Tzu-Yi Pai

Modeling biogas production from organic fraction of MSW co-digested with MSWI ashes in anaerobic bioreactors

This study aims at investigating the effects of MSW incinerator fly ash (FA) and bottom ash (BA) on the anaerobic co-digestion of OFMSW with FA or BA. It also simulates the biogas production from various dosed and control bioreactors. Results showed that suitable ashes addition (FA/MSW 10 and 20 g L(-1) and BA/MSW 100 g L(-1)) could improve the MSW anaerobic digestion and enhance the biogas production rates. FA/MSW 20 g L(-1) bioreactor had the higher biogas production and rate implying the potential option for MSW anaerobic co-digestion. Modeling studies showed that exponential plot simulated better for FA/MSW 10 g L(-1) and control bioreactors while Gaussian plot was applicable for FA/MSW 20 g L(-1) one. Linear and exponential plot of descending limb both simulated better for BA/MSW 100 g L(-1) bioreactor. Modified Gompertz plot showed higher correlation of biogas accumulation than exponential rise to maximum plot for all bioreactors.

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

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.

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

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.

Modelling the steady-state effluent characteristics of the TNCU process under different return mixed liquid

Abstract The Activated Sludge Model No. 2d (ASM2d) was employed and modified to predict the effluent qualities of the modified enhanced biological phosphate removal (EBPR) system – TNCU. The TNCU process was composed of anaerobic/anoxic/oxide (A2O) process and rotating biological contactors (RBC) in each reactor. There were three modifications for the model in this study: (1) the biosorption effect of the soluble COD, (2) the ammonification of the organic nitrogen in influent wastewater, and (3) the growth of the heterotrophic organisms in the anaerobic tank. The influent wastewater qualities were fixed, the ratios of return sludge and SRT were 0.5 and 11 days, and the model plant was operated at three different mixed liquid recycling ratios (MLRR, 0.5, 1.25, 2). When a steady state was reached, the comparisons between the measured values and the model predicted values in each test were made. It shows a good consistency between them. According to the consistent results, the biosorption effect of the soluble COD and the ammonification of the organic nitrogen in influent wastewater were the important effects in activated sludge system. Additionally, the heterotrophic organisms might grow in the anaerobic tank. The heterotrophic organisms, phosphorus accumulating organisms and autotrophic organisms would decrease in the anaerobic tanks because the lysis reactions were the major reactions in the anaerobic tanks. They would increase in the aerobic tanks. Furthermore, it was indirectly proved that the denitrifying PAOs existed in the EBPR system.

Biostabilization assessment of MSW co-disposed with MSWI fly ash in anaerobic bioreactors

Municipal solid waste incinerator (MSWI) fly ash has been examined for possible use as landfill interim cover. For this aim, three anaerobic bioreactors, 1.2m high and 0.2m in diameter, were used to assess the co-digestion or co-disposal performance of MSW and MSWI fly ash. Two bioreactors contained ratios of 10 and 20 g fly ash per liter of MSW (or 0.2 and 0.4 g g(-1) VS, that is, 0.2 and 0.4 g fly ash per gram volatile solids (VS) of MSW). The remaining bioreactor was used as control, without fly ash addition. The results showed that gas production rate was enhanced by the appropriate addition of MSWI fly ash, with a rate of approximately 6.5l day(-1)kg(-1)VS at peak production in the ash-added bioreactors, compared to approximately 4l day(-1)kg(-1)VS in control. Conductivity, alkali metals and VS in leachate were higher in the fly ash-added bioreactors compared to control. The results show that MSW decomposition was maintained throughout at near-neutral pH and might be improved by release of alkali and trace metals from fly ash. Heavy metals exerted no inhibitory effect on MSW digestion in all three bioreactors. These phenomena indicate that proper amounts of MSWI fly ash, co-disposed or co-digested with MSW, could facilitate bacterial activity, digestion efficiency and gas production rates.

Effects of spiked metals on the MSW anaerobic digestion

This study aimed to investigate the effects of eight metals on the anaerobic digestion of the organic fraction of municipal solid waste (OFMSW) in bioreactors. Anaerobic bioreactors containing 200 mL MSW mixed completely with 200 mL sludge seeding. Ca and K (0, 1000, 2000 and 6000 mg L−1) and Cr, Ni, Zn, Co, Mo and W (0, 5, 50 and 100 mg L−1) of various dose were added to anaerobic bioreactors to examine their anaerobic digestion performance. Results showed that except K and Zn, Ca (~728 to ~1461 mg L−1), Cr (~0.0022 to ~0.0212 mg L−1), Ni (~0.801 to ~5.362 mg L−1), Co (~0.148 to ~0.580 mg L−1), Mo (~0.044 to ~52.94 mg L−1) and W (~0.658 to ~40.39 mg L−1) had the potential to enhance the biogas production. On the other hand, except Mo and W, inhibitory concentrations IC50 of Ca, K, Cr, Ni, Zn and Co were found to be ~3252, ~2097, ~0.124, ~7.239, ~0.482, ~8.625 mg L−1, respectively. Eight spiked metals showed that they were adsorbed by MSW to a different extent resulting in different liquid metals levels and potential stimulation and inhibition on MSW anaerobic digestion. These results were discussed and compared to results from literature.

Novel modeling concept for evaluating the effects of cadmium and copper on heterotrophic growth and lysis rates in activated sludge process.

A new modeling concept to evaluate the effects of cadmium and copper on heterotrophic growth rate constant (mu(H)) and lysis rate constant (b(H)) in activated sludge was introduced. The oxygen uptake rate (OUR) was employed to measure the constants. The results indicated that the mu(H) 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 b(H) value increased from 0.31 to 0.35 d(-1) or by 11%. When adding 0.7 mg L(-1) of copper, the mu(H) value decreased to 2.80 d(-1) or by 38%. The b(H) 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 b(H) values and heavy metal concentrations agreed with exponential type well. The heavy metal would enhance b(H) 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 mu(H) but also that the enhancement effect on b(H) should be considered when heavy metal presented.

Improving neural network prediction of effluent from biological wastewater treatment plant of industrial park using fuzzy learning approach

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 (SSeff), chemical oxygen demand (CODeff), and pHeff 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 SSeff, CODeff and pHeff could be achieved using ANFIS. The maximum values of correlation coefficient for SSeff, CODeff, and pHeff 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 SSeff, CODeff, and pHeff could also be achieved.

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

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.

Evaluating Taiwan's air quality variation trends using grey system theory

Abstract According to geographical characteristics and air quality conditions, the Taiwan Environmental Protection Agency has divided the island into 7 air quality regions (AQRs) including Northern, Chu‐Miao, Central, Yun‐Chia‐Nan, Kao‐Ping, I‐lan and Hwa‐Tung AQRs. The grey relational grade (GRG) of all AQRs and nationwide grade were calculated to comprehend the level of contamination. Then the grey model GM (0, N) was used to evaluate the effects of 5 primary contaminants on air quality. The results indicated that the ranking of air quality for the 7 AQRs from the best to the worst were as follows: Hwa‐Tung > I‐lan > Chu‐Miao > Northern > Yun‐Chia‐Nan > Central > Kao‐Ping. The 5 most common contaminants from the greatest to the least were as follows: CO > SO2 > NO > O3 > PM10.