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Prof. Tzu–Yi Pai
Department of Science Education and Application, National Taichung University of Education, Taichung, Taiwan

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0 Mathematical Modeling
0 optimization algorithm
0 biological wastewater treatment
0 Intelligent computation
0 Autom

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biological wastewater treatment
Activated sludge model

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Short Biography

Master Program of Environmental Education and Management, Department of Science Education and Ap-plication, National Taichung University of Education, Taichung, 40306, Taiwan,

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Journal article
Published: 03 June 2021 in Water
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A sewer dynamic model (SDM), an innovative use of combined models, was established to describe the reactions of compounds in a pilot sewer pipe. The set of ordinary differential equations in the SDM was solved simultaneously using the fourth-order Runge–Kutta algorithm. The SDM was validated by calculating the consistency between the simulation and observation values. After the SDM was validated, the reaction rate was analyzed. For heterotrophs in the water phase and biofilm, their growth rates were greater than the organism decay rate. For ammonia, the supply rate was greater than the consumption rate at the initial time, but the supply rate was smaller than the consumption rate from the 3rd hour. The supply rate was smaller than the consumption rate for the other six compounds. The supply rate of oxygen was smaller than the consumption rate before the 4th hour because of the microorganism activities, and, subsequently, the supply rate was greater than the consumption rate after the 4th hour because of reaeration. The results of this study provide an insight into the reaction rates of different compounds in urban sewer pipes and an urban water network modeling reference for policymaking and regulation.

ACS Style

Tzu-Yi Pai; Huang-Mu Lo; Terng-Jou Wan; Ya-Hsuan Wang; Yun-Hsin Cheng; Meng-Hung Tsai; Hsuan Tang; Yu-Xiang Sun; Wei-Cheng Chen; Yi-Ping Lin. A Sewer Dynamic Model for Simulating Reaction Rates of Different Compounds in Urban Sewer Pipe. Water 2021, 13, 1580 .

AMA Style

Tzu-Yi Pai, Huang-Mu Lo, Terng-Jou Wan, Ya-Hsuan Wang, Yun-Hsin Cheng, Meng-Hung Tsai, Hsuan Tang, Yu-Xiang Sun, Wei-Cheng Chen, Yi-Ping Lin. A Sewer Dynamic Model for Simulating Reaction Rates of Different Compounds in Urban Sewer Pipe. Water. 2021; 13 (11):1580.

Chicago/Turabian Style

Tzu-Yi Pai; Huang-Mu Lo; Terng-Jou Wan; Ya-Hsuan Wang; Yun-Hsin Cheng; Meng-Hung Tsai; Hsuan Tang; Yu-Xiang Sun; Wei-Cheng Chen; Yi-Ping Lin. 2021. "A Sewer Dynamic Model for Simulating Reaction Rates of Different Compounds in Urban Sewer Pipe." Water 13, no. 11: 1580.

Journal article
Published: 16 January 2015 in Sustainability
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This study involved developing a natural disaster risk assessment framework based on the consideration of three phases: a pre-disaster phase, disaster impact phase, and post-disaster recovery phase. The exposure of natural disasters exhibits unique characteristics. The interactions of numerous factors should be considered in risk assessment as well as in monitoring environment to provide natural disaster warnings. In each phase, specific factors indicate the relative status in the area subjected to risk assessment. Three types of natural disaster were assessed, namely debris flows, floods, and droughts. The Chishan basin in Taiwan was used as a case study and the adequacy of the relocation of Xiaolin village was evaluated. Incorporating resilience into the assessment revealed that the higher the exposure is, the higher the resilience becomes. This is because highly populated areas are typically allocated enough resources to respond to disasters. In addition, highly populated areas typically exhibit high resilience. The application of this analysis in the policy of relocation of damaged village after disaster provides valuable information for decision makers to achieve the sustainability of land use planning.

ACS Style

Tai-Li Lee; Ching-Ho Chen; Tzu-Yi Pai; Ray-Shyan Wu. Development of a Meteorological Risk Map for Disaster Mitigation and Management in the Chishan Basin, Taiwan. Sustainability 2015, 7, 962 -987.

