☆ 4.8 Article

Machine Learning Models for Inverse Design of the Electrochemical Oxidation Process for Water Purification

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2023)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Engineering, Environmental

Prospective Life Cycle Assessment for the Electrochemical Oxidation Wastewater Treatment Process: From Laboratory to Industrial Scale

Ye Sun et al.

Summary: Electrochemical oxidation (EO) is a promising technology for water purification, but the indirect environmental burdens associated with electrode preparation and process operation should be considered. This study used prospective life cycle assessment (LCA) to evaluate the life cycle environmental impacts of EO technology from laboratory scale to industrial scale. The results showed that EO is effective for destroying organic pollutants, but the indirect environmental burdens may outweigh the direct benefits. Attention should be given to overall life cycle profiles, including reactor design, anode materials, electrolyte and flow pattern, and renewable power generation, for the practical application of EO technology in wastewater treatment plants.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2023)

添加到收藏夹

Article Engineering, Environmental

Predicting Solute Descriptors for Organic Chemicals by a Deep Neural Network (DNN) Using Basic Chemical Structures and a Surrogate Metric

Kai Zhang et al.

Summary: In this research, models using deep neural networks (DNN) and the PaDEL chemical representation were built to accurately estimate poly-parameter linear free energy relationships (pp-LFERs) descriptors. These models demonstrated good performance in modeling various chemical properties. By using logP as a surrogate metric, the overall accuracy of the estimated pp-LFER descriptors was evaluated. The results showed that the estimated descriptors had lower errors for chemicals with accurate pp-LFER descriptors.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2022)

添加到收藏夹

Article Engineering, Environmental

Machine Learning-Assisted QSAR Models on Contaminant Reactivity Toward Four Oxidants: Combining Small Data Sets and Knowledge Transfer

Shifa Zhong et al.

Summary: The study demonstrated the effectiveness of combining small, similar data sets and transferring knowledge between them to improve the predictive performance of machine learning models.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2022)

添加到收藏夹

Article Engineering, Environmental

Data-Driven Machine Learning in Environmental Pollution: Gains and Problems

Xian Liu et al.

Summary: The complexity and dynamics of the environment pose challenges for predicting and tracing pollution changes. However, the development of machine learning methods provides opportunities for environmental pollution research. The use of machine learning in environmental process studies still has limitations, and its application in areas other than air pollution needs to be increased.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2022)

添加到收藏夹

Article Automation & Control Systems

Selecting an appropriate supervised machine learning algorithm for predictive maintenance

Abdelfettah Ouadah et al.

Summary: This paper aims to improve the performance of predictive maintenance and achieve its goals by selecting the most suitable supervised machine learning algorithm. Three supervised machine learning algorithms, Random forest, Decision tree and KNN, were selected from a comparative study and tested on real-world and simulation scenarios. The results showed that Random forests and Decision trees had slightly different performance, with KNN being a better classification algorithm for extensive volumes of data and Random forest performing better in the case of small datasets.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2022)

添加到收藏夹

Article Engineering, Environmental

Revealing Drivers of Haze Pollution by Explainable Machine Learning

Linlu Hou et al.

Summary: This study used machine learning methods to investigate the roles of meteorological factors, emissions, and chemistry in five severe haze episodes that occurred before or during the COVID-19 lockdown in China. The study found that in addition to high levels of primary emissions, meteorological factors played a dominant role in PM2.5 pollution in these episodes, followed by chemistry.

ENVIRONMENTAL SCIENCE & TECHNOLOGY LETTERS (2022)

添加到收藏夹

Article Environmental Sciences

Electrochemical oxidation of ciprofloxacin in different aqueous matrices using synthesized boron-doped micro and nano-diamond anodes

Alexsandro J. dos Santos et al.

Summary: This study investigated the electrocatalytic performance of two different morphologies of boron doped diamond film electrodes in electrochemical oxidation for the removal of the antibiotic ciprofloxacin. The results showed that nanocrystalline diamond had higher sp2 carbon content and greater doping level, leading to improved surface roughness and electrocatalytic activity compared to microcrystalline diamond. The study also demonstrated the effectiveness of the nanocrystalline diamond in CIP degradation in various aqueous matrices.

ENVIRONMENTAL RESEARCH (2022)

添加到收藏夹

Article Engineering, Environmental

Prediction of Soil Heavy Metal Immobilization by Biochar Using Machine Learning

Kumuduni N. Palansooriya et al.

