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The performances of persulfate activators to degrade the persistent organic pollutants in industrial wastewater

Case Studies in Chemical and Environmental Engineering 2023年 8卷

作者： Van Tri, Dao Barcelo, Damia Le Luu, Tran Master Program in Water Technology Reuse and Management Vietnamese German University Ho Chi Minh City Viet Nam Catalan Institute for Water Research (ICRA-CERCA) Emili Grahit 101 Girona 17003 Spain Institute of Environmental Assessment and Water Research (IDAEA-CSIC) Jordi Girona 18-26 Barcelona 08034 Spain

Over the past few decades, there has been significant research and application of advanced oxidation processes (AOPs) that rely on in-situ radicals as a potential treatment for persistently organic contaminated industrial wastewaters that contain residual pharmaceuticals, dyes … While persulfate (PS) oxidation alone is insufficient for the complete elimination of contaminants in wastewater, combining it with activators such as transition metals, alkalis, heat, microwave (MW), ultrasound (US), ultraviolet (UV), electrochemical (EC) energy, or electron transfer mediators is a promising approach to generate sulfate radical (SO₄^−•) and facilitate further degradation of persistent organic pollutants (POPs). PS activation methods that rely on reducing agents (metal ions, alkali) and external energy (heat, MW, US, UV, and EC) are highly suitable for treating persistent organic pollutants (POPs) in industrial effluent. Meanwhile, PS activation method that uses activated carbon (AC) materials is deemed the most efficient system for removing residual antibiotics in pharmaceutical wastewater due to their high efficiency and short processing time. Notably, the pH value, reaction temperature, water matrices, ratio of catalyst/PS, and PS concentration are critical factors that exert significant effects on the treatment process. Accordingly, this review offers a broad understanding of sulfate radical oxidation processes (SR-AOPs) to aid in selecting suitable treatment approaches for POPs in industrial wastewater. © 2023 The Authors

关键词： Activators Advanced oxidation processes Industrial wastewaters Persistent organic pollutants Persulfate

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Contribution of Manggarai gate improvement to flood in Manggarai Village based on recorded flood event 5

Contribution of Manggarai gate improvement to flood in Mangg...

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5th International Seminar on Sustainable Urban Development, ISoSUD 2020

作者： Kesuma, T.N.A. Saputra, D. Farid, M. Kusuma, M.S.B. Kuntoro, A.A. Civil Engineering Doctoral Program Faculty of Civil and Environmental Engineering Bandung Institute of Technology Jawa Barat Bandung Indonesia Civil Engineering Master Program Faculty of Civil and Environmental Engineering Bandung Institute of Technology Jawa Barat Bandung Indonesia Water Resources Engineering Research Group Faculty of Civil and Environmental Engineering Bandung Institute of Technology Jawa Barat Bandung Indonesia Center for Coastal and Marine Development Institut Teknologi Bandung Jawa Barat Bandung Indonesia

Manggarai Gate is one of the most important flood control structures of the Jakarta Flood Control System. Its performance capacity is a compulsory parameter for flood risk reduction of the Jakarta Flood Prone Area. This paper aims to evaluate the contribution of the capacity improvement of Manggarai Gate to reduce the flood hazard at Manggarai flood-prone areas located in the upper part of this gate. An additional gate section was conducted in 2015 as the structural flood hazard reduction effort to increase the capacity of this gate from 330 m3/s to 507 m3/s. Recorded floods characteristic event before and after this improvement was compared to determine the flood hazard changes during the flood observation period. Based on this determination results it is found that the contribution of the increment capacity of Manggarai gates in reducing the flood hazard depends on the sustainability of the river capacity, river water quality, and flood control operation. It could also be concluded the sustainability of the river capacity and river water quality is very sensitive to the social behavior of the community and the river stakeholder along the river catchment area in addressing the environmental condition of the river catchment area. © Published under licence by IOP Publishing Ltd.

