Journal of water process engineering最新文献

{"title":"Nonionic surfactant-mediated polyester loose nanofiltration membranes for efficient dye desalination","authors":"Xiangxiang Zhang, Yalong Liu, Shaoqi Zhou, Ke Zheng","doi":"10.1016/j.jwpe.2026.110783","DOIUrl":"https://doi.org/10.1016/j.jwpe.2026.110783","url":null,"abstract":"","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"92 1","pages":"110783-110783"},"PeriodicalIF":0.0,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148861176","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":"Fe-Co/sodalite derived from coal gangue via in-situ Fe utilization for synergistic peroxymonosulfate activation: Degradation of sulfamethoxazole, pathway elucidation, and toxicity attenuation","authors":"Yiwen Li, Huangsiyu Zhao, Jiang Yu, Pengxinyue Huang, Ming Chen, Yinying Jiang, Jingyi Xiong, Jinchao Huang, Hongbin Jiang","doi":"10.1016/j.jwpe.2026.110728","DOIUrl":"https://doi.org/10.1016/j.jwpe.2026.110728","url":null,"abstract":"","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"92 1","pages":"110728-110728"},"PeriodicalIF":0.0,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148861173","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":"Two-stage synergistic flocculation based on chitosan/sodium hyaluronate polyelectrolyte complexes for rapid underwater visual clarification in turbid environments","authors":"Jiawei Fu, Junjie Li, Hang Xu","doi":"10.1016/j.jwpe.2026.110697","DOIUrl":"https://doi.org/10.1016/j.jwpe.2026.110697","url":null,"abstract":"","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"92 1","pages":"110697-110697"},"PeriodicalIF":0.0,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148861172","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":"Corrigendum to “Calcium silicate hydrate from simulated alkali-activated industrial waste residue: Adsorption and immobilization mechanism of Pb2+ and Mn2+” [J. Water Process Eng. 87 (2026) 110126]","authors":"Wenzhuo Lu , Qingyuan Liu , Huiyi Huang , Junzhang Ling , Ouxu Pan , Rongmiao Gan , Wanting Liu , Xinru Yang , Tengfa Long","doi":"10.1016/j.jwpe.2026.110227","DOIUrl":"10.1016/j.jwpe.2026.110227","url":null,"abstract":"","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"88 ","pages":"Article 110227"},"PeriodicalIF":6.7,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178869","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":"Corrigendum to “Advancements in piezo-photocatalysts for sustainable hydrogen generation and pollutant degradation: A comprehensive overview of piezo-photocatalysis” [J. Water Process Eng. 71 (2025) 107172]","authors":"Daniel Masekela , Sheriff A. Balogun , Tunde L. Yusuf , Seshibe Makgato , Kwena D. Modibane","doi":"10.1016/j.jwpe.2026.109926","DOIUrl":"10.1016/j.jwpe.2026.109926","url":null,"abstract":"","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"85 ","pages":"Article 109926"},"PeriodicalIF":6.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147599928","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":"JWPE perspectives for 2026 and for AI in publishing","authors":"Alicia An , Angela Zhang , Ludovic Dumée","doi":"10.1016/j.jwpe.2026.109816","DOIUrl":"10.1016/j.jwpe.2026.109816","url":null,"abstract":"","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"85 ","pages":"Article 109816"},"PeriodicalIF":6.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147599927","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":"Material differentiation: Unique applications of cobalt(II,III) oxide@manganese dioxide complex on titanium matrix in electrochemical chlorine deposition and ammonia nitrogen degradation","authors":"Ting Peng ,&nbsp;KeXuan Wu ,&nbsp;Jing Cao","doi":"10.1016/j.jwpe.2026.109563","DOIUrl":"10.1016/j.jwpe.2026.109563","url":null,"abstract":"<div><div>In the field of electrochemistry, while Co₃O₄@MnO₂ composite materials are no longer novel, research on their application in chlorine evolution reaction (CER) and ammonia nitrogen degradation remains scarce. This study employs a two-step hydrothermal synthesis to design a Co₃O₄@MnO₂/Ti catalyst featuring a unique nano-flower structure. This structure significantly increases electrochemical active sites and enhances charge transfer, thereby driving a remarkable improvement in CER performance. During secondary hydrothermal treatment and calcination, electrons transfer from Co²⁺ to Mn⁴⁺ (Co²⁺ + Mn⁴⁺ → Co³⁺ + Mn³⁺). Co₃O₄ incorporation promotes oxygen vacancy formation, and the synergistic interaction between Co³⁺ and Mn³⁺ dual active sites modulates the local electronic structure, effectively suppressing competitive OER and significantly improving CER selectivity. With a specific surface area of 109.9 m²/g and a charge transfer resistance reduced to 1.47 Ω, Co₃O₄@MnO₂ catalyst not only achieves an impressive current efficiency of 92.5% in neutral 0.6 M sodium chloride solution—over 200% higher than conventional MnO₂ (current efficiency of 28.3%) — but also demonstrates a 30% enhancement in ammonia nitrogen degradation efficiency. Furthermore, it also exhibits superior performance compared to Co₃O₄. These significant innovations and distinctive features provide valuable guidance for optimizing composite material designs.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"83 ","pages":"Article 109563"},"PeriodicalIF":6.