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This paper represents the first study to simulate the biodegradation rates (BDRs) of several components in the sewer using the sewer biodegradation model (SBDM). In order to verify the fitness between the experimental values and model values, experiments were conducted in a 21 m long sewer pilot plant and the results showed high fitness (All correlational coefficient values were greater than 0.91). Since the SBDM was validated, the biodegradation rate (BDR) was simulated. The production of hydrogen sulfide was also simulated. The results revealed that aerobic growth of heterotrophs in biofilm predominated the biodegradation. The growth rate of heterotrophs in biofilm was greater than the decay rate from initial time to the 3rd hour, but it changed after the 4th hour because of low substrate concentration. During the experimental period, the BDRs were greater than the supply rate for five components. For two components, the supply rates were greater than the BDRs. The BDR of dissolved oxygen was greater than the supply rate before the 3rd hour but that reversed after the 3rd hour. According to the results, the sewer biodeterioration could be simulated using SBDM.

Agricultural organic wastes (AOW) have the potential to provide bioenergy particularly found in biogas by anaerobic digestion (AD). In this study, the biogas production (BP) of AOW was obtained by batch AD with anaerobic digesters (500 mL) at 35°C incubator. The results showed that BP values in terms of volatile solids (VS) from rice husk, rice straw, flower residues, fruit and vegetable residues, wasted oyster shell residue (WOSR), fishery residues, livestock and poultry manures, livestock and poultry slaughter wastes (LPSW), and eight equally mixed wastes (EEMW) were 84.03, 193.36, 153.32, 76.27, 150.48, 63.26, 169.63, 615.74, and 172.83 mL/g VS, respectively. LPSW showed the highest μ m of 16.99 mL/g VS-d, the highest BP of 615.74 mL/g VS and the highest bioconversion efficiency of 65.98% compared to the other organic wastes. BP from the most AOW in Taiwan by AD was estimated to be 768,567,753 (743,522,223, excluding WOSR) m 3 /year. The annual BP of 768,567,753 m 3 /year of the eight total major AOW by AD was lower (∼20.11%) than 961,989,781 m 3 /year of the EEMW by anaerobic co-digestion. Result also showed that modified Gompertz equation was suitable to describe BP accumulation and BP rate.

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.

The organic content of municipal solid waste has long been an attractive source of renewable energy, mainly as a solid fuel in waste-to-energy plants. This study focuses on the potential to use microbial fuel cells to convert municipal solid waste organics into energy using various operational conditions. The results showed that two-chamber microbial fuel cells with carbon felt and carbon felt allocation had a higher maximal power density (20.12 and 30.47 mW m-2 for 1.5 and 4 L, respectively) than those of other electrode plate allocations. Most two-chamber microbial fuel cells (1.5 and 4 L) had a higher maximal power density than single-chamber ones with corresponding electrode plate allocations. Municipal solid waste with alkali hydrolysis pre-treatment and K3Fe(CN)6 as an electron acceptor improved the maximal power density to 1817.88 mW m-2 (~0.49% coulomb efficiency, from 0.05–0.49%). The maximal power density from experiments using individual 1.5 and 4 L two-chamber microbial fuel cells, and serial and parallel connections of 1.5 and 4 L two-chamber microbial fuel cells, was found to be in the order of individual 4 L (30.47 mW m-2) > serial connection of 1.5 and 4 L (27.75) > individual 1.5 L (20.12) > parallel connection of 1.5 and 4 L (17.04) two-chamber microbial fuel cells . The power density using municipal solid waste microbial fuel cells was compared with information in the literature and discussed.

