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Changes in water quality along the process of coal chemical reverse osmosis wastewater advanced treatment: Spectral analysis and molecular level exploration

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SEPARATION AND PURIFICATION TECHNOLOGY 2025年 360卷

作者： Yuan, Run Qin, Yihe Wang, Hao he, xuwen Dongguan Univ Technol Sch Environm & Civil Engn Dongguan 523808 Peoples R China CNPC Res Inst Safety & Environm Technol Beijing 102206 Peoples R China China Univ Min & Technol Sch Chem & Environm Engn Beijing 100083 Peoples R China

Investigating changes in the organic pollutant composition of coal chemical wastewater during the advanced treatment process is crucial for enhancing the efficiency of wastewater treatment systems. In this study, the molecular composition and changes in dissolved organic matter (DOM) were analyzed in a typical coal gasification reverse osmosis concentrate during various advanced treatment processes, including flocculation, ultrafiltration, adsorption, and catalytic ozonation, were investigated using conventional water quality analysis, ultraviolet spectroscopy, fluorescence spectroscopy, molecular weight fractionation analysis, organic component fractionation methods, and Fourier-transform ion cyclotron resonance mass spectrometry. After the advanced treatment process, the concentrations of organic pollutants in the effluent decreased significantly, and the final treated effluent COD was reduced to 90 mg/L. Analysis of the molecular weight fractions showed that after treatment, the proportion of low-molecular-weight organic pollutants (<3k Da) in the coal chemical reverse osmosis concentrate exceeded 50 %. The mass-to-charge ratio of organics in wastewater ranged between 100 and 800, most DOM molecules were unsaturated reduced-state recalcitrant organic molecules, and the DOM molecules in wastewater were mainly distributed among lignin-like, condensed aromatic, and protein-like organics. Compared to the other advanced treatment processes, catalytic ozonation efficiently removed refractory ligninlike organics from wastewater. We revealed the characteristics of organic pollutants in coal chemical reverse osmosis concentrates during the advanced treatment process using various characterization methods, which can assist in further exploring the degradation mechanisms of organics in actual wastewater to enhance the efficiency of wastewater treatment units.

关键词： Water quality analysis Coal chemical wastewater Advanced treatment technology Dissolved organic pollutants

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Prediction model for coal chemical wastewater quality after catalytic ozonation process treatment based on deep learning algorithm: Performance evaluation and model comparisons

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2025年 197卷

作者： Qin, Yihe Yuan, Run Zhang, Xuewei he, xuwen Dongguan Univ Technol Sch Environm & Civil Engn Dongguan 523808 Peoples R China Tianjin Univ Sch Earth Syst Sci Tianjin 300072 Peoples R China China Univ Min & Technol Sch Chem & Environm Engn Beijing 100083 Peoples R China

In this study, we tested different models for predicting the effluent water quality of coal chemical wastewater treated by the catalytic ozonation process. The optimal model was further modified using the norm feature vector selection methods and intelligent algorithms. The average removal amount and average removal rate of COD in coal chemical wastewater treated by catalytic ozonation process were 223.62 mg/L and 57.98 %, respectively. AAO-pH, AAO-ammonia nitrogen, AAO-total phosphorus, AAO-COD, AAO-total phenol, SST-COD, SST-total phenol, and SST-turbidity were inputs for each model, while the catalytic ozonation-COD was used as the output for each model. We collected 500 samples of one year for the training, validation, and testing of various models. Compared to other models, the RF model had better predictive performance. Additionally, L1 method had a stronger optimization ability for the RF model than L2 method. Compared with BOA and SSA, IGWO had a larger contribution to improving the predictive ability. The L1-RF-IGWO model showed the best predictive ability (test set R2 of 0.8717). The present results can aid coal chemical plants achieve data-driven management of water quality prediction and monitoring, providing ideas for the construction of models for predicting water quality in engineering.

