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Featured researches published by Y.P. Tsai.


Computers & Chemical Engineering | 2007

Grey and neural network prediction of suspended solids and chemical oxygen demand in hospital wastewater treatment plant effluent

Tzu-Yi Pai; Y.P. Tsai; Huang-Mu Lo; C.H. Tsai; Ching-Yuan Lin

Abstract Grey model (GM) and artificial neural network (ANN) was employed to predict suspended solids (SS) and chemical oxygen demand (COD) in the effluent from sequence batch reactors of a hospital wastewater treatment plant (HWWTP). The results indicated that the minimum mean absolute percentage errors (MAPEs) of 23.14% and 51.73% for SS and COD could be achieved using genetic algorithm ANN (GAANN). The minimum prediction accuracy of 23.14% and 55.11% for SS and COD could be achieved. Contrarily, GM only required a small amount of data and the prediction accuracy was analogous to that of GAANN. In the first type of application, the MAPE values of SS for model prediction using GM (1, N ) and GM (1, 2) lay between 23.14% and 26.67%. The MAPE values of COD using GM (1, N ) were smaller than those of GM (1, 2). The results showed that the fitness was good for both GM (1, N ) and GM (1, 2) to predict SS. However, only GM (1, N ) was better for COD prediction as comparing to GM (1, 2). In the second type application, the MAPE values of SS and COD prediction using GM (1, 1) and rolling GM (1, 1) (RGM, i.e., 8 data before the point at which was considered to be predicted were used to construct model) lay between 24–28% and 37–52%, respectively. Furthermore, it was observed that influent pH has affected effluent SS and COD significantly. It suggested that if the influent pH could be adjusted appropriately, a better effluent SS and COD could be obtained.


Computers & Chemical Engineering | 2009

Using fuzzy inference system to improve neural network for predicting hospital wastewater treatment plant effluent

Tzu-Yi Pai; Terng-Jou Wan; S.T. Hsu; Tien-Chin Chang; Y.P. Tsai; Ching-Yuan Lin; H.C. Su; L.F. Yu

Abstract In this study, three types of adaptive neuro fuzzy inference system (ANFIS) and artificial neural network (ANN) were employed to predict suspended solids (SS eff ) and chemical oxygen demand (COD eff ) in the effluent from a hospital wastewater treatment plant. The results indicated that ANFIS statistically outperforms ANN in terms of effluent prediction. The minimum mean absolute percentage errors of 11.99% and 12.75% for SS eff and COD eff could be achieved using ANFIS. The maximum values of correlation coefficient for SS eff and COD eff were 0.75 and 0.92, respectively. The minimum mean square errors of 0.17 and 19.58, and the minimum root mean square errors of 0.41 and 4.42 for SS eff and COD eff could also be achieved. ANFISs architecture consists of both ANN and fuzzy logic including linguistic expression of membership functions and if–then rules, so it can overcome the limitations of traditional neural network and increase the prediction performance.


Environmental Monitoring and Assessment | 2008

Comparisons of grey and neural network prediction of industrial park wastewater effluent using influent quality and online monitoring parameters

Tzu-Yi Pai; S.H. Chuang; Terng-Jou Wan; Huang-Mu Lo; Y.P. Tsai; H.C. Su; L.F. Yu; H.C. Hu; Pao-Jui Sung

In this study, Grey model (GM) and artificial neural network (ANN) were employed to predict suspended solids (SSeff) and chemical oxygen demand (CODeff) in the effluent from a wastewater treatment plant in industrial park of Taiwan. When constructing model or predicting, the influent quality or online monitoring parameters were adopted as the input variables. ANN was also adopted for comparison. The results indicated that the minimum MAPEs of 16.13 and 9.85% for SSeff and CODeff could be achieved using GMs when online monitoring parameters were taken as the input variables. Although a good fitness could be achieved using ANN, they required a large quantity of data. Contrarily, GM only required a small amount of data (at least four data) and the prediction results were even better than those of ANN. Therefore, GM could be applied successfully in predicting effluent when the information was not sufficient. The results also indicated that these simple online monitoring parameters could be applied on prediction of effluent quality well.


International Biodeterioration & Biodegradation | 2005

Effect of cadmium on composition and diversity of bacterial communities in activated sludges

Y.P. Tsai; Sheng-Jie You; Tzu-Yi Pai; Ko-Wei Chen


Chemosphere | 2004

Microbial kinetic analysis of three different types of EBNR process

Tzu-Yi Pai; Y.P. Tsai; Y.J. Chou; H.Y. Chang; Horng-Guang Leu; Chang-Feng Ou-Yang


Journal of Environmental Engineering | 2006

Effect of Cd(II) on Different Bacterial Species Present in a Single Sludge Activated Sludge Process for Carbon and Nutrient Removal

Y.P. Tsai; Sheng-Jie You; Tzu-Yi Pai; Ko-Wei Chen


Applied Mathematical Modelling | 2009

Using an extended activated sludge model to simulate nitrite and nitrate variations in TNCU2 process

Tzu-Yi Pai; H.Y. Chang; Terng-Jou Wan; S.H. Chuang; Y.P. Tsai


Clean-soil Air Water | 2010

Effect of Sludge Retention Time on Nitrifiers' Biomass and Kinetics in an Anaerobic/Oxic Process

Tzu-Yi Pai; Terng-Jou Wan; Y.P. Tsai; Chwen-Jeng Tzeng; Hsiao-Hsing Chu; Yao-Sheng Tsai; Ching-Yuan Lin


Environmental Engineering Science | 2008

Interaction of Colloidal Fe/Mn Oxides Concentration and Shear Stress on Biofilm Formation

Y.P. Tsai; Tzu-Yi Pai; Qi-Zhang Yang


Water Science and Technology | 2004

Biofilm bacteria inactivation by citric acid and resuspension evaluations for drinking water production systems

Y.P. Tsai; Tzu-Yi Pai; J.Y. Hsin; Terng-Jou Wan

Collaboration


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Tzu-Yi Pai

National Taichung University of Education

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Terng-Jou Wan

National Yunlin University of Science and Technology

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Ching-Yuan Lin

Chaoyang University of Technology

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H.C. Su

Chaoyang University of Technology

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Huang-Mu Lo

Chaoyang University of Technology

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L.F. Yu

Chaoyang University of Technology

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H.C. Hu

Chaoyang University of Technology

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Horng-Guang Leu

Environmental Protection Administration

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Ko-Wei Chen

National Chi Nan University

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Pao-Jui Sung

Chaoyang University of Technology

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