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Behera SK, Karthika S, Mahanty B, Meher SK, Zafar M, Baskaran D, Rajamanickam R, Das R, Pakshirajan K, Bilyaminu AM, Rene ER. Application of artificial intelligence tools in wastewater and waste gas treatment systems: Recent advances and prospects. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 370:122386. [PMID: 39260284 DOI: 10.1016/j.jenvman.2024.122386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 08/17/2024] [Accepted: 08/31/2024] [Indexed: 09/13/2024]
Abstract
The non-linear complex relationships among the process variables in wastewater and waste gas treatment systems possess a significant challenge for real-time systems modelling. Data driven artificial intelligence (AI) tools are increasingly being adopted to predict the process performance, cost-effective process monitoring, and the control of different waste treatment systems, including those involving resource recovery. This review presents an in-depth analysis of the applications of emerging AI tools in physico-chemical and biological processes for the treatment of air pollutants, water and wastewater, and resource recovery processes. Additionally, the successful implementation of AI-controlled wastewater and waste gas treatment systems, along with real-time monitoring at the industrial scale are discussed.
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Affiliation(s)
- Shishir Kumar Behera
- Process Simulation Research Group, School of Chemical Engineering, Vellore Institute of Technology, Vellore, 632 014, Tamil Nadu, India.
| | - S Karthika
- Department of Chemical Engineering, Alagappa College of Technology, Anna University, Chennai, 600 025, Tamil Nadu, India
| | - Biswanath Mahanty
- Division of Biotechnology, Karunya Institute of Technology & Sciences, Coimbatore, 641 114, Tamil Nadu, India
| | - Saroj K Meher
- Systems Science and Informatics Unit, Indian Statistical Institute, Bangalore, 560059, India
| | - Mohd Zafar
- Department of Applied Biotechnology, College of Applied Sciences & Pharmacy, University of Technology and Applied Sciences - Sur, P.O. Box: 484, Zip Code: 411, Sur, Oman
| | - Divya Baskaran
- Department of Chemical and Biomolecular Engineering, Chonnam National University, Yeosu, Jeonnam, 59626, South Korea; Department of Biomaterials, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Chennai, 600 077, Tamil Nadu, India
| | - Ravi Rajamanickam
- Department of Chemical Engineering, Annamalai University, Chidambaram, 608002, Tamil Nadu, India
| | - Raja Das
- Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, 632 014, Tamil Nadu, India
| | - Kannan Pakshirajan
- Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati, 781 039, Assam, India
| | - Abubakar M Bilyaminu
- Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, P. O. Box 3015, 2601, DA Delft, the Netherlands
| | - Eldon R Rene
- Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, P. O. Box 3015, 2601, DA Delft, the Netherlands
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Barbusiński K, Szeląg B, Parzentna-Gabor A, Kasperczyk D, Rene ER. Application of a generalized hybrid machine learning model for the prediction of H 2S and VOCs removal in a compact trickle bed bioreactor (CTBB). CHEMOSPHERE 2024; 360:142181. [PMID: 38685329 DOI: 10.1016/j.chemosphere.2024.142181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/05/2024] [Accepted: 04/26/2024] [Indexed: 05/02/2024]
Abstract
This study presents a generalized hybrid model for predicting H2S and VOCs removal efficiency using a machine learning model: K-NN (K - nearest neighbors) and RF (random forest). The approach adopted in this study enabled the (i) identification of odor removal efficiency (K) using a classification model, and (ii) prediction of K <100%, based on inlet concentration, time of day, pH and retention time. Global sensitivity analysis (GSA) was used to test the relationships between the inputs and outputs of the K-NN model. The results from classification model simulation showed high goodness of fit for the classification models to predict the removal of H2S and VOCs (SPEC = 0.94-0.99, SENS = 0.96-0.99). It was shown that the hybrid K-NN model applied for the "Klimzowiec" WWTP, including the pilot plant, can also be applied to the "Urbanowice" WWTP. The hybrid machine learning model enables the development of a universal system for monitoring the removal of H2S and VOCs from WWTP facilities.
