Artificial Neural Network Modeling for Organic and Total Nitrogen Removal of Aerobic Granulation

Simple item page
dc.contributor.advisorTay, Joo Hwa
dc.contributor.authorGong, Heli
dc.contributor.committeememberDu, Ke
dc.contributor.committeememberHe, Jianxun
dc.date2018-11
dc.date.accessioned2018-06-20T20:52:19Z
dc.date.available2018-06-20T20:52:19Z
dc.date.issued2018-06-12
dc.description.abstractAerobic granulation is a recent technology with high level of complexity and sensitivity to environmental and operational conditions. To understand this technology properly, mathematical modeling would be an invaluable tool. In this study, Artificial neural network (ANN), a computational tool capable of describing complex nonlinear systems, was selected to simulate the treatment performance of aerobic granulation technology. The model capability in predicting chemical oxygen demand (COD) and total nitrogen (TN) removal efficiencies of aerobic granular reactors under start-up and steady-state condition was thoroughly investigated. The capability of ANN has been examined and compared to a novel and a traditional modeling approach, namely Support Vector Machine (SVM) and Multiple Linear Regression (MLR), respectively. The models were fed with vast datasets collected from laboratory-, pilot, and fullscale studies on aerobic granulation reported in the literature. Statistical error analysis demonstrated that the ANN method yielded comparable or superior prediction accuracy, compared to other methods. The results of this study showed that ANN-based models were capable simulation approach to predict a complicated process like aerobic granulation.en_US
dc.identifier.citationGong, H. (2018). Artificial Neural Network Modeling for Organic and Total Nitrogen Removal of Aerobic Granulation (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/31992en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/31992
dc.identifier.urihttp://hdl.handle.net/1880/106766
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.facultySchulich School of Engineering
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subject.classificationEngineering--Civilen_US
dc.subject.classificationEngineering--Environmentalen_US
dc.titleArtificial Neural Network Modeling for Organic and Total Nitrogen Removal of Aerobic Granulation
dc.typemaster thesis
thesis.degree.disciplineCivil Engineering
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.item.requestcopytrue
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ucalgary_2018_gong_heli.pdf
Size:
3.37 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.74 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Open Theses and Dissertations