Microalgae produce increased lipid content accompanied by a significant decrease in cell density with decreasing nitrate concentration. Magnetic fields (MF) have been reported as a factor that could accelerate metabolism and growth in microalgae culture. Thus, this study aimed to optimize the influence of MF and nitrate concentration (sodium nitrate, N) on the growth and lipid productivity of Nannochloropsis oculata. A single-factor experiment integrated with response surface methodology (RSM) via central composite design (CCD) was performed. The results showed that the maximum specific growth rate (0.24 d−1) and maximum lipid productivity (38 mg L−1 d−1) obtained in this study were higher than those of the control culture (by 166% and 103%, respectively). This study also found that the two-way interaction term MF × N had a significant effect on cell growth but not on lipid production. It was concluded that to design appropriate MF for enhanced lipid productivity due to cell growth, further research must focus on developing an understanding of the relationship between the bioeffects of the magnetic field and the proteomic changes involved in lipid accumulation strategies. This approach would enable the design of conditions to obtain inexpensive high-value products from N. oculata.

This paper represents the first study to compare seven types of first–order and one–variable grey differential equation model [abbreviated as GM (1, 1)] and back-propagation artificial neural network (BPNN) for predicting hourly particulate matter (PM) including PMio and PM 2.5 concentrations in Dali area of Taichung City, Taiwan. Their prediction performance was also compared. The results indicated that the minimum mean absolute percentage error (MAPE), mean squared error (MSE), and root mean squared error (RMSE) was 16.76%, 132.95, and 11.53, respectively for PM 10 prediction. For PM 2.5 prediction, the minimum MAPE, MSE, and RMSE value of 21.64%, 40.41, and 6.36, 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) could predict the hourly PM variation precisely even comparing with BPNN.

This paper represents the first study to use the grey model (GM) for predicting CO 2 , SO 2 and O 2 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 CO 2 , SO 2 and O 2 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.

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.

In this study, the adaptive network-based fuzzy inference system (ANFIS) was used to generate the fuzzy rules for discussing the effects of gas and electricity price on residential sector’s energy consumption rate (RSECR) in Taiwan. Twenty seven fuzzy rules of Sugeno type were generated from ANFIS. It was found that the fuzzy rules did not reveal the significance when the situation was extreme, i.e. one input variable was low but another one was high. When all three input variables fell in the low degree of membership, the output RSECR values tended to be “high”. When all three input variables fell in the high degree of membership, the output RSECR values tended to be “low” even “negative”. It suggested that higher energy price would inhibit RSEC.

In this study, the back-propagation neural network (BPNN) was used to predict the number of low-income households (NLIH) in Taiwan, taking the seasonally adjusted annualized rates (SAAR) for real gross domestic product (GDP) as input variables. The results indicated that the lowest mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and highest correlation coefficient (R) for training and testing were 4.759 % versus 19.343 %, 24429972.268 versus 781839890.859, 4942.669 versus 27961.400, and 0.945 versus 0.838, respectively.

The effect of sludge retention time (SRT) on biomass, kinetic parameters, and stoichiometric parameters of ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) in anaerobic/anoxic/oxic (A2O) process were explored in this study. The results showed that the growth rate constants were 1.52, 1.22, and 0.85 day−1, respectively, for AOB, those were 1.59, 1.19, and 0.87 day−1, respectively, for NOB when SRT was 20, 10, and 5 days. The lysis rate constants of AOB and NOB were 0.14 and 0.09 day−1, respectively. The yield coefficients were 0.23 and 0.22, respectively, for AOB and NOB. They did not change with SRT obviously. The biomass of AOB was 50.94, 26.35, and 14.68 mg L−1, respectively, and the biomass of NOB was 116.77, 60.00, and 44.25 mg L−1, respectively, at SRT of 20, 10, and 5 days. When SRT diminished from 20 to 5 days, the biomass of AOB and NOB diminished by 36.26 and 75.52 mg L−1, respectively. The removal efficiency of NH4 +–N diminished by 68.9 %. The removal efficiency of total nitrogen diminished by 42.9 %.

In this study, exact radial basis function neural network (ERBFNN) was used to predict the concrete compressive strength based on physical properties of electric arc furnace oxidizing slag. The mean absolute percentage error (MAPE) was used to evaluate the predicting performance. The results indicated the minimum MAPE of 0.08 % and 5.28 % could be achieved when training and predicting, respectively. According to the results, it revealed that ERBFNN was an efficiently tool for providing information.

