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Zhou Z, Sun T, Li X, Ren J, Lu Z, Liu Y, Li K, Qu F. Reliable assessment and prediction of moderate preoxidation of sodium hypochlorite for algae-laden water treatment. WATER RESEARCH 2024; 266:122398. [PMID: 39244865 DOI: 10.1016/j.watres.2024.122398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Revised: 09/02/2024] [Accepted: 09/04/2024] [Indexed: 09/10/2024]
Abstract
Chemical moderate preoxidation for algae-laden water is an economical and prospective strategy for controlling algae and exogenous pollutants, whereas it is constrained by a lack of effective on-line evaluation and quick-response feedback method. Herein, excitation-emission matrix parallel factor analysis (EEM-PARAFAC) was used to identify cyanobacteria fluorophores after preoxidation of sodium hypochlorite (NaClO) at Excitation/Emission wavelength of 260(360)/450 nm, based on which the algal cell integrity and intracellular organic matter (IOM) release were quantitatively assessed. Machine learning modeling of fluorescence spectral data for prediction of moderate preoxidation using NaClO was established. The optimal NaClO dosage for moderate preoxidation depended on algal density, growth phases, and organic matter concentrations in source water matrices. Low doses of NaClO (<0.5 mg/L) led to short-term desorption of surface-adsorbed organic matter (S-AOM) without compromising algal cell integrity, whereas high doses of NaClO (≥0.5 mg/L) quickly caused cell damage. The optimal NaClO dosage increased from 0.2-0.3 mg/L to 0.9-1.2 mg/L, corresponding to the source water with algal densities from 0.1 × 10⁶ to 2.0 × 10⁶ cells/mL. Different growth stages required varying NaClO doses: stationary phase cells needed 0.3-0.5 mg/L, log phase cells 0.6-0.8 mg/L, and decaying cells 2.0-2.5 mg/L. The presence of natural organic matter and S-AOM increased the NaClO dosage limit with higher dissolved organic carbon (DOC) concentrations (1.00 mg/L DOC required 0.8-1.0 mg/L NaClO, while 2.20 mg/L DOC required 1.5-2.0 mg/L). Compared to other predictive models, the machine learning model (Gaussian process regression-Matern (0.5)) performed best, achieving R2 values of 1.000 and 0.976 in training and testing sets. Optimal preoxidation followed by coagulation effectively removed algal contaminants, achieving 91%, 92%, and 92% removal for algal cells, turbidity, and chlorophyll-a, respectively, thereby demonstrating the effectiveness of moderate preoxidation. This study introduces a novel approach to dynamically adjust NaClO dosage by monitoring source water qualities and tracking post-preoxidation fluorophores, enhancing moderate preoxidation technology application in algae-laden water treatment.
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Affiliation(s)
- Zhiwei Zhou
- College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China
| | - Tianjie Sun
- College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China
| | - Xing Li
- College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China
| | - Jiawei Ren
- College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China
| | - Zedong Lu
- College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China
| | - Yuankun Liu
- College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China
| | - Kai Li
- School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China
| | - Fangshu Qu
- Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Guangzhou University, Guangzhou 510006, China.
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Yalcin S, Ayyildiz E. Analyzing the impact of artificial intelligence on operational efficiency in wastewater treatment: a comprehensive neutrosophic AHP-based SWOT analysis. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:51000-51024. [PMID: 39106015 DOI: 10.1007/s11356-024-34430-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 07/15/2024] [Indexed: 08/07/2024]
Abstract
The escalating global challenges of population growth, climate crisis, and resource depletion have intensified water scarcity, emphasizing the critical role of wastewater treatment (WWT) in environmental preservation. While discharging untreated wastewater poses extinction risks to various species, effective WWT operations are indispensable for ecosystem continuity and sustainable water sources. Recognizing the complexity of WWT management, this study delves into the potential of artificial intelligence (AI) in strategic planning and decision-making within the WWT domain. Through a comprehensive SWOT analysis, this study evaluates the strengths, weaknesses, opportunities, and threats associated with AI integration in WWT processes. Utilizing the SWOT analysis framework, key criteria are identified, and their importance weights are assessed via the interval-valued neutrosophic analytical hierarchy process (IVN-AHP). According to analysis, the strengths in WWT are crucial, but potential opportunities and threats should not be ignored. The results of the study highlight several key findings regarding the integration of AI in WWT processes. While concerns about the reduction in human resources and potential unemployment, as well as the activation time and high energy consumption of AI systems, are identified as significant challenges, the study underscores the success of AI in data analytics as a strong aspect. Specifically, advanced data analysis techniques and the ability to proactively prevent problems emerge as important strengths of AI in WWT. WWT operators and practitioners are encouraged to prioritize the adoption of advanced data analysis techniques and proactive problem-solving strategies to maximize the effectiveness of AI integration in WWT processes.
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Affiliation(s)
- Selin Yalcin
- Department of Industrial Engineering, Istanbul Beykent University, Istanbul, Türkiye
| | - Ertugrul Ayyildiz
- Department of Industrial Engineering, Karadeniz Technical University, Trabzon, Türkiye.
- Department of Computer Science, Western Caspian University, Baku, Azerbaijan.
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Nagpal M, Siddique MA, Sharma K, Sharma N, Mittal A. Optimizing wastewater treatment through artificial intelligence: recent advances and future prospects. WATER SCIENCE AND TECHNOLOGY : A JOURNAL OF THE INTERNATIONAL ASSOCIATION ON WATER POLLUTION RESEARCH 2024; 90:731-757. [PMID: 39141032 DOI: 10.2166/wst.2024.259] [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: 01/31/2024] [Accepted: 07/17/2024] [Indexed: 08/15/2024]
Abstract
Artificial intelligence (AI) is increasingly being applied to wastewater treatment to enhance efficiency, improve processes, and optimize resource utilization. This review focuses on objectives, advantages, outputs, and major findings of various AI models in the three key aspects: the prediction of removal efficiency for both organic and inorganic pollutants, real-time monitoring of essential water quality parameters (such as pH, COD, BOD, turbidity, TDS, and conductivity), and fault detection in the processes and equipment integral to wastewater treatment. The prediction accuracy (R2 value) of AI technologies for pollutant removal has been reported to vary between 0.64 and 1.00. A critical aspect explored in this review is the cost-effectiveness of implementing AI systems in wastewater treatment. Numerous countries and municipalities are actively engaging in pilot projects and demonstrations to assess the feasibility and effectiveness of AI applications in wastewater treatment. Notably, the review highlights successful outcomes from these initiatives across diverse geographical contexts, showcasing the adaptability and positive impact of AI in revolutionizing wastewater treatment on a global scale. Further, insights on the ethical considerations and potential future directions for the use of AI in wastewater treatment plants have also been provided.