Summary: This study uses machine learning approaches to predict the immobilization efficiency of heavy metals in biochar-amended soils, and finds that the nitrogen content and application rate of biochar are the most significant features affecting the immobilization.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2022)

添加到收藏夹

Article Engineering, Chemical

Searching for Sustainable Refrigerants by Bridging MolecularModeling with Machine Learning

Ismail I. I. Alkhatib et al.

Summary: We present a novel integrated approach using machine learning algorithms to predict thermo-physical properties of fluids. The approach successfully models different types of refrigerants and provides accurate predictions when evaluating their thermophysical properties.

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2022)

添加到收藏夹

Article Engineering, Chemical

Deep learning meets quantitative structure-activity relationship (QSAR) for leveraging structure-based prediction of solute rejection in organic solvent nanofiltration

Gergo Ignacz et al.

Summary: Methods for determining solute rejection in organic solvent nanofiltration are time-consuming and expensive. This study presents two prediction methods based on quantitative structure-activity relationship using traditional machine learning and deep learning models, providing a new platform for a more in-depth investigation into membrane-solute interactions.

JOURNAL OF MEMBRANE SCIENCE (2022)

添加到收藏夹

Article Engineering, Environmental

A novel method for micropollutant quantification using deep learning and multi-objective optimization

Daeun Yun et al.

Summary: This study developed a rapid and cost-effective analytical approach based on deep learning and multi-objective optimization to estimate the concentrations of micropollutants (MPs) in aquatic systems. By considering internal standards and natural organic matter information, the method reduces instrumental error and matrix effect.

WATER RESEARCH (2022)

添加到收藏夹

Article Engineering, Environmental

Interpretable tree-based ensemble model for predicting beach water quality

Lingbo Li et al.

Summary: Tree-based machine learning models offer low-cost and timely solutions for predicting microbial fecal contamination in beach water. However, many of these models are difficult to interpret. This study evaluates five tree-based models and employs the SHAP explanation method to improve interpretability. LightGBM and XGBoost achieve the highest precision and recall scores in predicting Escherichia coli concentration in beach water.

WATER RESEARCH (2022)

添加到收藏夹

Article Chemistry, Multidisciplinary

Experimental design, machine learning approaches for the optimization and modeling of caffeine adsorption

N. Taoufik et al.

Summary: This study comparatively investigated the sorption of caffeine on fresh and calcined Cu-Al layered double hydroxide, developing reliable models through data mining methods. Various characterization techniques were used to analyze the characteristics of HDL, revealing its structural and functional features. The proposed machine learning models proved to be reliable computer methods for monitoring and simulating the adsorption of pollutants by Cu-Al-LDH from aqueous solutions.

MATERIALS TODAY CHEMISTRY (2022)

添加到收藏夹

Article Engineering, Environmental

Automated machine learning-based prediction of microplastics induced impacts on methane production in anaerobic digestion

Run-Ze Xu et al.

Summary: This study applied automated machine learning methods to model the effects of microplastics on anaerobic digestion processes and conducted explainable analysis. The results showed that the gradient boosting machine model had better prediction performance and the type of microplastics had a significant impact on methane production.

WATER RESEARCH (2022)

添加到收藏夹

Article Engineering, Environmental

Data-Driven Based In-Depth Interpretation and Inverse Design of Anaerobic Digestion for CH4-Rich Biogas Production

Jie Li et al.

Summary: This study utilized ensemble machine learning algorithms to predict and understand the anaerobic digestion (AD) system and developed a reversible design method. The critical factors influencing methane yield and content were identified and the computational results were experimentally validated in a real-world food waste AD process.

ACS ES&T ENGINEERING (2022)

添加到收藏夹

Article Engineering, Environmental

Shedding light on Black Box machine learning models for predicting the reactivity of HO• radicals toward organic compounds

Shifa Zhong et al.

Summary: Developed a machine learning-assisted method for an environmental task, constructed QSAR models using deep neural networks and gradient boosting algorithms for predicting the reactivity of organic compounds towards HO radicals, and achieved satisfactory predictive performance with an ensemble model.

CHEMICAL ENGINEERING JOURNAL (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A Convolutional Neural Network Using Dinucleotide One-hot Encoder for identifying DNA N6-Methyladenine Sites in the Rice Genome

Zhibin Lv et al.