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Affiliation(s)
- Krzysztof Barbusiński
- Department of Water and Wastewater Engineering, Silesian University of Technology, Konarskiego 18, 44-100, Gliwice, Poland
| | - Bartosz Szeląg
- Warsaw University of Life Sciences, Nowoursynowska 166, 02-787, Warsaw, Poland.
| | | | | | - Eldon R Rene
- Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, P.O. Box 3015, 2601DA Delft, Netherlands
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Zhai J, Jiang C, Xue X, Wang H. Biofiltration of toluene and ethyl acetate mixture by a fungal-bacterial biofilter: Performance and community structure analysis. Heliyon 2024; 10:e31984. [PMID: 38882306 PMCID: PMC11176807 DOI: 10.1016/j.heliyon.2024.e31984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 04/30/2024] [Accepted: 05/27/2024] [Indexed: 06/18/2024] Open
Abstract
The inhibitory effect of hydrophilic volatile organic compounds (VOCs) on hydrophobic VOCs removal was found to be efficiently reduced by the fungal-bacterial biofilters (F&B-BFs) developed in the present study. Overall, the toluene and ethyl acetate mixture removal efficiencies (REs) and elimination capacities (ECs) of F&B-BFs were superior to those of bacterial biofilters (B-BFs). The REs for toluene and ethyl acetate were 32.5 ± 0.8 % and 74.6 ± 1.0 %, respectively, for F&B-BFs, in comparison to 8.0 ± 0.3 % and 60 ± 1.3 % for B-BFs. The ECs for toluene and ethyl acetate were 13.0 g m-3 h-1 and 149.2 g m-3 h-1, respectively, for the F&B-BF, compared to 3.2 g m-3 h-1 and 119.6 g m-3 h-1 for the B-BFs. This was achieved at a constant empty bed residence time (EBRT) of 45 s. F&B-BFs exhibited a superior mineralization efficiencies (MEs) compared to B-BFs for a VOC mixture of toluene and ethyl acetate (≈36.1 % vs ~ 29.6 %). This is attributed to the direct capture of VOCs by the presence of fungi, increased the contact time between VOCs and VOCs-degrading bacteria, and even distribution of VOCs-degrading bacteria in the F&B-BFs. Moreover, compared with B-BFs, the coupling effect of genus Pseudomonas degradation, and unclassified_f_Herpotrichiellaceae and unclassified_p_Ascomycota adsorption of F&B-BF resulted in a reduction in the impact of the presence of hydrophilic VOCs on the removal of hydrophobic VOCs, thereby enhancing the biofiltration performance of mixtures of hydrophilic and hydrophobic VOCs.
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Affiliation(s)
- Jian Zhai
- Department of Printing and Packaging Engineering, Shanghai Publishing and Printing College, Shanghai, People's Republic of China
| | - Chunhua Jiang
- Department of Printing and Packaging Engineering, Shanghai Publishing and Printing College, Shanghai, People's Republic of China
| | - Xiaojuan Xue
- School of Environmental Engineering, Gansu Forestry Polytechnic, Tianshui, Gansu Province, People's Republic of China
| | - Hai Wang
- School of Environmental Engineering, Gansu Forestry Polytechnic, Tianshui, Gansu Province, People's Republic of China
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Liu J, Han Y, Dou X, Liang W. Effect of toluene on m-xylene removal in a biotrickling filter: Performance, biofilm characteristics, and microbial analysis. ENVIRONMENTAL RESEARCH 2024; 245:117978. [PMID: 38142726 DOI: 10.1016/j.envres.2023.117978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 12/13/2023] [Accepted: 12/16/2023] [Indexed: 12/26/2023]
Abstract
Hydrophobic volatile organic compounds (VOCs) pose a challenge to the removal efficiency in biotrickling filters (BTFs). The addition of relatively hydrophilic substances presents a promising approach for enhancing the elimination of hydrophobic VOCs. In this study, toluene was introduced into the BTF system alongside m-xylene, and their mixing ratios were changed to explore the interactions and mechanisms under different conditions. The result showed that the most pronounced synergistic interaction occurred when the mixing concentration ratio of m-xylene and toluene was 2:1. The removal efficiency (RE) of m-xylene increased from 88% to 97%, and the elimination capacity (EC) of m-xylene changed from 64 to 72 g m-3 h-1. Under this condition, there was a notable increase in biomass, extracellular polymeric substance (EPS) content, and relative hydrophobicity. Microbial diversity was enhanced observably with Berkeleyces and Mycobacterium potentially playing a positive role in co-degradation. Meanwhile, microbial metabolic function prediction indicated a significant enhancement in metabolic functions. Therefore, the introduction of relatively hydrophilic VOCs represents an effective strategy for enhancing the removal of hydrophobic VOCs in the BTFs.