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Affiliation(s)
- Mudita Nagpal
- Department of Applied Sciences, Vivekananda Institute of Professional Studies-Technical Campus, Delhi 110034, India E-mail:
| | - Miran Ahmad Siddique
- Department of Applied Sciences, Vivekananda Institute of Professional Studies-Technical Campus, Delhi 110034, India
| | - Khushi Sharma
- Department of Applied Sciences, Vivekananda Institute of Professional Studies-Technical Campus, Delhi 110034, India
| | - Nidhi Sharma
- Department of Applied Sciences, Vivekananda Institute of Professional Studies-Technical Campus, Delhi 110034, India
| | - Ankit Mittal
- Department of Chemistry, Shyam Lal College, University of Delhi, Delhi 110032, India
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Wasilewski T, Kamysz W, Gębicki J. AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring. BIOSENSORS 2024; 14:356. [PMID: 39056632 PMCID: PMC11274923 DOI: 10.3390/bios14070356] [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: 05/09/2024] [Revised: 06/25/2024] [Accepted: 06/28/2024] [Indexed: 07/28/2024]
Abstract
The steady progress in consumer electronics, together with improvement in microflow techniques, nanotechnology, and data processing, has led to implementation of cost-effective, user-friendly portable devices, which play the role of not only gadgets but also diagnostic tools. Moreover, numerous smart devices monitor patients' health, and some of them are applied in point-of-care (PoC) tests as a reliable source of evaluation of a patient's condition. Current diagnostic practices are still based on laboratory tests, preceded by the collection of biological samples, which are then tested in clinical conditions by trained personnel with specialistic equipment. In practice, collecting passive/active physiological and behavioral data from patients in real time and feeding them to artificial intelligence (AI) models can significantly improve the decision process regarding diagnosis and treatment procedures via the omission of conventional sampling and diagnostic procedures while also excluding the role of pathologists. A combination of conventional and novel methods of digital and traditional biomarker detection with portable, autonomous, and miniaturized devices can revolutionize medical diagnostics in the coming years. This article focuses on a comparison of traditional clinical practices with modern diagnostic techniques based on AI and machine learning (ML). The presented technologies will bypass laboratories and start being commercialized, which should lead to improvement or substitution of current diagnostic tools. Their application in PoC settings or as a consumer technology accessible to every patient appears to be a real possibility. Research in this field is expected to intensify in the coming years. Technological advancements in sensors and biosensors are anticipated to enable the continuous real-time analysis of various omics fields, fostering early disease detection and intervention strategies. The integration of AI with digital health platforms would enable predictive analysis and personalized healthcare, emphasizing the importance of interdisciplinary collaboration in related scientific fields.
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Affiliation(s)
- Tomasz Wasilewski
- Department of Inorganic Chemistry, Faculty of Pharmacy, Medical University of Gdansk, Hallera 107, 80-416 Gdansk, Poland
| | - Wojciech Kamysz
- Department of Inorganic Chemistry, Faculty of Pharmacy, Medical University of Gdansk, Hallera 107, 80-416 Gdansk, Poland
| | - Jacek Gębicki
- Department of Process Engineering and Chemical Technology, Faculty of Chemistry, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, Poland;
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Ibrahim M, Haider A, Lim JW, Mainali B, Aslam M, Kumar M, Shahid MK. Artificial neural network modeling for the prediction, estimation, and treatment of diverse wastewaters: A comprehensive review and future perspective. CHEMOSPHERE 2024; 362:142860. [PMID: 39019174 DOI: 10.1016/j.chemosphere.2024.142860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 07/03/2024] [Accepted: 07/14/2024] [Indexed: 07/19/2024]
Abstract
The application of artificial neural networks (ANNs) in the treatment of wastewater has achieved increasing attention, as it enhances the efficiency and sustainability of wastewater treatment plants (WWTPs). This paper explores the application of ANN-based models in WWTPs, focusing on the latest published research work, by presenting the effectiveness of ANNs in predicting, estimating, and treatment of diverse types of wastewater. Furthermore, this review comprehensively examines the applicability of the ANNs in various processes and methods used for wastewater treatment, including membrane and membrane bioreactors, coagulation/flocculation, UV-disinfection processes, and biological treatment systems. Additionally, it provides a detailed analysis of pollutants viz organic and inorganic substances, nutrients, pharmaceuticals, drugs, pesticides, dyes, etc., from wastewater, utilizing both ANN and ANN-based models. Moreover, it assesses the techno-economic value of ANNs, provides cost estimation and energy analysis, and outlines promising future research directions of ANNs in wastewater treatment. AI-based techniques are used to predict parameters such as chemical oxygen demand (COD) and biological oxygen demand (BOD) in WWTP influent. ANNs have been formed for the estimation of the removal efficiency of pollutants such as total nitrogen (TN), total phosphorus (TP), BOD, and total suspended solids (TSS) in the effluent of WWTPs. The literature also discloses the use of AI techniques in WWT is an economical and energy-effective method. AI enhances the efficiency of the pumping system, leading to energy conservation with an impressive average savings of approximately 10%. The system can achieve a maximum energy savings state of 25%, accompanied by a notable reduction in costs of up to 30%.
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Affiliation(s)
- Muhammad Ibrahim
- Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Adnan Haider
- Department of Environmental and IT Convergence Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
| | - Jun Wei Lim
- HICoE-Centre for Biofuel and Biochemical Research, Institute of Sustainable Energy and Resources, Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, 32610, Seri Iskandar, Perak Darul Ridzuan, Malaysia; Department of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, 602105, Chennai, India
| | - Bandita Mainali
- School of Engineering, Faculty of Science and Engineering, Macquarie University, Sydney 2109, Australia
| | - Muhammad Aslam
- Membrane Systems Research Group, Department of Chemical Engineering, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan; Faculty of Engineering & Quantity Surveying, INTI International University (INTI-IU), Persiaran Perdana BBN, Putra Nilai, Nilai, 71800, Negeri Sembilan, Malaysia
| | - Mathava Kumar
- Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu, 600036, India
| | - Muhammad Kashif Shahid
- Department of Environmental and IT Convergence Engineering, Chungnam National University, Daejeon 34134, Republic of Korea; School of Engineering, Faculty of Science and Engineering, Macquarie University, Sydney 2109, Australia; Faculty of Civil Engineering and Architecture, National Polytechnic Institute of Cambodia (NPIC), Phnom Penh 12409, Cambodia.
