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Effect of pre-coagulation on catalytic ozonation in the tertiary treatment of coking wastewater: Kinetic and ozone consumption analysis

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JOURNAL OF WATER PROCESS ENGINEERING 2022年 48卷

作者： Wang, Hao Zhang, Siyu he, Can Yuan, Run Wu, Xu Guo, Shaoqing he, xuwen Van Hulle, Stijn W. H. China Univ Min & Technol Beijing Sch Chem & Environm Engn Beijing 100083 Peoples R China Univ Ghent Dept Green Chem & Technol LIWET Campus KortrijkGraaf Karel De Goedelaan 5 B-8500 Kortrijk Belgium Katholieke Univ Leuven Dept Chem Engn PET Lab J De Nayerlaan 5 B-2860 St Katelijne Waver Belgium

The ozone-based advanced oxidation process in wastewater effluent degrades refractory organics efficiently and significantly. Nevertheless, high energy consumption is a challenge that hinders its widespread application. In this work, the pre-coagulation enhanced catalytic ozonation process for the treatment of biologically treated coking wastewater (BTCW) was investigated. The chemical oxygen demand (COD) removal results indicated that catalytic ozonation has an inadequate removal effect on the organic pollutants in BTCW. Moreover, the two-stage degradation occurred in the COD ozonation process, of which the slow reaction rate in the second stage was the main reason that limited its overall effect. The pre-coagulation overcame the obstacle of the limited effect of catalytic ozonation treatment of BTCW because of its ability to remove hydrophobic organics. The combination of pre-coagulation with catalytic ozonation could increase the COD removal efficiency from 45% to 69% compared with catalytic ozonation alone. There was a linear relationship between specific ozone consumption and ozone dosage regardless of whether BTCW was subjected to pre-coagulation. The fact that the slope of this linear relationship with pre-coagulation was much smaller than that without pre-coagulation. It indicates that pre-coagulation could significantly reduce ozone consumption. The primary role of pre-coagulation was to reduce ozone consumption considerably in the second stage of the ozonation process. The ozone consumption per unit mass of COD in the second stage reached 24.3 gO(3)/gCOD without pre-coagulation, while that was only 6.8 gO(3)/gCOD with pre-coagulation.

关键词： Coking wastewater Catalytic ozonation Pre-coagulation Kinetic model Consumption analysis

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Removal of viscous and clogging suspended solids in the wastewater from acrylonitrile-butadiene-styrene resin production by a new dissolved air release device

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2021年 148卷 524-535页

作者： Zhang, Xiaohang Zhang, Xuewei Liu, Yanping Zhang, Qing Yang, Shipeng he, xuwen Chinese Acad Sci Ctr Agr Resources Res Inst Genet & Dev Biol Key Lab Agr Water Resources 286 Huaizhong Rd Shijiazhuang 050021 Hebei Peoples R China China Univ Min & Technol Beijing Sch Chem & Environm Engn Xueyuan Rd Beijing 100083 Peoples R China Handan Municipal Publ Util Investment Grp Co Ltd Dept Engn & Technol 58 Guoyuan Rd Handan City 056000 Hebei Peoples R China Tsinghua Univ State Key Joint Lab Environm Simulat & Pollut Con 30 Shuangqing Rd Beijing 100084 Peoples R China

The suspended solids in the acrylonitrile-butadiene-styrene(ABS) wastewater are in the form of viscouslatex, which strengthens the blocking phenomenon of the release device, increases the suspended solids in the return water and reduces the removal effect of the air flotation apparatus. Therefore, we designed a new release device. To achieve the goal of controlling the bubbles in the target diameter and enlarge the hole size as much as possible to reduce blockage, we studied the relationship between the geometry structure and performance of the new, which could be describeed by fixed polynomials. The best demulsification effect could be obtained by the hybrid of the polyferric sulfate and FO4440SSH, and the highest removal rate was 92.51 %. After a long-running in the ABS sewage treatment plant of the petrochemical industry, the removal rate of the new could maintain at a high level(>90 %), and the removal effect was stable. Compared with other release devices that reported in the literatures, the new one had advantages significant in both removal efficiency and operation cost. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Acrylonitrile-butadiene-styrene wastewater Dissolved air flotation The volume of gas release The flow rate Demulsification

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Pilot-scale catalytic ozonation pretreatment for improving the biodegradability of fixed-bed coal gasification wastewater

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2021年 148卷 13-19页

作者： Chen, Xiaoya Wang, Chunrong Jiang, Longxin Li, Haiyan Wang, Jianbing he, xuwen China Univ Min & Technol Beijing Sch Chem & Environm Engn Beijing 100083 Peoples R China

