Microalgae-based wastewater treatment – Microalgae-bacteria consortia, multi-omics approaches and algal stress response

The Science of The Total Environment, Год журнала: 2022, Номер 845, С. 157110 - 157110

Опубликована: Июль 2, 2022

Язык: Английский

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Interpretable prediction, classification and regulation of water quality: A case study of Poyang Lake, China

The Science of The Total Environment, Год журнала: 2024, Номер 951, С. 175407 - 175407

Опубликована: Авг. 9, 2024

Язык: Английский

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Revolutionizing wastewater treatment toward circular economy and carbon neutrality goals: Pioneering sustainable and efficient solutions for automation and advanced process control with smart and cutting-edge technologies

Journal of Water Process Engineering, Год журнала: 2024, Номер 63, С. 105486 - 105486

Опубликована: Май 30, 2024

Wastewater treatment plants (WWTPs) play a crucial role in ensuring safe environment by effectively removing contaminants and minimizing pollutant discharges. Compliance with stringent regulations the search for sustainable processes pose new challenges provide opportunities innovative solutions. These solutions include using wastewater as resource to recover value-added by-products, such clean water, renewable energy, nutrients, while optimizing energy consumption reducing operating costs without compromising performance. To drive continuous innovation treatment, integration of advanced technologies robust monitoring control systems is imperative. This review explores advancements automation process within WWTPs. In this context, Internet Things (IoT), cloud computing, big data analytics, artificial intelligence (AI), blockchain, robotics, drones, virtual/augmented reality (VR/AR), digital twin are identified promising tools developing innovative, smart, efficient systems. While these offers many benefits, further research essential optimize their performance cost-effectiveness. A detailed overview current future applications smart provided, emphasizing strengths, limitations, improvements.

Язык: Английский

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A Review of Computational Modeling in Wastewater Treatment Processes

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

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A novel deep learning ensemble model based on two-stage feature selection and intelligent optimization for water quality prediction

Environmental Research, Год журнала: 2023, Номер 224, С. 115560 - 115560

Опубликована: Фев. 25, 2023

Язык: Английский

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DNN model development of biogas production from an anaerobic wastewater treatment plant using Bayesian hyperparameter optimization

Chemical Engineering Journal, Год журнала: 2023, Номер 471, С. 144671 - 144671

Опубликована: Июль 12, 2023

Язык: Английский

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Artificial Intelligence-Assisted Prediction of Effluent Phosphorus in a Full-Scale Wastewater Treatment Plant with Missing Phosphorus Input and Removal Data

ACS ES&T Water, Год журнала: 2023, Номер 4(3), С. 880 - 889

Опубликована: Янв. 9, 2023

Although artificial intelligence (AI) such as machine learning (ML) and deep (DL) has been recognized an emerging promising tool, its application becomes challenging with incomplete data collection. Herein, in the absence of influent phosphorus load chemical dosage for removal, we employed ML/DL models to predict effluent using nine-year from a small-scale wastewater treatment plant. Attempts were made select essential model input features 42 variables by Pearson correlation analysis reveal internal correlations among variables. First, five ML regression used load, maximum coefficient determination (R2) 0.637 was achieved support vector model. Then, DL named long short-term memory could one-day advance R2 value 0.496. Finally, on basis historical data, anomaly alarm design proposed minimize chance exceeding discharge permit accuracy 79.7% concentration after comparing seven classification models. This study provides example applying AI process improvement potential cost reduction sets.

Язык: Английский

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

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

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Enhancing wastewater treatment efficiency through machine learning-driven effluent quality prediction: A plant-level analysis

Journal of Water Process Engineering, Год журнала: 2024, Номер 58, С. 104758 - 104758

Опубликована: Янв. 9, 2024

Язык: Английский

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Artificial intelligence in wastewater treatment: Research trends and future perspectives through bibliometric analysis

Case Studies in Chemical and Environmental Engineering, Год журнала: 2024, Номер 10, С. 100926 - 100926

Опубликована: Авг. 31, 2024

Язык: Английский

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