This study adopted analytical hierarchical process (AHP) to evaluate the significance of the criterion of policy regarding promoting biomass energy, taking environmental social groups’ points of view into consideration. The analytical results of the study are as follows. The experts from environmental social groups suggested that the most important major criteria are in the order as follows: policy criterion layer, technical criterion layer, economy criterion layer, and energy education criterion layer. As for the global weights of the criteria, the criteria with top five weights are in the order as follows: “active development of green energy industry by government”, “supply and consumption method of low-carbon and low-pollution energy”, “increase of percentage of power generation from renewable energy in total power supply”, “encourage manufacturers to develop new biomass power generation technology”, and “continually develop the technology projects which has potential and prospects in the future”.

The analytical hierarchical process (AHP) was adopted in this study to determine the priority of the policy criterion related to promoting wind energy, considering the environmental practitioners’ points of view. The results were described as follows. The practitioners from environmental organizations suggested that the most important major criteria are in the following order: policy criterion layer, technical criterion layer, economy criterion layer, and energy education criterion layer. For the global weights of the criteria, the criteria with top five weights were in the order as follows: “active development of green energy industry by government”, “supply and consumption method of low-carbon and low-pollution energy”, “increase of percentage of power generation from renewable energy in total power supply”, “encourage manufacturers to develop new wind power generation technology”, and “continually develop the technology projects which has potential and prospects in the future”.

This study adopted analytical hierarchical process (AHP) to evaluate the significance of the policy criterion for promoting solar energy, regarding social publics’ points of view. The results of the study are described as follows. The social publics suggested that the most important major criteria are in the following order: policy, technique, educational promotion, and economy incentives. For the global weights of the criteria, the criteria with top five weights are in the following order: “patents for solar energy products“ (0.183), “encourage R&D of new techniques and reduce power cost“ (0.168), “improve power saving technique“ (0.095), “encourage the firms to develop new techniques of solar energy power“ (0.082), and “increase percentage of solar energy power in total power capacity“ (0.075).

This paper represents the first research to simultaneously study the agricultural water management and the influences on energy, food, and social security in Taiwan from 1980s. According to the survey, the average agricultural water use was 13.663 × 109 tons with standard deviation of 1.226 × 109 tons. The average value of agricultural water use percentage was 75.58% with standard deviation of 4.62%. The energy consumption in agricultural sector decreased year by year from 1983. The average energy consumption in agricultural sector was 130.841 × 109 L oil equivalent (OE) with standard deviation of 23 × 109 L OE. The percentage of agricultural gross domestic product (GDP) to total GDP decreased from 7.33% in 1981 to 1.83% in 2011. The magnitude and frequency of agricultural disasters increased severely in the past two decades. The loss of agricultural disasters account for 5% of the agricultural GDP and resulted in the fluctuation of crop production, farmers' family income, and social security. To lower the loss of agricultural disasters, the adaptation measures for agricultural water management, energy, food, and social security should ensure the stability of water, energy, and food supply.

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 the hardness of four groundwater monitoring stations in Kaohsiung City of Taiwan. The mean absolute percentage error (MAPE) was used to evaluate the predicting performance. The results indicated the minimum MAPE of 4.71 %, 3.15 %, 2.66 %, and 16.63 % could be achieved when predicting hardness of Fonsi, Datung, Shaukang, and Chihsien stations, respectively. According to the results, it revealed that GM (1, 1) was an efficiently early warning tool for providing groundwater quality information to the competent authority.

This study adopted analytical hierarchy process (AHP) to evaluate the significance of the criterion of social education (SE) regarding promoting energy saving and carbon reduction (ESCR), taking the points of view of scholars from universities and research institutes into consideration. The analytical results of the study are as follows. Regarding the hierarchy of evaluation criteria, on the whole, the academic experts suggest that the most important major criteria are in the following order: positive actions of the government, implementation of education and propaganda activities, support from SE units, and integration with social resources.

