4.7 Article

Machine learning classification algorithms for inadequate wastewater treatment risk mitigation

Related references

Note: Only part of the references are listed.
Article Agricultural Engineering

Machine learning modeling and analysis of biohydrogen production from wastewater by dark fermentation process

Ahmad Hosseinzadeh et al.

Summary: This study used machine learning (ML) procedures to model and analyze H-2 production from wastewater during dark fermentation, and found that gradient boosting machine (GBM), support vector machine (SVM), random forest (RF), and AdaBoost were the most appropriate models. By optimizing the models with grid search and analyzing them deeply with permutation variable importance (PVI), the research identified the relative importance of process variables.

BIORESOURCE TECHNOLOGY (2022)

Article Environmental Sciences

Prediction of effluent arsenic concentration of wastewater treatment plants using machine learning and kriging-based models

Mohammad Zounemat-Kermani et al.

Summary: This study evaluated the potential of kriging-based and machine learning models in predicting effluent arsenic concentration, with results showing that the kriging-logistic method performed the best and incorporating feature selection enhanced model performance by around 7.8%.

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2022)

Article Environmental Sciences

Towards better process management in wastewater treatment plants: Process analytics based on SHAP values for tree-based machine learning methods

Dong Wang et al.

Summary: Understanding pollutant removal mechanisms in WWTPs is crucial for controlling effluent quality. An upgraded ML framework with tree-based models and advanced SHAP interpretation system was used to study cause-and-effect relationships. Results showed XGBoost models to be optimal for TSSe and PO4(e) labels, providing insights for model comparison and interpretation in WWTP applications.

JOURNAL OF ENVIRONMENTAL MANAGEMENT (2022)

Article Green & Sustainable Science & Technology

Applications of machine learning algorithms for biological wastewater treatment: Updates and perspectives

Batsuren Sundui et al.

Summary: This study highlights the importance and advantages of using algae-bacteria consortia for wastewater treatment to achieve nutrient uptake and resource recovery. Machine learning algorithms play a crucial role in predicting uncertainties in the treatment process and provide reliable support for real-time monitoring, optimization, and fault detection in wastewater treatment systems.

CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY (2021)

Review Environmental Sciences

Treatment technologies for emerging contaminants in wastewater treatment plants: A review

Prangya R. Rout et al.

Summary: Emerging contaminants (ECs) are unregulated anthropogenic chemicals that persist in the environment and are considered contaminants of emerging environmental concerns. Major classes of ECs include pharmaceuticals and personal care products, surfactants, plasticizers, pesticides, fire retardants, and nanomaterials. In wastewater treatment plants, EC removal efficiency ranges from 20-50% in the primary treatment step, 30-70% in the secondary treatment step, and >90% in the tertiary treatment step.

SCIENCE OF THE TOTAL ENVIRONMENT (2021)

Article Environmental Sciences

Machine-learning insights into nitrate-reducing communities in a full-scale municipal wastewater treatment plant

Youngjun Kim et al.

Summary: This study conducted machine-learning modeling with activated sludge microbiome data to predict the operational characteristics of a full-scale municipal wastewater treatment plant, finding that linear models had high prediction performance in association with microbial taxa specific to anoxic processes.

JOURNAL OF ENVIRONMENTAL MANAGEMENT (2021)

Article Biotechnology & Applied Microbiology

Enhancement of microbiome management by machine learning for biological wastewater treatment

Wenfang Cai et al.

Summary: It is proposed to develop microbiome-based machine learning models to predict and design biological wastewater treatment systems in order to enhance system stability and prevent system failure.

MICROBIAL BIOTECHNOLOGY (2021)

Article Engineering, Environmental

Forecasting effluent and performance of wastewater treatment plant using different machine learning techniques

Mustafa El-Rawy et al.

Summary: This study introduces two methods for predicting and forecasting the removal efficiency of pollutants in wastewater treatment plants: traditional feed-forward, deep feed-forward backpropagation, and deep cascade-forward backpropagation networks; and deep learning time series forecasting with a long short-term memory network. The results show that the DCB network has the highest accuracy and is recommended for evaluating and predicting WWTP performance.

JOURNAL OF WATER PROCESS ENGINEERING (2021)

Article Engineering, Environmental

Machine learning application reveal dynamic interaction of polyphosphate-accumulating organism in full-scale wastewater treatment plant

Seungdae Oh et al.

