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Olawuni OA, Sadare OO, Moothi K. The adsorption routes of 4IR technologies for effective desulphurization using cellulose nanocrystals: Current trends, challenges, and future perspectives. Heliyon 2024; 10:e24732. [PMID: 38312585 PMCID: PMC10835247 DOI: 10.1016/j.heliyon.2024.e24732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 01/12/2024] [Indexed: 02/06/2024] Open
Abstract
The combustion of liquid fuels as energy sources for transportation and power generation has necessitated governments worldwide to direct petroleum refineries to produce sulphur-free fuels for environmental sustainability. This review highlights the novel application of artificial intelligence for optimizing and predicting adsorptive desulphurization operating parameters and green isolation conditions of nanocellulose crystals from lignocellulosic biomass waste. The shortcomings of the traditional modelling and optimization techniques are stated, and artificial intelligence's role in overcoming them is broadly discussed. Also, the relationship between nanotechnology and artificial intelligence and the future perspectives of fourth industrial revolution (4IR) technologies for optimization and modelling of the adsorptive desulphurization process are elaborately discussed. The current study surveys different adsorbents used in adsorptive desulphurization and how biomass-based nanocellulose crystals (green adsorbents) are suitable alternatives for achieving cleaner fuels and environmental sustainability. Likewise, the present study reports the challenges and potential solutions to fully implementing 4IR technologies for effective desulphurization of liquid fuels in petroleum refineries. Hence, this study provides insightful information to benefit a broad audience in waste valorization for sustainability, environmental protection, and clean energy generation.
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Affiliation(s)
- Oluwagbenga A Olawuni
- Department of Chemical Engineering, Faculty of Engineering and the Built Environment, University of Johannesburg, Doornfontein Campus, Johannesburg, 2028, South Africa
| | - Olawumi O Sadare
- Department of Chemical Engineering, Faculty of Engineering and the Built Environment, University of Johannesburg, Doornfontein Campus, Johannesburg, 2028, South Africa
- Department of Chemical Engineering, Water Innovation and Research Centre (WIRC), University of Bath, Claveton Down, Bath, North East Somerset, BA27AY, South West, United Kingdom
| | - Kapil Moothi
- Department of Chemical Engineering, Faculty of Engineering and the Built Environment, University of Johannesburg, Doornfontein Campus, Johannesburg, 2028, South Africa
- School of Chemical and Minerals Engineering, Faculty of Engineering, North-West University, Potchefstroom, 2520, South Africa
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Nguyen DV, Park J, Lee H, Han T, Wu D. Assessing industrial wastewater effluent toxicity using boosting algorithms in machine learning: A case study on ecotoxicity prediction and control strategy development. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 341:123017. [PMID: 38008256 DOI: 10.1016/j.envpol.2023.123017] [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: 08/28/2023] [Revised: 11/09/2023] [Accepted: 11/19/2023] [Indexed: 11/28/2023]
Abstract
Trace heavy metals have a tendency to persist in the effluent of industrial wastewater treatment facilities, leading to toxic effects on downstream water bodies. Traditional assessment methods relied on animal testing, but ethical concerns have rendered them unacceptable. An alternative solution is to evaluate wastewater toxicity using trophic-level aquatic organisms as bioassays. However, these bioassay methods involve costly and time-consuming chemical and biological analytical experiments. In this study, an artificial intelligence-powered water quality assessment (AiWA) approach is proposed for predicting industrial effluent ecotoxicity to further enhance the quick and cost-effective ecotoxicity assessment process. Initially, 99 samples were collected from industrial wastewater treatment plants representing 21 different industries in the Republic of Korea. Fourteen parameters were measured, encompassing both physicochemical and ecotoxicological aspects. Boosting algorithms, especially extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost), were employed for model development. XGBoost outperformed AdaBoost in terms of model performance. Feature selection analysis revealed that conductivity, copper, lead, selenium, pH, and zinc concentrations were the most suitable inputs for training the boosting model. The innovated XGBoost-based AiWA model demonstrated significantly higher performance (i.e., up to 80%) compared to conventional models with an R2 value of exceeding 0.94 and root mean square error of 3.5 toxicity unit for predicting the integrated toxicity unit (ITU). Additionally, pH and conductivity emerged as crucial indicators for reflecting ecotoxicity levels. Specially, this case study indicated that non-toxic/directly dischargeable levels (TU ≤ 1) were achieved when the pH ranged from 6.8 to 8.4 and the conductivity remained below 1651 μS/cm. These findings are expected to facilitate rapid and cost-effective detection of heavy metal ecotoxicity in industrial wastewater effluents, aiding decision-making in wastewater management.