In this study, seven types of first-order and one-variable grey differential equation model (abbreviated as GM (1, 1) model) were used to forecast hourly roadside particulate matter (PM) including PM10 and PM2.5 concentrations in Taipei County of Taiwan. Their forecasting 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 11.70%, 60.06, 7.75, and 0.90%, respectively when forecasting PM10. When forecasting PM2.5, the minimum MAPE, MSE, RMSE, and maximum R-value of 16.33%, 29.78, 5.46, and 0.90, respectively could be achieved. All statistical values revealed that the forecasting 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) was an efficiently early warning tool for providing PM information to the roadside inhabitants.

In this study, several types of adaptive network-based fuzzy inference system (ANFIS) with different membership functions (MFs) and artificial neural network (ANN) were employed to predict hourly photochemical oxidants that were oxidizing substances such as ozone and peroxiacetyl nitrate produced by photochemical reactions. The results indicated that ANFIS statistically outperforms ANN in terms of hourly oxidant prediction. The minimum mean absolute percentage errors (MAPEs) of 4.99% could be achieved using ANFIS with bell shaped MFs. The maximum correlation coefficient, the minimum mean square errors, and the minimum root mean square errors were 0.99, 0.15, and 0.39, respectively. ANFIS's architecture consists of both ANN and fuzzy logic including linguistic expression of MFs and if-then rules, so it can overcome the limitations of traditional neural network and increase the prediction performance.

A mathematical model was established to describe the transportation and transformation of nitrogen compounds in the sewer pipe. In order to verify the consistency between the experimental data and model simulation data, four runs of experiments were carried out in a 21 m long, 0.15 m diameter model sewer. The results showed a good consistency between the experimental and simulation values (all correlation coefficients >0.81). According to the good consistency, it was proved that the attached heterotrophic biofilm on the sewer bottom played a dominant role on degradation of compounds in the system. Readily biodegradable substrate ( S S ) decreased with the test time due to aerobic and anoxic growth of heterotrophs. Ammonia and ammonium nitrogen ( S NH ) increased with the test time. But nitrate and nitrite nitrogen ( S NO ) and soluble organic nitrogen ( S ND ) decreased. Dissolved oxygen ( S O ) declined due to the microbial consumption and subsequently increased due to reaeration, forming a sag curve. Furthermore, the transformation pathways of different compounds were identified in this study.

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.

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.

In this study, the effect of sludge retention time on ammonia oxidizing bacteria (AOB) and nitrite oxidizing bacteria (NOB) in an anaerobic/oxic (AO) process, was explored. The results indicated that the growth rate constants of AOB were 0.97, 0.88, and 0.79 d ―1 , respectively, meanwhile, those of NOB were 1.22, 1.03, and 0.93 d ―1 , respectively, when the sludge retention time (SRT) was 15 days, 10 days and 5 days. The relation between the growth rate constant and the SRT could be best described using a simple exponential curve and a second type hyperbolic curve. The lysis rate constants for AOB and NOB were 0.13 and 0.18 d ―1 , respectively. The yield coefficients values of AOB and NOB were 0.22 and 0.21, respectively. The percentage of AOB to mixed liquid suspended solids (MLSS) was 0.64%, 0.53%, and 0.35%, respectively. Meanwhile, the percentage of NOB was 2.24%, 1.87%, and 1.11%, respectively, at SRT values of 15 days, 10 days and 5 days. When the SRT value decreased, the AOB and NOB biomass levels decreased by 12.75 and 47.01 mg L ―1 , respectively. Meanwhile the removal efficiency of NH 4 + -N decreased from 90 to 26%, while the removal efficiency of total nitrogen (TN) decreased from 14 to 8%.

