ,&nbsp;Yongchang Sun","doi":"10.1016/j.jwpe.2025.108769","DOIUrl":"10.1016/j.jwpe.2025.108769","url":null,"abstract":"<div><div>The utilization of high-efficiency and environmentally friendly materials for pollutant removal has remained a research hotspot in groundwater remediation studies. Herein, a boron (B) and nitrogen (N) co-doped lignin-based carbon nanotube-coated nano zero-valent iron (nZVI) composite (Fe⁰@LC-NB) was synthesized via a straightforward one-step pyrolysis protocol for Cr(VI) remediation from groundwater. This synthetic approach simultaneously achieves the carbonization of lignin, growth of carbon nanotubes (CNTs), formation of nZVI, and co-doping of boron and nitrogen, resulting in a tubular structure that effectively prevents the aggregation of nZVI and enhances its reactivity. The abundant active sites of B–N–C, B<img>C, and pyridinic N generated by B/N co-doping are absent in singly-doped materials, significantly boosting the Cr(VI) adsorption capacity. Simultaneously, the electron donor-acceptor system formed by B, N, and Fe⁰ markedly increases the interfacial electron transfer rate, thereby achieving exceptional redox activity of the material. The results showed that the maximal adsorption capacity of Fe⁰@LC-NB reached 232.94 mg/g, which was 1.5 times that of un-modified lignin-based carbon nanotube-coated nZVI (Fe⁰@LC). The mechanism analysis indicates that electrostatic adsorption, redox reactions, and complexation processes were the major reasons for Cr(VI) removal. Moreover, unlike conventional nZVI materials that undergo rapid passivation in application, the protective carbon shell enables the composite to maintain outstanding performance even in complex aquatic environments, offering a cost-effective and efficient method for groundwater remediation.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"78 ","pages":"Article 108769"},"PeriodicalIF":6.7,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An attention-based parallel model with sliding window decomposition algorithm for water quality prediction","authors":"Yahong Yang ,&nbsp;Pengtang Zhang ,&nbsp;Yali Wang","doi":"10.1016/j.jwpe.2025.108751","DOIUrl":"10.1016/j.jwpe.2025.108751","url":null,"abstract":"<div><div>Accurate forecasting of effluent water quality is essential for enhancing the safety and economic efficiency of wastewater treatment plants (WWTPs) due to the volatility and time-varying nature of effluent water quality. Representative neural networks, such as Long Short-Term Memory (LSTM), have been extensively employed in time-series prediction. However, as the volume of water quality data increases, these models become unstable, making accurate prediction challenging. This study proposes a hybrid prediction method, DVIBM, based on optimized decomposition for forecasting effluent water quality. DVIBM integrates the Dung Beetle Optimization (DBO) algorithm, Variational Mode Decomposition (VMD), Informer, Bidirectional Long Short-Term Memory (BiLSTM) network, and the multi-scale attention mechanism (MUSE). The DBO algorithm is employed to optimize the hyperparameters <span><math><mi>α</mi></math></span> and <span><math><mi>k</mi></math></span> in VMD, within a sliding window framework, to determine the decomposition bandwidth and the number of modes. The original water quality time-series is decomposed into multiple sub-series, with future data excluded during the process to effectively extract features while preventing data leakage. DVIBM couples Informer and BiLSTM via the MUSE attention mechanism, adaptively fusing multi-scale long- and short-term features, thereby reducing error accumulation and propagation in cascaded or single-architecture. Across varying sliding-window parameter combinations and time steps, as well as in ablation comparisons, DVIBM achieves MAE/MSE/R² of 0.104/0.017/0.975 for effluent TN and 0.071/0.008/0.969 for TP, significantly outperforming the benchmark models. Global and local interpretability analyses of effluent TN and TP are conducted using the SHAP (Shapley Additive Explanations) algorithm, providing theoretical support for the interpretability of wastewater treatment systems.