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Papers
61,005 resultsShowing papers similar to Prediction of Biochar Adsorption of Uranium in Wastewater and Inversion of Key Influencing Parameters Based on Ensemble Learning
ClearHighly Efficient Adsorption of Norfloxacin by Low-Cost Biochar: Performance, Mechanisms, and Machine Learning-Assisted Understanding
Researchers produced biochar from medicinal plant residue using potassium carbonate activation and demonstrated its effectiveness in removing the antibiotic norfloxacin from wastewater. The biochar achieved a high surface area and strong adsorption performance through multiple binding mechanisms including hydrogen bonding and electrostatic interactions. The study also employed machine learning to predict adsorption outcomes, offering a cost-effective approach to treating pharmaceutical contamination in water.
Biochar-mediated remediation of uranium-contaminated soils: evidence, mechanisms, and perspectives
This meta-analysis found that adding biochar to uranium-contaminated soils significantly reduced uranium bioavailability by about 59% and shoot uranium accumulation by about 40%. Biochar works through adsorption, complexation, and by enhancing soil microbial communities, demonstrating its potential as a practical remediation tool for heavy metal contamination in agricultural lands.
AI-guided investigation of biochar’s efficacy in Pb immobilization for remediation of Pb contaminated agricultural land
Researchers evaluated ten types of biochar made from different biomass feedstocks for their ability to immobilize lead in contaminated agricultural soil. They used a machine learning approach to predict long-term immobilization effects and found that oilseed rape straw biochar pyrolyzed at 700 degrees was most effective. The study also accounted for simulated microplastic contamination during long-term incubation, providing a novel framework for predicting biochar performance in real-world remediation scenarios.
Biochar : A Review of its History, Characteristics, Factors that Influence its Yield, Methods of Production, Application in Wastewater Treatment and Recent Development
This review examines biochar's history, physicochemical properties, production methods, and applications in wastewater treatment, highlighting its high porosity and diverse functional groups that enable effective adsorption of contaminants including heavy metals and organic pollutants.
Design of an Efficient Model for Microplastic Removal in Wastewater using Advanced Filtration, Nanotechnology, and Bioremediation
This paper proposed an advanced machine learning model to design and optimize microplastic removal in wastewater treatment, using process parameters to predict removal efficiency. The intelligent model outperformed conventional design approaches in predicting treatment outcomes.
Mechanistic and machine-learning insights into microplastic adsorption on modified magnetic biochar for circular-economy applications
Researchers investigated stearic acid-modified magnetic biochar for removing polystyrene microplastics from water, achieving approximately 94% removal efficiency. Machine learning analysis identified contact time, pH, and adsorbent type as the key predictors of removal performance, and the microplastic-laden adsorbent was successfully upcycled for dye removal, demonstrating a circular-economy approach to water treatment.
Functionalized Biochars for Enhanced Removal of Heavy Metals from Aqueous Solutions: Mechanism and Future Industrial Prospects
This review examined functionalized biochar materials as adsorbents for removing heavy metals from water, comparing surface modification strategies that enhance metal uptake capacity and selectivity. Functionalized biochars showed substantially improved adsorption performance over unmodified biochar and low-cost conventional materials.
Predictive Model Based on K-Nearest Neighbor Coupled with the Gray Wolf Optimizer Algorithm (KNN_GWO) for Estimating the Amount of Phenol Adsorption on Powdered Activated Carbon
Researchers developed a machine learning model combining K-nearest neighbor with the gray wolf optimizer algorithm to predict phenol adsorption on activated carbon, achieving high accuracy and outperforming traditional isotherm models for estimating removal efficiency.
Advancements in Biochar as a Sustainable Adsorbent for Water Pollution Mitigation
This review examines how biochar, a charcoal-like material made from plant waste, can remove over 80% of microplastics and nanoplastics from contaminated water, along with heavy metals and other pollutants. Advances in biochar production and machine learning optimization are making it a promising, sustainable tool for cleaning microplastic-polluted water before it reaches people.
Machine Learning to Predict the Adsorption Capacity of Microplastics
Researchers developed machine learning models to predict the adsorption capacity of microplastics for chemical pollutants, providing a computational tool to better understand how microplastics act as vectors for contaminant dispersal in aquatic environments.
Machine learning model for the optimization and kinetics of petroleum industry effluent treatment using aluminum sulfate
Researchers developed machine learning models trained on jar test data from aluminum sulfate coagulation of petroleum industry effluent to optimize operating conditions and model time-evolution kinetics for turbidity removal, comparing the predictive capacity of four ML algorithms from scikit-learn for treatment unit design.
