Date of Publication
12-10-2022
Document Type
Bachelor's Thesis
Degree Name
Bachelor of Science in Chemical Engineering
Subject Categories
Chemical Engineering | Engineering
College
Gokongwei College of Engineering
Department/Unit
Chemical Engineering
Thesis Advisor
Arnel B. Beltran
Kathleen B. Aviso
Defense Panel Chair
Michael Angelo B. Promentilla
Defense Panel Member
Angelo Earvin S. Choi
Aileen H. Orbecido
Abstract/Summary
The performance of chlorination wastewater treatment plants (WWTPs) must be determined to identify its effectiveness in reducing pollutants in wastewater. This is directly affected by influent conditions (ICs), which reflects the behavior of the plant’s external environment. These effects were integrated with artificial neural network (ANN) modeling through Mahalanobis distance-based support vector machine (SVM), an anomaly detection algorithm. The analysis was done for two chlorination WWTPs: one with a moving bed biofilm reactor system (MBBR), while the other with conventional activated sludge (CAS) system for secondary treatment. Optimal SVM networks for classifying anomalies and non-anomalies were created using 266 and 221 datapoints, for the MBBR and CAS systems, respectively. Both utilized a fine Gaussian SVM architecture resulting in an area under the receiver operating characteristic curve of 0.90 (MBBR system) and 0.86 (CAS system). ANN networks were created to predict values for effluent biological oxygen demand (BOD), chemical oxygen demand (COD), and total coliform (TC), based on their SVM classification. The optimal networks had different transfer functions and network architectures reinforced the importance of specialized networks for different ICs. The performance of all ANNs was identified through their correlation coefficient (R), ranging from 0.818 to 0.997. The generalization capabilities for pairing ANN with SVM was evaluated using a new set of data from the same WWTP cases. The mean absolute error values for MBBR and CAS, respectively, were 4.95 and 2.32 ppm for BOD, 12.18 and 18.84 ppm for COD, and 26.29 and 198.96 MPN/100 mL for TC. The framework was able to capture the trend of the test datasets, reinforcing its ability for effluent parameter prediction for both case studies. Its errors were attributed to the presence of overfitting, lack of datapoints representing anomaly ICs, an infrequent grab sampling method, and exclusion of process parameters.
Abstract Format
html
Language
English
Format
Electronic
Keywords
Sewage disposal plants; Sewage—Purification—Chlorination; Chlorination
Recommended Citation
Jaluague, A. (2022). Development of prediction model for chlorination wastewater treatment plants using artificial neural networks with Mahalanobis distance - based support vector machine. Retrieved from https://animorepository.dlsu.edu.ph/etdb_chemeng/24
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Embargo Period
1-18-2023
