AI and Big Data in Water Environments DOI
Huichun Zhang

ACS ES&T Water, Год журнала: 2022, Номер 2(6), С. 904 - 906

Опубликована: Май 25, 2022

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTAI and Big Data in Water EnvironmentsHuichun ZhangHuichun ZhangMore by Huichun ZhangView Biographyhttps://orcid.org/0000-0002-5683-5117Cite this: ACS EST 2022, 2, 6, 904–906Publication Date (Web):May 25, 2022Publication History Received10 May 2022Published online25 inissue 10 June 2022https://pubs.acs.org/doi/10.1021/acsestwater.2c00203https://doi.org/10.1021/acsestwater.2c00203editorialACS PublicationsCopyright © 2022 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions free to access through this site. Learn MoreArticle Views3134Altmetric-Citations3LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum full text article downloads since November 2008 (both PDF HTML) across all institutions individuals. These metrics regularly updated reflect usage leading up last few days.Citations number other articles citing article, calculated Crossref daily. Find more information about citation counts.The Altmetric Attention Score a quantitative measure attention that research has received online. Clicking on donut icon will load page at altmetric.com with additional details score social media presence for given article. how calculated. Share Add toView InAdd Full Text ReferenceAdd Description ExportRISCitationCitation abstractCitation referencesMore Options onFacebookTwitterWechatLinked InRedditEmail (1 MB) Get e-AlertscloseSUBJECTS:Algorithms,Drinking water,Manufacturing,Nanoparticles,Water treatment e-Alerts

Язык: Английский

Deep learning based data-driven model for detecting time-delay water quality indicators of wastewater treatment plant influent DOI

Yituo Zhang,

Chaolin Li,

Hengpan Duan

и другие.

Chemical Engineering Journal, Год журнала: 2023, Номер 467, С. 143483 - 143483

Опубликована: Май 15, 2023

Язык: Английский

Процитировано

67

Prediction of effluent total nitrogen and energy consumption in wastewater treatment plants: Bayesian optimization machine learning methods DOI
Gang Ye, Jinquan Wan,

Zhicheng Deng

и другие.

Bioresource Technology, Год журнала: 2024, Номер 395, С. 130361 - 130361

Опубликована: Янв. 28, 2024

Язык: Английский

Процитировано

23

A Review of Computational Modeling in Wastewater Treatment Processes DOI Creative Commons
M. Salomé Duarte, Gilberto Martins, Pedro Oliveira

и другие.

ACS ES&T Water, Год журнала: 2023, Номер 4(3), С. 784 - 804

Опубликована: Авг. 24, 2023

Wastewater treatment companies are facing several challenges related to the optimization of energy efficiency, meeting more restricted water quality standards, and resource recovery potential. Over past decades, computational models have gained recognition as effective tools for addressing some these challenges, contributing economic operational efficiencies wastewater plants (WWTPs). To predict performance WWTPs, numerous deterministic, stochastic, time series-based been developed. Mechanistic models, incorporating physical empirical knowledge, dominant predictive models. However, represent a simplification reality, resulting in model structure uncertainty constant need calibration. With increasing amount available data, data-driven becoming attractive. The implementation can revolutionize way manage WWTPs by permitting development digital twins process simulation (near) real-time. In is not explicitly specified but instead determined searching relationships data. Thus, main objective present review discuss machine learning prediction WWTP effluent characteristics inflows well anomaly detection studies consumption WWTPs. Furthermore, an overview considering merging both mechanistic hybrid presented promising approach. A critical assessment gaps future directions on mathematical modeling processes also presented, focusing topics such explainability use Transfer Learning processes.

Язык: Английский

Процитировано

42

Energy consumption prediction in water treatment plants using deep learning with data augmentation DOI Creative Commons
Fouzi Harrou, Abdelkader Dairi, Abdelhakim Dorbane

и другие.

Results in Engineering, Год журнала: 2023, Номер 20, С. 101428 - 101428

Опубликована: Сен. 26, 2023

Wastewater treatment plants (WWTPs) are energy-intensive facilities that play a critical role in meeting stringent effluent quality regulations. Accurate prediction of energy consumption WWTPs is essential for cost savings, process optimization, regulatory compliance, and reducing carbon footprint. This paper introduces an efficient approach predicting WWTPs, leveraging deep learning models, data augmentation, feature selection. Specifically, Spline Cubic interpolation enriches the dataset, while Random Forest model identifies important features. The study investigates impact lagged to capture temporal dependencies. Comparative analysis five models on original augmented datasets from Melbourne WWTP demonstrates substantial performance improvement with data. Incorporating further enhances accuracy, providing valuable insights effective management. Notably, Long Short-Term Memory (LSTM) Bidirectional Gated Recurrent Unit (BiGRU) achieve Mean Absolute Percentage Error (MAPE) values 1.36% 1.436%, outperforming state-of-the-art methods.

