Machine-learning-based prediction and optimization of emerging contaminants' adsorption capacity on biochar materials

[1]  Daniel C W Tsang,et al.  Machine learning-assisted exploration for carbon neutrality potential of municipal sludge recycling via hydrothermal carbonization. , 2022, Bioresource technology.

[2]  M. Xue,et al.  Integrated adsorption and photodegradation of tetracycline by bismuth oxycarbonate/biochar nanocomposites , 2022, Chemical Engineering Journal.

[3]  Jingchun Tang,et al.  A review of antibiotics and antibiotic resistance genes (ARGs) adsorption by biochar and modified biochar in water. , 2022, The Science of the total environment.

[4]  Yongping Li,et al.  Development of iron-based biochar for enhancing nitrate adsorption: Effects of specific surface areas, electrostatic forces, and functional groups. , 2022, The Science of the total environment.

[5]  K. Chon,et al.  Machine learning approaches to predict the photocatalytic performance of bismuth ferrite-based materials in the removal of malachite green. , 2022, Journal of hazardous materials.

[6]  Haiping Yang,et al.  Facile synthesis of Cu-BTC@biochar with controlled morphology for effective toluene adsorption at medium-high temperature , 2022, Chemical Engineering Journal.

[7]  Tao Chen,et al.  Prediction of uranium adsorption capacity on biochar by machine learning methods , 2022, Journal of Environmental Chemical Engineering.

[8]  T. H. Huyen Nguyen,et al.  Machine learning-assisted evaluation of potential biochars for pharmaceutical removal from water. , 2022, Environmental research.

[9]  Daniel C W Tsang,et al.  Waste-derived biochar for water pollution control and sustainable development , 2022, Nature Reviews Earth & Environment.

[10]  John L. Zhou,et al.  Application of machine learning algorithms in predicting the photocatalytic degradation of perfluorooctanoic acid , 2022, Catalysis Reviews.

[11]  R. Dantas,et al.  New graphene oxide-safranin modified@polyacrylonitrile membranes for removal of emerging contaminants: The role of chemical and morphological features , 2022, Chemical Engineering Journal.

[12]  K. Chae,et al.  Facilitated physisorption of ibuprofen on waste coffee residue biochars through simultaneous magnetization and activation in groundwater and lake water: Adsorption mechanisms and reusability , 2022, Journal of Environmental Chemical Engineering.

[13]  Haibo Lin,et al.  Bone/muscle-inspired polymer porous matrix toughened carbon nanofibrous catalytic membranes for robust emerging contaminants removal , 2022, Chemical Engineering Journal.

[14]  R. Elmoubarki,et al.  Experimental design, machine learning approaches for the optimization and modeling of caffeine adsorption , 2022, Materials Today Chemistry.

[15]  Mingzhi Huang,et al.  Water quality prediction model using Gaussian process regression based on deep learning for carbon neutrality in papermaking wastewater treatment system. , 2022, Environmental research.

[16]  Daniel C W Tsang,et al.  Machine learning exploration of the direct and indirect roles of Fe impregnation on Cr(VI) removal by engineered biochar , 2022 .

[17]  E. Lichtfouse,et al.  Removal of emerging contaminants from wastewater using advanced treatments. A review , 2022, Environmental Chemistry Letters.

[18]  J. Qiu,et al.  Mechanism of biochar functional groups in the catalytic reduction of tetrachloroethylene by sulfides. , 2022, Environmental pollution.

[19]  Fan Yang,et al.  Application of machine learning in predicting the adsorption capacity of organic compounds onto biochar and resin. , 2022, Environmental research.

[20]  Yaoyu Zhou,et al.  Novel insights into the adsorption of organic contaminants by biochar: A review. , 2022, Chemosphere.

[21]  Thi Tuyet Hanh Nguyen,et al.  Potential application of machine learning for exploring adsorption mechanisms of pharmaceuticals onto biochars. , 2022, Chemosphere.

[22]  Yingjie Tian,et al.  A comprehensive survey on regularization strategies in machine learning , 2021, Inf. Fusion.

[23]  M. Sajid,et al.  Removal of pharmaceuticals from water using sewage sludge-derived biochar: A review. , 2021, Chemosphere.

[24]  C. Su,et al.  Machine learning method for simulation of adsorption separation: Comparisons of model’s performance in predicting equilibrium concentrations , 2021, Arabian Journal of Chemistry.

[25]  Roshan Thotagamuge,et al.  Machine learning approaches to predict adsorption capacity of Azolla pinnata in the removal of methylene blue , 2021, Journal of the Taiwan Institute of Chemical Engineers.