Breakthrough Curves Prediction of Selenite Adsorption on Chemically Modified Zeolite Using Boosted Decision Tree Algorithms for Water Treatment Applications
Researchers developed iron-coated natural zeolite adsorbents for removing selenite contamination from water using packed-bed adsorption columns. The study applied machine learning algorithms to predict breakthrough curves and optimize the removal process, demonstrating that this cost-effective natural material can efficiently retard selenium mobility in contaminated water.
Elucidating microplastic adsorption mechanisms in biomass composite materials through interpretable machine learning
Researchers used interpretable machine learning to study how biomass composite materials adsorb microplastics from water. They found that initial microplastic concentration and surface electrical potential were the most important factors determining adsorption effectiveness. The study demonstrates that data-driven approaches can help design more efficient and sustainable materials for removing microplastics from contaminated water.
Machine Learning-Driven Prediction of Organic Compound Adsorption onto Microplastics in Freshwater
Seven machine learning algorithms were trained on 173 published measurements to predict how strongly organic contaminants adsorb onto different types of microplastics in freshwater. Accurate adsorption predictions are essential for assessing environmental risk, because microplastics that strongly bind pollutants become vectors that concentrate and transport toxic chemicals through aquatic food webs.
Machine Learning to Predict the Adsorption Capacity for Microplastics
Researchers developed and compared three machine learning models — random forest, support vector machine, and artificial neural network — to predict microplastic/water partition coefficients (log Kd) for chemical pollutant adsorption, addressing the limited experimental data available on microplastic adsorption capacity in aquatic environments.
Comparing the Performance of Machine Learning and Deep Learning Algorithms in Wastewater Treatment Process
This study compared machine learning and deep learning algorithms for predicting wastewater treatment plant performance, finding that modified ensemble and stacked models performed best. Machine learning approaches for optimizing wastewater treatment could improve the removal of microplastics alongside conventional pollutants.
The state-of-the-art review on biochar as green additives in cementitious composites: performance, applications, machine learning predictions, and environmental and economic implications
Researchers reviewed how biochar — a carbon-rich material made by heating biomass — can be added to cement to reduce carbon emissions and improve building material performance, while also examining how machine learning models can predict composite properties and support more sustainable construction practices.
Predictive modeling of microplastic adsorption in aquatic environments using advanced machine learning models
Scientists used advanced machine learning models to predict how microplastics interact with and absorb organic pollutants in water. The results showed that microplastics with certain chemical properties attract more toxic compounds, which matters because contaminated microplastics in waterways can concentrate harmful chemicals that may eventually reach humans through drinking water and seafood.
Enhancing water quality prediction: a machine learning approach across diverse water environments
Researchers compared seven machine learning models for predicting water quality parameters using six years of wastewater treatment plant data. The gradient boosting model performed best overall, accurately predicting parameters related to water contamination. While the study focuses on general water quality rather than microplastics specifically, these predictive tools could be applied to monitoring microplastic-relevant conditions in treatment systems.
Predicting aqueous sorption of organic pollutants on microplastics with machine learning
Researchers developed machine learning models to predict how organic pollutants bind to microplastics in water, using data from 475 published experiments. The models outperformed traditional approaches by accounting for properties of both the microplastics and the pollutants simultaneously. The study provides a more universal tool for understanding how microplastics can transport and concentrate harmful chemicals in freshwater systems.
Hydraulic behaviour of sand-biochar mixtures in water and wastewater treatment applications
Researchers investigated how mixing biochar — a charcoal-like material made from organic matter — into sand affects the flow of water through filter systems used to treat drinking water and wastewater, including for removing microplastics. By accounting for the unique pore structure inside biochar particles, they significantly improved the accuracy of models predicting how well these filters perform.
Machine Learning Prediction of Adsorption Behavior of Xenobiotics on Microplastics under Different Environmental Conditions
Researchers developed a machine learning model to predict how different xenobiotic chemicals adsorb onto microplastics under varying environmental conditions, providing a computational tool to assess microplastics as vectors for pollutant transport without requiring extensive laboratory experiments.
Assessing comparable bioconcentration potentials for nanoparticles in aquatic organisms via combined utilization of machine learning and toxicokinetic models
Researchers developed an eXtreme Gradient Boosting-derived toxicokinetic (XGB-TK) model combining machine learning and toxicokinetic modelling to predict bioconcentration factors for a broad range of metallic and carbonaceous nanoparticles in aquatic organisms, addressing the scarcity of experimental data for estimating nanoparticle bioaccumulation potential.
Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning
Researchers developed machine learning models using molecular descriptors to predict the adsorption capacity of microplastics for organic pollutants in aqueous environments, achieving high accuracy across multiple polymer types and enabling faster environmental risk assessment.