Artificial intelligence (AI) applications in adsorption of heavy metals using modified biochar

Lakshmi, D. Vidhya; Akhil, Dilipkumar; Kartik, Ashokkumar; Gopinath, Kannappan Panchamoorthy; Arun, Jayaseelan; Bhatnagar, Amit; Rinklebe, Jörg; Kim, Woong; Govarthanan, Muthusamy

doi:10.1016/j.scitotenv.2021.149623

Cited by 81 publications

(38 citation statements)

References 155 publications

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“…As a result, one of the primary challenges inherent in this process is the appropriate selection and optimisation of process variables in order to maximise biochar yield and performance. Therefore, the operation of predictive models serves to reduce the burden on the environment, improve method greenness, and contribute to the long-term goals of sustainability and circular bioeconomy (El-Azazy et al 2020 ; El-Azazy et al 2021a ; Bhagat et al 2020 ; Hiranjit 2015 ; Hodgson et al 2016 ; Karimifard and Alavi Moghaddam 2018 ; Kostić et al 2016 ; Lakshmi et al 2021 ; Puccini et al 2017 ).…”

Section: Environmental Remediationmentioning

confidence: 99%

Biochar for agronomy, animal farming, anaerobic digestion, composting, water treatment, soil remediation, construction, energy storage, and carbon sequestration: a review

et al. 2022

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In the context of climate change and the circular economy, biochar has recently found many applications in various sectors as a versatile and recycled material. Here, we review application of biochar-based for carbon sink, covering agronomy, animal farming, anaerobic digestion, composting, environmental remediation, construction, and energy storage. The ultimate storage reservoirs for biochar are soils, civil infrastructure, and landfills. Biochar-based fertilisers, which combine traditional fertilisers with biochar as a nutrient carrier, are promising in agronomy. The use of biochar as a feed additive for animals shows benefits in terms of animal growth, gut microbiota, reduced enteric methane production, egg yield, and endo-toxicant mitigation. Biochar enhances anaerobic digestion operations, primarily for biogas generation and upgrading, performance and sustainability, and the mitigation of inhibitory impurities. In composts, biochar controls the release of greenhouse gases and enhances microbial activity. Co-composted biochar improves soil properties and enhances crop productivity. Pristine and engineered biochar can also be employed for water and soil remediation to remove pollutants. In construction, biochar can be added to cement or asphalt, thus conferring structural and functional advantages. Incorporating biochar in biocomposites improves insulation, electromagnetic radiation protection and moisture control. Finally, synthesising biochar-based materials for energy storage applications requires additional functionalisation.

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Section: Environmental Remediationmentioning

confidence: 99%

Biochar for agronomy, animal farming, anaerobic digestion, composting, water treatment, soil remediation, construction, energy storage, and carbon sequestration: a review

et al. 2022

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“…Despite a number of published technical studies on the simulation of HMs using AI-based models such as artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support vector machines (SVM), etc. others that work on the application of AI in the rea of HMs include [12,16,32,33]. Recently, Yaseen [3] conducted a comprehensive review for modelling HMs using soft computing models.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variability Assessment of Trace Metals Based on Subsurface Water Quality Impact Integrated with Artificial Intelligence-Based Modeling

et al. 2022

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Increasing anthropogenic emissions due to rapid industrialization have triggered environmental pollution and pose a threat to the well-being of the ecosystem. In this study, the first scenario involved the spatio-temporal assessment of topsoil contamination with trace metals in the Dammam region, and samples were taken from 2 zones: the industrial (ID), and the agricultural (AG) area. For this purpose, more than 130 spatially distributed samples of topsoil were collected from residential, industrial, and agricultural areas. Inductively coupled plasma—optical emission spectroscopy (ICP-OES)—was used to analyze the samples for various trace metals. The second scenario involved the creation of different artificial intelligence (AI) models, namely an artificial neural network (ANN) and a support vector regression (SVR), for the estimation of zinc (Zn), copper (Cu), chromium (Cr), and lead (Pb) using feature-based input selection. The experimental outcomes depicted that the average concentration levels of HMs were as follows: Chromium (Cr) (31.79 ± 37.9 mg/kg), Copper (Cu) (6.76 ± 12.54 mg/kg), Lead (Pb) (6.34 ± 14.55 mg/kg), and Zinc (Zn) (23.44 ± 84.43 mg/kg). The modelling accuracy, based on different evaluation criteria, showed that agricultural and industrial stations showed performance merit with goodness-of-fit ranges of 51–91% and 80–99%, respectively. This study concludes that AI models could be successfully applied for the rapid estimation of soil trace metals and related decision-making.

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“…The 18 O isotope for the 18 F production used in the positron emission tomography, respectively, the 13 C and 14 C isotopes, which are used in the respiratory tests, plays an important role in medicine nowadays. Another important domain in which the stable isotopes of 18 O, 15 N, and 13 C are used as tracer agents in agriculture [5], improving the usage of the chemical fertilizer. The most frequent stable isotopes separation methods use the distillation procedures of the compound that contains the followed isotope in liquid or in gaseous phase, respectively, the isotopic exchange in countercurrent on columns with packing.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, the usage of a separation cascade, containing on its structure two separation columns, is considered for the practical implementation of the separation procedure through the system NO, NO 2 -H 2 O, HNO 3 [6,[14][15][16][17][18][19]. This procedure is used for the separation of the 18 O isotope.…”

Section: Introductionmentioning

confidence: 99%

“…The main scope of the paper is to prove the higher accuracy obtained by using the AI (Artificial Intelligence) [15][16][17] methods in modelling the operation of the separation cascade, in comparison with the accuracy obtained by using classic methods. The problem of modeling and controlling the separation processes is presented in several papers of the technical literature for different types of isotopes: [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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AI versus Classic Methods in Modelling Isotopic Separation Processes: Efficiency Comparison

et al. 2021

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In the paper, the comparison between the efficiency of using artificial intelligence methods and the efficiency of using classical methods in modelling the industrial processes is made, considering as a case study the separation process of the 18O isotope. Firstly, the behavior of the considered isotopic separation process is learned using neural networks. The comparison between the efficiency of these methods is highlighted by the simulations of the process model, using the mentioned modelling techniques. In this context, the final part of the paper presents the proposed model being simulated in different scenarios that can occur in practice, thus resulting in some interesting interpretations and conclusions. The paper proves the feasibility of using artificial intelligence methods for industrial processes modeling; the obtained models being intended for use in designing automatic control systems.

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Artificial intelligence (AI) applications in adsorption of heavy metals using modified biochar

Cited by 81 publications

References 155 publications

Biochar for agronomy, animal farming, anaerobic digestion, composting, water treatment, soil remediation, construction, energy storage, and carbon sequestration: a review

Biochar for agronomy, animal farming, anaerobic digestion, composting, water treatment, soil remediation, construction, energy storage, and carbon sequestration: a review

Spatiotemporal Variability Assessment of Trace Metals Based on Subsurface Water Quality Impact Integrated with Artificial Intelligence-Based Modeling

AI versus Classic Methods in Modelling Isotopic Separation Processes: Efficiency Comparison

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