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Doménech-Sánchez A, Laso E, Albertí S. Prevalence and Control of Pseudomonas aeruginosa in Tourist Facilities across the Canary Islands, Spain. Pathogens 2024; 13:501. [PMID: 38921799 PMCID: PMC11207077 DOI: 10.3390/pathogens13060501] [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: 05/09/2024] [Revised: 06/07/2024] [Accepted: 06/07/2024] [Indexed: 06/27/2024] Open
Abstract
Pseudomonas aeruginosa is a common pathogen associated with recreational water facilities and poses risks to public health. However, data on the prevalence of P. aeruginosa in tourist destinations like the Canary Islands, Spain, remain limited. We assessed P. aeruginosa prevalence in 23 tourist facilities from 2016 to 2019. Compliance with water quality standards was evaluated, and 3962 samples were collected and analyzed. We examined different types of recreational water installations, including outer swimming pools, whirlpools, and cold wells. Of the sampled facilities, 31.2% did not comply with the current legislation's parametric values, mainly due to inadequate disinfectant levels, water temperature, and P. aeruginosa presence. The prevalence of P. aeruginosa was 4.8%, comparable to some European countries but lower than others. Cold wells displayed the highest non-compliance rate (89.2%) and yet exhibited a lower P. aeruginosa prevalence (1.9%) than outer swimming pools and whirlpools. Children's presence did not significantly impact P. aeruginosa contamination. Chlorine-based disinfectants are more effective than bromine-based ones in controlling P. aeruginosa. Regional variability in contamination was observed, with Fuerteventura showing lower colonization rates. Disinfectant levels play a critical role in P. aeruginosa control, and maintaining adequate levels is essential, particularly in bromine-treated installations. Our findings provide valuable insights into the prevalence and distribution of P. aeruginosa in recreational waters within tourist facilities. Tailored strategies are needed to ensure water safety in different Spanish regions. Continued monitoring and assessment, combined with artificial intelligence and machine learning, will enable the implementation of targeted interventions to protect the health of recreational water users.
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Affiliation(s)
- Antonio Doménech-Sánchez
- Instituto Universitario de Investigación en Ciencias de la Salud (IUNICS), Universidad de las Islas Baleares, Carretera de Valldemossa km 7.5, 07122 Palma de Mallorca, Spain;
- Saniconsult Ibérica SL, Can Foradí 37 bajos, 07009 Palma de Mallorca, Spain;
- Instituto de Investigación Sanitaria de les Illes Balears (IdIsBa), Edificio S, Hospital Universitario Son Espases, Carretera de Valldemossa 79, 07120 Palma de Mallorca, Spain
| | - Elena Laso
- Saniconsult Ibérica SL, Can Foradí 37 bajos, 07009 Palma de Mallorca, Spain;
| | - Sebastián Albertí
- Instituto Universitario de Investigación en Ciencias de la Salud (IUNICS), Universidad de las Islas Baleares, Carretera de Valldemossa km 7.5, 07122 Palma de Mallorca, Spain;
- Instituto de Investigación Sanitaria de les Illes Balears (IdIsBa), Edificio S, Hospital Universitario Son Espases, Carretera de Valldemossa 79, 07120 Palma de Mallorca, Spain
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Anandhi G, Iyapparaja M. Photocatalytic degradation of drugs and dyes using a maching learning approach. RSC Adv 2024; 14:9003-9019. [PMID: 38500628 PMCID: PMC10945304 DOI: 10.1039/d4ra00711e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 03/02/2024] [Indexed: 03/20/2024] Open
Abstract
The waste management industry uses an increasing number of mathematical prediction models to accurately forecast the behavior of organic pollutants during catalytic degradation. With the increasing quantity of waste generated, these models are critical for reinforcing the efficiency of wastewater treatment strategies. The application of machine-learning techniques in recent years has notably improved predictive models for waste management, which are essential for mitigating the impact of toxic commercial waste on global water supply. Organic contaminants, dyes, pesticides, surfactants, petroleum by-products, and prescription drugs pose risks to human health. Because traditional techniques face challenges in ensuring water quality, modern strategies are vital. Machine learning has emerged as a valuable tool for predicting the photocatalytic degradation of medicinal drugs and dyes, providing a promising avenue for addressing urgent demands in removing organic pollutants from wastewater. This research investigates the synergistic application of photocatalysis and machine learning for pollutant degradation, showcasing a sustainable solution with promising effects on environmental remediation and computational efficiency. This study contributes to green chemistry by providing a clever framework for addressing present-day water pollution challenges and achieving era-driven answers.
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Affiliation(s)
- Ganesan Anandhi
- Department of Smart Computing, School of Computer Science Engineering and Information Systems, Vellore Institute of Technology Vellore 632014 Tamil Nadu India
| | - M Iyapparaja
- Department of Smart Computing, School of Computer Science Engineering and Information Systems, Vellore Institute of Technology Vellore 632014 Tamil Nadu India
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Jannat JN, Islam ARMT, Mia MY, Pal SC, Biswas T, Jion MMMF, Islam MS, Siddique MAB, Idris AM, Khan R, Islam A, Kormoker T, Senapathi V. Using unsupervised machine learning models to drive groundwater chemistry and associated health risks in Indo-Bangla Sundarban region. CHEMOSPHERE 2024; 351:141217. [PMID: 38246495 DOI: 10.1016/j.chemosphere.2024.141217] [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: 07/22/2023] [Revised: 12/17/2023] [Accepted: 01/12/2024] [Indexed: 01/23/2024]
Abstract
Groundwater is an essential resource in the Sundarban regions of India and Bangladesh, but its quality is deteriorating due to anthropogenic impacts. However, the integrated factors affecting groundwater chemistry, source distribution, and health risk are poorly understood along the Indo-Bangla coastal border. The goal of this study is to assess groundwater chemistry, associated driving factors, source contributions, and potential non-carcinogenic health risks (PN-CHR) using unsupervised machine learning models such as a self-organizing map (SOM), positive matrix factorization (PMF), ion ratios, and Monte Carlo simulation. For the Sundarban part of Bangladesh, the SOM clustering approach yielded six clusters, while it yielded five for the Indian Sundarbans. The SOM results showed high correlations among Ca2+, Mg2+, and K+, indicating a common origin. In the Bangladesh Sundarbans, mixed water predominated in all clusters except for cluster 3, whereas in the Indian Sundarbans, Cl--Na+ and mixed water dominated in clusters 1 and 2, and both water types dominated the remaining clusters. Coupling of SOM, PMF, and ionic ratios identified rock weathering as a driving factor for groundwater chemistry. Clusters 1 and 3 were found to be influenced by mineral dissolution and geogenic inputs (overall contribution of 47.7%), while agricultural and industrial effluents dominated clusters 4 and 5 (contribution of 52.7%) in the Bangladesh Sundarbans. Industrial effluents and agricultural activities were associated with clusters 3, 4, and 5 (contributions of 29.5% and 25.4%, respectively) and geogenic sources (contributions of 23 and 22.1% in clusters 1 and 2) in Indian Sundarbans. The probabilistic health risk assessment showed that NO3- poses a higher PN-CHR risk to human health than F- and As, and that potential risk to children is more evident in the Bangladesh Sundarban area than in the Indian Sundarbans. Local authorities must take urgent action to control NO3- emissions in the Indo-Bangla Sundarbans region.