Summary: This study advanced the understanding of the diversity and interactions of PAOs and GAOs in EBPR systems using machine learning models, identifying specific species populations strongly correlated with water temperature and validating their positive association in real-scale processes.

JOURNAL OF WATER PROCESS ENGINEERING (2021)

Article Environmental Sciences

Machine Learning Models for Predicting Water Quality of Treated Fruit and Vegetable Wastewater

Gurvinder Mundi et al.

Summary: The study evaluated and modeled various water treatment methods for fruit and vegetable processing wastewater, with multiple linear regression and group method of data handling models selected for predicting post-treatment water quality. These models were able to generate robust equations for selecting appropriate treatment methods based on original wastewater characteristics and required standards.

WATER (2021)

Article Multidisciplinary Sciences

Wastewater Plant Reliability Prediction Using the Machine Learning Classification Algorithms

Lazar Z. Velimirovic et al.

Summary: The paper aims to develop a model to predict water pump failure using machine learning algorithms, improving the efficiency and management of wastewater treatment systems. By using classification algorithms, future values can be predicted based on current values, along with probabilities of each sample belonging to each class.

SYMMETRY-BASEL (2021)

Article Engineering, Environmental

Comparative study on total nitrogen prediction in wastewater treatment plant and effect of various feature selection methods on machine learning algorithms performance

Faramarz Bagherzadeh et al.

Summary: This study evaluated the effect of seven different Feature Selection methods on enhancing the prediction accuracy for total nitrogen in wastewater treatment plants. The results showed that scenario IV suggested by Mutual Information had the best performance. In addition, Gradient Boosting Machine demonstrated the best performance on unseen data-set, indicating its effectiveness for wastewater components prediction.

JOURNAL OF WATER PROCESS ENGINEERING (2021)

Article Environmental Sciences

A machine learning framework to improve effluent quality control in wastewater treatment plants

Dong Wang et al.

Summary: This paper presents a novel ML-based framework designed to improve effluent quality control in WWTPs by clarifying the relationships between operational variables and effluent parameters. The study shows that influent temperature has a significant impact on TSSe and PO4(e), but in different ways; PO4(e) strongly depends on the TSS concentration in aeration basins, and the impact of TSS in aeration basins on effluent parameters increases with the distances of the basin from the merging outlet; Returning excessive amounts of sludge through the second return sludge pipe should be avoided due to its adverse impact on effluent quality.

SCIENCE OF THE TOTAL ENVIRONMENT (2021)

Article Green & Sustainable Science & Technology

Optimizing of operation strategies of the single-stage partial nitrification-anammox process

Guangjiao Chen et al.

JOURNAL OF CLEANER PRODUCTION (2020)

Article Computer Science, Artificial Intelligence

Online sequential extreme learning machine based adaptive control for wastewater treatment plant

Weiwei Cao et al.

NEUROCOMPUTING (2020)

Article Engineering, Environmental

The potential of new ensemble machine learning models for effluent quality parameters prediction and related uncertainty

Ahmad Sharafati et al.

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION (2020)

Article Engineering, Environmental

Performance improvement of wastewater treatment processes by application of machine learning

O. Icke et al.

WATER SCIENCE AND TECHNOLOGY (2020)

Article Engineering, Environmental

Real-time model predictive control of a wastewater treatment plant based on machine learning

A. Bernardelli et al.

WATER SCIENCE AND TECHNOLOGY (2020)

Article Engineering, Civil

Heavy Metal Removal Investigation in Conventional Activated Sludge Systems

Magdi Buaisha et al.

CIVIL ENGINEERING JOURNAL-TEHRAN (2020)

Article Environmental Sciences

Optimization of the wastewater treatment plant: From energy saving to environmental impact mitigation

Sina Borzooei et al.

SCIENCE OF THE TOTAL ENVIRONMENT (2019)

Article Construction & Building Technology

Deep learning approach for sustainable WWTP operation: A case study on data-driven influent conditions monitoring

Abdelkader Dairi et al.

SUSTAINABLE CITIES AND SOCIETY (2019)

Review Biotechnology & Applied Microbiology

A review on the advances in nitrifying biofilm reactors and their removal rates in wastewater treatment

Mona Chaali et al.