AMA Style

Tai-Li Lee, Ching-Ho Chen, Tzu-Yi Pai, Ray-Shyan Wu. Development of a Meteorological Risk Map for Disaster Mitigation and Management in the Chishan Basin, Taiwan. Sustainability. 2015; 7 (1):962-987.

Chicago/Turabian Style

Tai-Li Lee; Ching-Ho Chen; Tzu-Yi Pai; Ray-Shyan Wu. 2015. "Development of a Meteorological Risk Map for Disaster Mitigation and Management in the Chishan Basin, Taiwan." Sustainability 7, no. 1: 962-987.

Journal article
Published: 28 September 2014 in Applied Mathematical Modelling
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This paper represents the first study to use the grey model (GM) for predicting CO2, SO2 and O2 in the emissions from a medical incinerator. The artificial neural network (ANN) was also employed for comparison. Four control parameters were served as the input variables. The results indicated that two control parameters of temperature highly influenced air pollutant emissions. The minimum mean absolute percentage errors of 3.70%, 6.11% and 1.08% for CO2, SO2 and O2 could be achieved using GMs, meanwhile the minimum root mean squared errors for three air pollutant were 0.1660, 2.4521 and 0.2112. The control parameters could be applied to the prediction of air pollutant emissions. It also revealed that GM could predict the air pollutant emissions even though emission data were not sufficient.

ACS Style

Tzu-Yi Pai; Huang-Mu Lo; Terng-Jou Wan; Li Chen; Pei-Shan Hung; Hsuan-Hao Lo; Wei-Jia Lai; Hsin-Yi Lee. Predicting air pollutant emissions from a medical incinerator using grey model and neural network. Applied Mathematical Modelling 2014, 39, 1513 -1525.

AMA Style

Tzu-Yi Pai, Huang-Mu Lo, Terng-Jou Wan, Li Chen, Pei-Shan Hung, Hsuan-Hao Lo, Wei-Jia Lai, Hsin-Yi Lee. Predicting air pollutant emissions from a medical incinerator using grey model and neural network. Applied Mathematical Modelling. 2014; 39 (5-6):1513-1525.

Chicago/Turabian Style

Tzu-Yi Pai; Huang-Mu Lo; Terng-Jou Wan; Li Chen; Pei-Shan Hung; Hsuan-Hao Lo; Wei-Jia Lai; Hsin-Yi Lee. 2014. "Predicting air pollutant emissions from a medical incinerator using grey model and neural network." Applied Mathematical Modelling 39, no. 5-6: 1513-1525.

Book chapter
Published: 15 July 2014 in Studies in Big Data
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For controlling air pollution, the Taiwan Environmental Protection Administration (TEPA) installed automatic air quality monitoring stations (AQMSs) and TEPA prescribed the industries to install continuous emission monitoring systems (CEMS). By 2014, there were a total of 76 AQMS and 351 CEMS in the entire nation. Therefore, the huge amount of air quality monitoring data forms big data. The processing, interpretation, collection and organization of air quality monitoring big data (AQMBD) have emerged in air quality control including industry management, traffic reduction, and residential health. In this chapter, the application of computational intelligence on analysis of air quality monitoring big data was reviewed worldwide. Additionally, the application of computational intelligence (CI) including artificial neural network, fuzzy theory, and adaptive network-based fuzzy inference system (ANFIS) was discussed. Finally, the implementation of CI on AQMBD granular computing was proposed.

ACS Style

Tzu-Yi Pai; Moo-Been Chang; Shyh-Wei Chen. Application of Computational Intelligence on Analysis of Air Quality Monitoring Big Data. Studies in Big Data 2014, 427 -441.

AMA Style

Tzu-Yi Pai, Moo-Been Chang, Shyh-Wei Chen. Application of Computational Intelligence on Analysis of Air Quality Monitoring Big Data. Studies in Big Data. 2014; ():427-441.

Chicago/Turabian Style

Tzu-Yi Pai; Moo-Been Chang; Shyh-Wei Chen. 2014. "Application of Computational Intelligence on Analysis of Air Quality Monitoring Big Data." Studies in Big Data , no. : 427-441.