In this study, the variation of sewage quality was investigated and the active fraction of different microbial functional groups in biofilm was quantified in a 5.6-km trunk sewer line. The sewage quality including suspended solids, biochemical oxygen demand, total chemical oxygen demand (COD), total nitrogen, total Kjeldahl nitrogen, ammonia nitrogen, nitrite nitrogen, and nitrate nitrogen were measured and compared with the values in literatures. The results indicated that since the wastewater treatment plant was not operated at its full capacity, the concentrations of different compounds were lower compared with the values in literatures. The values of heterotrophic growth rate constant lay between 5.6 and 8.6 day(-1). Its average value was 7.7 day(-1). The values of heterotrophic lysis rate constant lay between 0.2 and 0.4 day(-1). The active heterotrophic biomass in biofilm varied from 240 to 800 mg COD m(-2) and average value was 497 mg COD m(-2). The biofilm mass varied from 880 to 1,080 mg m(-2). The percentage of heterotroph to biofilm mass fall within the range of 24.0-90.9% and average value was 52.9%. In the oxygen uptake rate batch tests, the biomass, growth rate constant, and lysis rate constant of autotroph could not be determined because the fraction of autotroph in biofilm was relatively few. It revealed that the degradation of organic matters, nitrification, and denitrification occurred in the trunk sewer line. But the results indicate that the condition seem favorable for nitrification.

BACKGROUND: Pesticides are sometimes non-biodegradable and, moreover, toxic to microorganisms. If pesticides exceed the tolerance of microorganisms, failure of the activated sludge process (ASP) occurs. Therefore the effects of two types of organophosphate pesticides on heterotrophic growth rate constant in sludge from ASP were investigated. Oxygen uptake rate was employed to measure the rate constants. RESULTS: The results indicated that the value of heterotrophic growth rate constant decreased from 3.88 d−1 to 1.46 d−1 or by 62% when 0.5 mg L−1 of glyphosate was added. When adding 0.5 mg L−1 of malathion, the value of heterotrophic growth rate constant decreased to 1.33 d−1 or by 66%. The value of heterotrophic growth rate constant decreased to 1.98 d−1 or by 49% when 0.5 mg L−1 of pesticide combination (50% for each) was added. CONCLUSIONS: The inhibitory effects of glyphosate and malathion were in good agreement with non-competitive inhibition kinetics, but pesticide combination did not follow non-competitive kinetics. The inhibition coefficient values for glyphosate, malathion and their combination were 0.29, 0.29 and 0.58 mg L−1, respectively. For comparison, linear and exponential types of models were derived by regression. According to non-competitive kinetics, and linear and exponential models, the inhibitory effects of glyphosate and malathion were almost consistent. Finally, the degree of inhibition was simulated using different types of model. It was found that the toxicity of the two pesticides agreed with the antagonism well. Copyright © 2009 Society of Chemical Industry

In this study, an extended activated sludge model was established to describe the transformation of nitrite ( S NO 2 ) , nitrate ( S NO 3 ) and other components in TNCU2 process (National Central University of Taiwan No. 2) that consisted of anaerobic, oxic, anoxic, oxic zones in sequence. The significant differences between this extended model and other models were that two-stage nitrification, multi-stage denitrification, and phosphorus removal were taken into account simultaneously. The results indicated that the growth rate constants of X AOB and X NOB were 1.4 and 0.4 d −1 , respectively. Y AOB value was 0.14 and Y NOB value was 0.04. According to model simulation, the heterotrophic microorganism ( X H ), phosphorus accumulating organism ( X PAO ), X AOB and X NOB concentrations were 1160–1322, 182–226, 21–26 and 13–17 mg l −1 , respectively, in TNCU2 process. X H , X PAO , X AOB , and X NOB decreased in the anaerobic tanks because of the lysis reaction. Then X H , X PAO , X AOB , and X NOB increased in the aerobic tanks due to aerobic growth. X H , X PAO , X AOB , and X NOB increased in quantities by about 5%, 6%, 6% and 4% in the first aerobic tank and decreased in quantities by about 12%, 19%, 20% and 19% in the anoxic tank in which the step feeding influent flowed. The ratio of total nitrifying species to total active biomass was about 3% in each tank.

