Journal of water process engineering最新文献

{"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}

{"title":"Green synthesis and advanced modeling of yttrium oxide nanoparticles for efficient antimony removal from contaminated water","authors":"Amine El Azizi ,&nbsp;Konouz Hamidallah ,&nbsp;Yassine El Miz ,&nbsp;Mohamed Elsenety ,&nbsp;Noureddine El Messaoudi ,&nbsp;Mouslim Messali ,&nbsp;Mohamed Loutou","doi":"10.1016/j.jwpe.2026.109646","DOIUrl":"10.1016/j.jwpe.2026.109646","url":null,"abstract":"<div><div>Yttrium oxide nanoparticles (Y₂O₃ NPs) were synthesized via a green route using <em>Pinus</em> leaf extract and evaluated for Sb³⁺ removal from aqueous solutions. Structural characterization confirmed the formation of highly crystalline nanoparticles with an average size of approximately 16.4 nm. Batch adsorption experiments demonstrated a high maximum adsorption capacity of 228.54 mg/g. Equilibrium data were best described by the Langmuir isotherm, indicating monolayer adsorption, whereas kinetic data followed a pseudo-first-order model, indicating rapid Sb³⁺ uptake. Thermodynamic analysis showed that the adsorption process is spontaneous and exothermic (ΔG° &lt; 0, ΔH° = −23.52 kJ/mol). Process optimization using Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) showed excellent agreement with experimental results, with ANN providing superior predictive accuracy (R² &gt; 0.99). Regeneration experiments demonstrated that the nanoparticles retained more than 62% of their adsorption capacity after eight reuse cycles, confirming their reusability. These results highlight the potential of green-synthesized Y₂O₃ nanoparticles as efficient, reusable adsorbents for removing Sb³⁺ from contaminated water.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"83 ","pages":"Article 109646"},"PeriodicalIF":6.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189780","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":"Preparation of environmentally friendly nano-CaCO3/PDMS-modified polyurethane sponge and its application in oil-water remediation","authors":"Shaolang Wang,&nbsp;Jia Luo,&nbsp;Kai Yang,&nbsp;Bo Yang,&nbsp;Guiju Li,&nbsp;Zhichao Zhang,&nbsp;Yong Jiang","doi":"10.1016/j.jwpe.2026.109633","DOIUrl":"10.1016/j.jwpe.2026.109633","url":null,"abstract":"<div><div>Efficient treatment of oilfield produced water is a critical challenge for promoting sustainable development in the petroleum industry. To address the high toxicity and environmental hazards associated with conventional fluorine-containing modifiers, this study designed an environmentally friendly nano-CaCO₃/PDMS-modified polyurethane sponge (CaCO₃/PDMS@PU) for oil-water separation. Silanization was achieved through condensation between the epoxy groups of the silane coupling agent KH560 and hydroxyl groups on nano-CaCO₃. Polydopamine self-polymerization formed an adhesive layer that uniformly loaded the modified nano-CaCO₃ onto the sponge framework. Subsequent PDMS cross-linking and curing constructed a low surface energy hydrophobic layer, resulting in a water contact angle of 136°. Adsorption experiments demonstrated a maximum adsorption capacity of 23.5 g/g for various organic solvents, with the adsorption process fitting pseudo-second-order kinetics and the Freundlich isotherm model. Continuous oil-water separation tests verified high efficiency and stability under dynamic conditions. The material maintained excellent adsorption performance in strong acidic (pH = 1) and alkaline (pH = 12) environments. After eighteen adsorption-desorption cycles, approximately 87% of the initial chloroform adsorption capacity was retained. This study provides a novel strategy for developing green and efficient oil-water separation materials with strong environmental adaptability and promising application potential.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"83 ","pages":"Article 109633"},"PeriodicalIF":6.