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Affiliation(s)
- Jannatun Nahar Jannat
- Department of Disaster Management, Begum Rokeya University, Rangpur, 5400, Bangladesh.
| | - Abu Reza Md Towfiqul Islam
- Department of Disaster Management, Begum Rokeya University, Rangpur, 5400, Bangladesh; Department of Development Studies, Daffodil International University, Dhaka, 1216, Bangladesh.
| | - Md Yousuf Mia
- Department of Disaster Management, Begum Rokeya University, Rangpur, 5400, Bangladesh.
| | - Subodh Chandra Pal
- Department of Geography, The University of Burdwan, Purba Bardhaman, West Bengal, 713104, India.
| | - Tanmoy Biswas
- Department of Geography, The University of Burdwan, Purba Bardhaman, West Bengal, 713104, India.
| | | | - Md Saiful Islam
- Department of Soil Science, Patuakhali Science and Technology University, Dumki, Patuakhali, 8602, Bangladesh.
| | - Md Abu Bakar Siddique
- Institute of National Analytical Research and Service (INARS), Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhanmondi, Dhaka 1205, Bangladesh.
| | - Abubakr M Idris
- Department of Chemistry, College of Science, King Khalid University, Abha 62529, Saudi Arabia; Research Center for Advanced Materials Science (RCAMS), King Khalid University, Abha, Saudi Arabia.
| | - Rahat Khan
- Institute of Nuclear Science & Technology, Bangladesh Atomic Energy Commission (BAEC), Savar, Dhaka 1349, Bangladesh.
| | - Aznarul Islam
- Department of Geography, Aliah University, 17 Gora Chand Road, Kolkata-700 014, India.
| | - Tapos Kormoker
- Department of Science and Environmental Studies, The Education University of Hong Kong, Tai Po, New Territories 999077, Hong Kong.
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Mathaba M, Banza J. A comprehensive review on artificial intelligence in water treatment for optimization. Clean water now and the future. JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH. PART A, TOXIC/HAZARDOUS SUBSTANCES & ENVIRONMENTAL ENGINEERING 2024; 58:1047-1060. [PMID: 38293764 DOI: 10.1080/10934529.2024.2309102] [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: 12/06/2023] [Accepted: 01/13/2024] [Indexed: 02/01/2024]
Abstract
Given the severe effects that toxic compounds present in wastewater streams have on humans, it is imperative that water and wastewater streams pollution be addressed globally. This review comprehensively examines various water and wastewater treatment methods and water quality management methods based on artificial intelligence (AI). Machine learning (ML) and AI have become a powerful tool for addressing problems in the real world and has gained a lot of interest since it can be used for a wide range of activities. The foundation of ML techniques involves training of a network with collected data, followed by application of learned network to the process simulation and prediction. The creation of ML models for process simulations requires measured data. In order to forecast and simulate chemical and physical processes such chemical reactions, heat transfer, mass transfer, energy, pharmaceutics and separation, a variety of machine-learning algorithms have recently been developed. These models have shown to be more adept at simulating and modeling processes than traditional models. Although AI offers many advantages, a number of disadvantages have kept these methods from being extensively applied in actual water treatment systems. Lack of evidence of application in actual water treatment scenarios, poor repeatability and data availability and selection are a few of the main problems that need to be resolved.
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Affiliation(s)
- Machodi Mathaba
- Department of Chemical Engineering, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa
| | - JeanClaude Banza
- Department of Chemical Engineering, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa
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Bankole AO, Moruzzi R, Negri RG, Bressane A, Reis AG, Sharifi S, James AO, Bankole AR. Machine learning framework for modeling flocculation kinetics using non-intrusive dynamic image analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 908:168452. [PMID: 37956843 DOI: 10.1016/j.scitotenv.2023.168452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 10/27/2023] [Accepted: 11/07/2023] [Indexed: 11/15/2023]
Abstract
The implementation of a machine learning (ML) model to improve both the effectiveness and sustainability of the water treatment system is a significant challenge in the water sector, with the optimization of flocculation processes being a major setback. The objective of this study was to develop a ML model for predicting flocs evolution of the flocculation process in water treatment. Furthermore, we have devised a framework for its potential adoption in large-scale water treatment. Therefore, the paper can be split into two parts. In the first one, flocculation evolution has been studied from an experimental setup, using a non-intrusive image acquisition method. Subsequently, the ML framework has been implemented. Batch assay data of two velocity gradients (Gf 20 and 60 s-1) and flocculation time of three hours were partitioned into five groups for flocs length range 0.27-3.5 mm and upscaled using linear method. Multilayer Perceptron (MLP) and Long-Short Term Memory (LSTM) models, and traditional time series model, Auto Regressive Integrated Moving Average (ARIMA) were explored to predict floc length evolution data. The experiments illustrate the kinetics of flocculation, where the initial stage is characterized by a rapid floc growth followed by a plateau during which floc length fluctuates within a narrow range. Results demonstrate that ML is sensitive to flocculation; however, the model should be selected with care. ARIMA model is not suitable for predicting number of flocs with negative test accuracy (R2). In contrast, MLP recorded R2 of 0.86-1.0 for training and 0.92-1.0 for testing, across Gf 20 s-1 and Gf 60 s-1. LSTM model has the best prediction R2 of 0.92-1.00 for Gf 20 s-1 and accurately predicts the number of flocs across all groups and Gfs. Our study has proven that the developed framework could be replicated for water treatment modeling and promotes the application of smart technology in water treatment.