Book chapter
Published: 10 December 2013 in Econometrics for Financial Applications
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In this study, a conceptual social network, in which the artificial neural network (ANN) was used, was adopted to evaluate impact of economic growth rate (EGR) and national income indices (NII) on crude birth rate (CBR) in Taiwan. The NII included gross domestic product (GDP), GDP per capita (GDPPC), gross national product (GNP), GNP per capita (GNPPC), national income (NI), and NI per capita (NIPC). To establish the ANN model, the EGR and NII were taken as the input variables, and the CBR was taken as the output variable. The results indicated that the minimum mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and maximum correlation coefficient (R) was 23.29 %, 46.02, 6.78, and 0.85, respectively when training. Those for testing were 28.93 %, 35.82, 5.99, and 0.70, respectively. The results showed that the CBR appeared to have a negative sensitivity towards three per capita indices including GDPPC (−0.0369), GNPPC (−0.1314), and NIPC (−0.3822). It suggested that the “capital dilution” would result in CBR decline. But positive EGR in a previous year should stimulate the CBR in the current year, as well as the positive macroeconomic factors including GDP, GNP, and NI. It suggested that the economic development would cause the fertility will to occur, thus increased the CBR.

ACS Style

Yi-Ti Tung; Tzu-Yi Pai. Using Neural Network Model to Evaluate Impact of Economic Growth Rate and National Income Indices on Crude Birth Rate in Taiwan. Econometrics for Financial Applications 2013, 427 -437.

AMA Style

Yi-Ti Tung, Tzu-Yi Pai. Using Neural Network Model to Evaluate Impact of Economic Growth Rate and National Income Indices on Crude Birth Rate in Taiwan. Econometrics for Financial Applications. 2013; ():427-437.

Chicago/Turabian Style

Yi-Ti Tung; Tzu-Yi Pai. 2013. "Using Neural Network Model to Evaluate Impact of Economic Growth Rate and National Income Indices on Crude Birth Rate in Taiwan." Econometrics for Financial Applications , no. : 427-437.

Journal article
Published: 31 August 2011 in Applied Mathematical Modelling
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In this study, three types of adaptive neuro fuzzy inference system (ANFIS) were employed to predict effluent suspended solids (SSeff), chemical oxygen demand (CODeff), and pHeff from a wastewater treatment plant in industrial park. For comparison, artificial neural network (ANN) was also used. The results indicated that ANFIS statistically outperformed ANN in terms of effluent prediction. The minimum mean absolute percentage errors of 2.67%, 2.80%, and 0.42% for SSeff, CODeff, and pHeff could be achieved using ANFIS. The maximum values of correlation coefficient for SSeff, CODeff, and pHeff were 0.96, 0.93, and 0.95, respectively. The minimum mean square errors of 0.19, 2.25, and 0.00, and the minimum root mean square errors of 0.43, 1.48, and 0.04 for SSeff, CODeff, and pHeff could also be achieved. ANFIS’s architecture can overcome the limitations of traditional neural network. It also revealed that the influent indices could be applied to the prediction of effluent quality.

ACS Style

T.Y. Pai; P.Y. Yang; S.C. Wang; M.H. Lo; C.F. Chiang; J.L. Kuo; H.H. Chu; H.C. Su; L.F. Yu; Y.H. Chang. Predicting effluent from the wastewater treatment plant of industrial park based on fuzzy network and influent quality. Applied Mathematical Modelling 2011, 35, 3674 -3684.

AMA Style

T.Y. Pai, P.Y. Yang, S.C. Wang, M.H. Lo, C.F. Chiang, J.L. Kuo, H.H. Chu, H.C. Su, L.F. Yu, Y.H. Chang. Predicting effluent from the wastewater treatment plant of industrial park based on fuzzy network and influent quality. Applied Mathematical Modelling. 2011; 35 (8):3674-3684.

Chicago/Turabian Style

T.Y. Pai; P.Y. Yang; S.C. Wang; M.H. Lo; C.F. Chiang; J.L. Kuo; H.H. Chu; H.C. Su; L.F. Yu; Y.H. Chang. 2011. "Predicting effluent from the wastewater treatment plant of industrial park based on fuzzy network and influent quality." Applied Mathematical Modelling 35, no. 8: 3674-3684.