Achieving zero liquid dischargeis a requirement for the development of the coal chemical industry;high pollutant concentration, strong biotoxicity, and poor biodegradability (BOD5/COD=0.1-0.2) make fixed-bed coal gasification wastewater difficult to treat. This study synthesized a Fe-Mn/lava catalyst (FMLC) and performed a pilot-scale test on catalytic ozonation for the pretreatment of fixed-bed coal gasification wastewater. The effects of ozone and catalyst dosages were investigated, and the changes in the organics, biotoxicity, and biodegradability of wastewater were analyzed using three-dimensional fluorescence spectroscopy, BOD5/COD, and specific oxygen uptake rate (SOUR). Energy consumption was also calculated. The removal efficiencies (at a flow rate of 1.0 m(3)/h, ozone dosage of 179 g/h, and catalyst dosage of 40 kg/m(3)) of COD and BOD5 were 61.77% and 16.98%, respectively, after 30 days of continuous operation. Toxic refractory macromolecular organic pollutants were transformed into small, biodegradable substances. BOD5/COD increased from 0.16 to 0.35, and SOUR decreased from 2.8460 to 2.3180 mgO(2)/(gMLSS.h). The operating cost was 1.24 kWh/kg COD. Catalytic ozonation as a pretreatment technology markedly improved biodegradability, reduced biotoxicity, and looks to be promising for treating fixed-bed coal gasification wastewater. (C) 2020 Published by Elsevier B.V. on behalf of Institution of Chemical Engineers.

关键词： Fixed-bed coal gasification wastewater Pilot-scale Catalytic ozonation Fe-Mn/lava catalyst Pretreatment Biodegradability

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煤矿矿井水资源化利用技术创新

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煤炭科学技术 2023年第1期51卷 523-530页

作者：何绪文王绍州张学伟李福勤中国矿业大学(北京)化学与环境工程学院北京100083 河北工程大学能源与环境工程学院河北邯郸056038

国家对煤矿矿井水资源化利用高度重视,排放标准的不断提高和智慧矿山概念的不断深化,给矿井水处理技术提出了新的挑战。通过对矿井水地面处理技术与井下多级过滤处理技术的分析和比较,在总结了现有技术存在问题的基础上,提出了聚瓷膜超... 详细信息

国家对煤矿矿井水资源化利用高度重视,排放标准的不断提高和智慧矿山概念的不断深化,给矿井水处理技术提出了新的挑战。通过对矿井水地面处理技术与井下多级过滤处理技术的分析和比较,在总结了现有技术存在问题的基础上,提出了聚瓷膜超滤高效处理矿井水新技术。新技术相比于传统处理技术的多流程而言,因处理过程无需加药,可省去整个加药系统,故处理过程仅包括高压预处理与常压过滤处理2个主要单元,解决了矿井水传统处理方法必须加混凝剂的难题,可真正做到矿井水处理全过程无人值守,符合智慧矿山的要求。新技术具有流程短、占地小、无需加药、出水水质稳定、可自动运行等特点,同时还可实现模块化、智能化,可适用于不同场景下含高悬浮物矿井水的直接过滤,或作为矿井水苦咸水的预处理单元,其中特别适合用于矿井水的井下处理就地复用。其核心组件聚瓷膜更是具有强度高、通量大、超亲水疏油、耐受悬浮物(SS)质量浓度可达10 000 mg/L等优势。通过实验室试验和矿井水地面处理改造工程案例可知,在进水SS质量浓度为0~10 000 mg/L,聚瓷膜直滤后出水SS均能稳定达到1 mg/L以下,浊度稳定达到1 NTU以下,该技术在煤矿矿井水资源化利用方面有明显优势,可为将来的大规模工程化推广应用提供技术参考。

关键词：矿井水悬浮物聚瓷膜资源化利用煤矿

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A Three-Dimensional Cyclic Multiplication - Convolution Neural Network for Hyperspectral Image Classification 19

A Three-Dimensional Cyclic Multiplication - Convolution Neur...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Xiong, Xiaorui Huang, Zihan Sheng, Zhongyin Xu, Zihan he, xuwen Lin, Qiaoling Wuhan Polytechnic University School of Mathematics and Computer Science Wuhan China

ISBN: (纸本)9798350346558

Hyperspectral images have a large amount of spectral and spatial information, and play a significant role in remote sensing. However, the traditional image classification techniques are no longer applicable to the developing hyperspectral remote sensing images, and how to reasonably utilize the rich feature information of hyperspectral images to obtain high-accuracy classification results has become one of the most important research topics at present. This paper proposes a new 3D convolutional neural network to solve the problem of feature wastage in hyperspectral images. The framework cycles the convolutional kernel size, and the number of convolutional kernels increases in a multiplicative relationship for hyperspectral image classification (CM-CNN). Compared with existing neural network frameworks on different datasets, CMCNN achieves the highest Kappa coefficient and confusion matrix accuracy, which are stable at above 95%. From the classification results, there are almost no obvious classification errors. © 2022 IEEE.