Язык: Английский

Процитировано

41

Unlocking the Potential of Wastewater Treatment: Machine Learning Based Energy Consumption Prediction DOI Open Access

Yasminah Alali,

Fouzi Harrou, Ying Sun

и другие.

Water, Год журнала: 2023, Номер 15(13), С. 2349 - 2349

Опубликована: Июнь 25, 2023

Wastewater treatment plants (WWTPs) are energy-intensive facilities that fulfill stringent effluent quality norms. Energy consumption prediction in WWTPs is crucial for cost savings, process optimization, compliance with regulations, and reducing the carbon footprint. This paper evaluates compares a set of 23 candidate machine-learning models to predict WWTP energy using actual data from Melbourne WWTP. To this end, Bayesian optimization has been applied calibrate investigated machine learning models. Random Forest XGBoost (eXtreme Gradient Boosting) were assess how incorporated features influenced prediction. In addition, study consideration information past improving accuracy by incorporating time-lagged measurements. Results showed dynamic outperformed static reduced The shows including lagged measurements model improves accuracy, results indicate K-nearest neighbors dominates state-of-the-art methods reaching promising predictions.

Язык: Английский

Процитировано

30

Maximizing energy efficiency in wastewater treatment plants: A data-driven approach for waste heat recovery and an economic analysis using Organic Rankine Cycle and thermal energy storage DOI
Sameer Al‐Dahidi, Mohammad Alrbai, Loiy Al‐Ghussain

и другие.

Applied Energy, Год журнала: 2024, Номер 362, С. 123008 - 123008

Опубликована: Март 16, 2024

Язык: Английский

Процитировано

13

Analysis of factors influencing the energy efficiency in Chinese wastewater treatment plants through machine learning and SHapley Additive exPlanations DOI
Jinze Li,

Zexuan Du,

Junyan Liu

и другие.

The Science of The Total Environment, Год журнала: 2024, Номер 920, С. 171033 - 171033

Опубликована: Фев. 17, 2024

Язык: Английский

Процитировано

11

Enhanced Insights into Effluent Prediction in Wastewater Treatment Plants: Comprehensive Deep Learning Model Explanation Based on SHAP DOI

Ruojia Li,

Kuanliang Feng,

Tong An

и другие.

ACS ES&T Water, Год журнала: 2024, Номер 4(4), С. 1904 - 1915

Опубликована: Апрель 2, 2024

Models are increasingly being utilized to improve the understanding and operation of wastewater treatment plants (WWTPs) in face escalating water resource challenges. Abundant operational data provide extensive opportunities for development machine learning (ML) deep (DL) models. However, coupling time lag among features exacerbate black-box nature such models, hindering their application WWTPs. In this study, we construct a DL model using long short-term memory (LSTM) algorithm capable accurately predicting effluent quality full-scale WWTP with finely tuned hyperparameters rationally chosen input features. Comprehensive explanation based on Shapley additive explanations (SHAP) is implemented clarify contributions multivariate series (MTS) inputs predicted results terms feature dimensions. The LSTM models exhibit excellent accuracy (R2 0.96, 0.95, 0.76 MAPE 5.49, 7.17, 13.37%, respectively) chemical oxygen demand (COD), total phosphorus (TP), nitrogen (TN) better than other baseline ML SHAP quantify what most important when they exert influence how impact results. analysis from temporal dimension further explains characteristics process justifies introduction MTS. Compared correlation without engineering, selection method by significantly enhances predictive accuracy. combinations adjusted values, strong interactions significant output identified. This novel attempt both explainability MTS prediction work shows potential applying WWTPs performance.

Язык: Английский

Процитировано

11

Adaptive prediction for effluent quality of wastewater treatment plant: Improvement with a dual-stage attention-based LSTM network DOI
Tong An,

Kuanliang Feng,

Peijin Cheng

и другие.

Journal of Environmental Management, Год журнала: 2024, Номер 359, С. 120887 - 120887

Опубликована: Апрель 27, 2024

Язык: Английский

Процитировано

10

Evidence of autotrophic direct electron transfer denitrification (DETD) by Thiobacillus species enriched on biocathodes during deep polishing of effluent from a municipal wastewater treatment plant DOI
Haoyong Li,

Yuhao Xu,

He Dong

и другие.

Chemical Engineering Journal, Год журнала: 2024, Номер 495, С. 153389 - 153389

Опубликована: Июнь 21, 2024

Язык: Английский

Процитировано

10