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First-Attempt Application of Sponge Membrane Bioreactor Treating 3,4-Dichlorophenol Contaminated Wastewater at Different Hydraulic Retention Times

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SSRN 2024年

作者： Nhu Nguyet, Phan Nguyen, Thi-Kim-Ngan Thuan, Ngo Thi Yen, Duong Hoang Vo, Thi-Kim-Quyen Nguyen, Thi-Yen-Phuong Bui, Xuan-Thanh Watari, Takahiro Yamaguchi, Takashi Hatamoto, Masashi Faculty of Environment University of Science Ho Chi Minh City Viet Nam Ho Chi Minh City Viet Nam Ho Chi Minh City Viet Nam School of Chemical and Environmental Engineering International University Ho Chi Minh City Viet Nam Master Program in Water Technology Reuse and Management Vietnamese German University Binh Duong Province Viet Nam Ho Chi Minh City Viet Nam Department of Civil and Environmental Engineering Nagaoka University of Technology Niigata Japan Department of Science of Technology Innovation Nagaoka University of Technology Niigata Japan

Chlorophenols (CPs) are priority pollutants which persist in the environment for up to 28 years. The release of Chlorophenol derivatives into aquatic environments poses serious risks to living beings because of their mutagenic, carcinogenic, genotoxic, and teratogenic effects. This study highlights the first application of a Sponge Membrane Bioreactor (Sponge-MBR) for treating 3,4-Dichlorophenol (3,4-DCP)-contaminated wastewater. Herein, a lab-scale Sponge-MBR was conducted to treat 3,4-DCP-contaminated wastewater at various hydraulic retention times (HRTs) ranging from 5 to 15 h in terms of organic matter and 3,4-DCP removal, and microbial community shifts. The sludge characteristics were also performed. Results showed that a rise in HRT at 5, 10 and 15 h increased the removal efficiencies of COD (84 – 94%) and 3,4-DCP (90 – 96%). Under 30-day operation period at optimal HRT (15 h), corresponding to OLR of 2.84 ± 0.18 kg COD·m-3·day-1, the removal efficiencies of both 3,4-DCP and COD were 96% with the average removal rate of 356 ± 83 mg COD·g-SS-1·day-1 and 1.62 ± 0.38 mg 3,4-DCP·g-SS-1·day -1. The attached biomass in sponge media was recorded higher than suspended biomass and contributed 49 – 66% of the total biomass, indicating that the microbial community shifts could degrade organic matter and remove 3,4-DCP. Proteobacteria dominated the microbial community because of their adaptability to environmental changes. The findings derived from this study provide useful perspectives on the application of Sponge-MBR treating 3,4-DCP-contaminated wastewater and offer Sponge-MBR as a potential alternative for treating wastewater contaminated with toxic chemicals. © 2024, The Authors. All rights reserved.

关键词： Biomass

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Prediction of Hydrogen Production Rate in Anaerobic Fermentation Using Grey Relation Analysis and Machine Learning

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SSRN 2023年

作者： Wang, Yifan Wu, Jinghui Tseng, Yu-Yao Zhao, Chunliang Li, Keqing Wang, Xianze Wang, Ming-Hung Lay, Chyi-How Huo, Mingxin Engineering Research Center of Low-Carbon Treatment Green Development of Polluted Water in Northeast China Ministry of Education Changchun130117 China Science and Technology Innovation Center for Municipal Wastewater Treatment and Water Quality Protection Northeast Normal University Changchun130117 China Key Laboratory of Songliao Aquatic Environment Ministry of Education Jilin Jianzhu University Changchun130118 China Master's Program of Green Energy Science and Technology Feng Chia University Taichung40724 Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Chiayi County Taiwan

