A hybrid study of a 4-stage compressed solar distiller based on experimental, computational and deep learning methods DOI

Razieh Akhlaghi Ardekani,

Ali Kianifar, Mohammad Mustafa Ghafurian

и другие.

Desalination, Год журнала: 2023, Номер 568, С. 117016 - 117016

Опубликована: Сен. 27, 2023

Язык: Английский

Biochar-based persulfate activation: Rate constant prediction, key variables identification, and system optimization DOI
Nurul Alvia Istiqomah, Donghwi Jung,

Jeehyeong Khim

и другие.

Journal of Water Process Engineering, Год журнала: 2024, Номер 65, С. 105839 - 105839

Опубликована: Июль 30, 2024

Язык: Английский

Процитировано

4
Optimizing water resources for sustainable desalination: The integration of expert systems and solar energy in experimental applications DOI Creative Commons
Wissam H. Alawee, Alaa Abdulhady Jaber, Z.M. Omara

и другие.

Desalination and Water Treatment, Год журнала: 2024, Номер 320, С. 100683 - 100683

Опубликована: Авг. 3, 2024

The integration of renewable energy sources with multi-energy systems present challenges and opportunities to enhance sustainability. Among these, solar stills have emerged as a solution for water desalination. With the advent expert system technologies, avenues are opened improving operational efficiency distillers. This paper presents an innovative approach utilizing correlation analysis, ReliefF feature selection, k-Nearest Neighbor (kNN) algorithm forecasting cumulative distillate output double slope still. analysis is based on 6-cases-based dataset, which includes variations in relative different operational-environmental conditions. Key features that significantly impact overall performance were identified manage distiller productivity. findings reveal maximum was 1610 ML/m2.day due incorporating reflective materials phase change (PCM) enhancing distillation rates. kNN model evaluated its R2, RMSE, CVRMSE, best models achieving scores 0.995, 0.0033, 0.1666, respectively. These metrics underscore effectiveness proposed machine learning predicting output, thereby enabling informed management processes. Combining technologies computational intelligence holds significant promise sustainable environmental management, study presented.

Язык: Английский

Процитировано

4
Enhancing solar power forecasting with machine learning using principal component analysis and diverse statistical indicators DOI Creative Commons

Youcef Djeldjeli,

Lakhdar Taouaf,

Sultan Alqahtani

и другие.

Case Studies in Thermal Engineering, Год журнала: 2024, Номер 61, С. 104924 - 104924

Опубликована: Авг. 6, 2024

_Predicting solar energy is essential for efficient power system planning and the successful integration of renewable sources. This study aims to develop a framework evaluating various machine learning models feature selection strategies prediction. The research applies six models, i.e., linear regression (LR), random forest (RF), neural networks (NN), K-nearest neighbor (KNN), gradient boosting (GB), AdaBoost, datasets from 2019 2021 collected at Abiod Sid Cheikh station in southern Algeria. Various statistical indicators, including R2, RMSE, MAE, Adj-R, were analyzed assess model performance. analysis revealed that R2 values ranged 0.591 0.996 kW/m2, RMSE 0.510 1.78 MAE 0.357 0.856 kW/m2 across different models. KNN NN showed significant errors, while GB RF demonstrated strong accuracies (RMS = 0.9). AdaBoost LR excelled real-time short-term predictions, exhibiting an RMS 0.99. offers comprehensive evaluation method selecting most suitable findings can assist planners engineers choosing appropriate accurate prediction, thereby enhancing efficiency systems. Improved prediction contribute more reliable into grids, supporting transition cleaner sources reducing environmental impacts.

Язык: Английский

Процитировано

4
Advancements in CNT research: Integrating machine learning with microscopic simulations, macroscopic techniques, and application of performance prediction and functional optimization DOI

Dianming Chu,

Chenyu Gao,

Zongchao Ji

и другие.

Materials Today Chemistry, Год журнала: 2025, Номер 45, С. 102616 - 102616

Опубликована: Март 5, 2025

Язык: Английский

Процитировано

0
Experimental and machine learning optimization of ultrasonic-enhanced heat convection in a spiral heater DOI
Farzad Azizi Zade,

Razieh Abedini,

Amir Abdullah

и другие.

Journal of Thermal Analysis and Calorimetry, Год журнала: 2025, Номер unknown

Опубликована: Апрель 28, 2025

Язык: Английский

Процитировано

0
Machine learning-guided exploration of carbon-based photothermal materials for solar evaporation DOI
Gang Bai,

Zihui Wang,

Hao Wang

и другие.

Separation and Purification Technology, Год журнала: 2025, Номер unknown, С. 133627 - 133627

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
A hybrid study of a 4-stage compressed solar distiller based on experimental, computational and deep learning methods DOI

Razieh Akhlaghi Ardekani,

Ali Kianifar, Mohammad Mustafa Ghafurian

и другие.

Desalination, Год журнала: 2023, Номер 568, С. 117016 - 117016

Опубликована: Сен. 27, 2023

Язык: Английский

Процитировано

2