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Affiliation(s)
- Duc-Viet Nguyen
- Centre for Environmental and Energy Research, Ghent University Global Campus, Incheon 21985, Republic of Korea; Department of Green Chemistry and Technology, Ghent University, Centre for Advanced Process Technology for Urban Resource Recovery (CAPTURE), Ghent B9000, Belgium
| | - Jihae Park
- Centre for Environmental and Energy Research, Ghent University Global Campus, Incheon 21985, Republic of Korea; Department of Animal Sciences and Aquatic Ecology, Ghent University, Ghent B9000, Belgium; Bio Environmental Science and Technology (BEST) Lab, Ghent University Global Campus, 119-5 Songdomunhwa-ro, Incheon 21985, Republic of Korea
| | - Hojun Lee
- Bio Environmental Science and Technology (BEST) Lab, Ghent University Global Campus, 119-5 Songdomunhwa-ro, Incheon 21985, Republic of Korea
| | - Taejun Han
- Centre for Environmental and Energy Research, Ghent University Global Campus, Incheon 21985, Republic of Korea; Department of Animal Sciences and Aquatic Ecology, Ghent University, Ghent B9000, Belgium; Bio Environmental Science and Technology (BEST) Lab, Ghent University Global Campus, 119-5 Songdomunhwa-ro, Incheon 21985, Republic of Korea
| | - Di Wu
- Centre for Environmental and Energy Research, Ghent University Global Campus, Incheon 21985, Republic of Korea; Department of Green Chemistry and Technology, Ghent University, Centre for Advanced Process Technology for Urban Resource Recovery (CAPTURE), Ghent B9000, Belgium.
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Chen Z, Yang S, Zhang L, Duan F. Degradative solvent extraction of cyanobacteria: From reaction kinetics to potential organic matter evolution mechanism. BIORESOURCE TECHNOLOGY 2023; 386:129547. [PMID: 37488019 DOI: 10.1016/j.biortech.2023.129547] [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: 05/17/2023] [Revised: 07/20/2023] [Accepted: 07/21/2023] [Indexed: 07/26/2023]
Abstract
This study proposed a new continuous lumped reaction kinetics model to accurately reveal the control mechanism of cyanobacteria at each stage of degradative solvent extraction and discussed the potential evolution mechanism of organic matter. Results showed that degradation solvent extraction successfully separated nitrogen and phosphorus from cyanobacteria. The solute has high carbon and volatile contents, is almost ash-free, and forms a phosphorus-rich residue. The lowest fitting degree of the continuous lumped reaction model kinetics was 94.5%, suggesting that this model worked well. The depolymerization of the residue dominated between 200 and 350 °C, whereas solute decomposition dominated at 400 °C. Nitrogen-containing compounds, which originate from protein decarboxylation or deamination to generate amides, are the main components of the solute, and amino acids react with reducing sugars to generate nitrogen heterocyclic compounds, which are useful for preparing nitrogen-containing chemicals.
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Affiliation(s)
- Zongqi Chen
- Engineering Technology-Research Center of Anhui Education Department for Energy Saving and Pollutant Control in Metallurgical Process, School of Energy and Environment, Anhui University of Technology, Maanshan 243002, Anhui Province, PR China
| | - Shoumeng Yang
- Engineering Technology-Research Center of Anhui Education Department for Energy Saving and Pollutant Control in Metallurgical Process, School of Energy and Environment, Anhui University of Technology, Maanshan 243002, Anhui Province, PR China
| | - Lihui Zhang
- Engineering Technology-Research Center of Anhui Education Department for Energy Saving and Pollutant Control in Metallurgical Process, School of Energy and Environment, Anhui University of Technology, Maanshan 243002, Anhui Province, PR China.