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Affiliation(s)
- Abayomi O Bankole
- Civil and Environmental Engineering Department, Faculty of Engineering, Sao Paulo State University, Bauru 17033-360, Brazil; Water Resources Management and Agrometeorology Department, COLERM, Federal University of Agriculture, Abeokuta, Nigeria.
| | - Rodrigo Moruzzi
- Civil and Environmental Engineering Department, Faculty of Engineering, Sao Paulo State University, Bauru 17033-360, Brazil; Environmental Engineering Department, Institute of Science and Technology, Sao Paulo State University, Sao Jose dos Campos 12245-000, Brazil.
| | - Rogerio G Negri
- Environmental Engineering Department, Institute of Science and Technology, Sao Paulo State University, Sao Jose dos Campos 12245-000, Brazil
| | - Adriano Bressane
- Civil and Environmental Engineering Department, Faculty of Engineering, Sao Paulo State University, Bauru 17033-360, Brazil; Environmental Engineering Department, Institute of Science and Technology, Sao Paulo State University, Sao Jose dos Campos 12245-000, Brazil
| | - Adriano G Reis
- Civil and Environmental Engineering Department, Faculty of Engineering, Sao Paulo State University, Bauru 17033-360, Brazil; Environmental Engineering Department, Institute of Science and Technology, Sao Paulo State University, Sao Jose dos Campos 12245-000, Brazil
| | - Soroosh Sharifi
- Department of Civil Engineering, Faculty of Engineering, University of Birmingham, United Kingdom
| | - Abraham O James
- Civil and Environmental Engineering Department, Faculty of Engineering, Sao Paulo State University, Bauru 17033-360, Brazil; Environmental Management and Toxicology Department, COLERM, Federal University of Agriculture, Abeokuta, Nigeria
| | - Afolashade R Bankole
- Civil and Environmental Engineering Department, Faculty of Engineering, Sao Paulo State University, Bauru 17033-360, Brazil
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Bekbolatova M, Mayer J, Ong CW, Toma M. Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives. Healthcare (Basel) 2024; 12:125. [PMID: 38255014 PMCID: PMC10815906 DOI: 10.3390/healthcare12020125] [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: 10/11/2023] [Revised: 12/27/2023] [Accepted: 01/02/2024] [Indexed: 01/24/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of improving patient outcomes and optimizing healthcare delivery. By harnessing machine learning algorithms, natural language processing, and computer vision, AI enables the analysis of complex medical data. The integration of AI into healthcare systems aims to support clinicians, personalize patient care, and enhance population health, all while addressing the challenges posed by rising costs and limited resources. As a subdivision of computer science, AI focuses on the development of advanced algorithms capable of performing complex tasks that were once reliant on human intelligence. The ultimate goal is to achieve human-level performance with improved efficiency and accuracy in problem-solving and task execution, thereby reducing the need for human intervention. Various industries, including engineering, media/entertainment, finance, and education, have already reaped significant benefits by incorporating AI systems into their operations. Notably, the healthcare sector has witnessed rapid growth in the utilization of AI technology. Nevertheless, there remains untapped potential for AI to truly revolutionize the industry. It is important to note that despite concerns about job displacement, AI in healthcare should not be viewed as a threat to human workers. Instead, AI systems are designed to augment and support healthcare professionals, freeing up their time to focus on more complex and critical tasks. By automating routine and repetitive tasks, AI can alleviate the burden on healthcare professionals, allowing them to dedicate more attention to patient care and meaningful interactions. However, legal and ethical challenges must be addressed when embracing AI technology in medicine, alongside comprehensive public education to ensure widespread acceptance.
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Affiliation(s)
- Molly Bekbolatova
- Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USA; (M.B.); (J.M.)
| | - Jonathan Mayer
- Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USA; (M.B.); (J.M.)
| | - Chi Wei Ong
- School of Chemistry, Chemical Engineering, and Biotechnology, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459, Singapore
| | - Milan Toma
- Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USA; (M.B.); (J.M.)
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12
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Xiao X, Peng Y, Zhang W, Yang X, Zhang Z, Ren B, Zhu G, Zhou S. Current status and prospects of algal bloom early warning technologies: A Review. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 349:119510. [PMID: 37951110 DOI: 10.1016/j.jenvman.2023.119510] [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: 07/26/2023] [Revised: 10/21/2023] [Accepted: 10/31/2023] [Indexed: 11/13/2023]
Abstract
In recent years, frequent occurrences of algal blooms due to environmental changes have posed significant threats to the environment and human health. This paper analyzes the reasons of algal bloom from the perspective of environmental factors such as nutrients, temperature, light, hydrodynamics factors and others. Various commonly used algal bloom monitoring methods are discussed, including traditional field monitoring methods, remote sensing techniques, molecular biology-based monitoring techniques, and sensor-based real-time monitoring techniques. The advantages and limitations of each method are summarized. Existing algal bloom prediction models, including traditional models and machine learning (ML) models, are introduced. Support Vector Machine (SVM), deep learning (DL), and other ML models are discussed in detail, along with their strengths and weaknesses. Finally, this paper provides an outlook on the future development of algal bloom warning techniques, proposing to combine various monitoring methods and prediction models to establish a multi-level and multi-perspective algal bloom monitoring system, further improving the accuracy and timeliness of early warning, and providing more effective safeguards for environmental protection and human health.
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Affiliation(s)
- Xiang Xiao
- College of Civil Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China
| | - Yazhou Peng
- College of Civil Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China.
| | - Wei Zhang
- School of Hydraulic and Environmental Engineering, Changsha University of Science & Technology, Changsha, 410114, China.
| | - Xiuzhen Yang
- College of Civil Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China
| | - Zhi Zhang
- Laboratory of Three Gorges Reservoir Region, Chongqing University, Chongqing, 400045, China
| | - Bozhi Ren
- School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan, 411201, Hunan, China
| | - Guocheng Zhu
- College of Civil Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China
| | - Saijun Zhou
- College of Civil Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China
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13
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Sahu S, Kaur A, Singh G, Kumar Arya S. Harnessing the potential of microalgae-bacteria interaction for eco-friendly wastewater treatment: A review on new strategies involving machine learning and artificial intelligence. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 346:119004. [PMID: 37734213 DOI: 10.1016/j.jenvman.2023.119004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/06/2023] [Accepted: 09/13/2023] [Indexed: 09/23/2023]
Abstract
In the pursuit of effective wastewater treatment and biomass generation, the symbiotic relationship between microalgae and bacteria emerges as a promising avenue. This analysis delves into recent advancements concerning the utilization of microalgae-bacteria consortia for wastewater treatment and biomass production. It examines multiple facets of this symbiosis, encompassing the judicious selection of suitable strains, optimal culture conditions, appropriate media, and operational parameters. Moreover, the exploration extends to contrasting closed and open bioreactor systems for fostering microalgae-bacteria consortia, elucidating the inherent merits and constraints of each methodology. Notably, the untapped potential of co-cultivation with diverse microorganisms, including yeast, fungi, and various microalgae species, to augment biomass output. In this context, artificial intelligence (AI) and machine learning (ML) stand out as transformative catalysts. By addressing intricate challenges in wastewater treatment and microalgae-bacteria symbiosis, AI and ML foster innovative technological solutions. These cutting-edge technologies play a pivotal role in optimizing wastewater treatment processes, enhancing biomass yield, and facilitating real-time monitoring. The synergistic integration of AI and ML instills a novel dimension, propelling the fields towards sustainable solutions. As AI and ML become integral tools in wastewater treatment and symbiotic microorganism cultivation, novel strategies emerge that harness their potential to overcome intricate challenges and revolutionize the domain.