Anaerobic fermentation for hydrogen production has many environmental factors that limit microbial activity, but machine learning has enormous potential in handling the complexity of biological processes. This study explores the potential of machine learning in predicting hydrogen production rates (HPR) from anaerobic fermentation of biomass energy. Grey relation analysis was conducted to determine the correlation between operational parameters and HPR. Five machine learning algorithms, such as decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and K-nearest neighbor (KNN), were then paired with operating conditions and water quality performance as features and HPR as a label, with mean squared error (MSE) and R2 as evaluation indexes. Butyric acid, oxidation-reduction potential (ORP), and volatile suspended solids (VSS) were found to play crucial roles in hydrogen production from sucrose anaerobic fermentation. XGBoost had the highest accuracy with R2 of 0.91 and MSE of 0.0052. © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Influences of key factors on river water quality in urban and rural areas: A review

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Case Studies in Chemical and Environmental Engineering 2023年 8卷

作者： Anh, Nguyen Tuan Can, Le Duy Nhan, Nguyen Thi Schmalz, Britta Luu, Tran Le Doctoral Training Program in Sustainable Urban Development Vietnamese German University Viet Nam Nong Lam University of Ho Chi Minh City Gia Lai Campus Viet Nam Department of Mechatronics and Sensor Systems Technology Vietnamese German University Vanh Dai 4 Quarter 4 Thoi Hoa Ward Ben Cat Binh Duong Province Viet Nam Master Program in Water Technology Reuse and Management Vietnamese German University Vanh Dai 4 Quarter 4 Thoi Hoa Ward Ben Cat Binh Duong Province Viet Nam Chair of Engineering Hydrology and Water Management Technical University of Darmstadt Germany

water quality in rivers is deteriorating in urban and rural areas due to natural and anthropogenic factors. Understanding how changes and factors affect river water quality is crucial for managing water quality in river basins. This review focuses on analyzing key factors affecting water quality, and the temporal and spatial variations of water quality in rivers flowing in rural and urban areas. Natural processes such as weathering of rocks, evapotranspiration, atmospheric deposition, climate change, and natural disasters cause changes in the quality of river water. Anthropogenic factors could stem from industrial effluents, domestic activities, and agricultural activities such as the application of fertilizers, manures, pesticides, animal husbandry activities, irrigation practices, deforestation, and aquaculture. The seasonal variations in river water quality are discussed, and land use or cover could affect water quality parameters in a negative or positive way. In addition to traditional contaminants such as biodegradable organic matter, heavy metals, and pathogens, emerging and persistent pollutants such as organochlorine pesticides (OCPs), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), perfluoroalkyl and polyfluoroalkyl substances (PFASs), and phamaceutic active compounds (PhACs) has been found in many rivers, which could pose a threat to human and animal health. The comparison of key factors and parameters in urban and rural areas is also clarified, which provides authorities and policymakers with a deep understanding and supports decision-making in sustainable water management. © 2023 The Authors

关键词： River basin River water quality Rural areas Urban areas water pollution

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Reconstructing suspended sediment concentrations in the Mekong River Basin via semi-supervised-based deep neural networks

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Science of the Total Environment 2024年 955卷 176758页

作者： Nguyen, Thi Thu Ha Vu, Duc Quang Doan, Ngoc Phu Chi, Huynh Thi Khanh Li, Peixin Binh, Doan Van An, Yimeng Dung, Pham Tuan Hoang, Tuan A. Son, Mai Thai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat City Viet Nam

The Mekong River Basin (MRB) is crucial for the livelihoods of over 60 million people across six Southeast Asian countries. Understanding long-term sediment changes is crucial for management and contingency plans, but the sediment concentration data in the MRB are extremely sporadic, making analysis challenging. This study focuses on reconstructing long-term suspended sediment concentration (SSC) data using a novel semi-supervised machine learning (ML) model. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on daily hydroclimate and SSC data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the superior performance of the proposed method compared to the state-of-the-art supervised techniques (i.e., Random Forest, XGBoost, CatBoost, MLP, CNN, and LSTM), and surpasses existing semi-supervised methods (i.e., CoReg, ⊓ Model, ICT, and Mean Teacher). This approach is the first semi-supervised method to reconstruct sediment data in the field and has the potential for broader application in other river systems. © 2024 The Author(s)