关键词： Predictive model Water quality analysis Coal chemical wastewater Catalytic ozonation

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Catalytic ozonation process for treating coking biochemical wastewater based on an Fe-Mn/Al2O3 catalyst

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COLLOIDS AND SURFACES A-PHYSICOCheMICAL AND ENGINEERING ASPECTS 2025年 713卷

作者： Qin, Yihe Yuan, Run Luo, Shaojun Huang, Haiming he, xuwen Dongguan Univ Technol Sch Environm & Civil Engn Dongguan 523808 Peoples R China Tianjin Univ Sch Earth Syst Sci Tianjin 300072 Peoples R China China Univ Min & Technol Sch Chem & Environm Engn Beijing 100083 Peoples R China

Catalysts with high catalytic performance are crucial for the catalytic ozonation of organic wastewater. This study synthesized an efficient, stable, and practical Fe-Mn/Al2O3 ozone catalyst for wastewater treatment using an impregnation calcination method, and applied it in the catalytic ozonation of wastewater. When the concentration of manganese nitrate was 0.15 mol/L, the Mn/Fe ratio was 3:1, the immersion time was 24 h, the calcination temperature was 500 degrees C, and the calcination time was 4 h, the prepared Fe-Mn/Al2O3 had the best catalytic effect on simulated wastewater, with a chemical oxygen demand (COD) removal efficiency of 73.6 %. At the same time, the physicochemical properties of the catalyst were investigated, and the catalytic effect of the catalyst on actual coking wastewater was investigated on a laboratory scale. When the ozone concentration was 20 mg/L, the gas flow rate was 0.5 L/min, the catalyst concentration was 400 g/L, and the pH was 7.02, the actual COD removal efficiency of coking wastewater was 71.6 %. The pilot test results showed that the COD of the treated coking wastewater could stably meet the standard (COD<80 mg/L), with an average COD removal rate of about 58.3 %, indicating that the Fe-Mn/Al2O3 had high catalytic activity and good stability. Kinetic analysis showed that the degradation process of organic pollutants followed the pseudo-second-order kinetic equation. The results of water quality analysis such as conventional water quality indicators, fluorescence spectroscopy, and molecular weight classification indicated that the large molecular organic pollutants were efficiently degraded. Mechanism analysis indicated that the active components of the catalyst reacted with ozone through electron transfer to enhance the ozone utilization efficiency and efficiently remove organic pollutants. This study can provide theoretical support and technical reference for the standardized discharge of coking wastewater in the future.

关键词： Catalytic ozonation Coking wastewater Mechanism exploration Water quality analysis

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Binding characteristics of Pb and Zn to low-temperature feces-based biochar-derived DOM revealed by EEM-PARAFAC combined with general and moving-window two-dimensional correlation analyses

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第10期30卷 27525-27538页

作者： he, Changjun he, xuwen Yuan, Run Li, Na Jiang, Jinyuan Chinese Res Inst Environm Sci Res Ctr Environm Pollut Control Technol Beijing 100012 Peoples R China China Univ Min & Technol Beijing Sch Chem & Environm Engn Beijing 100083 Peoples R China

Pyrolysis carbonization of human feces has shown potential for converting feces biomass into a soil amendment. However, little is known about the interactions of DOM derived from feces-based biochar produced at low-temperature with heavy metals (HMs). In this study, the binding properties of Pb(II) and Zn(II) with DOM derived from feces-based biochar produced at low pyrolysis temperatures were investigated using EEM-PARAFAC combined with general, and moving-window two-dimensional correlation analyses (2D-COS). The results revealed that DOM from biochar produced at 280 degrees C exhibited a higher Pb(II) and Zn(II) affinity and more binding sites than DOM produced at 380 degrees C. The fulvic-like and humic-like components exhibited obvious fluorescence quenching after the heavy metal addition, and the complexes formed with Pb(II) and Zn(II) were more stable. C-H groups exhibited the fastest response to Pb(II) and Zn(II) binding in the FB280 DOM, while the COO- groups of carboxylic acids in the FB380 DOM exhibited the fastest response to Pb(II) and Zn(II). Moreover, the mutation concentration range of components and functional groups in DOM, as analyzed by MW2D-COS, was greater for Zn(II) than for Pb(II). These results provide a more detailed molecular-level understanding of the interaction mechanisms between heavy metals and feces-based biochar-derived DOM and the effect of HM concentration on DOM binding. Further, these results will help to provide a reasonable reference for feces management and feces-based biochar in controlling soil HMs.