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Affiliation(s)
- Sudarshan Sahu
- Department of Biotechnology Engineering, University Institute of Engineering and Technology, Panjab University, Chandigarh, India
| | - Anupreet Kaur
- Department of Biotechnology Engineering, University Institute of Engineering and Technology, Panjab University, Chandigarh, India
| | - Gursharan Singh
- Department of Medical Laboratory Sciences, Lovely Professional University, Phagwara, 144411, Punjab, India
| | - Shailendra Kumar Arya
- Department of Biotechnology Engineering, University Institute of Engineering and Technology, Panjab University, Chandigarh, India.
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14
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Yu X, Chen S, Zhang X, Wu H, Guo Y, Guan J. Research progress of the artificial intelligence application in wastewater treatment during 2012-2022: a bibliometric analysis. WATER SCIENCE AND TECHNOLOGY : A JOURNAL OF THE INTERNATIONAL ASSOCIATION ON WATER POLLUTION RESEARCH 2023; 88:1750-1766. [PMID: 37830995 PMCID: wst_2023_296 DOI: 10.2166/wst.2023.296] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
This study identified literatures from the Web of Science Core Collection on the application of artificial intelligence in wastewater treatment from 2011 to 2022, through bibliometrics, to summarize achievements and capture the scientific and technological progress. The number of papers published is on the rise, and especially, the number of papers issued after 2018 has increased sharply, with China contributing the most in this regard, followed by the US, Iran and India. The University of Tehran has the largest number of papers, WATER is the most published journal, and Nasr M has the largest number of articles. Collaborative network has been developed mainly through cooperation between European countries, China and the US. Remote sensing in developing countries needs to be further integrated with water quality monitoring programs. It is worth noting that artificial neural network is a research hotspot in recent years. Through keyword clustering analysis, 'machine learning' and 'deep learning' are hot keywords that have emerged since 2019. The use of neural networks for predicting the effectiveness of treatment of difficult to degrade wastewater is a future research trend. The rapid advancement of deep learning provides the opportunity to build automated pipeline defect detection systems through image recognition.
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Affiliation(s)
- Xiaoman Yu
- School of Resources and Environmental Engineering, Shanghai Polytechnic University, Shanghai 201209, China E-mail:
| | - Shuai Chen
- School of Resources and Environmental Engineering, Shanghai Polytechnic University, Shanghai 201209, China; Anhui International Joint Research Center for Nano Carbon-based Materials and Environmental Health, Huainan 232001, China
| | - Xiaojiao Zhang
- School of Resources and Environmental Engineering, Shanghai Polytechnic University, Shanghai 201209, China
| | - Hongcheng Wu
- Shanghai Wobai Environmental Development Co. Ltd, Shanghai 201209, China
| | - Yaoguang Guo
- School of Resources and Environmental Engineering, Shanghai Polytechnic University, Shanghai 201209, China
| | - Jie Guan
- School of Resources and Environmental Engineering, Shanghai Polytechnic University, Shanghai 201209, China
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15
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Rao KS, Tirth V, Almujibah H, Alshahri AH, Hariprasad V, Senthilkumar N. Optimization of water reuse and modelling by saline composition with nanoparticles based on machine learning architectures. WATER SCIENCE AND TECHNOLOGY : A JOURNAL OF THE INTERNATIONAL ASSOCIATION ON WATER POLLUTION RESEARCH 2023; 87:2793-2805. [PMID: 37318924 PMCID: wst_2023_161 DOI: 10.2166/wst.2023.161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Water is a necessary resource that enables the existence of all life forms, including humans. Freshwater usage has become increasingly necessary in recent years. Facilities for treating seawater are less dependable and effective. Deep learning methods have the ability to improve salt particle analysis in saltwater's accuracy and efficiency, which will enhance the performance of water treatment plants. This research proposes a novel technique in optimization of water reuse with nanoparticle analysis based on machine learning architecture. Here, the optimization of water reuse is carried out based on nanoparticle solar cell for saline water treatment and the saline composition has been analyzed using a gradient discriminant random field. Experimental analysis is carried out in terms of specificity, computational cost, kappa coefficient, training accuracy, and mean average precision for various tunnelling electron microscope (TEM) image datasets. The bright-field TEM (BF-TEM) dataset attained a specificity of 75%, kappa coefficient of 44%, training accuracy of 81%, and mean average precision of 61%, whereas the annular dark-field scanning TEM (ADF-STEM) dataset produced specificity of 79%, kappa coefficient of 49%, training accuracy of 85%, and mean average precision of 66% as compared with the existing artificial neural network (ANN) approach.
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Affiliation(s)
- Koppula Srinivas Rao
- Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, Telangana, India
| | - Vineet Tirth
- Mechanical Engineering Department, College of Engineering, King Khalid University, Abha, Asir 61421, Saudi Arabia; Research Center for Advanced Materials Science (RCAMS), King Khalid University, Guraiger, P.O. Box 9004, Abha, Asir 61413, Saudi Arabia
| | - Hamad Almujibah
- Department of Civil Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif City 21974, Saudi Arabia
| | - Abdullah H Alshahri
- Department of Civil Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif City 21974, Saudi Arabia
| | - V Hariprasad
- Department of Aerospace Engineering, Jain (Deemed-to-be) University, Jain Global Campus, Jakkasandra Post, Kanakapura 562112, India
| | - N Senthilkumar
- Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai 602105, India E-mail:
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16
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de Camargo ET, Spanhol FA, Slongo JS, da Silva MVR, Pazinato J, de Lima Lobo AV, Coutinho FR, Pfrimer FWD, Lindino CA, Oyamada MS, Martins LD. Low-Cost Water Quality Sensors for IoT: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094424. [PMID: 37177633 PMCID: PMC10181703 DOI: 10.3390/s23094424] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 04/20/2023] [Accepted: 04/25/2023] [Indexed: 05/15/2023]
Abstract
In many countries, water quality monitoring is limited due to the high cost of logistics and professional equipment such as multiparametric probes. However, low-cost sensors integrated with the Internet of Things can enable real-time environmental monitoring networks, providing valuable water quality information to the public. To facilitate the widespread adoption of these sensors, it is crucial to identify which sensors can accurately measure key water quality parameters, their manufacturers, and their reliability in different environments. Although there is an increasing body of work utilizing low-cost water quality sensors, many questions remain unanswered. To address this issue, a systematic literature review was conducted to determine which low-cost sensors are being used for remote water quality monitoring. The results show that there are three primary vendors for the sensors used in the selected papers. Most sensors range in price from US$6.9 to US$169.00 but can cost up to US$500.00. While many papers suggest that low-cost sensors are suitable for water quality monitoring, few compare low-cost sensors to reference devices. Therefore, further research is necessary to determine the reliability and accuracy of low-cost sensors compared to professional devices.