关键词： Self-supervised learning

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Nitrogen Source Recovery Efficiency in the Catfish Farming Zero Emission Integrated System in Mekong Delta, Viet Nam

SSRN

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SSRN 2022年

作者： Le Thanha, Hai Nguyen Thi Phuong, Thao Thi, Hieu Tran Tran Nguyen Trung, Kien Viet, Thang Nguyen Van, Thanh Tran Le Thanh, Son Le, Luu Tran Thanh, Hung Nguyen Mai, Linh Duong Schnitzer, Hans Institute for Environment and Resources National University of Ho Chi Minh City Ho Chi Minh740500 Viet Nam Master program in Water Technology Reuse and Management Vietnamese – German University Binh Duong Viet Nam An Giang University National University of Ho Chi Minh City Ho Chi Minh740500 Viet Nam

The purpose of this study is to optimize the nitrogen recovery efficiency in the catfish farming agro-based zero emission integrated system in Vietnam with special focuses on limiting the nitrogen loss into the environment, as well as on saving cost for other agriculture activities. Nitrogen source was recovered from catfish wastewater, sediment and dead fish bodies. Optimal nitrogen recovery system was estimated based on the amount of waste and nitrogen concentration in the treatment processes within the integrated system. In this study, LINGO software was used to estimate nitrogen recovery efficiency, and the construction area for the system. In addition, Material Flow Analysis technique was used to monitor the transportation and accumulation ratio of nitrogen circulating within the integrated zero emission system. This study results indicate that high nitrogen recovery efficiency was obtained in the study system which helps to reduce nitrogen discharging rate into the environment. The total nitrogen quantity of 12,049.9 KgN was recovered for the total designed farming area of 120,062m2, and the optimal area ratio between fish ponds and other treatment components (composting area, grass garden, pond, cow cage etc) in the integrated system was also obtained. This designed system has been successfully applied to a local catfish farm with an area of 10,000m2 which produced valuable by-products such as organic fertilizer (with 1.7% total Nitrogen), hydrolyzed protein solution (with 3.08% total Nitrogen). Moreover, the nitrogen recovery efficiency from catfishpond wastewater for further supply to corn cultivation and water hyacinth were 47.9% and 79.5%, respectively. © 2022, The Authors. All rights reserved.

关键词： Recovery

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Experimental Instrumentation of water Stage Monitoring Stations in Mountain Streams Bedrock Using Pressure Transducers

SSRN

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SSRN 2023年

作者： Quezada, Victor M. Hernandez Anguiano, Jesus Horacio Miranda, Raúl Padilla, Francisco Li, Yanmei Knappett, Peter S.K. Murrieta, Daniel Vázquez, Antonio Bian, Jianmin Doctoral Program of Water Science and Technology Engineering Division University of Guanajuato Guanajuato Guanajuato36000 Mexico Department of Mining Metallurgy and Geology Engineering University of Guanajuato Guanajuato Guanajuato36020 Mexico Department of Geomatics and Hydraulic Engineering University of Guanajuato Guanajuato Guanajuato36000 Mexico ETS of Engineering of Roads Canals and Ports University of La Coruña C. de Elviña 15071 Spain Department of Geology and Geophysics Texas A&M University College StationTX778432 United States Master Program of Water Science Engineering Division University of Guanajuato Guanajuato Guanajuato36000 Mexico Key Laboratory of Groundwater Resources and Environment Ministry of Education Jilin University Changchun130021 China College of New Energy and Environment Jilin University Changchun130021 China