Journal article
Published: 20 August 2010 in Water, Air, & Soil Pollution
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In this study, seven types of first-order and one-variable grey differential equation model (abbreviated as GM (1, 1) model) were used to predict hourly particulate matter (PM) including PM10 and PM2.5 concentrations in Banciao City of Taiwan. Their prediction performance was also compared. The results indicated that the minimum mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and maximum correlation coefficient (R) was 14.10%, 25.62, 5.06, and 0.96, respectively, when predicting PM10. When predicting PM2.5, the minimum MAPE, MSE, RMSE, and maximum R value of 15.24%, 11.57, 3.40, and 0.93, respectively, could be achieved. All statistical values revealed that the predicting performance of GM (1, 1, x (0)), GM (1, 1, a), and GM (1, 1, b) outperformed other GM (1, 1) models. According to the results, it revealed that GM (1, 1) GM (1, 1) was an efficiently early warning tool for providing PM information to the inhabitants.

ACS Style

Tzu-Yi Pai; Ching-Lin Ho; Shyh-Wei Chen; Huang-Mu Lo; Pao-Jui Sung; Shu-Wen Lin; Wei-Jia Lai; Shih-Chi Tseng; Shu-Ping Ciou; Jui-Ling Kuo; Jing-Tang Kao. Using Seven Types of GM (1, 1) Model to Forecast Hourly Particulate Matter Concentration in Banciao City of Taiwan. Water, Air, & Soil Pollution 2010, 217, 25 -33.

AMA Style

Tzu-Yi Pai, Ching-Lin Ho, Shyh-Wei Chen, Huang-Mu Lo, Pao-Jui Sung, Shu-Wen Lin, Wei-Jia Lai, Shih-Chi Tseng, Shu-Ping Ciou, Jui-Ling Kuo, Jing-Tang Kao. Using Seven Types of GM (1, 1) Model to Forecast Hourly Particulate Matter Concentration in Banciao City of Taiwan. Water, Air, & Soil Pollution. 2010; 217 (1-4):25-33.

Chicago/Turabian Style

Tzu-Yi Pai; Ching-Lin Ho; Shyh-Wei Chen; Huang-Mu Lo; Pao-Jui Sung; Shu-Wen Lin; Wei-Jia Lai; Shih-Chi Tseng; Shu-Ping Ciou; Jui-Ling Kuo; Jing-Tang Kao. 2010. "Using Seven Types of GM (1, 1) Model to Forecast Hourly Particulate Matter Concentration in Banciao City of Taiwan." Water, Air, & Soil Pollution 217, no. 1-4: 25-33.

Journal article
Published: 25 October 2009 in World Journal of Microbiology and Biotechnology
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In this study, the variation of biomass, kinetic parameters, and stoichiometric parameters for ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) in TNCU3 process were explored at different aerobic hydraulic retention time (AHRT). The results indicated that the growth rate constants of AOB were 0.92, 0.88, and 0.95 days−1, respectively, meanwhile, those of NOB were 2.58 1.41, and 1.40 days−1, respectively, when AHRT was 5, 6, and 7 h. The lysis rate constants for AOB and NOB were 0.13 and 0.17 days−1, respectively. When AHRT was 5, 6, and 7 h, the yield coefficients of AOB were 0.20, 0.23, and 0.28 g COD g−1 N, respectively, meanwhile those of NOB were 0.23, 0.19, and 0.22 g COD g−1 N, respectively. The average percentage of AOB was 0.44, 0.61, and 0.64%, respectively, while that of NOB was 0.46, 0.61, and 0.74%, respectively. The relation between the biomass percentage of AOB and AHRT was in a good agreement with first type hyperbolic curve. The relation between the biomass percentage of NOB and AHRT was in a good agreement with seven types of curve including simple exponential curve, power exponential curve, and first type hyperbolic curve etc. When the AHRT increased from 5 to 7 h, the removal efficiency of NH4 +–N increased from 80.2 to 94.8%, or by 14.6%. Meanwhile, the removal efficiency of total nitrogen increased from 63.6 to 70.9%, or by 7.3%.