[26]  J. Abdi,et al.  Estimation of tetracycline antibiotic photodegradation from wastewater by heterogeneous metal-organic frameworks photocatalysts. , 2021, Chemosphere.

[27]  Daniel C W Tsang,et al.  Insights into the adsorption of pharmaceuticals and personal care products (PPCPs) on biochar and activated carbon with the aid of machine learning. , 2021, Journal of hazardous materials.

[28]  Hern Kim,et al.  Process optimization and kinetics analysis for photocatalytic degradation of emerging contaminant using N-doped TiO2-SiO2 nanoparticle: Artificial Neural Network and Surface Response Methodology approach , 2021, Environmental Technology & Innovation.

[29]  M. Bashir,et al.  Insight into two-dimensional MXenes for environmental applications: Recent progress, challenges, and prospects , 2021, FlatChem.

[30]  S. Dube,et al.  Ball-milling synthesis of biochar and biochar–based nanocomposites and prospects for removal of emerging contaminants: A review , 2021 .

[31]  Javad Roostaei,et al.  Comparative study on total nitrogen prediction in wastewater treatment plant and effect of various feature selection methods on machine learning algorithms performance , 2021 .

[32]  E. Carissimi,et al.  Wastewater containing emerging contaminants treated by residues from the brewing industry based on biochar as a new CuFe2O4 / biochar photocatalyst , 2021, Process Safety and Environmental Protection.

[33]  P. S. Kumar,et al.  Application of adsorption process for effective removal of emerging contaminants from water and wastewater. , 2021, Environmental pollution.

[34]  E. Postnikov,et al.  The CatBoost as a tool to predict the isothermal compressibility of ionic liquids , 2021, Journal of Molecular Liquids.

[35]  A. Gupta,et al.  A review on occurrences, eco-toxic effects, and remediation of emerging contaminants from wastewater: Special emphasis on biological treatment based hybrid systems , 2021 .

[36]  K. Cho,et al.  Effects of NaOH Activation on Adsorptive Removal of Herbicides by Biochars Prepared from Ground Coffee Residues , 2021, Energies.

[37]  Daniel C W Tsang,et al.  Machine learning for the selection of carbon-based materials for tetracycline and sulfamethoxazole adsorption , 2021 .

[38]  N. Gilani,et al.  Construction of graphene based photocatalysts for photocatalytic degradation of organic pollutant and modeling using artificial intelligence techniques , 2021 .

[39]  Md. Anwarul Islam,et al.  A critical review on silver nanoparticles: From synthesis and applications to its mitigation through low-cost adsorption by biochar. , 2021, Journal of environmental management.

[40]  Pan Wu,et al.  Adsorption of emerging contaminants from water and wastewater by modified biochar: A review. , 2021, Environmental pollution.

[41]  Yongeun Park,et al.  Competitive adsorption of pharmaceuticals in lake water and wastewater effluent by pristine and NaOH-activated biochars from spent coffee wastes: Contribution of hydrophobic and π-π interactions. , 2020, Environmental pollution.

[42]  Mohamed Shuaib Mohamed Saheed,et al.  Conventional and emerging technologies for removal of antibiotics from wastewater. , 2020, Journal of hazardous materials.

[43]  Yao Yevenyo Ziggah,et al.  Principal component analysis (PCA) based hybrid models for the accurate estimation of reservoir water saturation , 2020, Comput. Geosci..

[44]  Gopal Achari,et al.  Development of an ensemble of machine learning algorithms to model aerobic granular sludge reactors. , 2020, Water research.

[45]  P. Show,et al.  A review on effective removal of emerging contaminants from aquatic systems: Current trends and scope for further research. , 2020, Journal of hazardous materials.

[46]  John T. Hancock,et al.  CatBoost for big data: an interdisciplinary review , 2020, Journal of Big Data.

[47]  Yongeun Park,et al.  Single and competitive adsorptions of micropollutants using pristine and alkali-modified biochars from spent coffee grounds. , 2020, Journal of hazardous materials.

[48]  M. Naushad,et al.  High removal of emerging contaminants from wastewater by activated carbons derived from the shell of cashew of Para , 2020, Carbon Letters.

[49]  John T. Hancock,et al.  Survey on categorical data for neural networks , 2020, Journal of Big Data.

[50]  A. Mohamed,et al.  Magnetically recoverable Pd-loaded BiFeO3 microcomposite with enhanced visible light photocatalytic performance for pollutant, bacterial and fungal elimination , 2020 .

[51]  J. Kwak,et al.  Effects of the Pyrolysis Temperature on Adsorption of Carbamazepine and Ibuprofen by NaOH Pre-treated Pine Sawdust Biochars , 2020 .

