Predicting the effluent total coliform count of UV disinfection for wastewater treatment using artificial neural networks
Clicks: 4
ID: 284858
2021
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Popular Article
0.9
/100
4 views
0 readers
AI Quality Assessment
Not analyzed
Readership in this journal
PopularRanked #323 of 3,757 articles by views in Malay Journal
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 3,757 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
The use of UV transmittance as the wastewater disinfection method has been increasing among local water utilities. Due to technological advancements in computing, artificial neural networks (ANN) have been proven to be a more appropriate modeling tool than process-based modeling. In this study, two WWTPs were examined as individual case studies. The first case study employed a conventional activated sludge (CAS) system, while the second case study employed a moving bed biofilm reactor system (MBBR) for secondary treatment. Both employ UV transmittance as its disinfection system. For each case study, an ANN model was trained and optimized by varying its network properties, particularly the network architecture and transfer functions in the hidden layers. A total of 80 and 146 data points were used in obtaining the optimal network parameters for CAS and MBBR systems, respectively. The resulting optimal network for both cases employed the hyperbolic tangent transfer function with a network architecture of three hidden layers with ten neurons per hidden layer (5-10-10-10-1). The performance of these networks was evaluated using the correlation coefficient (R) which resulted in significantly high values of 0.9955 and 0.9862 for the CAS and MBBR system, respectively. Mathematical relationships established between the network layers were used to formulate a general equation as a function of the input parameters, weights, and bias weights of each layer. To assess the generalization capability of the optimal networks, these were utilized to predict the effluent total coliform count of a new set of actual wastewater data obtained from the same WWTP for each system. However, this resulted in MAE values of 472 and 261 MPN/100mL for CAS and MBBR, respectively. These errors were attributed to several factors such as the presence of outliers in the simulation data, overfitting, the method of data collection, and the inclusion of repetitive and irrelevant input parameters.
| Reference Key |
persistent_1760653895_68f17247a57d5
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Que, Jaena Mae Ong |
| Journal | Malay Journal |
| Year | 2021 |
| DOI |
DOI not found
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.