关键词： Human feces Biochar Dissolved organic matter heavy metals binding Moving-window analysis

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Temporal assembly patterns of microbial communities in three parallel bioreactors treating low-concentration coking wastewater with differing carbon source concentrations

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Journal of Environmental Sciences 2024年第3期137卷 455-468页

作者： Weijia Li Yu Xia Na Li Jie Chang Jing Liu Pei Wang xuwen he School of Chemical and Environmental Engineering China University of Mining and Technology-BeijingBeijing 100083China

Carbon source is an important factor of biological treatment systems, the effects of which on their temporal community assembly patterns are not sufficiently understood *** this study, the temporal dynamics and driving mechanisms of the communities in three parallel bioreactors for low-concentration coking wastewater(CWW) treatment with differing carbon source concentrations(S0 with no glucose addition, S1 with 200 mg/L glucose addition and S2 with 400 mg/L glucose addition) were comprehensively studied. Highthroughput sequencing and bioinformatics analyses including network analysis and Infer Community Assembly Mechanisms by Phylogenetic bin-based null model(iCAMP) were used. The communities of three systems showed turnover rates of 0.0029~0.0034 every 15days. Network analysis results showed that the S0 network showed higher positive correlation proportion(71.43%) and clustering coefficient(0.33), suggesting that carbon source shortage in S0 promoted interactions and cooperation of microbes. The neutral community model analysis showed that the immigration rate increased from 0.5247 in S0 to 0.6478in S2. The iCAMP analysis results showed that drift(45.89%) and homogeneous selection(31.68%) dominated in driving the assembly of all the investigated microbial *** contribution of homogeneous selection increased with the increase of carbon source concentrations, from 27.92% in S0 to 36.08% in S2. The OTUs participating in aerobic respiration and tricarboxylic acid(TCA) cycle were abundant among the bins mainly affected by deterministic processes, while those related to the metabolism of refractory organic pollutants in CWW such as alkanes, benzenes and phenols were abundant in the bins dominated by stochastic processes.

关键词： Coking wastewater treatment Temporal dynamics Network analysis Deterministic processes Stochastic processes

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A low-temperature process for lithium extraction from lithium-bearing mineral: Conversion behavior and mechanisms

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SEPARATION AND PURIFICATION TECHNOLOGY 2025年 357卷

作者： Luo, Shaojun Zheng, Xiaohong Liu, Haichao Zhang, Qing he, Mingming Wang, Yuehai he, xuwen Sun, Zhi China Univ Min & Technol Beijing Sch Chem & Environm Engn Beijing 100083 Peoples R China Chinese Acad Sci Inst Proc Engn Natl Engn Res Ctr Green Recycling Strateg Met Res Beijing 100190 Peoples R China China Univ Geosci Beijing Sch Mat Sci & Technol Beijing Key Lab Mat Utilizat Nonmet Minerals & Sol Natl Lab Mineral Mat Beijing 100083 Peoples R China Shanghai Polytech Univ 2360 Jinhai Rd Shanghai 201209 Peoples R China