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"78 ","pages":"Article 108751"},"PeriodicalIF":6.7,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time effluent water quality prediction model based on BiLSTM and KAN for wastewater treatment plants","authors":"Siyu Liu ,&nbsp;Zhaocai Wang","doi":"10.1016/j.jwpe.2025.108750","DOIUrl":"10.1016/j.jwpe.2025.108750","url":null,"abstract":"<div><div>Predicting effluent water quality in wastewater treatment plants (WWTPs) is essential for operation optimization, resource efficiency, and regulatory compliance. However, traditional methods struggle with complex temporal dynamics and nonlinear interactions, and current research lacks unified approaches for feature interaction, noise robustness, and multiscale modeling. In this study, we introduce a hybrid model combining bidirectional long short-term memory (BiLSTM) and Kolmogorov-Arnold networks (KAN), alongside a feature-selection mechanism that fuses Spearman, Kendall, and maximal information coefficient (MIC) metrics to identify key water-quality drivers. The feature-selection strategy integrates three methods to capture both monotonic and non-monotonic associations, reducing noise by focusing on impactful predictors. The model synergistically combines BiLSTM's bidirectional temporal feature extraction (capturing past-future context of time-series data) with KAN's strong nonlinear approximation power (modeling complex interactions via spline-based univariate function combinations, based on the Kolmogorov-Arnold theorem), optimizing spatiotemporal feature integration through a dynamic weighted gating mechanism. Experimental results show that, compared with benchmark models such as long short-term memory (LSTM), the model reduces the root mean square error (RMSE) in predicting effluent chemical oxygen demand (COD) by 7.67 % to 45.17 % and improves the coefficient of determination (R²) by 0.96 % to 14.76 %, demonstrating superior forecasting performance. Temporal differential analysis uncovers water quality fluctuations within a day, while multiscale forecasting achieves R² &gt; 0.92, validating the model's ability to capture dynamic changes and perform nonlinear mapping. This study further applies SHapley Additive Explanation (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) for interpretability: SHAP identifies key drivers while LIME clarifies how these variables influence specific predictions, aiding operational adjustments. Noise-injection tests confirm robustness, ensuring reliability under sensor drift. This framework offers a comprehensive, interpretable, and resilient solution for real-time WWTP control (e.g., dynamic carbon source dosing) and advances smart water management.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"78 ","pages":"Article 108750"},"PeriodicalIF":6.7,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced electrochemical detection of the antibiotic levofloxacin using temperature optimized Er2MoO6 nanomaterials for environmental monitoring","authors":"Kumar Gokulkumar ,&nbsp;Sri Balaji Natarajan ,&nbsp;Shen-Ming Chen ,&nbsp;Sakthivel Kogularasu ,&nbsp;Shih-Hsuan Chen ,&nbsp;Kun-Mu Lee","doi":"10.1016/j.jwpe.2025.108757","DOIUrl":"10.1016/j.jwpe.2025.108757","url":null,"abstract":"<div><div>Levofloxacin (LFX) is widely used in healthcare and aquaculture due to its structural stability and physicochemical properties, which allow its residues to persist in the environment and pose significant risks to human health. However, conventional sensors often lack the efficiency and sensitivity required for detecting complex molecular compounds. In this work, a temperature-optimized erbium molybdate (Er₂MoO₆)-based electrochemical sensor was developed for the highly sensitive and selective detection of LFX. The Er₂MoO₆ nanoparticles, synthesized via a hydrothermal process at two different temperatures (160 °C and 200 °C), exhibited excellent stability and reusability for sustained sensing. Notably, the sample (Er₂MoO₆-T2) synthesized at 200 °C demonstrated significant improvements in crystallinity, morphology, and surface properties. The Er₂MoO₆-T2 modified glassy carbon electrode achieved an ultra-low detection limit of 0.00146 μM, a wide linear range (0.0025–2125.5 μM), and outstanding selectivity, reproducibility, and long-term stability. Furthermore, the temperature-optimized Er₂MoO₆ nanoparticles enabled high recovery rates of 98.6 % in complex matrices such as human urine, blood, lake water, and pond water, confirming the sensor's reliability for real-world applications. This study highlights the potential of Er₂MoO₆-based electrodes as high-performance electrochemical platforms for antibiotic monitoring.