Date of Publication

12-17-2021

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Chemical Engineering

Subject Categories

Chemical Engineering

College

Gokongwei College of Engineering

Department/Unit

Chemical Engineering

Thesis Advisor

Aileen H. Orbecido
Lawrence P. Belo
John Paolo L. Lazarte
Kathleen B. Aviso

Defense Panel Chair

Arnel B. Beltran

Defense Panel Member

Vergel C. Bungay
Denvert C. Pangayao

Abstract/Summary

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.

Abstract Format

html

Language

English

Format

Electronic

Keywords

Sewage—Purification—Ultraviolet treatment; Enterobacteriaceae; Neural networks (Computer science)

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Embargo Period

3-1-2022

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