Summary: N6-methyladenine (m(6)A) is a crucial epigenetic modification related to the control of various DNA processes. The iRicem6A-CNN protocol, using machine learning, achieved high accuracy in identifying m(6)A sites in the rice genome, outperforming other predictors.

NEUROCOMPUTING (2021)

添加到收藏夹

Article Physics, Multidisciplinary

An Insider Data Leakage Detection Using One-Hot Encoding, Synthetic Minority Oversampling and Machine Learning Techniques

Taher Al-Shehari et al.

Summary: Insider threats, carried out by authorized employees within an organization, pose a significant cybersecurity challenge by potentially causing more damage than external attacks. This paper proposes a machine learning-based model to detect data leakage attacks executed by malicious insiders before leaving an organization, addressing bias in detection results and data imbalance with feature scaling, one-hot encoding, and synthetic minority oversampling techniques. Experimental results demonstrate the model's superior performance in detecting insider data leakage events.

ENTROPY (2021)

添加到收藏夹

Article Engineering, Environmental

Molecular image-convolutional neural network (CNN) assisted QSAR models for predicting contaminant reactivity toward OH radicals: Transfer learning, data augmentation and model interpretation

Shifa Zhong et al.

Summary: The study demonstrated that data augmentation and transfer learning can enhance the predictive performance of molecular image-CNN models for predicting compound rate constants toward OH radicals. The models showed comparable performance to molecular fingerprint-based models, with a broader applicability domain and the ability to reliably predict the reactivity of new compounds.

CHEMICAL ENGINEERING JOURNAL (2021)

添加到收藏夹

Article Engineering, Environmental

Quantitative structure activity relationships (QSARs) and machine learning models for abiotic reduction of organic compounds by an aqueous Fe(II) complex

Yidan Gao et al.

Summary: This study developed predictive models for the abiotic reduction of 60 organic compounds using both conventional chemical descriptor-based and machine learning approaches, finding that the machine learning model provided similar prediction accuracy to conventional QSARs across all compound classes and conditions.

WATER RESEARCH (2021)

添加到收藏夹

Article Engineering, Environmental

Polyethylene-water partition coefficients for polychlorinated biphenyls: Application of QSPR predictions models with experimental validation

Tengyi Zhu et al.

Summary: The issues of water environmental recalcitrance and ecotoxicity caused by polychlorinated biphenyls (PCBs) are of international concern. Partition coefficients with PCBs between low-density polyethylene (LDPE) and water (KPE-w) play a significant role in assessing their environmental transport. Through a combination of quantitative structure-property relationships (QSPRs) and machine-learning algorithms, 10 in-silico models were developed for a quick estimate of KPE-w, showing good goodness-of-fit and external prediction performances. Further experimental verification demonstrated that the number of chlorine atoms and ortho-substituted chlorines are the key parameters affecting KPE-w values.

WATER RESEARCH (2021)

添加到收藏夹

Article Chemistry, Physical

How Machine Learning Will Revolutionize Electrochemical Sciences

Aashutosh Mistry et al.

Summary: The development time of electrochemical systems is limited by the identification of new materials and understanding their electrochemical response. To shorten this cycle, machine learning can be used for data-driven predictions.

ACS ENERGY LETTERS (2021)

添加到收藏夹

Article Chemistry, Physical

New insights into hydrogen uptake on porous carbon materials via explainable machine learning

Muhammad Irfan Maulana Kusdhany et al.

Summary: The study found that pressure and BET surface area are the two strongest predictors of excess hydrogen uptake in porous carbon materials, with oxygen content also showing a positive correlation. Additionally, ultramicropores are more positively correlated with excess uptake than micropores, although the effect is relatively small compared to other factors such as BET surface area and total pore volume.

CARBON (2021)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Performance prediction of trace metals and cod in wastewater treatment using artificial neural network

Anthony Njuguna Matheri et al.

Summary: Artificial intelligence is increasingly being integrated into daily operations, with a focus on applications such as wastewater treatment. The study successfully predicted the interrelationship between parameters in the wastewater treatment process using techniques like artificial neural networks, providing a reliable tool for environmental management and emerging technologies.

COMPUTERS & CHEMICAL ENGINEERING (2021)

添加到收藏夹

Article Engineering, Environmental

Estimation of Unit Process Data for Life Cycle Assessment Using a Decision Tree-Based Approach

Bu Zhao et al.