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%.

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.

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. 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.

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.

Six test runs were carried out to explore the interaction of shear stress and Fe/Mn oxides concentration on the growth of heterotrophic micro-organisms in biofilms. Experimental results revealed that the existence of Fe/Mn colloids was beneficial to biofilm formation. Significant interactions existed (p < 0.001) between shear stress and influent Fe/Mn concentration on biofilm bacteria growth and dissolved organic carbon consumption. Biofilm bacteria numbers were positively related to the dissolved organic carbon consumption rate. This implies that the influent Fe/Mn concentration did not interfere with the uptake of the carbon source by biofilm bacteria. At a higher Fe/Mn concentration (HCS), the biofilm HPC number significantly increased with an increase in shear stress. However, the opposite relation was found at a lower Fe/Mn concentration (LCS). A significant interaction existed between influent colloidal Fe/Mn concentration and shear stress on the kinetic and yield parameters specific growth rate (μ ...

Five types of gray models (GMs) were employed to predict suspended solids (SSeff), chemical oxygen demand (CODeff), and pHeff in the effluent from a wastewater treatment plant (WWTP) in industrial park of Taiwan. For comparison, an artificial neural network (ANN) was also used. Results indicated that the minimum MAPEs of 18.91, 6.10, and 0.86% for SSeff, CODeff, and pHeff could be achieved using GMs. A good fitness could be achieved using ANN also, but they required a large quantity of data for constructing model. Contrarily, GM only required a small amount of data (at least four data), and the prediction results were even better than those of ANN. In the first type of application, the MAPE values for predicting SSeff and pHeff were lower when using GM1N2-1. MAPE value of CODeff using GM1N3-1 was lower when predicting. In the second type, the MAPE value of SSeff using GM (1, 1) was lower when predicting. When predicting CODeff and pHeff, the values using rolling GM (1, 1) (RGM, i.e., four data before the ...

Grey model (GM) and artificial neural network (ANN) were employed to predict suspended solids (SSeff), chemical oxygen demand (CODeff) and pHeff in the effluent from conventional activated process of an industrial wastewater treatment plant using simple online monitoring parameters (pH in the equalization pond effluent; pH, temperature, and dissolved oxygen in the aeration tank). The results indicated that the minimum mean absolute percentage errors of 20.79, 6.09 and 0.71% for SSeff, CODeff and pHeff, respectively, could be achieved using different types of GMs. GM only required a small amount of data (at least four data) and the prediction results were even better than those of ANN. According to the results, the online monitoring parameters could be applied on the prediction of effluent quality. It also revealed that GM could predict the industrial effluent variation as its effluent data was insufficient.

In this study, an extended activated sludge model was established to describe the transformation of nitrite ( S N O 2 ), nitrate ( S N O 3 ) and other components in A2O process when mixed liquid recycling ratio (MLRR) varied. The significant differences between the extended model and other models are listed as follows: (1) the contribution and processes of heterotrophs (XH) which used different carbon sources to reduce S N O 2 and S N O 3 under anoxic conditions, (2) the contribution and processes of phosphorus accumulating organisms (XPAO) which reduce S N O 2 and S N O 3 under anoxic condition and (3) the contribution and two-stage nitrification processes of ammonia oxidizing bacteria (XAOB) and nitrite oxidizing bacteria (XNOB) which oxidized ammonia ( S N H 4 ) and S N O 2 under aerobic condition. The results showed that the variation of S N O 2 and S N O 3 could be modeled successfully using the extended model. The μAOB and μNOB were 0.8 and 0.4 day−1, respectively. YAOB value was 0.18, and YNOB value was 0.06. According to model simulation, XH, XPAO, XAOB and XNOB concentrations were 1081–1203, 377–407, 19–21 and 11–12 mg L−1 in three test runs, respectively. From anoxic tank to aerobic tank, XAOB and XNOB increased from 20 to 21 mg L−1 and from 11 to 12 mg L−1 when MLRR was equal to 0.5 but their concentrations maintained at 20 and 11 mg L−1 in both tanks when MLRR was equal to 2.0. In the aerobic effluent, XH decreased from 1203 to 1164 mg L−1 and XPAO increased from 397 to 407 mg L−1 when MLRR increased from 0.5 to 2.0. The ratio of total nitrifying species to total active biomass was about 2% in each tank.