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"78 ","pages":"Article 108757"},"PeriodicalIF":6.7,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Highly efficient (Au)(III) recovery from acid wastewater by thiosemicarbazide modified Zr-MOF","authors":"Guowen He ,&nbsp;Dan Wu ,&nbsp;Dechong Ma ,&nbsp;Jiaqi Bu ,&nbsp;Zecheng Cheng","doi":"10.1016/j.jwpe.2025.108743","DOIUrl":"10.1016/j.jwpe.2025.108743","url":null,"abstract":"<div><div>The design and development of new precious metal recovery adsorbents have significant socio-economic benefits; this study successfully synthesized a high-performance gold ion adsorbent, UiO-66-TMB, by modifying UiO-66-NH₂ with thiosemicarbazide, a common industrial chemical rich in amino and thiol groups. The obtained material exhibits excellent stability and adsorption performance, with the maximum adsorption capacity of 729.15 mg·g⁻¹ at pH = 2. The adsorbent has good recyclability and can maintain superior selectivity in environments containing multiple impurity ions. In addition, even under strong acidic conditions with low initial gold ion concentration, a saturated adsorption capacity of 705.6 mg·g⁻¹ can be maintained. Mechanism studies have shown that there is a synergistic effect between electrostatic attraction, coordination binding, and redox reactions between Au and functional groups containing N and S elements on the surface of materials. The functionalization strategy proposed in this work opens new research directions for developing efficient and stable precious metal adsorption materials, offering a potential solution for sustainable resource recovery from industrial wastewater and electronic waste.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"78 ","pages":"Article 108743"},"PeriodicalIF":6.7,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thiophosphoryltriamide encapsulated into magnetic MCM-41-NH2 as a novel magnetically recoverable mesoporous adsorbent for Hg2+ removal in wastewater: Crystal structure and molecular calculations","authors":"Hadis Mohammadpour ,&nbsp;Niloufar Dorosti ,&nbsp;Harald Krautscheid ,&nbsp;Rahime Eshaghi Malekshah","doi":"10.1016/j.jwpe.2025.108722","DOIUrl":"10.1016/j.jwpe.2025.108722","url":null,"abstract":"<div><div>Water contaminants using heavy metal ions particularly Hg²⁺, due to harmful effects on human health and aquatic ecosystem, are challenging global environmental impact. Hence, the objective of this work is the synthesis of a new absorbent thiophosphoric triamide (PS) decorated on magnetic amine-functionalized mesoporous silica (MCM-41-NH₂) for Hg²⁺ removal. First, HgCl₂[P(S)(C₆H₁₁NH)₃]₂ (C) was synthesized by reaction of mercuric chloride and the thiophosphoric triamide ligand (PS). A distorted tetrahedral geometry surrounding the Hg²⁺ ion was revealed for the obtained complex. Nano-cubic structures of complex and its corresponding ligand (<strong>PS</strong>´ and <strong>Ć</strong>) were prepared with size between 60 and 80 nm at chloroform solvent as well. According to the strong interaction of PS to coordination with mercury (II), a newly thiophosphoryl functionalized magnetic mesoporous silica adsorbent, named Fe₃O₄@MCM-41-NH₂/PS, was synthesized with narrow pore size distribution, high specific surface area, and total pore volume, respectively, 7.726 nm, 93.353 m²/g, and 0.137 cm³/g. Initial concentration of Hg²⁺, adsorbent dosage, pH, temperature, and interfering ions were studied to eliminate Hg²⁺ from aqueous solution. Moreover, the material exhibited excellent recyclability owing to its magnetic properties, facilitating easy separation and reuse. The adsorption behavior of Hg²⁺ onto Fe₃O₄@MCM-41-NH₂/PS was best described by the Langmuir isotherm and pseudo-second-order kinetic models and the maximum adsorption capacity was determined to be 161.29 mg g⁻¹ at pH 8 and the temperature of 25 °C. Further, Monte Carlo simulations exhibited the decisive role of S, O, and NH groups for the elevated adsorption of mercury ions on Fe₃O₄@MCM-41-NH₂/PS in the presence of water.