This study presents a new method of installing non-vented pressure transducers with dataloggers to monitor river/streams stage in mountain areas. The installation was performed in the bedrock of the streams using a protective copper casing, next to streams natural control and without affecting their characteristics since it does not require infrastructure. This allowed the installation of 18 pressure transducers in areas with mining influence, urban and natural protected areas, among others. The results obtained show that the risks of damage to the pressure transducer due to extreme floods or vandalism are significantly reduced, and that it is possible to build monitoring stations using the natural section/channel controls or structure controls. The proposed method considerably reduces infrastructure and maintenance costs for stage monitoring, making it feasible to install in mountain rivers and streams with similar characteristics to those presented in this study. © 2023, The Authors. All rights reserved.

关键词： Pressure sensors

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Reconstructing Suspended Sediment Concentrations in the Mekong River Basin Via Semi-Supervised-Based Deep Neural Networks

SSRN

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SSRN 2024年

作者： Nguyen, Thi-Thu-Ha Vu, Duc Quang Doan, Ngoc Phu Thi Khanh Chi, Huynh Li, Peixin Van Binh, Doan An, Yimeng Tuan Dung, Pham Hoang, Tuan A. Thai Son, Mai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City70000 Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City70000 Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat Town820000 Viet Nam

The Mekong River Basin (MRB) is one of the highest biodiversity areas in the world and is the vital source of livelihoods for more than 60 millions of people over six countries in Southeast Asia. However, it is suffering from a severe sediment starvation problem caused by climate change and human activities, particularly dam constructions. Understanding long-term sediment changes thus plays a crucial role for management and contingency plans. Current sediment concentration data in the MRB are extremely sporadic, making it a non-trivial task to analyze them. This paper focuses on the sediment reconstruction to provide long-term reliable suspended sediment concentration (SSC) data using Machine Learning (ML) perspectives. In particular, a novel semi-supervised ML model, which can effectively deal with data sparsity, is introduced to predict/reconstruct missing sediment data. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the performances of the proposed method compared to other state-of-the-art supervised techniques and traditional simulation methods commonly used in previous sediment forecasting/reconstruction studies. To the best of our knowledge, this approach is the first semi-supervised method to reconstruct sediment data in the field. Though this study focuses on the MRB, sediment sparsity is a very common problem in other river systems worldwide. Thus, this method can be widely applied for them for more effective sediment reconstruction/prediction in general. © 2024, The Authors. All rights reserved.

关键词： Biodiversity

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Synthesis of activated carbon from macadamia nutshells activated by H2SO4 and K2CO3 for methylene blue removal in water

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Bioresource technology Reports 2020年 12卷

作者： Dao, Trung Minh Le Luu, Tran Department of Environmental Science Thu Dau Mot University Binh Duong Viet Nam Master Program in Water Technology Reuse and Management Vietnamese German University Binh Duong Viet Nam

Macadamia nutshell is a new absorbent material for wastewater treatment. This work is a report on the synthesis of activated carbon as an absorbent materials prepared from macadamia nutshells using H2SO4 and K2CO3 as activating agents for methylene blue (MB) removal in water. The results show that MB adsorption capacity reaches its highest at 250.22 mg/g in preparation conditions H2SO4:coke 1:1 after 45 min calcination time when using a H2SO4 agent. The MB adsorption capacity of the absorbent using the K2CO3 agent reaches the highest at 261.52 mg/g in preparation conditions K2CO3:coke 1:1 and 60 min calcination time. Activated carbon prepared from macadamia nutshells activated by H2SO4 agents achieved the best MB removal efficiency of 67.29% in optimal conditions at a dose of 1 g/L with a treatment time of 60 min. The K2CO3agent achieved the best MB removal efficiency of 98.55% in optimal conditions at a dose of 0.5 g/L at a treatment time of 45 min. Macadamia nutshells are a potential new material to prepare the activated carbon in order to remove toxic organic compounds in wastewater. © 2020 Elsevier Ltd

关键词： Activated carbon

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