JOURNAL OF CHEMICAL TECHNOLOGY AND BIOTECHNOLOGY (2018)

Article Green & Sustainable Science & Technology

Prediction of effluent quality parameters of a wastewater treatment plant using a supervised committee fuzzy logic model

Ata Allah Nadiri et al.

JOURNAL OF CLEANER PRODUCTION (2018)

Article Environmental Sciences

Statistical monitoring of a wastewater treatment plant: A case study

Fouzi Harrou et al.

JOURNAL OF ENVIRONMENTAL MANAGEMENT (2018)

Article Engineering, Environmental

Wastewater treatment plant performance analysis using artificial intelligence - an ensemble approach

Vahid Nourani et al.

WATER SCIENCE AND TECHNOLOGY (2018)

Article Environmental Sciences

Machine Learning Algorithms for the Forecasting of Wastewater Quality Indicators

Francesco Granata et al.

WATER (2017)

Review Biochemical Research Methods

The membrane-biofilm reactor (MBfR) as a counter-diffusional biofilm process

Robert Nerenberg

CURRENT OPINION IN BIOTECHNOLOGY (2016)

Review Marine & Freshwater Biology

The re-eutrophication of Lake Erie: Harmful algal blooms and hypoxia

Susan B. Watson et al.

HARMFUL ALGAE (2016)

Article Biotechnology & Applied Microbiology

Effect of COD/N ratio on nitrogen removal in a membrane-aerated biofilm reactor

Jiayi Lin et al.

INTERNATIONAL BIODETERIORATION & BIODEGRADATION (2016)

Article Environmental Sciences

Simulation and optimization of a coking wastewater biological treatment process by activated sludge models (ASM)

Xiaohui Wu et al.

JOURNAL OF ENVIRONMENTAL MANAGEMENT (2016)

Article Environmental Sciences

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Hong Guo et al.

JOURNAL OF ENVIRONMENTAL SCIENCES (2015)

Review Environmental Sciences

Assessing and addressing the re-eutrophication of Lake Erie: Central basin hypoxia

Donald Scavia et al.

JOURNAL OF GREAT LAKES RESEARCH (2014)

Review Engineering, Environmental

Current state of sewage treatment in China

Lingyun Jin et al.

WATER RESEARCH (2014)

Article Multidisciplinary Sciences

Record-setting algal bloom in Lake Erie caused by agricultural and meteorological trends consistent with expected future conditions

Anna M. Michalak et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2013)

Article Engineering, Multidisciplinary

Application of Artificial Neural Network (ANN) for the prediction of EL-AGAMY wastewater treatment plant performance-EGYPT

Mahmoud S. Nasr et al.

ALEXANDRIA ENGINEERING JOURNAL (2012)

Article Biotechnology & Applied Microbiology

Modelling and dynamic simulation of hybrid moving bed biofilm reactors: Model concepts and application to a pilot plant

Giorgio Mannina et al.

BIOCHEMICAL ENGINEERING JOURNAL (2011)

Article Biotechnology & Applied Microbiology

Effect of Oxygen Gradients on the Activity and Microbial Community Structure of a Nitrifying, Membrane-Aerated Biofilm

Leon S. Downing et al.

BIOTECHNOLOGY AND BIOENGINEERING (2008)

Article Green & Sustainable Science & Technology

Amelioration of carbon removal prediction for an activated sludge process using an artificial neural network (ANN)

Duenyamin Gueclue et al.

CLEAN-SOIL AIR WATER (2008)

Article Green & Sustainable Science & Technology

Prediction of chemical oxygen demand (COD) based on wavelet decomposition and neural networks

Davut Hanbay et al.

CLEAN-SOIL AIR WATER (2007)

Article Environmental Sciences

Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance

Farouq S. Mjalli et al.

JOURNAL OF ENVIRONMENTAL MANAGEMENT (2007)

Article Biotechnology & Applied Microbiology

Feasibility of a membrane-aerated biofilm reactor to achieve controllable nitrification

A Terada et al.

BIOCHEMICAL ENGINEERING JOURNAL (2006)

Article Computer Science, Interdisciplinary Applications

Prediction of wastewater treatment plant performance using artificial neural networks

MM Hamed et al.

ENVIRONMENTAL MODELLING & SOFTWARE (2004)