Summary: The study developed a machine learning model to estimate missing unit process data, which can complement primary LCI data, successfully classify zero and nonzero flows, and accurately estimate the values of nonzero flows.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2021)

添加到收藏夹

Article Engineering, Environmental

pySiRC: Machine Learning Combined with Molecular Fingerprints to Predict the Reaction Rate Constant of the Radical-Based Oxidation Processes of Aqueous Organic Contaminants

Flavio Olimpio Sanches-Neto et al.

Summary: A web application utilizing machine learning and molecular fingerprint algorithm was developed to calculate reaction rate constant of oxidative processes of organic pollutants in water. Three machine learning algorithms were evaluated, resulting in high goodness-of-fit for training set and good predictive capacity for test set. The model was able to interpret the reactivity of radicals based on the SHAP method.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2021)

添加到收藏夹

Article Engineering, Environmental

Applied Machine Learning for Prediction of CO2 Adsorption on Biomass Waste-Derived Porous Carbons

Xiangzhou Yuan et al.

Summary: Researchers analyzed the adsorption characteristics and composition of biomass waste-derived porous carbons (BWDPCs) through machine learning and found that the gradient boosting decision trees (GBDT) model performed the best in prediction, showing high accuracy in CO2 adsorption.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2021)

添加到收藏夹

Article Engineering, Environmental

Machine Learning: New Ideas and Tools in Environmental Science and Engineering

Shifa Zhong et al.

Summary: The article discusses the potential of machine learning to revolutionize data analysis in the field of environmental science and engineering, covering applications and considerations.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2021)

添加到收藏夹

Article Multidisciplinary Sciences

Deep exploration of random forest model boosts the interpretability of machine learning studies of complicated immune responses and lung burden of nanoparticles

Fubo Yu et al.

Summary: This study successfully predicts the pulmonary immune responses and lung burden of nanoparticles using a tree-based random forest feature importance and feature interaction network analysis framework (TBRFA), overcoming feature importance bias brought by small datasets.

SCIENCE ADVANCES (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Inverse design of nanoporous crystalline reticular materials with deep generative models

Zhenpeng Yao et al.

Summary: Reticular frameworks are crystalline porous materials that have desirable properties for various applications, but the diversity of building blocks presents challenges for materials design. An automated nanoporous materials discovery platform powered by a supramolecular variational autoencoder is proposed for generative design of reticular materials, showing high fidelity in capturing structural features and promising optimization capability for gas separation.

NATURE MACHINE INTELLIGENCE (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Application and comparison of several machine learning algorithms and their integration models in regression problems

Jui-Chan Huang et al.

NEURAL COMPUTING & APPLICATIONS (2020)

添加到收藏夹

Article Chemistry, Multidisciplinary

Machine Learning Assisted Synthesis of Metal-Organic Nanocapsules

Yunchao Xie et al.

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2020)

添加到收藏夹

Article Engineering, Environmental

Low-Temperature Removal of Refractory Organic Pollutants by Electrochemical Oxidation: Role of Interfacial Joule Heating Effect

Shuzhao Pei et al.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2020)

添加到收藏夹

Article Chemistry, Multidisciplinary

Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning

Chun-Teh Chen et al.

ADVANCED SCIENCE (2020)

添加到收藏夹

Article Engineering, Environmental

Degradation of hydroxychloroquine by electrochemical advanced oxidation processes

Nasr Bensalah et al.

CHEMICAL ENGINEERING JOURNAL (2020)

添加到收藏夹

Article Chemistry, Multidisciplinary

Artificial intelligence: the silver bullet for sustainable materials development

Rifan Hardian et al.

GREEN CHEMISTRY (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Unsupervised Semantic-Preserving Adversarial Hashing for Image Search

Cheng Deng et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2019)

添加到收藏夹

Article Medical Informatics

Comparing different supervised machine learning algorithms for disease prediction

Shahadat Uddin et al.

BMC MEDICAL INFORMATICS AND DECISION MAKING (2019)

添加到收藏夹

Review Chemistry, Multidisciplinary

The Prospect of Electrochemical Technologies Advancing Worldwide Water Treatment

Brian P. Chaplin

ACCOUNTS OF CHEMICAL RESEARCH (2019)

添加到收藏夹

Article Engineering, Environmental

Characteristics and difference of oxidation and coagulation mechanisms for the removal of organic compounds by quantum parameter analysis

Zhiwen Cheng et al.

CHEMICAL ENGINEERING JOURNAL (2018)

添加到收藏夹

Article Environmental Sciences

Electro-oxidation of organic pollutants by reactive electrochemical membranes

Clement Trellu et al.