Lithium extraction from lithium-bearing clay mineral (LiBC) has received significant attention due to the increasing demand of lithium for the advanced renewable energy industry. However, the low contents of valuable elements and stable physiochemical properties of LiBC result in high energy consumption and low efficiency in extracting lithium from LiBC. This study demonstrates a low-temperature strategy using (NH4)2SO4 as an additive to effectively extract lithium from LiBC. In details, the thermodynamic analysis verifies that Li, Al, and Fe can be converted to sulfates at 250-450 degrees C during thermal treatment, indicating the possibility for extraction of Li under low-temperature conditions. Subsequently, the leaching behavior of lithium under different roasting temperatures and water leaching conditions was investigated. The results show that 83.43 % of Li can be extracted under optimized conditions. The mechanism study was carried out through a number of characterization techniques including 3D microscope, TG-DSC, XRD, FTIR, and SEM. It reveals that formation of liquidus phase of molten salt is the key to achieve low-temperature roasting and transfer and reactions at the interface of molten salt and LiBC can be significantly enhanced that can easily breakdown the interlayer structures. Consequently, it offers distinct advantages, since the roasting temperature can be significantly decreased with minor impurity ions being leached into the solutions. This research therefore provides an efficient and environmentally friendly method for lithium extraction from minerals.

关键词： Lithium-bearing mineral Molten salt roasting Solid-liquid reaction Lithium Extraction Ammonium sulfate

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Pilot-Scale Application of Red Mud-Based Slag Pellets in Steelmaking: Process Optimization, Mechanism, and Life Cycle Assessment

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ACS OMEGA 2025年第16期10卷 16762-16776页

作者： Qin, Yihe Yuan, Run Li, Han he, xuwen Chen, Jingjing Chen, Xingtong Huang, Suran Huang, Haiming Dongguan Univ Technol Sch Environm & Civil Engn Dongguan 523808 Peoples R China China Univ Min & Technol Sch Chem & Environm Engn Beijing Peoples R China

The resource utilization of red mud (RM) is crucial for the aluminum industry and for addressing environmental concerns. In this study, RM-based slag pellets were prepared through a cold pressing process and used for converter steelmaking. In the pellet preparation experiment, the effects of binder starch and carboxymethyl cellulose sodium (CMC) dosage, moisture content, drying temperature, particle size, and forming pressure on the strength of RM-based pellets were studied. The formation mechanism of RM particles was determined by analyzing the changes in the crystal structure, functional groups, elemental valence state information, and density functional theory calculations. Finally, the environmental benefits of the pellet production process were analyzed. Under the optimal reaction conditions, the compressive strength of RM pellets prepared based on starch and CMC can reach 1162.06 and 1345.6 N/P, which can meet the production needs of the converter. Mechanism analysis shows that organic adhesives adsorb and solidify particles through chemical or hydrogen bonding, resulting in a denser particle structure. RM particles have an excellent slag and phosphorus removal performance. The results of the life cycle assessment clarify the potential contributions of each stage of particle production. The raw materials and transportation distance have significant impacts on the particle production system. The experimental results provide theoretical guidance and a sustainable evaluation for the production of RM slag aggregates.

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Influence of ballasted material properties in enhancing the separation of high concentration suspended solids in coal mining water

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2022年第0期157卷 458-465页

作者： Zhang, Xiaohang Liu, Yanping Wei, Ming Tan, Jinlong Yang, Tingchao he, xuwen China Univ Min & Technol Beijing Sch Chem & Environm Engn Beijing 100083 Peoples R China Handan Municipal Publ Util Investment Grp Co Ltd Dept Engn & Technol Handan 056000 Hebei Peoples R China Hebei Vocat Coll Polit & Law Party Comm Shijiazhuang 050061 Hebei Peoples R China Hebei Univ Engn Sch Min & Geomat Engn Handan 056038 Hebei Peoples R China Hebei Prov Coal Geol Bureanu Business Dev Sect Hydrol Geol Team Handan 056001 Hebei Peoples R China