关键词： Image classification

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Preparation of a novel dual structure graded cemented carbides induced by bidirectional diffusion

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MATERIALS LETTERS 2022年 312卷 131689-131689页

作者： Wang, Qiwei he, xuwen Li, Na Zhang, Weibin Du, Yong Shandong Univ Key Lab Liquid Solid Struct Evolut & Proc Mat Minist Educ Jinan 250061 Peoples R China Shandong Univ Suzhou Inst Suzhou 215123 Jiangsu Peoples R China China Acad Engn Phys Inst Mat Mianyang 621908 Sichuan Peoples R China Cent South Univ State Key Lab Powder Met Changsha 410083 Hunan Peoples R China

Based on the thermodynamic calculations, a novel dual graded WC-Co-Ti(C, N)-ZrC-HfC cemented carbides was designed and prepared by introducing the combination of spinodal decomposition and denitrification process. The dual graded structures of the cemented carbides have a smooth fcc-rich surface and toughness fcc-free interlayer, which can improve the coating adhesion and inhibit the cracks propagation. Microstructure characterizations and composition distributions elucidated the principle of multi-factors diffusion migration and the formation mechanism of dual structure graded layers.

关键词： Cemented carbides Thermodynamics Sintering Multilayer structure

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

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

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

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

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

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.

关键词： Training Accuracy Noise Lighting Object detection Feature extraction Pattern recognition Detection algorithms Monitoring Farming

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Leather Defect Detection Algorithm Based on an Improved YOLOv5s

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

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

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

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

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.

关键词： YOLO Industries Deep learning Accuracy Attention mechanisms Prediction algorithms Robustness Classification algorithms Manufacturing Defect detection

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heavy Metal Distribution and Microbial Diversity of the Surrounding Soil and Tailings of Two Cu Mines in China

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WATER AIR AND SOIL POLLUTION 2023年第4期234卷 241-241页

作者： Xia, Yu Liu, Jing Chang, Jie Li, Weijia Xia, Kaiyu Liu, Zilong Liu, Yizhen he, xuwen China Univ Min & Technol Beijing Sch Chem & Environm Engn Beijing 100083 Peoples R China China Natl Gold Grp Co Ltd Beijing 100011 Peoples R China

Mining activities can cause heavy metal pollution which affects soil microbial diversity. Current understanding on the impact of Cu mines especially that of bioavailable heavy metals on microbial communities is still limited. This study investigated heavy metal distribution and microbial diversity of the surrounding soil and tailings of two Cu mines from Tibet (JM) and Zhejiang (JD), China. Cu, Pb, Zn, and Cr concentrations in the soil around JM were 107.33 +/- 75.48, 29.21 +/- 19.66, 73.89 +/- 28.79, and 17.88 +/- 4.91 mg center dot kg(-1), respectively, and those of JD were 40.59 +/- 21.86, 27.00 +/- 19.40, 95.45 +/- 54.36, and 30.87 +/- 11.55 mg center dot kg(-1), respectively. For each mine, the sites closer to the mining area displayed higher PINemerow values and acid extractable contents of Cu (Cu-_AE), Zn (Zn-_AE), and Pb (Pb-_AE). The tailings samples showed lower microbial diversity and higher abundances of heavy metal-resistant organisms including the ones from the phyla Firmicutes and Bacteroidetes and the genera Bacteroides, Lactobacillus, and Staphylococcus than the surrounding soil samples. Canonical corresponding analysis (CCA) revealed that water content, altitude, electrical conductivity (EC), total concentration of Zn (Zn-_total), Cu-_AE, Zn-_AE, and Pb-_AE, and pH value were effective environmental factors shaping the structure of all the investigated communities. Acid extractable contents of Pb and Zn rather than their total concentrations were significantly correlated to JM surrounding soil microbial structure. Microbial structure differentiations of the surrounding soil of JM and JD were largely attributed to stochastic processes, suggested by the beta nearest taxon index (beta NTI).

关键词： Acid extractable fraction Cu mine Effective factors heavy metal Microbial communities

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End-to-End Individual Pig Detection Based on Transfer Learning

End-to-End Individual Pig Detection Based on Transfer Learni...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

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

Nowadays, monitoring animals manually on a large scale is challenging, while external devices can provide more prosperous and accurate information. Therefore, there are already applications incorporating wearable sensors to monitor individual pigs. With continued breakthroughs in computer vision, low-cost visible light cameras can help monitor pigs. CNN-based object detection methods have been applied to individual pig detection, but most rely on hand-designed components and require prior knowledge. Based on previous researchers' progress, we propose using DETR, an end-to-end object detection method, to detect individual pigs. Also, to adapt to our natural pig farm environment, we trained the model with transfer learning to achieve an AP of 0.995 on our collected dataset.

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