A Parameter Study of the Performance of Atmospheric Water Generators and Their Forecasting Models

dc.contributor.advisorFeng, Zhi-Gang
dc.contributor.advisorCastillo, Krystel
dc.contributor.authorHoner, Benjamin
dc.contributor.committeeMemberWan, Hung-Da
dc.creator.orcidhttps://orcid.org/0000-0001-5606-022X
dc.date.accessioned2024-02-09T22:25:54Z
dc.date.available2024-02-09T22:25:54Z
dc.date.issued2017
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractThe purpose of this research was to analyze the parameters affecting the performance of two atmospheric water generators and to create and compare two forecast models that will predict how well the generators perform in varying geographic locations. As climate conditions vary throughout the different seasons of the year, so will the amount of water produced from atmospheric water generators. By running two atmospheric water generators over a period of seven months and recording the amount of water produced along with the varying climate condition values, each parameter affecting water production was analyzed and compared to the others in order to distinguish which parameter had the greatest impact on the performance of the generators. Relative humidity was found to be the most important parameter affecting water production, followed by temperature and air pressure. The forecast models created in this study include a neural network model and a multiple linear regression model. With R2 values of 86.51% and 58.73% for each respective generator, the neural network model outperformed the multiple, linear regression model whose R2 values were 76.19% and 55.79%, respectively. Additionally, simplified versions for each type of model were produced to provide a generalized solution for those who do not have detailed climate condition data readily available.
dc.description.departmentMechanical Engineering
dc.format.extent118 pages
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/20.500.12588/4023
dc.languageen
dc.subjectatmospheric water generator
dc.subjectforecasting
dc.subjectlinear regression
dc.subjectneural network
dc.subjectparameter study
dc.subjectsustainability
dc.subject.classificationMechanical engineering
dc.subject.classificationSustainability
dc.titleA Parameter Study of the Performance of Atmospheric Water Generators and Their Forecasting Models
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentMechanical Engineering
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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