AIMC Topic: Charcoal

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Machine learning application for predicting key properties of activated carbon produced from lignocellulosic biomass waste with chemical activation.

Bioresource technology Mar 21, 2024
The successful application of gradient boosting regression (GBR) in machine learning to forecast surface area, pore volume, and yield in biomass-derived activated carbon (AC) production underscores its potential for enhancing manufacturing processes....
Biomass Charcoal Machine Learning Temperature
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Application of machine learning in prediction of Pb adsorption of biochar prepared by tube furnace and fluidized bed.

Environmental science and pollution research international Mar 20, 2024
Data mining by machine learning (ML) has recently come into application in heavy metals purification from wastewater, especially in exploring lead removal by biochar that prepared using tube furnace (TF-C) and fluidized bed (FB-C) pyrolysis methods. ...
Adsorption Charcoal Water Pollutants, Chemical Machine Learning Lead
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Machine learning models for predicting biochar properties from lignocellulosic biomass torrefaction.

Bioresource technology Mar 2, 2024
This study developed six machine learning models to predict the biochar properties from the dry torrefaction of lignocellulosic biomass by using biomass characteristics and torrefaction conditions as input variables. After optimization, gradient boos...
Biomass Charcoal Machine Learning Temperature
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Artificial intelligence-enabled optimization of Fe/Zn@biochar photocatalyst for 2,6-dichlorophenol removal from petrochemical wastewater: A techno-economic perspective.

Chemosphere Feb 19, 2024
While numerous studies have addressed the photocatalytic degradation of 2,6-dichlorophenol (2,6-DCP) in wastewater, an existing research gap pertains to operational factors' optimization by non-linear prediction models to ensure a cost-effective and ...
Chlorophenols Wastewater Zinc Phenols Charcoal Artificial Intelligence
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Tree-structured parzen estimator optimized-automated machine learning assisted by meta-analysis for predicting biochar-driven NO mitigation effect in constructed wetlands.

Journal of environmental management Feb 17, 2024
Biochar is a carbon-neutral tool for combating climate change. Artificial intelligence applications to estimate the biochar mitigation effect on greenhouse gases (GHGs) can assist scientists in making more informed solutions. However, there is also e...
Artificial Intelligence Machine Learning Nitrogen Wetlands Soil Charcoal Nitrous Oxide
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Efficient remediation of different concentrations of Cr-contaminated soils by nano zero-valent iron modified with carboxymethyl cellulose and biochar.

Journal of environmental sciences (China) Dec 18, 2023
Nano zero-valent iron (nZVI) is widely used in soil remediation due to its high reactivity. However, the easy agglomeration, poor antioxidant ability and passivation layer of Fe-Cr coprecipitates of nZVI have limited its application scale in Cr-conta...
Charcoal Carboxymethylcellulose Sodium Metal Nanoparticles Environmental Restoration and Remediation Iron Soil Pollutants Soil Chromium
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Multitask Deep Learning Enabling a Synergy for Cadmium and Methane Mitigation with Biochar Amendments in Paddy Soils.

Environmental science & technology Dec 12, 2023
Biochar has demonstrated significant promise in addressing heavy metal contamination and methane (CH) emissions in paddy soils; however, achieving a synergy between these two goals is challenging due to various variables, including the characteristic...
Soil Deep Learning Charcoal Methane Oryza Artificial Intelligence Cadmium
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Predicting the efficiency of arsenic immobilization in soils by biochar using machine learning.

Journal of environmental sciences (China) Nov 27, 2023
Arsenic (As) pollution in soils is a pervasive environmental issue. Biochar immobilization offers a promising solution for addressing soil As contamination. The efficiency of biochar in immobilizing As in soils primarily hinges on the characteristics...
Machine Learning Soil Arsenic Soil Pollutants Models, Chemical Charcoal
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Transformer-based deep learning models for adsorption capacity prediction of heavy metal ions toward biochar-based adsorbents.

Journal of hazardous materials Oct 12, 2023
Biochar adsorbents synthesized from food and agricultural wastes are commonly applied to eliminate heavy metal (HM) ions from wastewater. However, biochar's diverse characteristics and varied experimental conditions make the accurate estimation of th...
Deep Learning Adsorption Charcoal Ions Metals, Heavy
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Role of modeling and artificial intelligence in process parameter optimization of biochar: A review.

Bioresource technology Oct 10, 2023
Enhancement of crop yield, conservation and quality upgradation of soil, and efficient water management are the main objectives of sustainable agriculture and mitigating climate change's impact on agriculture. In recent days, biochar, obtained via th...
Charcoal Soil Agriculture Artificial Intelligence
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