| | - Feng Duan
- Engineering Technology-Research Center of Anhui Education Department for Energy Saving and Pollutant Control in Metallurgical Process, School of Energy and Environment, Anhui University of Technology, Maanshan 243002, Anhui Province, PR China
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Esmaeili F, Mafakheri F, Nasiri F. Biomass supply chain resilience: integrating demand and availability predictions into routing decisions using machine learning. SMART SCIENCE 2023. [DOI: 10.1080/23080477.2023.2176749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Affiliation(s)
- Foad Esmaeili
- Department of Building, Civil, and Environmental Engineering, Concordia University, Montreal, Canada
| | - Fereshteh Mafakheri
- École nationale d’administration publique, Université du Québec, Montréal, Canada
| | - Fuzhan Nasiri
- Department of Building, Civil, and Environmental Engineering, Concordia University, Montreal, Canada
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Zhang W, Chen Q, Chen J, Xu D, Zhan H, Peng H, Pan J, Vlaskin M, Leng L, Li H. Machine learning for hydrothermal treatment of biomass: A review. BIORESOURCE TECHNOLOGY 2023; 370:128547. [PMID: 36584720 DOI: 10.1016/j.biortech.2022.128547] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 12/24/2022] [Accepted: 12/26/2022] [Indexed: 06/17/2023]
Abstract
Hydrothermal treatment (HTT) (i.e., hydrothermal carbonization, liquefaction, and gasification) is a promising technology for biomass valorization. However, diverse variables, including biomass compositions and hydrothermal processes parameters, have impeded in-depth mechanistic understanding on the reaction and engineering in HTT. Recently, machine learning (ML) has been widely employed to predict and optimize the production of biofuels, chemicals, and materials from HTT by feeding experimental data. This review comprehensively analyzed the application of ML for HTT of biomass and systematically illustrated basic ML procedure and descriptors for inputs and outputs of ML models (e.g., biomass compositions, operation conditions, yield and physicochemical properties of derived products) that could be applied in HTT. Moreover, this review summarized ML-aided HTT prediction of yield, compositions, and physicochemical properties of HTT hydrochar or biochar, bio-oil, syngas, and aqueous phase. Ultimately, future prospects were proposed to enhance predictive performance, mechanistic interpretation, process optimization, data sharing, and model application during ML-aided HTT.
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Affiliation(s)
- Weijin Zhang
- School of Energy Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Qingyue Chen
- School of Minerals Processing and Bioengineering, Central South University, Changsha, Hunan 410083, China
| | - Jiefeng Chen
- School of Energy Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Donghai Xu
- Key Laboratory of Thermo-Fluid Science & Engineering, Ministry of Education, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, China
| | - Hao Zhan
- School of Energy Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Haoyi Peng
- School of Energy Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Jian Pan
- School of Minerals Processing and Bioengineering, Central South University, Changsha, Hunan 410083, China
| | - Mikhail Vlaskin
- Joint Institute for High Temperatures of the Russian Academy of Sciences, Moscow 125412, Russia
| | - Lijian Leng
- School of Energy Science and Engineering, Central South University, Changsha, Hunan 410083, China.
| | - Hailong Li
- School of Energy Science and Engineering, Central South University, Changsha, Hunan 410083, China
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Predicting Carbon Residual in Biomass Wastes Using Soft Computing Techniques. ADSORPT SCI TECHNOL 2022. [DOI: 10.1155/2022/8107196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
In recent decades, the development of complex materials developed a class of biomass waste-derived porous carbons (BWDPCs), which are used for carbon capture and sustainable waste management. It is difficult in understanding the adsorption mechanism of CO2 in the air as it has a wide range of properties associated with its diverse textures, functional group existence, pressure, and temperature of varying range. These properties influence diversely the adsorption mechanism of CO2 and pose serious challenges in the process. To resolve this multiobjective formulation, we use a machine learning classifier that maps systematically the CO2 adsorption as a function of compositional and textural properties and adsorption parameters. The machine learning classifier helps in the classification of various porous carbon materials during the time of training and testing. The results of the simulation show that the proposed method is more efficient in classifying the porous nature of the CO2 adsorption materials than other methods.
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