Use of magnetic fields and nitrate concentration to optimize the growth and lipid yield of Nannochloropsis oculata.

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.

Behaviors of biomass and kinetic parameter for nitrifying species in A²O process at different sludge retention time.

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 %.

Predicting the co-melting temperatures of municipal solid waste incinerator fly ash and sewage sludge ash using grey model and neural network.

A grey model (GM) and an artificial neural network (ANN) were employed to predict co-melting temperature of municipal solid waste incinerator (MSWI) fly ash and sewage sludge ash (SSA) during formation of modified slag. The results indicated that in the aspect of model prediction, the mean absolute percentage error (MAPEs) were between 1.69 and 13.20% when adopting seven different GM (1, N) models. The MAPE were 1.59 and 1.31% when GM (1, 1) and rolling grey model (RGM (1, 1)) were adopted. The MAPEs fell within the range of 0.04 and 0.50% using different types of ANN. In GMs, the MAPE of 1.31% was found to be the lowest when using RGM (1, 1) to predict co-melting temperature. This value was higher than those of ANN2-1 to ANN8-1 by 1.27, 1.25, 1.24, 1.18, 1.16, 1.14 and 0.81%, respectively. GM only required a small amount of data (at least four data). Therefore, GM could be applied successfully in predicting the co-melting temperature of MSWI fly ash and SSA when no sufficient information is available. It also indicates that both the composition of MSWI fly ash and SSA could be applied on the prediction of co-melting temperature.

Monitoring and assessing variation of sewage quality and microbial functional groups in a trunk sewer line.

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.

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 (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.