ACS Style

Tzu-Yi Pai; Ren-Jie Chiou; Chwen-Jeng Tzeng; Tung-Sheng Lin; Shan-Chun Yeh; Pao-Jui Sung; Chu-Hui Tseng; Chia-Ho Tsai; Yao-Sheng Tsai; Wen-Jui Hsu; Yuh-Ling Wei. Variation of biomass and kinetic parameters for nitrifying species in the TNCU3 process at different aerobic hydraulic retention times. World Journal of Microbiology and Biotechnology 2009, 26, 589 -597.

AMA Style

Tzu-Yi Pai, Ren-Jie Chiou, Chwen-Jeng Tzeng, Tung-Sheng Lin, Shan-Chun Yeh, Pao-Jui Sung, Chu-Hui Tseng, Chia-Ho Tsai, Yao-Sheng Tsai, Wen-Jui Hsu, Yuh-Ling Wei. Variation of biomass and kinetic parameters for nitrifying species in the TNCU3 process at different aerobic hydraulic retention times. World Journal of Microbiology and Biotechnology. 2009; 26 (4):589-597.

Chicago/Turabian Style

Tzu-Yi Pai; Ren-Jie Chiou; Chwen-Jeng Tzeng; Tung-Sheng Lin; Shan-Chun Yeh; Pao-Jui Sung; Chu-Hui Tseng; Chia-Ho Tsai; Yao-Sheng Tsai; Wen-Jui Hsu; Yuh-Ling Wei. 2009. "Variation of biomass and kinetic parameters for nitrifying species in the TNCU3 process at different aerobic hydraulic retention times." World Journal of Microbiology and Biotechnology 26, no. 4: 589-597.

Journal article
Published: 15 July 2009 in Journal of Hazardous Materials
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A new modeling concept to evaluate the effects of cadmium and copper on heterotrophic growth rate constant (μH) and lysis rate constant (bH) in activated sludge was introduced. The oxygen uptake rate (OUR) was employed to measure the constants. The results indicated that the μH value decreased from 4.52 to 3.26 d−1 or by 28% when 0.7 mg L−1 of cadmium was added. Contrarily the bH value increased from 0.31 to 0.35 d−1 or by 11%. When adding 0.7 mg L−1 of copper, the μH value decreased to 2.80 d−1 or by 38%. The bH value increased to 0.42 d−1 or by 35%. After regression, the inhibitory effect was in a good agreement with non-competitive inhibition kinetic. The inhibition coefficient values for cadmium and copper were 1.82 and 1.21 mg L−1, respectively. The relation between the bH values and heavy metal concentrations agreed with exponential type well. The heavy metal would enhance bH value. Using these data, a new kinetic model was established and used to simulate the degree of inhibition. It was evident that not only the inhibitory effect on μH but also that the enhancement effect on bH should be considered when heavy metal presented.

ACS Style

T.Y. Pai; S.C. Wang; H.M. Lo; C.F. Chiang; M.H. Liu; R.J. Chiou; W.Y. Chen; P.S. Hung; W.C. Liao; H.G. Leu. Novel modeling concept for evaluating the effects of cadmium and copper on heterotrophic growth and lysis rates in activated sludge process. Journal of Hazardous Materials 2009, 166, 200 -206.

AMA Style

T.Y. Pai, S.C. Wang, H.M. Lo, C.F. Chiang, M.H. Liu, R.J. Chiou, W.Y. Chen, P.S. Hung, W.C. Liao, H.G. Leu. Novel modeling concept for evaluating the effects of cadmium and copper on heterotrophic growth and lysis rates in activated sludge process. Journal of Hazardous Materials. 2009; 166 (1):200-206.

Chicago/Turabian Style

T.Y. Pai; S.C. Wang; H.M. Lo; C.F. Chiang; M.H. Liu; R.J. Chiou; W.Y. Chen; P.S. Hung; W.C. Liao; H.G. Leu. 2009. "Novel modeling concept for evaluating the effects of cadmium and copper on heterotrophic growth and lysis rates in activated sludge process." Journal of Hazardous Materials 166, no. 1: 200-206.

