An Intelligent Soft Sensor for Detecting Ammonia in a NanoFeCu Catalysed Sewage Treatment
A novel intelligent soft sensor, PCA-DA-E-LSSVR, was developed for nanoFeCu catalysed ammonia removal process in wastewater treatment. It accurately predicted ammonia concentration using process variables. Comparative analysis showed superior predictive accuracy over commonly used models, such as AN...
| Main Author: | |
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| Format: | Thesis |
| Published: |
Curtin University
2023
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| Online Access: | http://hdl.handle.net/20.500.11937/94977 |
| _version_ | 1848765951667863552 |
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| author | Ngu, Joyce Chen Yen |
| author_facet | Ngu, Joyce Chen Yen |
| author_sort | Ngu, Joyce Chen Yen |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | A novel intelligent soft sensor, PCA-DA-E-LSSVR, was developed for nanoFeCu catalysed ammonia removal process in wastewater treatment. It accurately predicted ammonia concentration using process variables. Comparative analysis showed superior predictive accuracy over commonly used models, such as ANN and SVR. The PCA-DA-E-LSSVR model, with coefficients of determination exceeding 0.7, outperformed in flow rate and nanoparticle regeneration parameter studies. This model also exhibited its efficacy in online ammonia concentration estimation and adaptability to online conditions. |
| first_indexed | 2025-11-14T11:43:25Z |
| format | Thesis |
| id | curtin-20.500.11937-94977 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:43:25Z |
| publishDate | 2023 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-949772024-05-03T01:10:10Z An Intelligent Soft Sensor for Detecting Ammonia in a NanoFeCu Catalysed Sewage Treatment Ngu, Joyce Chen Yen A novel intelligent soft sensor, PCA-DA-E-LSSVR, was developed for nanoFeCu catalysed ammonia removal process in wastewater treatment. It accurately predicted ammonia concentration using process variables. Comparative analysis showed superior predictive accuracy over commonly used models, such as ANN and SVR. The PCA-DA-E-LSSVR model, with coefficients of determination exceeding 0.7, outperformed in flow rate and nanoparticle regeneration parameter studies. This model also exhibited its efficacy in online ammonia concentration estimation and adaptability to online conditions. 2023 Thesis http://hdl.handle.net/20.500.11937/94977 Curtin University restricted |
| spellingShingle | Ngu, Joyce Chen Yen An Intelligent Soft Sensor for Detecting Ammonia in a NanoFeCu Catalysed Sewage Treatment |
| title | An Intelligent Soft Sensor for Detecting Ammonia in a NanoFeCu
Catalysed Sewage Treatment |
| title_full | An Intelligent Soft Sensor for Detecting Ammonia in a NanoFeCu
Catalysed Sewage Treatment |
| title_fullStr | An Intelligent Soft Sensor for Detecting Ammonia in a NanoFeCu
Catalysed Sewage Treatment |
| title_full_unstemmed | An Intelligent Soft Sensor for Detecting Ammonia in a NanoFeCu
Catalysed Sewage Treatment |
| title_short | An Intelligent Soft Sensor for Detecting Ammonia in a NanoFeCu
Catalysed Sewage Treatment |
| title_sort | intelligent soft sensor for detecting ammonia in a nanofecu
catalysed sewage treatment |
| url | http://hdl.handle.net/20.500.11937/94977 |