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189783","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":"Granular stability mechanism and microbial shifts of the anammox process operated at 15 °C","authors":"Xiaoliang Fan ,&nbsp;Yunzhi Qian ,&nbsp;Xuanhui Lv ,&nbsp;Yonghao Zhu ,&nbsp;Jiayao Ding ,&nbsp;Chenshun Lu ,&nbsp;Shilong He","doi":"10.1016/j.jwpe.2026.109659","DOIUrl":"10.1016/j.jwpe.2026.109659","url":null,"abstract":"<div><div>The low temperatures in winter can inhibit the activity of anaerobic ammonium oxidation (anammox) bacteria (AnAOB) and restrict the application of the anammox process. This study investigated the effects of hydraulic retention time (HRT) and influent total nitrogen (TN) concentration on the nitrogen removal performance, granular stability mechanism and microbial shifts in anammox granular sludge (AnGS) system at 15 °C. An HRT of 0.96 h led to denitrifying bacteria (DNB) proliferation, limiting the nitrogen removal rate (NRR) to 2.1 g N/L/d. A TN concentration of 400 mg/L promoted AnAOB activity, increasing the NRR to 4.2 g N/L/d. During this transition, particle size with high AnAOB activity and proportion has shifted from &gt;2.0 mm to 1.0–1.5 mm. At 4.2 g N/L/d, hydroxyapatite (HAP) selectively formed in AnGS sized &gt;1.0 mm due to differences in activity. AnAOB released less proteins (PN) under short HRT and less polysaccharides (PS) under high TN concentration to maintain settling properties by adjusting the PN/PS ratio (R² = 0.96, <em>p</em> &lt; 0.05). Reduced HRT enriched <em>Ca. Kuenenia</em> in AnGS with a size range of 0.5–2.0 mm, constituting 20.2% to 30.2%. While, elevated TN concentrations increased the abundance of <em>unclassified_f_Brocadiaceae</em> in AnGS, ranging from 20.7% to 33.0%. Furthermore, metagenomic analysis indicated that AnGS sized &gt;2.0 mm exhibited abundant key functional genes of anammox. This study serves as a reference for achieving stable treatment of low-temperature wastewater through the anammox process.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"83 ","pages":"Article 109659"},"PeriodicalIF":6.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189786","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":"Design calculation method of the interception ratio for combined sewer overflow abatement in the context of overflow frequency regulation","authors":"Xingpo Liu ,&nbsp;Shijie An ,&nbsp;Liang Shen","doi":"10.1016/j.jwpe.2026.109599","DOIUrl":"10.1016/j.jwpe.2026.109599","url":null,"abstract":"<div><div>The interception ratio is the critical parameter for the design and rehabilitation of the interceptive combined sewer systems. Currently, there are four categories of methods for determining the interception ratio: economic investment optimization methods, population density correlation methods, hydraulic model simulation methods, and standard (or specification) recommendation methods. However, these methods rarely consider the control requirements of combined sewer overflow (CSO) at the discharge outfalls, such as overflow frequency regulation. In this context, three new design calculation methods are proposed and compared for different scenarios of overflow frequency regulation, including the threshold sorting method (Method I), the annual multi-event-maxima (AMEM) sampling method (Method II) and the low return period rainfall intensity formula method (Method III). For three methods, nine minimum interevent time (MIET) scenarios (10, 15, 20, 30, 45, 60, 90, 120, 180 min) for rainfall event division are considered in calculating the interception ratio. A case study is conducted based on the ten-year recorded rainfall series (from 1/1/2008 to 12/31/2017). Results reveal that: (1) The mean interception ratios of the three methods are 2.61, 4.95 and [0.69, 2.65], respectively. (2) The standard deviations of three methods are 0.05, 0.00 and [0.01, 0.13], respectively. (3) The result of method II can be used as the upper limit of interception ratio. (4) Method III is applicable to situations where only the low return period rainfall intensity formula can be obtained.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"84 ","pages":"Article 109599"},"PeriodicalIF":6.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387108","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}