Journal article
Published: 03 March 2009 in Computers & Chemical Engineering
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In this study, three types of adaptive neuro fuzzy inference system (ANFIS) and artificial neural network (ANN) were employed to predict suspended solids (SSeff) and chemical oxygen demand (CODeff) 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 SSeff and CODeff could be achieved using ANFIS. The maximum values of correlation coefficient for SSeff and CODeff 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 SSeff and CODeff could also be achieved. ANFIS's 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.

ACS Style

T.Y. Pai; T.J. Wan; S.T. Hsu; T.C. Chang; Y.P. Tsai; C.Y. Lin; H.C. Su; L.F. Yu. Using fuzzy inference system to improve neural network for predicting hospital wastewater treatment plant effluent. Computers & Chemical Engineering 2009, 33, 1272 -1278.

AMA Style

T.Y. Pai, T.J. Wan, S.T. Hsu, T.C. Chang, Y.P. Tsai, C.Y. Lin, H.C. Su, L.F. Yu. Using fuzzy inference system to improve neural network for predicting hospital wastewater treatment plant effluent. Computers & Chemical Engineering. 2009; 33 (7):1272-1278.

Chicago/Turabian Style

T.Y. Pai; T.J. Wan; S.T. Hsu; T.C. Chang; Y.P. Tsai; C.Y. Lin; H.C. Su; L.F. Yu. 2009. "Using fuzzy inference system to improve neural network for predicting hospital wastewater treatment plant effluent." Computers & Chemical Engineering 33, no. 7: 1272-1278.

Journal article
Published: 01 March 2009 in Bioprocess and Biosystems Engineering
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Three types of adaptive network-based fuzzy inference system (ANFIS) in which the online monitoring parameters served as the input variable were employed to predict suspended solids (SS(eff)), chemical oxygen demand (COD(eff)), and pH(eff) in the effluent from a biological wastewater treatment plant in industrial park. Artificial neural network (ANN) was also used for comparison. The results indicated that ANFIS statistically outperforms ANN in terms of effluent prediction. When predicting, the minimum mean absolute percentage errors of 2.90, 2.54 and 0.36% for SS(eff), COD(eff) and pH(eff) could be achieved using ANFIS. The maximum values of correlation coefficient for SS(eff), COD(eff), and pH(eff) were 0.97, 0.95, and 0.98, respectively. The minimum mean square errors of 0.21, 1.41 and 0.00, and the minimum root mean square errors of 0.46, 1.19 and 0.04 for SS(eff), COD(eff), and pH(eff) could also be achieved.

ACS Style

Tzu-Yi Pai; S. C. Wang; C. F. Chiang; H. C. Su; L. F. Yu; P. J. Sung; C. Y. Lin; H. C. Hu. Improving neural network prediction of effluent from biological wastewater treatment plant of industrial park using fuzzy learning approach. Bioprocess and Biosystems Engineering 2009, 32, 781 -790.

AMA Style

Tzu-Yi Pai, S. C. Wang, C. F. Chiang, H. C. Su, L. F. Yu, P. J. Sung, C. Y. Lin, H. C. Hu. Improving neural network prediction of effluent from biological wastewater treatment plant of industrial park using fuzzy learning approach. Bioprocess and Biosystems Engineering. 2009; 32 (6):781-790.

Chicago/Turabian Style

Tzu-Yi Pai; S. C. Wang; C. F. Chiang; H. C. Su; L. F. Yu; P. J. Sung; C. Y. Lin; H. C. Hu. 2009. "Improving neural network prediction of effluent from biological wastewater treatment plant of industrial park using fuzzy learning approach." Bioprocess and Biosystems Engineering 32, no. 6: 781-790.