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080802","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":"A parsimonious hybrid model: Integrating wavelet neural networks and deep learning for water quality forecasting in Southern Iran","authors":"Mehri Saeidinia ,&nbsp;Laleh Divband Hafshejani ,&nbsp;Mohsen Shahsavar","doi":"10.1016/j.jwpe.2026.109478","DOIUrl":"10.1016/j.jwpe.2026.109478","url":null,"abstract":"<div><div>Drip irrigation in arid and semi-arid regions is frequently compromised by emitter clogging from calcium carbonate scaling, traditionally assessed via the Langelier Saturation Index (LSI) using laboratory-intensive measurements of calcium hardness and alkalinity that preclude real-time monitoring. This study develops a field-deployable, real-time LSI prediction framework using only three low-cost, continuously measurable sensor inputs: pH, temperature, and electrical conductivity (EC). A 25-year (1991–2015) hydrological dataset comprising 4,633 samples from Khuzestan Province, Iran, was used to train and compare seven optimized models—classical machine learning (GA-tuned SVR, RF, XGBoost), deep learning sequence models (Bayesian-tuned CNN, LSTM, GRU), and a novel hybrid Wavelet-Artificial Neural Network (WANN). Models were evaluated across seven input combinations, with performance assessed via RMSE, MAE, NSE, R², distributional tests (Kolmogorov–Smirnov), rank correlations (Kendall's τ), bootstrapped 99% confidence intervals, and feature importance analysis. The full three-input scenario (EC + pH + Temp) yielded the highest accuracy, with GA-XGBoost (NSE = 0.844, RMSE = 0.132) and Bayesian-WANN (NSE = 0.838, RMSE = 0.134) outperforming deep learning models. Random Forest-based feature importance revealed pH as the dominant driver (58.8%), followed by EC (29.6%) and temperature (11.5%), explaining the modest NSE gain from including temperature. Bootstrap hypothesis testing confirmed statistical equivalence among top performers (GA-XGBoost, GA-SVR, GA-RF, B-WANN). The EC + pH pairing proved a robust alternative (NSE ≈ 0.79–0.80) when temperature data are unavailable. By enabling proactive, sensor-driven scaling risk assessment on lightweight edge devices, this framework overcomes limitations of conventional equilibrium-based indices, offering a practical tool for clogging prevention and sustainable water management in resource-constrained agriculture.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"83 ","pages":"Article 109478"},"PeriodicalIF":6.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080896","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":"Enterprise-oriented optimization of carbon accounting methods for wastewater treatment plants: Comparative analysis, modeling, and application in China","authors":"Xiangyu Zhang,&nbsp;Baoyi Tian,&nbsp;Guanmin Li,&nbsp;Rongguang Li,&nbsp;Kai Ma,&nbsp;Xinfei Li,&nbsp;Shichao Jia,&nbsp;Changchun Xin","doi":"10.1016/j.jwpe.2026.109598","DOIUrl":"10.1016/j.jwpe.2026.109598","url":null,"abstract":"<div><div>This study examines challenges in carbon emission accounting for wastewater treatment enterprises, particularly the diverse guidelines and difficulty in selecting appropriate methods. A comparative analysis of six mainstream methodologies, focusing on accounting boundaries and emission factors, was conducted. The case calculations for three wastewater treatment plants in northern China indicate significant discrepancies in carbon emission estimates between different methods, with a range of 61% and a standard deviation of 17.35%. An improved accounting method was proposed, integrating operational and extended responsibility emissions, localized emission factors, and simplified procedures. The carbon emission intensity ranged from 0.78 to 1.02 kg CO₂-eq/m³, with electricity consumption, sodium hypochlorite usage, and nitrous oxide emissions as major contributors. Sensitivity analysis showed strong correlations between carbon intensity and nitrogen removal. Each additional 1 mg/L of nitrogen removed increased carbon intensity by approximately 0.014 kg CO₂-eq/m³. A ridge regression model confirmed total nitrogen and biochemical oxygen demand removal as key drivers. Furthermore, a support vector regression model using influent quality parameters achieved robust prediction performance with coefficient of determination values of 0.759, supporting feedforward carbon management for small- and medium-sized plants. This study offers a closed-loop framework for carbon emission management and practical tools for wastewater sector decarbonization.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"83 ","pages":"Article 109598"},"PeriodicalIF":6.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080891","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":"Modeling haloketones in drinking water using conformable neural networks: a case study of Jinhua, China","authors":"J.I. Johnson ,&nbsp;A.I. Mata ,&nbsp;A. Parrales ,&nbsp;J.E. Solís-Pérez ,&nbsp;A. Huicochea ,&nbsp;J.A. Hernández","doi":"10.1016/j.jwpe.2026.109542","DOIUrl":"10.1016/j.jwpe.2026.109542","url":null,"abstract":"<div><div>The prediction of halogenated ketones in drinking water is relevant for public health surveillance and treatment control. Sixty-three samples from Jinhua, China, with routinely monitored physicochemical parameters were used, targeting three objectives: 1,1-dichloro-2-propanone (DCP), 1,1,1-trichloro-2-propanone (TCP), and total haloketones (HK). We compared two simple baselines—multiple linear regression and random forests—with an artificial neural network using radial basis function activation. The models were trained with a fixed training/validation/test split, minimum-maximum scaling to [0.1, 0.9], and evaluated with R, RMSE, and MAPE. A global sensitivity analysis identified the most influential inputs. The baselines established realistic performance limits (e.g., for DCP: R ≈ 0.77 and RMSE≈0.23 for linear regression; R ≈ 0.77 and RMSE≈0.28 for random forest). The conformable activation network improved agreement with observations for all targets: averaging <em>R</em> = 0.94, RMSE = 0.398. Sensitivity analysis was consistent with known factors in the process. The proposed activation design achieved strong gains over linear and tree-based baselines on a small dataset while remaining computationally light. We document the assumptions, data ranges, and limitations to support its reuse in routine monitoring.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"83 ","pages":"Article 109542"},"PeriodicalIF":6.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080974","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}