With the continuous improvement and economic development of China's coal mining industry, research on the characteristics of wastewater discharge has gained attraction in recent years. Ballasted flocculation technology, has been widely used in recent years. However, its effect on the settling velocity of the flocs in wastewater has not been quantified. This study aimed to screen optimal reaction condition for ballasted flocculation and to derive the settling velocity of the ballasted flocs under these conditions. The results showed that, the size of the ballasted material had no significant effect on the turbidity and suspended solids removal rate;however, the settling time was effectively shortened, and the turbidity removal rate could reach more than 95% within 15 s. Owing to the optimization of the reaction conditions, 90% of the flocs sizes were distributed in the 80-200 mu m range. Moreover, the difference between the theoretical and the measured value of the ballasted flocculation was small, indicating that the derived formula could effectively quantify the settling velocity of the ballasted flocs under optimal conditions. These findings may serve as a critical reference for the treatment of high-suspended solids in coal mining water and have significant potential for engineering application. (c) 2021 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Ballasted flocculation Fractal dimension Settlement velocity Stress analysis Coal mining water

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IVDETR: A Multi-Source Image Pig Detection Method Based on Cross-Domain Feature Fusion 7

IVDETR: A Multi-Source Image Pig Detection Method Based on C...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Song, Rui Li, Hao Huang, Mengzhen Huang, Zihan Li, Peng he, xuwen Wuhan Polytechnic University School of Mathematics and Computer Science Wuhan China

ISBN: (纸本)9798350350890

In large-scale centralised pig farming, object detection algorithms can accurately track and monitor each pig. However, a significant proportion of current detection methods are based on visible images, and the colour differences between different pig breeds limit the applicability of the models. Furthermore, complex background interference, low illumination conditions, or high illumination intensity noise in visible scenes can likewise lead to reduced accuracy during detection. Therefore, we propose a cross-domain encoder based on the self-attention and design the IVDETR model, which integrates features from infrared and visible light images for pig object detection. In order to facilitate the training and evaluation of the model, we constructed a comprehensive, standardised infrared-visible light dataset for the first time. The experimental outcomes demonstrated that the detection accuracy was increased from 0.939 to 0.998 in a real pig breeding environment. © 2024 IEEE.

关键词： Mammals

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Leather Defect Detection Algorithm Based on an Improved YOLOv5s 7

Leather Defect Detection Algorithm Based on an Improved YOLO...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： he, xuwen Li, Hao Liu, Yifan Song, Rui Zhao, Yuchen Ou, Xiao School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan China

ISBN: (纸本)9798350350890

In the field of intelligent manufacturing, the realization of efficient and accurate automated surface defect detection is particularly critical for enhancing product quality, especially in the leather industry, which holds a significant position in global trade. Traditional defect detection methods encounter challenges in handling complex scenes and identifying minute defects. However, the advent of deep learning technologies has provided a new pathway to address these issues. This paper is dedicated to innovative research on the precise localization and detailed classification of leather surface defects through the enhancement of deep learning models, aiming to overcome the limitations of existing technologies and improve the accuracy and efficiency of detection. By extending the YOLOv5s model, this study has developed the CMR-YOLO algorithm. This algorithm integrates the Channel Attention mechanism (CA), an improved Multi-Dimensional Precision Intersection over Union (MPDIoU), and the Cross Stage Partial Residual feature enhancement module (C3R). These enhancements not only improve the classification accuracy but also bolster the model's stability and robustness in complex scenes. Through the comprehensive application of these techniques, the CMR-YOLO algorithm demonstrates improved performance in categorizing different types of leather defects, particularly effective in handling diverse defect characteristics and accurately classifying various defects. Compared to the traditional YOLOv5s, the CMR-YOLO algorithm also exhibits improved accuracy in defect classification, with the mAP50 increasing from 62.5% to 79.5%, effectively enhancing the overall performance of leather defect detection. These research outcomes not only achieve technological breakthroughs but also provide significant technical support for quality control and intelligent manufacturing in the leather goods industry. © 2024 IEEE.

关键词： Leather

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