Journal article
Published: 31 December 2008 in Waste Management
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In this study, the impact levels in environmental impact assessment (EIA) reports of 10 incinerator plants were quantified and discussed. The relationship between the quantified impact levels and the plant scale factors of BeiTou, LiZe, BaLi, LuTsao, RenWu, PingTung, SiJhou and HsinChu were constructed, and the impact levels of the GangShan (GS) and YongKong (YK) plants were predicted using grey model GM (1, N). Finally, the effects of plant scale factors on impact levels were evaluated using grey model GM (1, N) too. According to the predicted results of GM, the relative errors of topography/geology/soil, air quality, hydrology/water quality, solid waste, noise, terrestrial fauna/flora, aquatic fauna/flora and traffic in the GS plant were 17%, 14%, 15%, 17%, 75%, 16%, 13%, and 37%, respectively. The relative errors of the same environmental items in the YK plant were 1%, 18%, 10%, 40%, 37%, 3%, 25% and 33%, respectively. According to GM (1, N), design capacity (DC) and heat value (HV) were the plant scale factors that affected the impact levels significantly in each environmental item, and thus were the most significant plant scale factors. GM (1, N) was effective in predicting the environmental impact and analyzing the reasonableness of the impact. If there is an EIA for a new incinerator plant to be reviewed in the future, the official committee of the Taiwan EPA could review the reasonableness of impact levels in EIA reports quickly.

ACS Style

T.Y. Pai; R.J. Chiou; H.H. Wen. Evaluating impact level of different factors in environmental impact assessment for incinerator plants using GM (1, N) model. Waste Management 2008, 28, 1915 -1922.

AMA Style

T.Y. Pai, R.J. Chiou, H.H. Wen. Evaluating impact level of different factors in environmental impact assessment for incinerator plants using GM (1, N) model. Waste Management. 2008; 28 (10):1915-1922.

Chicago/Turabian Style

T.Y. Pai; R.J. Chiou; H.H. Wen. 2008. "Evaluating impact level of different factors in environmental impact assessment for incinerator plants using GM (1, N) model." Waste Management 28, no. 10: 1915-1922.

Comparative study
Published: 01 April 2004 in Chemosphere
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The disadvantages of developed biological nutrient removal (BNR) processes (additional energy for liquid circulation and addition of external carbon substrate for denitrification in anoxic zones) were improved by reconfiguring the process into (1) an anaerobic zone followed by multiple stages of aerobic-anoxic zones (TNCU3 process) or (2) anaerobic, oxic, anoxic, oxic zones in sequence (TNCU2 process). These two pilot plants were operated at a recycling sludge ratio of 0.5 without internal recycle of nitrified supernatant. The sludge retention time was maintained at 10 d. The main objective of this study is to analyze the kinetics of different microorganisms in these two processes and A2O process by using the Activated Sludge Model No. 2d. The effective removal efficiency of carbon, total phosphorus and total nitrogen at 87-98%, 92-100% and 63-80%, respectively, were achieved in the testing runs. According to model simulations, the microbial kinetics in the TNCU3 and TNCU2 processes would be affected by different operations. When the step feeding strategy was adopted, the HRT was longer due to the less influent flowrate in the front stages and the microbes would grow in quantities by about 6% in the aerobic reactors. In the followed anoxic reactors, the microbes would decrease in quantities by about 12% due to the dilution effect. The dilution effects in TNCU3 and TNCU2 processes did not take place in A2O process because the recycling mixed liquid from the aerobic reactor to the anoxic reactor still contained particulate components. The XH, XPAO, and XAUT concentrations in the effluent of the last tank were lower when the step-feeding mode was adopted. The TNCU3 and TNCU2 processes could be operated efficiently without nitrified liquid circulation and addition of external carbon substrate for denitrification.

ACS Style

T.Y. Pai; Yung-Pin Tsai; Y.J. Chou; H.Y. Chang; H.G. Leu; C.F. Ouyang. Microbial kinetic analysis of three different types of EBNR process. Chemosphere 2004, 55, 109 -118.

AMA Style

T.Y. Pai, Yung-Pin Tsai, Y.J. Chou, H.Y. Chang, H.G. Leu, C.F. Ouyang. Microbial kinetic analysis of three different types of EBNR process. Chemosphere. 2004; 55 (1):109-118.

Chicago/Turabian Style

T.Y. Pai; Yung-Pin Tsai; Y.J. Chou; H.Y. Chang; H.G. Leu; C.F. Ouyang. 2004. "Microbial kinetic analysis of three different types of EBNR process." Chemosphere 55, no. 1: 109-118.