[52]  K. Chon,et al.  Removal Efficiency of Pharmaceuticals Using Coffee Residues Biochar Activated with Zinc Chloride and Powdered Activated Carbon , 2019, Journal of Korean Society of Environmental Engineers.

[53]  Y. Ok,et al.  The application of machine learning methods for prediction of metal sorption onto biochars. , 2019, Journal of hazardous materials.

[54]  T. Stenström,et al.  Decision tree for identification and prediction of filamentous bulking at full-scale activated sludge wastewater treatment plant , 2019, Process Safety and Environmental Protection.

[55]  Yingjie Dai,et al.  The adsorption, regeneration and engineering applications of biochar for removal organic pollutants: A review. , 2019, Chemosphere.

[56]  J. Chen,et al.  Enhanced Adsorption of Aqueous Tetracycline Hydrochloride on Renewable Porous Clay-Carbon Adsorbent Derived from Spent Bleaching Earth via Pyrolysis. , 2019, Langmuir : the ACS journal of surfaces and colloids.

[57]  Yabin Shao,et al.  HIBoosting: A Recommender System Based on a Gradient Boosting Machine , 2019, IEEE Access.

[58]  A. Al-Dujaili,et al.  Phenol adsorption on biochar prepared from the pine fruit shells: Equilibrium, kinetic and thermodynamics studies. , 2018, Journal of environmental management.

[59]  M. Rizwan,et al.  A critical review of mechanisms involved in the adsorption of organic and inorganic contaminants through biochar , 2018, Arabian Journal of Geosciences.

[60]  G. Zeng,et al.  Investigating the adsorption behavior and the relative distribution of Cd2+ sorption mechanisms on biochars by different feedstock. , 2018, Bioresource technology.

[61]  Daniel C W Tsang,et al.  Wood-based biochar for the removal of potentially toxic elements in water and wastewater: a critical review , 2018, International Materials Reviews.

[62]  G. Murtaza,et al.  Effect of biochar on alleviation of cadmium toxicity in wheat (Triticum aestivum L.) grown on Cd-contaminated saline soil , 2018, Environmental Science and Pollution Research.

[63]  E. Bazrafshan,et al.  Photocatalytic degradation of COD in dairy wastewater using CuO nanoparticles , 2017 .

[64]  Daniel C W Tsang,et al.  Biochar-induced changes in soil properties affected immobilization/mobilization of metals/metalloids in contaminated soils , 2017, Journal of Soils and Sediments.

[65]  Pengwei Huo,et al.  Preparation of highly porous carbon from sustainable α-cellulose for superior removal performance of tetracycline and sulfamethazine from water , 2016 .

[66]  Chang-won Kim,et al.  Evaluation of the k-nearest neighbor method for forecasting the influent characteristics of wastewater treatment plant , 2016, Frontiers of Environmental Science & Engineering.

[67]  Wenjing Lu,et al.  Adsorption of cadmium by biochar derived from municipal sewage sludge: Impact factors and adsorption mechanism. , 2015, Chemosphere.

[68]  Qiuping Zhao,et al.  Direct synthesis of nitrogen-doped carbon nanosheets with high surface area and excellent oxygen reduction performance. , 2014, Langmuir : the ACS journal of surfaces and colloids.

[69]  N. Bolan,et al.  Biochar as a sorbent for contaminant management in soil and water: a review. , 2014, Chemosphere.

[70]  Ziying Wang,et al.  Impact of deashing treatment on biochar structural properties and potential sorption mechanisms of phenanthrene. , 2013, Environmental science & technology.

[71]  Chad L. Staiger,et al.  Screening metal-organic frameworks for selective noble gas adsorption in air: effect of pore size and framework topology. , 2013, Physical chemistry chemical physics : PCCP.

[72]  Sotiris B. Kotsiantis,et al.  Decision trees: a recent overview , 2011, Artificial Intelligence Review.

[73]  A. Avcı,et al.  Process optimization for Cr(VI) adsorption onto activated carbons by experimental design , 2011 .

[74]  Chunshan Song,et al.  Role of Surface Oxygen-Containing Functional Groups in Liquid-Phase Adsorption of Nitrogen Compounds on Carbon-Based Adsorbents , 2009 .

[75]  D. Lin,et al.  Adsorption of phenolic compounds by carbon nanotubes: role of aromaticity and substitution of hydroxyl groups. , 2008, Environmental science & technology.

[76]  Hongwei Wu,et al.  Role of unburnt carbon in adsorption of dyes on fly ash , 2005 .

[77]  J. Friedman Greedy function approximation: A gradient boosting machine. , 2001 .