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Affiliation(s)
- Edson Tavares de Camargo
- Federal University of Technology-Parana (UTFPR), Toledo 85902-490, Brazil
- Graduate Program in Computer Science, Western Paraná State University (UNIOESTE), Cascavel 85819-110, Brazil
| | - Fabio Alexandre Spanhol
- Federal University of Technology-Parana (UTFPR), Toledo 85902-490, Brazil
- Graduate Program in Computer Science, Western Paraná State University (UNIOESTE), Cascavel 85819-110, Brazil
| | | | | | - Jaqueline Pazinato
- Federal University of Technology-Parana (UTFPR), Toledo 85902-490, Brazil
| | - Adriana Vechai de Lima Lobo
- Sanitation Company of Paraná (SANEPAR), Curitiba 80215-900, Brazil
- Federal University of Parana (UFPR), Curitiba 80210-170, Brazil
| | | | | | | | - Marcio Seiji Oyamada
- Graduate Program in Computer Science, Western Paraná State University (UNIOESTE), Cascavel 85819-110, Brazil
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17
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Saleem F, Jiang JL, Atrache R, Paschos A, Edge TA, Schellhorn HE. Cyanobacterial Algal Bloom Monitoring: Molecular Methods and Technologies for Freshwater Ecosystems. Microorganisms 2023; 11:851. [PMID: 37110273 PMCID: PMC10144707 DOI: 10.3390/microorganisms11040851] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/15/2023] [Accepted: 03/24/2023] [Indexed: 03/29/2023] Open
Abstract
Cyanobacteria (blue-green algae) can accumulate to form harmful algal blooms (HABs) on the surface of freshwater ecosystems under eutrophic conditions. Extensive HAB events can threaten local wildlife, public health, and the utilization of recreational waters. For the detection/quantification of cyanobacteria and cyanotoxins, both the United States Environmental Protection Agency (USEPA) and Health Canada increasingly indicate that molecular methods can be useful. However, each molecular detection method has specific advantages and limitations for monitoring HABs in recreational water ecosystems. Rapidly developing modern technologies, including satellite imaging, biosensors, and machine learning/artificial intelligence, can be integrated with standard/conventional methods to overcome the limitations associated with traditional cyanobacterial detection methodology. We examine advances in cyanobacterial cell lysis methodology and conventional/modern molecular detection methods, including imaging techniques, polymerase chain reaction (PCR)/DNA sequencing, enzyme-linked immunosorbent assays (ELISA), mass spectrometry, remote sensing, and machine learning/AI-based prediction models. This review focuses specifically on methodologies likely to be employed for recreational water ecosystems, especially in the Great Lakes region of North America.
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Affiliation(s)
| | | | | | | | | | - Herb E. Schellhorn
- Department of Biology, McMaster University, Hamilton, ON L8S 4L8, Canada
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18
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Deep Study on Fouling Modelling of Ultrafiltration Membranes Used for OMW Treatment: Comparison Between Semi-empirical Models, Response Surface, and Artificial Neural Networks. FOOD BIOPROCESS TECH 2023. [DOI: 10.1007/s11947-023-03033-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
Abstract
AbstractOlive oil production generates a large amount of wastewater called olive mill wastewater. This paper presents the study of the effect of transmembrane pressure and cross flow velocity on the decrease in permeate flux of different ultrafiltration membranes (material and pore size) when treating a two-phase olive mill wastewater (olive oil washing wastewater). Both semi-empirical models (Hermia models adapted to tangential filtration, combined model, and series resistance model), as well as statistical and machine learning methods (response surface methodology and artificial neural networks), were studied. Regarding the Hermia model, despite the good fit, the main drawback is that it does not consider the possibility that these mechanisms occur simultaneously in the same process. According to the accuracy of the fit of the models, in terms of R2 and SD, both the series resistance model and the combined model were able to represent the experimental data well. This indicates that both cake layer formation and pore blockage contributed to membrane fouling. The inorganic membranes showed a greater tendency to irreversible fouling, with higher values of the Ra/RT (adsorption/total resistance) ratio. Response surface methodology ANOVA showed that both cross flow velocity and transmembrane pressure are significant variables with respect to permeate flux for all membranes studied. Regarding artificial neural networks, the tansig function presented better results than the selu function, all presenting high R2, ranging from 0.96 to 0.99. However, the comparison of all the analyzed models showed that depending on the membrane, one model fits better than the others. Finally, through this work, it was possible to provide a better understanding of the data modelling of different ultrafiltration membranes used for the treatment of olive mill wastewater.
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19
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Huang C, Gao W, Zheng Y, Wang W, Zhang Y, Liu K. Universal machine-learning algorithm for predicting adsorption performance of organic molecules based on limited data set: Importance of feature description. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 859:160228. [PMID: 36402319 DOI: 10.1016/j.scitotenv.2022.160228] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 11/09/2022] [Accepted: 11/12/2022] [Indexed: 06/16/2023]
Abstract
Adsorption of organic molecules from aqueous solution offers a simple and effective method for their removal. Recently, there have been several attempts to apply machine learning (ML) for this problem. To this end, polyparameter linear free energy relationships (pp-LFERs) were employed, and poor prediction results were observed outside model applicability domain of pp-LFERs. In this study, we improved the applicability of ML methods by adopting a chemical-structure (CS) based approach. We used the prediction of adsorption of organic molecules on carbon-based adsorbents as an example. Our results show that this approach can fully differentiate the structural differences between any organic molecules, while providing significant information that is relevant to their interaction with the adsorbents. We compared two CS feature descriptors: 3D-coordination and simplified molecular-input line-entry system (SMILES). We then built CS-ML models based on neural networks (NN) and extreme gradient boosting (XGB). They all outperformed pp-LFERs based models and are capable to accurately predict adsorption isotherm of isomers with similar physiochemical properties such as chiral molecules, even though they are trained with achiral molecules and racemates. We found for predicting adsorption isotherm, XGB shows better performance than NN, and 3D-coordinations allow effective differentiation between organic molecules.
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Affiliation(s)
- Chaoyi Huang
- Division of Environment and Resources, College of Engineering, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Wenyang Gao
- Division of Artificial Intelligence and Data Science, College of Engineering, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Yingdie Zheng
- Division of Environment and Resources, College of Engineering, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Wei Wang
- Division of Environment and Resources, College of Engineering, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Yue Zhang
- Division of Artificial Intelligence and Data Science, College of Engineering, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Kai Liu
- Division of Environment and Resources, College of Engineering, Westlake University, Hangzhou, Zhejiang 310024, China.
