AIMC Topic: Charcoal

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Machine learning analysis and prediction of N, NO, and O adsorption on activated carbon and carbon molecular sieve.

Environmental science and pollution research international Aug 13, 2022
This research focuses on predicting the adsorbed amount of N, O, and NO on carbon molecular sieve and activated carbon using the artificial neural network (ANN) approach. Experimental isotherm data (data set 1242) on adsorbent type, gas type, tempera...
Neural Networks, Computer Bayes Theorem Charcoal Algorithms Adsorption
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Modeling of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction onto Biochar.

Chemosphere May 23, 2022
Environmental distresses linked to heavy metal (HM) impurity in the water received significant attention among research communities. Recently, advancements in industrial sectors like paper industries, mining, non-ferrous metallurgy, electroplating, m...
Metals, Heavy Wastewater Adsorption Charcoal Deep Learning Water Pollutants, Chemical
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Artificial neural networks for the prediction of biochar yield: A comparative study of metaheuristic algorithms.

Bioresource technology Apr 22, 2022
In this study, an integrated framework of artificial neural networks (ANNs) and metaheuristic algorithms have been developed for the prediction of biochar yield using biomass characteristics and pyrolysis process conditions. Comparative analysis of s...
Pyrolysis Algorithms Charcoal Neural Networks, Computer
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A novel artificial intelligent model for predicting water treatment efficiency of various biochar systems based on artificial neural network and queuing search algorithm.

Chemosphere Sep 20, 2021
This study aims at providing a robust artificial intelligent model for predicting the efficiency of heavy metal removal from aqueous solutions of biochar systems with high accuracy and reliability. Not only is it environmentally significant, but it i...
Neural Networks, Computer Algorithms Humans Adsorption Charcoal Water Purification Reproducibility of Results
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Artificial intelligence (AI) applications in adsorption of heavy metals using modified biochar.

The Science of the total environment Aug 18, 2021
The process of removal of heavy metals is important due to their toxic effects on living organisms and undesirable anthropogenic effects. Conventional methods possess many irreconcilable disadvantages pertaining to cost and efficiency. As a result, t...
Artificial Intelligence Adsorption Charcoal Metals, Heavy
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Predicting the sorption efficiency of heavy metal based on the biochar characteristics, metal sources, and environmental conditions using various novel hybrid machine learning models.

Chemosphere Mar 9, 2021
Heavy metals in water and wastewater are taken into account as one of the most hazardous environmental issues that significantly impact human health. The use of biochar systems with different materials helped significantly remove heavy metals in the ...
Charcoal Machine Learning Metals, Heavy Humans Reproducibility of Results Artificial Intelligence
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Artificial neural network modelling for biodecolorization of Basic Violet 03 from aqueous solution by biochar derived from agro-bio waste of groundnut hull: Kinetics and thermodynamics.

Chemosphere Mar 6, 2021
In this study, Levenberg Marquardt back propagation algorithm was used to train the Artificial Neural Network (ANN) and to predict the adsorptive removal of cationic dye Basic Violet 03 (BV03) by biochar derived from biowaste of groundnut hull. The e...
Hydrogen-Ion Concentration Thermodynamics Kinetics Neural Networks, Computer Charcoal
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Graphitic carbon nitride/biochar composite synthesized by a facile ball-milling method for the adsorption and photocatalytic degradation of enrofloxacin.

Journal of environmental sciences (China) Nov 6, 2020
In order to enhance the removal performance of graphitic carbon nitride (g-CN) on organic pollutant, a simultaneous process of adsorption and photocatalysis was achieved via the compounding of biochar and g-CN. In this study, g-CN was obtained by a c...
Graphite Charcoal Enrofloxacin Adsorption Nitrogen Compounds Light
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Comparative study of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) for modeling of Cu (II) adsorption from aqueous solution using biochar derived from rambutan (Nephelium lappaceum) peel.

Environmental monitoring and assessment Jun 17, 2020
Presence of copper within water bodies deteriorates human health and degrades natural environment. This heavy metal in water is treated using a promising biochar derived from rambutan (Nephelium lappaceum) peel through slow pyrolysis. This research c...
Fuzzy Logic Charcoal Humans Linear Models Sapindaceae Neural Networks, Computer Environmental Monitoring Adsorption
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Fabrication of Carbon-Based Ionic Electromechanically Active Soft Actuators.

Journal of visualized experiments : JoVE Apr 25, 2020
Ionic electromechanically active capacitive laminates are a type of smart material that move in response to electrical stimulation. Due to the soft, compliant and biomimetic nature of this deformation, actuators made of the laminate have received inc...
Polymers Electrodes Charcoal Electric Conductivity Biomimetics Ions Robotics
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