Objective During present investigation the data of a laboratory-scale anoxic sulfide oxidizing （ASO） reactor were used in a neural network system to predict its performance. Methods Five uncorrelated components of t...Objective During present investigation the data of a laboratory-scale anoxic sulfide oxidizing （ASO） reactor were used in a neural network system to predict its performance. Methods Five uncorrelated components of the influent wastewater were used as the artificial neural network model input to predict the output of the effluent using back-propagation and general regression algorithms. The best prediction performance is achieved when the data are preprocessed using principal components analysis （PCA） before they are fed to a back propagated neural network. Results Within the range of experimental conditions tested, it was concluded that the ANN model gave predictable results for nitrite removal from wastewater through ASO process. The model did not predict the formation of sulfate to an acceptable manner. Conclusion Apart from experimentation, ANN model can help to simulate the results of such experiments in finding the best optimal choice for ASO based denitrification. Together with wastewater collection and the use of improved treatment systems and new technologies, better control of wastewater treatment plant （WTP） can lead to more effective maneuvers by its operators and, as a consequence, better effluent quality.展开更多
Sewage sludge composting is an important environmental measure. The reduction of nitrogen loss is a critical aim of compost maturation, and the addition of spent mushrooms (SMs) and herbal residues (HRs) may be helpfu...Sewage sludge composting is an important environmental measure. The reduction of nitrogen loss is a critical aim of compost maturation, and the addition of spent mushrooms (SMs) and herbal residues (HRs) may be helpful. To evaluate the nitrogen transformations during co-composting of sewage sludge, SMs, and HRs, windrows were constructed in a residual processing plant. Dewatered sewage sludge and sawdust were mixed with SMs and HRs at two proportions on a fresh weight basis, 3:1:1 (sewage sludge:sawdust:SMs or HRs) and 3:1:2 (sewage sludge:sawdust:SMs or HRs). The mixture was then composted for 40 d. Changes in the physicochemical charac- teristic of sewage sludge during composting were recorded and analyzed. Addition of SMs and HRs accelerated the temperature rise, mediating a quicker composting maturation time compared to control. The addition also resulted in lower nitrogen losses and higher nitrate nitrogen levels in the compost products. Among the windrows, SM and HR addition improved the nitrogen status. The total nitrogen (TN) and nitrogen losses for SM and HR treatments ranged from 22.45 to 24.99 g/kg and from 10.2% to 22.4% over the control values (18.66-21.57 g/kg and 40.5%-64.2%, respectively). The pile with the highest proportion of SMs (3:1:2 (sewage sludge:sawdust:SMs)) had the highest TN level and the lowest nitrogen loss. The germination index (GI) values for all samples at maturity were above 80%, demonstrating optimal maturity. The addition of SMs and HRs augments sewage composting.展开更多
文摘Objective During present investigation the data of a laboratory-scale anoxic sulfide oxidizing （ASO） reactor were used in a neural network system to predict its performance. Methods Five uncorrelated components of the influent wastewater were used as the artificial neural network model input to predict the output of the effluent using back-propagation and general regression algorithms. The best prediction performance is achieved when the data are preprocessed using principal components analysis （PCA） before they are fed to a back propagated neural network. Results Within the range of experimental conditions tested, it was concluded that the ANN model gave predictable results for nitrite removal from wastewater through ASO process. The model did not predict the formation of sulfate to an acceptable manner. Conclusion Apart from experimentation, ANN model can help to simulate the results of such experiments in finding the best optimal choice for ASO based denitrification. Together with wastewater collection and the use of improved treatment systems and new technologies, better control of wastewater treatment plant （WTP） can lead to more effective maneuvers by its operators and, as a consequence, better effluent quality.
基金Project (No. 2009ZX07317-008-06) supported by the Major Projects on Control and Management Technology of Water Pollution of the Ministry of Housing and Urban-Rural Development of China
文摘Sewage sludge composting is an important environmental measure. The reduction of nitrogen loss is a critical aim of compost maturation, and the addition of spent mushrooms (SMs) and herbal residues (HRs) may be helpful. To evaluate the nitrogen transformations during co-composting of sewage sludge, SMs, and HRs, windrows were constructed in a residual processing plant. Dewatered sewage sludge and sawdust were mixed with SMs and HRs at two proportions on a fresh weight basis, 3:1:1 (sewage sludge:sawdust:SMs or HRs) and 3:1:2 (sewage sludge:sawdust:SMs or HRs). The mixture was then composted for 40 d. Changes in the physicochemical charac- teristic of sewage sludge during composting were recorded and analyzed. Addition of SMs and HRs accelerated the temperature rise, mediating a quicker composting maturation time compared to control. The addition also resulted in lower nitrogen losses and higher nitrate nitrogen levels in the compost products. Among the windrows, SM and HR addition improved the nitrogen status. The total nitrogen (TN) and nitrogen losses for SM and HR treatments ranged from 22.45 to 24.99 g/kg and from 10.2% to 22.4% over the control values (18.66-21.57 g/kg and 40.5%-64.2%, respectively). The pile with the highest proportion of SMs (3:1:2 (sewage sludge:sawdust:SMs)) had the highest TN level and the lowest nitrogen loss. The germination index (GI) values for all samples at maturity were above 80%, demonstrating optimal maturity. The addition of SMs and HRs augments sewage composting.