Japan's Air Pollution Control Law signed in 1968 prescribes the maximum permissible limits of motor vehicle exhausts as well as establishing mechanisms for monitoring air pollution In this paper, the grey relational grade of air pollutants from ambient air pollution and roadside air pollution monitoring stations is used to look at the relationship between air pollution and transportation. The results indicated that the ambient and roadside air quality increased by rose from 1975 to 2004 but less fast than the growth in traffic. Some of this may be attributable to the legislation but there have also been other measures since 1968 that have also contributed.

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.

Heavy metal cadmium(II) was added stepwise into an A2 O pilot plant to investigate the toxic effects of Cd(II) on the removal efficiencies, kinetic parameters (yield coefficients and maximum specific growth rates) and reaction rates of carbon, nitrogen and phosphate for the acclimatized heterotrophic and autotrophic bacteria. Results showed that 2 mg∕L Cd(II) initially affected the biological reaction of phosphate removal. At Cd(II) 5 mg∕L , the efficiencies of total nitrogen removal and nitrification were substantially dropped. At the same time, the yield coefficient and maximum specific growth rate of heterotrophs were significantly decreased from 0.8 g COD∕g COD and 6.44 day−1 to 0.54 g COD∕g COD and 4.67 day−1 , respectively. And, the denitrification rate was inhibited by about 61%. The inhibition percentages of anaerobic release, anoxic and aerobic uptake rates of phosphate were about 76, 64, and 90%, respectively. When Cd(II) concentration was continually increased up to 35 mg∕L , removal efficiency...

The study investigates the inactivation of biofilm bacteria colonized on the surface of polyvinyl chloride (PVC) pipes delivering either groundwater or treated wastewater. It does so using a citric acid (C6H8O7) solution. The results of the study showed that the optimal conditions of the biofilm bacteria inactivation were over 10,000 mg/L citric acid concentration and 60 minutes of contact time at least. Under these conditions, the removal efficiency could reach above 99.999% for heterotrophic plate count (HPC) bacteria and 99.95% for coliform bacteria. The study also showed that the biofilm bacteria were the major sources of planktonic bacteria resuspended into water purified by drinking water production systems (DWPS).

A hybrid model which incorporated a biofilm model into the general dynamic model was developed to predict the effluent quality of a combined activated sludge and biofilm processTaiwan National Cent...

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.

The effect of sludge retention time (SRT) on biomass, kinetic parameters, and stoichiometric parameters of ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) in anaerobic/anoxic/oxic (A(2)O) process were explored in this study. The results showed that the growth rate constants were 1.52, 1.22, and 0.85 day(-1), respectively, for AOB, those were 1.59, 1.19, and 0.87 day(-1), respectively, for NOB when SRT was 20, 10, and 5 days. The lysis rate constants of AOB and NOB were 0.14 and 0.09 day(-1), respectively. The yield coefficients were 0.23 and 0.22, respectively, for AOB and NOB. They did not change with SRT obviously. The biomass of AOB was 50.94, 26.35, and 14.68 mg L(-1), respectively, and the biomass of NOB was 116.77, 60.00, and 44.25 mg L(-1), respectively, at SRT of 20, 10, and 5 days. When SRT diminished from 20 to 5 days, the biomass of AOB and NOB diminished by 36.26 and 75.52 mg L(-1), respectively. The removal efficiency of NH4 (+)-N diminished by 68.9 %. The removal efficiency of total nitrogen diminished by 42.9 %.