AIMC Topic: Charcoal

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Biosorption of cobalt and chromium from wastewater using manganese dioxide and iron oxide nanoparticles loaded on cellulose-based biochar: Modeling and optimization with machine learning (artificial neural network).

International journal of biological macromolecules Oct 28, 2024
In this study, two nanomaterials with excellent adsorption capacities were developed to remove heavy metals efficiently from wastewater. Manganese dioxide MnO nanoparticles and iron oxide FeO nanoparticles were successfully synthesized using cassava ...
Oxides Charcoal Chromium Adsorption Manganese Compounds Cobalt Ferric Compounds Thermodynamics Kinetics Hydrogen-Ion Concentration Wastewater Water Purification Cellulose Water Pollutants, Chemical Neural Networks, Computer Temperature Machine Learning
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Synergistic biochar and Serratia marcescens tackle toxic metal contamination: A multifaceted machine learning approach.

Journal of environmental management Sep 20, 2024
Metal contamination in soil poses environmental and health risks requiring effective remediation strategies. This study introduces an innovative approach of synergistically employing biochar and bacterial inoculum of Serratia marcescens to address to...
Serratia marcescens Machine Learning Soil Soil Pollutants Charcoal Metals
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Activated biochar production from young coconut waste (Cocos nucifera) as bioadsorbent: a pathway through Artificial Neural Network (ANN) optimization.

Environmental monitoring and assessment Sep 20, 2024
This pioneering work explores the immense potential of young coconut waste, a continuously marginalized residue of the food and beverage industry, to serve as an indispensable feedstock in the production of biochar. Through an examination of the key ...
Adsorption Charcoal Cocos Neural Networks, Computer
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Machine learning-driven prediction of phosphorus adsorption capacity of biochar: Insights for adsorbent design and process optimization.

Journal of environmental management Sep 4, 2024
Phosphorus (P) pollution in aquatic environments poses significant environmental challenges, necessitating the development of effective remediation strategies, and biochar has emerged as a promising adsorbent for P removal at the cost of extensive re...
Water Purification Water Pollutants, Chemical Adsorption Machine Learning Algorithms Charcoal Phosphorus
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Predicting and refining acid modifications of biochar based on machine learning and bibliometric analysis: Specific surface area, average pore size, and total pore volume.

The Science of the total environment Jul 6, 2024
Acid-modified biochar is a modified biochar material with convenient preparation, high specific surface area, and rich pore structure. It has great potential for application in the heavy metal remediation, soil amendments, and carrying catalysts. Spe...
Charcoal Porosity Bibliometrics Adsorption Machine Learning
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Optimal biochar selection for cadmium pollution remediation in Chinese agricultural soils via optimized machine learning.

Journal of hazardous materials Jun 27, 2024
Biochar is effective in mitigating heavy metal pollution, and cadmium (Cd) is the primary pollutant in agricultural fields. However, traditional trial-and-error methods for determining the optimal biochar remediation efficiency are time-consuming and...
Charcoal Soil Environmental Restoration and Remediation China Agriculture Machine Learning Cadmium Soil Pollutants
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The use of artificial neural network for modelling adsorption of Congo red onto activated hazelnut shell.

Environmental monitoring and assessment Jun 19, 2024
Activated hazelnut shell (HSAC), an organic waste, was utilized for the adsorptive removal of Congo red (CR) dye from aqueous solutions, and a modelling study was conducted using artificial neural networks (ANNs). The structure and characteristic fun...
Water Pollutants, Chemical Adsorption Neural Networks, Computer Charcoal Corylus Waste Disposal, Fluid Hydrogen-Ion Concentration Models, Chemical Congo Red Kinetics
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Multi-output neural network model for predicting biochar yield and composition.

The Science of the total environment Jun 14, 2024
In biomass pyrolysis for biochar production, existing prediction models face computational challenges and limited accuracy. This study curated a comprehensive dataset, revealing pyrolysis parameters' dominance in biochar yield (54.8 % importance). Py...
Neural Networks, Computer Biomass Charcoal Machine Learning Pyrolysis
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Predicting Cd(II) adsorption capacity of biochar materials using typical machine learning models for effective remediation of aquatic environments.

The Science of the total environment Jun 13, 2024
The screening and design of "green" biochar materials with high adsorption capacity play a pivotal role in promoting the sustainable treatment of Cd(II)-containing wastewater. In this study, six typical machine learning (ML) models, namely Linear Reg...
Charcoal Environmental Restoration and Remediation Cadmium Models, Chemical Waste Disposal, Fluid Machine Learning Water Pollutants, Chemical Adsorption
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Deep learning prediction and experimental investigation of specific capacitance of nitrogen-doped porous biochar.

Bioresource technology May 25, 2024
N-doped porous biochar is a promising carbon material for supercapacitor electrodes due to its developed pore structure and high chemical activity which greatly affect the capacitive performance. Predicting the capacitance and exploring the most infl...
Electrodes Electric Capacitance Deep Learning Porosity Charcoal Nitrogen
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