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20
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Gerevini L, Cerro G, Bria A, Marrocco C, Ferrigno L, Vitelli M, Ria A, Molinara M. An end-to-end real-time pollutants spilling recognition in wastewater based on the IoT-ready SENSIPLUS platform. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2023. [DOI: 10.1016/j.jksuci.2022.12.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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21
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Review of Latest Advances in Nature-Inspired Algorithms for Optimization of Activated Sludge Processes. Processes (Basel) 2022. [DOI: 10.3390/pr11010077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
The activated sludge process (ASP) is the most widely used biological wastewater treatment system. Advances in research have led to the adoption of Artificial Intelligence (AI), in particular, Nature-Inspired Algorithm (NIA) techniques such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) to optimize treatment systems. This has aided in reducing the complexity and computational time of ASP modelling. This paper covers the latest NIAs used in ASP and discusses the advantages and limitations of each algorithm compared to more traditional algorithms that have been utilized over the last few decades. Algorithms were assessed based on whether they looked at real/ideal treatment plant (WWTP) data (and efficiency) and whether they outperformed the traditional algorithms in optimizing the ASP. While conventional algorithms such as Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) were found to be successfully employed in optimization techniques, newer algorithms such as Whale Optimization Algorithm (WOA), Bat Algorithm (BA), and Intensive Weed Optimization Algorithm (IWO) achieved similar results in the optimization of the ASP, while also having certain unique advantages.
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22
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Ajeng AA, Rosli NSM, Abdullah R, Yaacob JS, Qi NC, Loke SP. Resource recovery from hydroponic wastewaters using microalgae-based biorefineries: A circular bioeconomy perspective. J Biotechnol 2022; 360:11-22. [PMID: 36272573 DOI: 10.1016/j.jbiotec.2022.10.011] [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: 05/13/2022] [Revised: 10/09/2022] [Accepted: 10/17/2022] [Indexed: 11/07/2022]
Abstract
As the world's population grows, it is necessary to rethink how countries throughout the world produce food in order to replace the conventional and unsustainable agricultural techniques. Microalgae cultivation using a nutrient-rich solution from hydroponic systems not only presents a novel approach to solving problems pertaining to the impact of the discharges on the natural environment but also provides a plethora of other biotechnological applications particularly in the productions of high value-added products and plants growth stimulants, which can be potentially assimilated into the circular bioeconomy (CBE) in the hydroponic sector. In this review, the potential and practicability of microalgae to be merged into hydroponics CBE are reviewed. Overall, the integration of microalgal biorefineries in hydroponics systems can be realized after considering their Technology Readiness Level and System Readiness Level beforehand. Several suggestions on strains and hydroponics system improvement using existing biotechnological tools, Artificial Intelligence (AI) and nanobiotechnology in support of the CBE will be covered.
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Affiliation(s)
- Aaronn Avit Ajeng
- Institute of Biological Sciences, Faculty of Science, Universiti Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Noor Sharina Mohd Rosli
- Institute of Biological Sciences, Faculty of Science, Universiti Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Rosazlin Abdullah
- Institute of Biological Sciences, Faculty of Science, Universiti Malaya, 50603 Kuala Lumpur, Malaysia; Centre for Research in Biotechnology for Agriculture (CEBAR), Institute of Biological Sciences, Faculty of Science, Universiti Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Jamilah Syafawati Yaacob
- Institute of Biological Sciences, Faculty of Science, Universiti Malaya, 50603 Kuala Lumpur, Malaysia; Centre for Research in Biotechnology for Agriculture (CEBAR), Institute of Biological Sciences, Faculty of Science, Universiti Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Ng Cai Qi
- Institute of Biological Sciences, Faculty of Science, Universiti Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Show Pau Loke
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia.
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23
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Marmolejo-Ramos F, Ospina R, García-Ceja E, Correa JC. Ingredients for Responsible Machine Learning: A Commented Review of The Hitchhiker’s Guide to Responsible Machine Learning. JOURNAL OF STATISTICAL THEORY AND APPLICATIONS 2022; 21:175-185. [PMID: 36160758 PMCID: PMC9483296 DOI: 10.1007/s44199-022-00048-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 09/02/2022] [Indexed: 11/25/2022] Open
Abstract
AbstractIn The hitchhiker’s guide to responsible machine learning, Biecek, Kozak, and Zawada (here BKZ) provide an illustrated and engaging step-by-step guide on how to perform a machine learning (ML) analysis such that the algorithms, the software, and the entire process is interpretable and transparent for both the data scientist and the end user. This review summarises BKZ’s book and elaborates on three elements key to ML analyses: inductive inference, causality, and interpretability.
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Affiliation(s)
- Fernando Marmolejo-Ramos
- Centre for Change and Complexity in Learning, University of South Australia, Adelaide, SA 5001 Australia
| | - Raydonal Ospina
- CASTLab, Department of Statistics, Universidade Federal de Pernambuco, Recife, Pernambuco 51280-000 Brazil
| | - Enrique García-Ceja
- Escuela de Ingeniería y Ciencias, Tecnológico de Monterrey, 64849 Monterrey, Nuevo León Mexico
| | - Juan C. Correa
- CESA Business School, Bogotá, Bogotá, DC, 110231 Colombia
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24
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A Bibliometric Analysis and Review of Resource Management in Internet of Water Things: The Use of Game Theory. WATER 2022. [DOI: 10.3390/w14101636] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
To understand the current state of research and to also reveal the challenges and opportunities for future research in the field of internet of water things for water quality monitoring, in this study, we conduct a bibliometric analysis and a comprehensive review of the published research from 2012 to 2022 on internet of water things for water quality monitoring. The bibliometric analysis method was used to analyze the collected published papers from the Scopus database. This helped to determine the majority of research topics in the internet of water things for water quality monitoring research field. Subsequently, an in depth comprehensive review of the relevant literature was conducted to provide insight into recent advances in internet of water things for water quality monitoring, and to also determine the research gaps in the field. Based on the comprehensive review of literature, we identified that reviews of the research topic of resource management in internet of water things for water quality monitoring is less common. Hence, this study aimed to fill this research gap in the field of internet of water things for water quality monitoring. To address the resource management challenges associated with the internet of water things designed for water quality monitoring applications, this paper is focused on the use of game theory methods. Game theory methods are embedded with powerful mathematical techniques that may be used to model and analyze the behaviors of various individual, or any group, of water quality sensors. Additionally, various open research issues are pointed out as future research directions.
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