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"78 ","pages":"Article 108722"},"PeriodicalIF":6.7,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transforming electrolytic manganese residue into Mn₃O₄ via acid activation: Structural evolution and leaching kinetics","authors":"Yaoyu Yan ,&nbsp;Shuchen Sun ,&nbsp;Jing Wei ,&nbsp;A. Shubo ,&nbsp;Faxin Xiao ,&nbsp;Ganfeng Tu","doi":"10.1016/j.jwpe.2025.108768","DOIUrl":"10.1016/j.jwpe.2025.108768","url":null,"abstract":"<div><div>Electrolytic manganese residue (EMR), a hazardous solid waste from electrolytic manganese metal (EMM) production, poses serious environmental risks due to its complex mineralogy and heavy metal mobility. However, it also holds potential as a secondary resource. We establish an end-to-end waste-to-materials flowsheet—sulfuric-acid curing → water leaching → impurity removal → one-step conversion—that selectively recovers Mn from EMR and upgrades it to phase-pure, high-value Mn₃O₄ nanomaterials. The effects of curing temperature and acid dosage on the leaching behaviors of Mn, Fe, Al, and Si were systematically investigated. At 240 °C and an acid dosage of 2.5 times the stoichiometric requirement, Mn leaching efficiency reached 95.35 %, while Si leaching remained below 50 % due to silicate encapsulation and gelation. Kinetic modeling using the shrinking core model revealed that Mn dissolution was primarily controlled by product-layer diffusion, with an apparent activation energy of 14–18 kJ·mol⁻¹. FTIR, XRD, SEM–EDS, and BET analyses showed that acid curing disrupted the dense silicate matrix and increased surface area from 9.4 to 55.6 m²·g⁻¹. Mn²⁺ in the purified leachate was directly precipitated and oxidized using an NH₃·H₂O–H₂O₂–EDTA system, producing uniformly sized Mn₃O₄ nanoparticles. Rather than a stand-alone synthesis, the impurity-tolerant process with defined operating windows is the core contribution, with the Mn₃O₄ product validating this waste-to-value pathway. This integrated route offers a scalable framework for hazardous-waste valorization while clarifying sulfuric-acid-curing transformation and leaching kinetics, advancing sustainable metal recovery.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"78 ","pages":"Article 108768"},"PeriodicalIF":6.7,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sustainable wastewater treatment via nitrogen/sulfur co-doped graphene composite: Mechanistic insights & process optimization","authors":"Humaira Seema ,&nbsp;Muhammad Arshad ,&nbsp;Arslan Maqbool ,&nbsp;Sumbal Zeb ,&nbsp;Ali Hamid ,&nbsp;Muhammad Umar ,&nbsp;Sajjad Hussain ,&nbsp;Hammad Khan","doi":"10.1016/j.jwpe.2025.108701","DOIUrl":"10.1016/j.jwpe.2025.108701","url":null,"abstract":"<div><div>The persistent discharge of dye-laden industrial effluents poses environmental and health risks due to the toxic, mutagenic, and carcinogenic nature of synthetic dyes. This study reports the synthesis of a nitrogen/sulfur co-doped three-dimensional graphene composite (NSGH) via a simple hydrothermal method for crystal violet (CV) dye removal from aqueous media. Characterization techniques (SEM, XRD, FTIR, EDX) confirmed successful heteroatom incorporation and a porous 3D structure with abundant active sites. Batch adsorption experiments were systematically designed using a Box–Behnken design to investigate the effects of pH, contact time, initial dye concentration, and adsorbent dosage on CV adsorption onto NSGH. Adsorption performance was evaluated using three key metrics: removal efficiency (RR), adsorption capacity (q), and effective adsorption capacity (EAC), a dimensionless parameter integrating both removal rate and capacity. Parametric modeling via response surface methodology (RSM) and artificial neural networks (ANN) revealed ANN's superior predictive accuracy (R² = 0.993) over RSM (R² = 0.975). Multi-objective optimization using the desirability function identified optimal conditions (pH: 7.0, 33.9 min, 39.9 mg L^-1, 0.019 g), achieving 90.84 % RR, 31.35 mg g⁻¹ q, and 1.08 EAC. Sensitivity analysis indicated initial dye concentration as the most influential variable across all metrics. Kinetic data were best fitted by the pseudo-first-order model, supporting diffusion-controlled physisorption, while statistical physics and thermodynamic analyses confirmed multilayer, spontaneous, and endothermic adsorption. DFT simulations reinforced experimental outcomes, showing strong π–π and electrostatic interactions on NSGH (−18.82 kcal mol⁻¹). Although a gradual decline in performance was observed over five adsorption–desorption cycles, NSGH demonstrated appreciable reusability, reinforcing its applicability as a high-efficiency adsorbent for dye-laden wastewater. By integrating dual doping, process modeling, and systematic evaluation, this work offers a practical framework for guiding the development of future water treatment materials.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"78 ","pages":"Article 108701"},"PeriodicalIF":6.7,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}