干旱区生态环境知识资源中心(Arid): Productivity modelling of an inclined stepped solar still for seawater desalination using boosting algorithms based on experimental data

Arid

DOI	10.5004/dwt.2022.28960
	Productivity modelling of an inclined stepped solar still for seawater desalination using boosting algorithms based on experimental data
	Wazirali, Raniyah; Abujazar, Mohammed Shadi S.; Abujayyab, Sohaib K. M.; Ahmad, Rami; Fatihah, Suja; Kabeel, A. E.; Karaagac, Sakine Ugurlu; Abu Amr, Salem S.; Alazaiza, Motasem Y. D.; Bashir, Mohammed J. K.; Sokar, Ibrahim Y.
通讯作者	Abujazar, MSS
来源期刊	DESALINATION AND WATER TREATMENT
ISSN	1944-3994
EISSN	1944-3986
出版年	2022
卷号	276 页码:28-39
英文摘要	Solar energy has recently become a viable option for desalinating seawater, primarily in arid regions. However, increasing the productivity of solar still by integrating experimental base and modelling methods is still subject to prediction errors; therefore, the main objective of this research is to postulate and test boosting algorithms for predicting the efficiency and productivity of the system. Five boosting regressors were deployed and evaluated: categorical boosting, adaptive boosting, extreme gradient boosting, gradient boosting machine, and gradient boosting machine (LightGBM). The proposed regressors are implemented based on the system's actual recorded dataset (consisting of 720 observations). The dataset consists of input variables, which are the wind speed (V), cloud cover, humidity, ambient temperature (T), solar radiation (SR), (T-io), (T-w), (T-v), and (T-t). Also, the output variable is represented by the productivity of the system. The dataset was separated into training (70%) and testing (30%) sets. In order to decrease regressors errors, hyperparameter optimization was employed. GradientBoosting approach provided the best prediction, with 95% R-2 accuracy and 39.57 root mean square error (RMSE) error. The LightGBM technique achieved 94% R-2 accuracy and 40.07 RMSE error in the testing dataset. The results reveal that GradientBoosting outperforms the cascaded forward neural network in predicting system productivity (CFNN).
英文关键词	Solar desalination Meteorological data Boosting algorithms Modelling Productivity evaluation
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000934038900003
WOS关键词	ARTIFICIAL NEURAL-NETWORKS ; PERFORMANCE EVALUATION ; SINGLE ; ENERGY ; PARAMETERS ; SYSTEM ; OPTIMIZATION ; QUALITY ; EXERGY ; DESIGN
WOS类目	Engineering, Chemical ; Water Resources
WOS研究方向	Engineering ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/392235
推荐引用方式 GB/T 7714	Wazirali, Raniyah,Abujazar, Mohammed Shadi S.,Abujayyab, Sohaib K. M.,et al. Productivity modelling of an inclined stepped solar still for seawater desalination using boosting algorithms based on experimental data[J],2022,276:28-39.
APA	Wazirali, Raniyah.,Abujazar, Mohammed Shadi S..,Abujayyab, Sohaib K. M..,Ahmad, Rami.,Fatihah, Suja.,...&Sokar, Ibrahim Y..(2022).Productivity modelling of an inclined stepped solar still for seawater desalination using boosting algorithms based on experimental data.DESALINATION AND WATER TREATMENT,276,28-39.
MLA	Wazirali, Raniyah,et al."Productivity modelling of an inclined stepped solar still for seawater desalination using boosting algorithms based on experimental data".DESALINATION AND WATER TREATMENT 276(2022):28-39.