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Affiliation(s)
- Jia Liu
- Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing, 100124, China.
| | - Yueyang Han
- Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing, 100124, China
| | - Xiaona Dou
- Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing, 100124, China
| | - Wenjun Liang
- Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing, 100124, China
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Sakhaei A, Zamir SM, Rene ER, Veiga MC, Kennes C. Neural network-based performance assessment of one- and two-liquid phase biotrickling filters for the removal of a waste-gas mixture containing methanol, α-pinene, and hydrogen sulfide. ENVIRONMENTAL RESEARCH 2023; 237:116978. [PMID: 37633629 DOI: 10.1016/j.envres.2023.116978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 08/04/2023] [Accepted: 08/23/2023] [Indexed: 08/28/2023]
Abstract
The performance of one- and two-liquid phase biotrickling filters (OLP/TLP-BTFs) treating a mixture of gas-phase methanol (M), α-pinene (P), and hydrogen sulfide (H) was assessed using artificial neural network (ANN) modeling. The best ANN models with the topologies 3-9-3 and 3-10-3 demonstrated an exceptional capacity for predicting the performance of O/TLP-BTFs, with R2 > 99%. The analysis of causal index (CI) values for the model of OLP-BTF revealed a negative impact of M on P removal (CI = -2.367), a positive influence of P and H on M removal (CI = +7.536 and CI = +3.931) and a negative effect of H on P removal (CI = -1.640). The addition of silicone oil in TLP-BTF reduced the negative impact of M and H on P degradation (CI = -1.261 and CI = -1.310, respectively) compared to the OLP-BTF. These findings suggested that silicone oil had the potential to improve P availability to the biofilm by increasing the concentration gradient of P between the air/gas and aqueous phases. Multi-objective particle swarm optimization (MOPSO) suggested an optimum operational condition, i.e. inlet M, P, and H concentrations of 1.0, 1.1, and 0.3 g m-3, respectively, with elimination capacities (ECs) of 172.1, 26.5, and 0.025 g m-3 h-1 for OLP-BTF. Likewise, one of the optimum operational conditions for TLP-BTF is achievable at inlet concentrations of 4.9, 1.7, and 0.8 g m-3, leading to the optimum ECs of 299.7, 52.9, and 0.072 g m-3 h-1 for M, P, and H, respectively. These results provide important insights into the treatment of complex waste gas mixtures, addressing the interactions between the pollutant removal characteristics in OLP/TLP-BTFs and providing novel approaches in the field of biological waste gas treatment.
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Affiliation(s)
- Amirmohammad Sakhaei
- Biochemical Engineering Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, P.O. Box 14115-114, Iran
| | - Seyed Morteza Zamir
- Biochemical Engineering Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, P.O. Box 14115-114, Iran.
| | - Eldon R Rene
- Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, Westvest 7, P. O. Box 3015, 2611AX, Delft, the Netherlands
| | - María C Veiga
- Chemical Engineering Laboratory, Faculty of Sciences and Centre for Advanced Scientific Research - Centro de Investigaciones Científicas Avanzadas (CICA), BIOENGIN Group, University of La Coruña, E - 15008, A Coruña, Spain
| | - Christian Kennes
- Chemical Engineering Laboratory, Faculty of Sciences and Centre for Advanced Scientific Research - Centro de Investigaciones Científicas Avanzadas (CICA), BIOENGIN Group, University of La Coruña, E - 15008, A Coruña, Spain
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Wang A, Qiao Y, Zhang Y, Jin R, Liu J, He Z, Jia M, Gao J, Guo C. Performance and Mechanism of Chlorine Dioxide on BTEX Removal in Liquid and Indoor Air. Molecules 2023; 28:molecules28114342. [PMID: 37298823 DOI: 10.3390/molecules28114342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 05/14/2023] [Accepted: 05/24/2023] [Indexed: 06/12/2023] Open
Abstract
With the development of the chemical industry, benzene, toluene, ethylbenzene, and xylene (BTEX) have gradually become the major indoor air pollutants. Various gas treatment techniques are widely used to prevent the physical and mental health hazards of BTEX in semi-enclosed spaces. Chlorine dioxide (ClO2) is an alternative to chlorine as a secondary disinfectant with a strong oxidation ability, a wide range of action, and no carcinogenic effects. In addition, ClO2 has a unique permeability which allows it to eliminate volatile contaminants from the source. However, little attention has been paid to the removal of BTEX by ClO2, due to the difficulty of removing BTEX in semi-enclosed areas and the lack of testing methods for the reaction intermediates. Therefore, this study explored the performance of ClO2 advanced oxidation technology on both liquid and gaseous benzene, toluene, o-xylene, and m-xylene. The results showed that ClO2 was efficient in the removal of BTEX. The byproducts were detected by gas chromatography-mass spectrometry (GC-MS) and the reaction mechanism was speculated using the ab initio molecular orbital calculations method. The results demonstrated that ClO2 could remove the BTEX from the water and the air without causing secondary pollution.
