AIMC Topic: Hydrogen-Ion Concentration
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Artificial neural network modeling for the oxidation kinetics of divalent manganese ions during chlorination and the role of arsenite ions in the binary/ternary systems.
Water research
Jun 3, 2024
This study investigated the coexistence and contamination of manganese (Mn(II)) and arsenite (As(III)) in groundwater and examined their oxidation behavior under different equilibrating parameters, including varying pH, bicarbonate (HCO) concentratio...
Machine-Learning-Assisted Rational Design of Si─Rhodamine as Cathepsin-pH-Activated Probe for Accurate Fluorescence Navigation.
Advanced materials (Deerfield Beach, Fla.)
May 31, 2024
High-performance fluorescent probes stand as indispensable tools in fluorescence-guided imaging, and are crucial for precise delineation of focal tissue while minimizing unnecessary removal of healthy tissue. Herein, machine-learning-assisted strateg...
Machine learning-driven prediction of phosphorus removal performance of metal-modified biochar and optimization of preparation processes considering water quality management objectives.
Bioresource technology
May 18, 2024
Developing an optimized and targeted design approach for metal-modified biochar based on water quality conditions and management is achievable through machine learning. This study leveraged machine learning to analyze experimental data on phosphate a...
Artificial neural network-based modeling of Malachite green adsorption onto baru fruit endocarp: insights into equilibrium, kinetic, and thermodynamic behavior.
International journal of phytoremediation
May 17, 2024
In this study, artificial neural network (ANN) tools were employed to forecast the adsorption capacity of Malachite green (MG) by baru fruit endocarp waste (B@FE) under diverse conditions, including pH, adsorbent dosage, initial dye concentration, co...
Sorption Behavior of Azo Dye Congo Red onto Activated Biochar from Waste: Gradient Boosting Machine Learning-Assisted Bayesian Optimization for Improved Adsorption Process.
International journal of molecular sciences
Apr 27, 2024
This work aimed to describe the adsorption behavior of Congo red (CR) onto activated biochar material prepared from waste (). The carbon precursor was soaked with phosphoric acid, followed by pyrolysis to convert the precursor into activated biochar...
Prediction models for bioavailability of Cu and Zn during composting: Insights into machine learning.
Journal of hazardous materials
Apr 23, 2024
Bioavailability assessment of heavy metals in compost products is crucial for evaluating associated environmental risks. However, existing experimental methods are time-consuming and inefficient. The machine learning (ML) method has demonstrated exce...
Predictive modeling of copper (II) adsorption from aqueous solutions by sawdust: a comparative analysis of adaptive neuro-fuzzy interference system (ANFIS) and artificial neural network (ANN) approaches.
Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering
Apr 12, 2024
Heavy metal ions are considered to be the most prevalent and toxic water contaminants. The objective of thois work was to investigate the effectiveness of employing the adsorption technique in a laboratory-size reactor to remove copper (II) ions from...
Integrated AI-driven optimization of Fenton process for the treatment of antibiotic sulfamethoxazole: Insights into mechanistic approach.
Chemosphere
Apr 7, 2024
Antibiotics, as a class of environmental pollutants, pose a significant challenge due to their persistent nature and resistance to easy degradation. This study delves into modeling and optimizing conventional Fenton degradation of antibiotic sulfamet...
Predicting maturity and identifying key factors in organic waste composting using machine learning models.
Bioresource technology
Apr 6, 2024
The measurement of germination index (GI) in composting is a time-consuming and laborious process. This study employed four machine learning (ML) models, namely Random Forest (RF), Artificial Neural Network (ANN), Support Vector Regression (SVR), and...
Statistical versus neural network-embedded swarm intelligence optimization of a metallo-neutral-protease production: activity kinetics and food industry applications.
Preparative biochemistry & biotechnology
Mar 16, 2024
An integrated approach involving response surface methodology (RSM) and artificial neural network-ant-colony hybrid optimization (ANN-ACO) was adopted to develop a bioprocess medium to increase the yield of neutral protease under submerged fermentat...
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