CHEMOSPHERE (2018)

添加到收藏夹

Article Engineering, Environmental

Inactivation of marine heterotrophic bacteria in ballast water by an Electrochemical Advanced Oxidation Process

Javier Moreno-Andres et al.

WATER RESEARCH (2018)

添加到收藏夹

Article Multidisciplinary Sciences

Nanophotonic particle simulation and inverse design using artificial neural networks

John Peurifoy et al.

SCIENCE ADVANCES (2018)

添加到收藏夹

Article Engineering, Environmental

Electrochemical Oxidation of Atrazine and Clothianidin on Bi-doped SnO2-TinO2n-1 Electrocatalytic Reactive Electrochemical Membranes

Pralay Gayen et al.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2018)

添加到收藏夹

Review Multidisciplinary Sciences

Inverse molecular design using machine learning: Generative models for matter engineering

Benjamin Sanchez-Lengeling et al.

SCIENCE (2018)

添加到收藏夹

Review Chemistry, Multidisciplinary

Single and Coupled Electrochemical Processes and Reactors for the Abatement of Organic Water Pollutants: A Critical Review

Carlos A. Martinez-Huitle et al.

CHEMICAL REVIEWS (2015)

添加到收藏夹

Review Engineering, Environmental

Challenges and Opportunities for Electrochemical Processes as Next-Generation Technologies for the Treatment of Contaminated Water

Jelena Radjenovic et al.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2015)

添加到收藏夹

Review Genetics & Heredity

Machine learning applications in genetics and genomics

Maxwell W. Libbrecht et al.

NATURE REVIEWS GENETICS (2015)

添加到收藏夹

Article Engineering, Environmental

Incorporation of electrochemical advanced oxidation processes in a multistage treatment system for sanitary landfill leachate

Francisca C. Moreira et al.

WATER RESEARCH (2015)

添加到收藏夹

Article Engineering, Environmental

Degradation mechanism study of organic pollutants in ozonation process by QSAR analysis

Huicen Zhu et al.

CHEMICAL ENGINEERING JOURNAL (2014)

添加到收藏夹

Article Engineering, Environmental

Mechanism of p-Substituted Phenol Oxidation at a Ti4O7 Reactive Electrochemical Membrane

Amr M. Zaky et al.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2014)

添加到收藏夹

Review Chemistry, Analytical

Critical review of electrochemical advanced oxidation processes for water treatment applications

Brian P. Chaplin

ENVIRONMENTAL SCIENCE-PROCESSES & IMPACTS (2014)

添加到收藏夹

Review Computer Science, Artificial Intelligence

A review of particle swarm optimization and its applications in Solar Photovoltaic system

Anula Khare et al.

APPLIED SOFT COMPUTING (2013)

添加到收藏夹

Article Engineering, Environmental

QSAR models for oxidation of organic micropollutants in water based on ozone and hydroxyl radical rate constants and their chemical classification

Sairam Sudhakaran et al.

WATER RESEARCH (2013)

添加到收藏夹

Article Engineering, Environmental

Electrocatalytic degradation kinetic of 4-chlorophenol by the Pd/C gas-diffusion electrode system

Z. Y. Bian et al.

WATER SCIENCE AND TECHNOLOGY (2013)

添加到收藏夹

Review Engineering, Environmental

Review of photochemical reaction constants of organic micropollutants required for UV advanced oxidation processes in water

B. A. Wols et al.

WATER RESEARCH (2012)

添加到收藏夹

Article Engineering, Environmental

Efficient Electrochemical Oxidation of Perfluorooctanoate Using a Ti/SnO2-Sb-Bi Anode

Qiongfang Zhuo et al.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2011)

添加到收藏夹

Article Biochemical Research Methods

Permutation importance: a corrected feature importance measure

Andre Altmann et al.

BIOINFORMATICS (2010)

添加到收藏夹

Article Engineering, Environmental

Essential explanation of the strong mineralization performance of boron-doped diamond electrodes

Xiuping Zhu et al.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2008)

添加到收藏夹

Article Engineering, Environmental

Electrochemical oxidation characteristics of p-substituted phenols using a boron-doped diamond electrode

Xiuping Zhu et al.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2007)

添加到收藏夹

Article Engineering, Environmental

Electrochemical oxidation of hydroquinone, resorcinol, and catechol on boron-doped diamond anodes

B Nasr et al.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2005)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.