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Affiliation(s)
- Anlong Wang
- School of Environment and Safety Engineering, North University of China, Taiyuan 030051, China
| | - Yina Qiao
- School of Environment and Safety Engineering, North University of China, Taiyuan 030051, China
| | - Yufan Zhang
- School of Environment and Safety Engineering, North University of China, Taiyuan 030051, China
| | - Riya Jin
- School of Environment and Safety Engineering, North University of China, Taiyuan 030051, China
| | - Jiaoqin Liu
- School of Environment and Safety Engineering, North University of China, Taiyuan 030051, China
| | - Zengdi He
- School of Environment and Safety Engineering, North University of China, Taiyuan 030051, China
| | - Mengye Jia
- School of Environment and Safety Engineering, North University of China, Taiyuan 030051, China
| | - Jingshuai Gao
- School of Environment and Safety Engineering, North University of China, Taiyuan 030051, China
| | - Chengjie Guo
- School of Environment and Safety Engineering, North University of China, Taiyuan 030051, China
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Giang HM, Huyen Nga NT, Rene ER, Ha HN, Varjani S. Performance and neural modeling of a compost-based biofilter treating a gas-phase mixture of benzene and xylene. ENVIRONMENTAL RESEARCH 2023; 217:114788. [PMID: 36403652 DOI: 10.1016/j.envres.2022.114788] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 10/26/2022] [Accepted: 11/09/2022] [Indexed: 06/16/2023]
Abstract
Biofilter (BF) has been regarded as a versatile gas treatment technology for removing volatile organic compounds (VOCs) from contaminated gas streams. In order for BF to be utilized in the industrial setting, it is essential to conduct research aimed at removing VOC mixtures under different inlet loading conditions, i.e. as a function of the gas flow rate and inlet VOC concentrations. The main aim of this study was to apply artificial neural networks (ANN) and determine the relationship between flow rate (FR), pressure drop (PD), inlet concentration (C), and removal efficiency (RE) in the BF treating gas-phase benzene and xylene mixtures. The ANN model was trained and tested to assess the removal efficiency of benzene (REB) and xylene (REX) under the influence of different FR, PD and C. The model's performance was assessed using a cross-validation method. The REb varied from 20% to >60%, while the REx varied from 10% to 70% during the different experimental phases of BF operation. The causal index (CI) technique was used to determine the sensitivity of the input parameters on the output variables. The ANN model with a topology of 4-4-2 performed the best in terms of predicting the RE profiles of both the pollutants. Furthermore, the effect was more pronounced for xylene because an increase in the benzene concentration reduced xylene removal (CI = -25.7170) more severely than benzene removal. An increase in the xylene concentration had a marginally positive effect on the benzene removal (CI = +0.1178).
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Affiliation(s)
- Hoang Minh Giang
- Faculty of Environmental Engineering, Hanoi University of Civil Engineering, 55 Giai Phong Road, Hai Ba Trung District, Hanoi, 113021, Viet Nam.
| | - Nguyen Thi Huyen Nga
- Faculty of Environmental Engineering, Hanoi University of Civil Engineering, 55 Giai Phong Road, Hai Ba Trung District, Hanoi, 113021, Viet Nam
| | - Eldon R Rene
- Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, Westvest 7, P.O. Box 3015, 2601DA, Delft, the Netherlands
| | - Hoang Ngoc Ha
- Faculty of Environmental Engineering, Hanoi University of Civil Engineering, 55 Giai Phong Road, Hai Ba Trung District, Hanoi, 113021, Viet Nam
| | - Sunita Varjani
- Gujarat Pollution Control Board, Gandhinagar, Gujarat, 382 010, India
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Artificial intelligence-based modeling and optimization of microbial electrolysis cell-assisted anaerobic digestion fed with alkaline-pretreated waste-activated sludge. Biochem Eng J 2022. [DOI: 10.1016/j.bej.2022.108670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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