A combined support vector regression with a firefly algorithm for prediction of energy consumption in wastewater treatment plants DOI Creative Commons
Mohammed Achite, Saeed Samadianfard, Nehal Elshaboury

et al.

Water Science & Technology, Journal Year: 2024, Volume and Issue: 90(10), P. 2747 - 2763

Published: Nov. 15, 2024

ABSTRACT Wastewater treatment plants (WWTPs) comprise energy-intensive processes, serving as primary contributors to overall WWTP costs. This research study proposes a novel approach that integrates support vector regression (SVR) with the firefly algorithm (FFA) for prediction of energy consumption in Chlef City, Algeria. The database comprises comprehensive set 1,653 samples, capturing diverse information categories. It includes chemical and physical characteristics, encompassing oxygen demand, 5-day biochemical potential hydrogen, water temperature, total suspended sediment basin, influent N-NH3 concentration, number aerators, operating time. Additionally, hydraulic energy-related parameters are represented by flow entered at station consumed respectively. Finally, meteorological data, comprising rainfall, relative humidity, aridity index, part dataset required analysis. In this regard, 15 different models correspond combinations input assessed study. results show SVR–FFA-15 can render an improvement accuracy WWTPs. provides useful tool managing wastewater makes insightful recommendations future savings.

Language: Английский

Calculation of carbon emissions in wastewater treatment and its neutralization measures: A review DOI
Zhixin Liu, Ziyi Xu, Xiaolei Zhu

et al.

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 912, P. 169356 - 169356

Published: Dec. 16, 2023

Language: Английский

Citations

77
Navigating the molecular landscape of environmental science and heavy metal removal: A simulation-based approach DOI Creative Commons

Iman Salahshoori,

Marcos A.L. Nobre, Amirhosein Yazdanbakhsh

et al.

Journal of Molecular Liquids, Journal Year: 2024, Volume and Issue: 410, P. 125592 - 125592

Published: July 20, 2024

Heavy metals pose a significant threat to ecosystems and human health because of their toxic properties ability bioaccumulate in living organisms. Traditional removal methods often fall short terms cost, energy efficiency, minimizing secondary pollutant generation, especially complex environmental settings. In contrast, molecular simulation offer promising solution by providing in-depth insights into atomic interactions between heavy potential adsorbents. This review highlights the for removing types pollutants science, specifically metals. These powerful tool predicting designing materials processes remediation. We focus on specific like lead, Cadmium, mercury, utilizing cutting-edge techniques such as Molecular Dynamics (MD), Monte Carlo (MC) simulations, Quantum Chemical Calculations (QCC), Artificial Intelligence (AI). By leveraging these methods, we aim develop highly efficient selective unravelling underlying mechanisms, pave way developing more technologies. comprehensive addresses critical gap scientific literature, valuable researchers protection health. modelling hold promise revolutionizing prediction metals, ultimately contributing sustainable solutions cleaner healthier future.

Language: Английский

Citations

19
Enhancing effluent quality prediction in wastewater treatment plants through the integration of factor analysis and machine learning DOI

Jia-Qiang Lv,

Lili Du,

Hongyong Lin

et al.

Bioresource Technology, Journal Year: 2023, Volume and Issue: 393, P. 130008 - 130008

Published: Nov. 18, 2023

Language: Английский

Citations

25
Advancement in Supercapacitors for IoT Applications by Using Machine Learning: Current Trends and Future Technology DOI Open Access
Qadeer Akbar Sial, Usman Safder, Shahid Iqbal

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(4), P. 1516 - 1516

Published: Feb. 10, 2024

Supercapacitors (SCs) are gaining attention for Internet of Things (IoT) devices because their impressive characteristics, including high power and energy density, extended lifespan, significant cycling stability, quick charge–discharge cycles. Hence, it is essential to make precise predictions about the capacitance lifespan supercapacitors choose appropriate materials develop plans replacement. Carbon-based supercapacitor electrodes crucial advancement contemporary technology, serving as a key component among numerous types electrode materials. Moreover, accurately forecasting storage may greatly improve efficient handling system malfunctions. Researchers worldwide have increasingly shown interest in using machine learning (ML) approaches predicting performance The driven by its noteworthy benefits, such improved accuracy predictions, time efficiency, cost-effectiveness. This paper reviews different charge processes, categorizes SCs, investigates frequently employed carbon components. supercapacitors, which applications, affected number capacity, cycle longevity. Additionally, we provide an in-depth review several recently developed ML-driven models used substance properties optimizing effectiveness. purpose these proposed ML algorithms validate anticipated accuracies, aid selection models, highlight future research topics field scientific computing. Overall, this highlights possibility techniques advancements energy-storing device development.

