The deep finding out network structure and integrate the modules connected to peak prediction. Furthermore, as a way to make the water good quality prediction model more robust and additional minimize the influence of climate adjust around the model prediction, we’ll collect years of water good quality data because the education dataset in future research. At the similar time, we are going to incorporate various factors, for instance seasonal alterations and climate modify, into the deep neural network as prior facts so that the prediction model can accomplish longer-term prediction benefits.Author Tetranor-PGDM Data Sheet Contributions: Conceptualization, Y.F. and Z.H.; methodology, Y.F. and Z.H.; software program, Y.F.; validation, Y.F.; formal evaluation, Y.F.; investigation, Y.F., Z.H. and Y.Z.; resources, Z.H.; information curation, Y.F., Z.H. and M.H.; writing–original draft preparation, Y.F.; writing–review and editing, Z.H., Y.Z. and M.H.; visualization, Y.F.; supervision, Z.H.; project administration, Z.H. and Y.Z.; funding acquisition, Z.H. All authors have read and agreed for the published version with the manuscript. Funding: This analysis was funded by the Hainan Province Organic Science Foundation of China (Grant No. 619QN195 and Grant No. 620RC564), the National Organic Science Foundation of China (Grant No. 61963012 and Grant No. 62161010). Conflicts of Interest: The authors declare no conflict of interest.
waterArticleComparison of Desalination Technologies Making use of Renewable Power Sources with Life Cycle, PESTLE, and Multi-Criteria Decision AnalysesHuyen Trang Do Thi 1 , Tibor Pasztor 1 , Daniel Fozer 1 , Flavio Manentiand Andras Jozsef Toth 1, Environmental and Procedure Engineering Study Group, Heliosupine N-oxide manufacturer Division of Chemical and Environmental Method Engineering, Budapest University of Technologies and Economics, Muegyetem rkp. 3, H-1111 Budapest, Hungary; [email protected] (H.T.D.T.); [email protected] (T.P.); [email protected] (D.F.) SuPER (Sustainable Procedure Engineering Analysis) Group, Polytechnic University of Milan, Piazza Leonardo da Vinci 32, 20133 Milan, Italy; [email protected] Correspondence: [email protected]; Tel.: +36-1-463-1490; Fax: +36-1-463-Citation: Do Thi, H.T.; Pasztor, T.; Fozer, D.; Manenti, F.; Toth, A.J. Comparison of Desalination Technologies Employing Renewable Energy Sources with Life Cycle, PESTLE, and Multi-Criteria Choice Analyses. Water 2021, 13, 3023. https://doi.org/10.3390/w13213023 Academic Editors: Robert Field and Muhammad Wakil Shahzad Received: 17 September 2021 Accepted: 19 October 2021 Published: 28 OctoberAbstract: Currently, desalination continues to expand globally, which is probably the most successful solutions to resolve the problem of the global drinking water shortage. However, desalination will not be a fail-safe approach and has lots of environmental and human well being consequences. This paper investigated the desalination process of seawater with diverse technologies, namely, multi-stage flash distillation (MSF), multi-effect distillation (MED), and reverse osmosis (RO), and with many energy sources (fossil energy, solar power, wind energy, nuclear power). The aim was to examine the diverse desalination technologies’ effectiveness with energy sources applying 3 assessment strategies, which have been examined separately. The life cycle assessment (LCA), PESTLE, and multicriteria selection analysis (MCDA) strategies have been used to evaluate every single process. LCA was based around the following influence analysis and evaluation procedures: ReCiPe 2016, Impact 2002+, and.