Language: Английский

Citations

16
Integrating advanced techniques and machine learning for landfill leachate treatment: Addressing limitations and environmental concerns DOI
Vivek Kumar Gaur, Krishna Gautam, Reena Vishvakarma

et al.

Environmental Pollution, Journal Year: 2024, Volume and Issue: 354, P. 124134 - 124134

Published: May 9, 2024

Language: Английский

Citations

8
Enhanced supercapacitive performance of lead vanadate hybrid architect on nickel foam with machine learning-driven capacitance prediction DOI
Qadeer Akbar Sial, Usman Safder, Rana Basit Ali

et al.

Journal of Power Sources, Journal Year: 2024, Volume and Issue: 608, P. 234580 - 234580

Published: May 4, 2024

Language: Английский

Citations

6
Machine learning framework for wastewater circular economy — Towards smarter nutrient recoveries DOI Creative Commons
Allan Soo, Li Gao, Ho Kyong Shon

et al.

Desalination, Journal Year: 2024, Volume and Issue: 592, P. 118092 - 118092

Published: Sept. 7, 2024

Language: Английский

Citations

6
Dynamic Real-Time Prediction of Reclaimed Water Volumes Using the Improved Transformer Model and Decomposition Integration Technology DOI Open Access
Xiangyu Sun,

Lina Zhang,

Chao Wang

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(15), P. 6598 - 6598

Published: Aug. 1, 2024

In recent years, wastewater reuse has become crucial for addressing global freshwater scarcity and promoting sustainable water resource development. Accurate inflow volume predictions are essential enhancing operational efficiency in treatment facilities effective utilization. Traditional decomposition integration models often struggle with non-stationary time series, particularly peak anomaly sensitivity. To address this challenge, a differential model based on real-time rolling forecasts been developed. This uses an initial prediction machine learning (ML) model, followed by using Complete Ensemble Empirical Mode Decomposition Adaptive Noise (CEEMDAN). A Time-Aware Outlier-Sensitive Transformer (TS-Transformer) is then applied integrated predictions. The ML-CEEMDAN-TSTF demonstrated superior accuracy compared to basic ML models, other Transformer-based models. hybrid explicitly incorporates time-scale differentiated information as feature, improving the model’s adaptability complex environmental data predictive performance. TS-Transformer was designed make more sensitive anomalies peaks issues such anomalous data, uncertainty suboptimal forecasting accuracy. results indicated that: (1) introduction of significantly enhanced accuracy; (2) higher ML-CEEMDAN-Transformer; (3) TS-Transformer-based consistently outperformed those LSTM eXtreme Gradient Boosting (XGBoost). Consequently, research provides precise robust method predicting reclaimed volumes, which holds significant implications clean environment management.

Language: Английский

Citations

4
Machine Learning Methods for the Prediction of Wastewater Treatment Efficiency and Anomaly Classification with Lack of Historical Data DOI Creative Commons
Igor Gulshin, Olga Kuzina

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(22), P. 10689 - 10689

Published: Nov. 19, 2024

This study examines an algorithm for collecting and analyzing data from wastewater treatment facilities, aimed at addressing regression tasks predicting the quality of treated classification preventing emergency situations, specifically filamentous bulking activated sludge. The feasibility using obtained under laboratory conditions simulating technological process as a training dataset is explored. A small collected actual plants considered test dataset. For both tasks, best results were achieved gradient-boosting models CatBoost family, yielding metrics SMAPE = 9.1 ROC-AUC 1.0. set most important predictors modeling was selected each target features.

Language: Английский

Citations

4
Estimation of prediction intervals for uncertainty assessment of artificial neural network based wastewater treatment plant effluent modeling DOI
Vahid Nourani,

Reza Shahidi Zonouz,

Mehdi Dini

et al.

Journal of Water Process Engineering, Journal Year: 2023, Volume and Issue: 55, P. 104145 - 104145

Published: Aug. 12, 2023

Language: Английский

Citations

11