Wastewater treatment is one of critical issues faced by water utilities, and receives more and more attentions recently. The energy consumption modeling in biochemical wastewater treatment was investigated in the study via a general and robust approach based on Bayesian semi-parametric quantile regression. The dataset was derived from a municipal wastewater treatment plant, where the energy consumption of unit chemical oxygen demand (COD) reduction was the response variable of interest. Via the proposed approach, the comprehensive regression pictures of the energy consumption and truly influencing factors, i.e., the regression relationships at lower, median and higher energy consumption levels were characterized respectively. Meanwhile, the proposals for energy saving in different cases were also facilitated specifically. First, the lower level of energy consumption was closely associated with the temperature of influent wastewater, and the chroma-rich wastewater also showed helpful in the execution of energy saving. Second, at median energy consumption level, the COD-rich wastewater played a determinative role in the reduction of energy consumption, while the higher quality of treated water led to slightly energy intensive. Third, the higher level of energy consumption was most likely to be attributed to the relatively high temperature of wastewater and total nitrogen (TN)-rich wastewater, and both of the factors were preferably to be avoided to alleviate the burden of energy consumption. The study provided an efficient approach to controlling the energy consumption of wastewater treatment in the perspective of statistical regression modeling, and offered valuable suggestions for the future energy saving. 相似文献
在RSSI(Received Signal Strength Indication)测距定位技术中,为抑制巷道信号NLOS(Non Line of Sight)传输对定位结果的影响,提出信号指纹定位和几何优化算法.在离线阶段利用高斯滤波最大值加权法和最小二乘法建立符合矿井巷道环境的无线信号测距模型,设计改进卡尔曼滤波器... 相似文献
Characterization of the typical petroleum pollutants, polycyclic aromatic hydrocarbons (PAHs) and n-alkanes, and indigenous microbial community structure and function in historically contaminated soil at petrol stations is critical. Five soil samples were collected from a petrol station in Beijing, China. The concentrations of 16 PAHs and 31 n-alkanes were measured by gas chromatography-mass spectrometry. The total concentrations of PAHs and n-alkanes ranged from 973 ± 55 to 2667 ± 183 μg/kg and 6.40 ± 0.38 to 8.65 ± 0.59 mg/kg (dry weight), respectively, which increased with depth. According to the observed molecular indices, PAHs and n-alkanes originated mostly from petroleum-related sources. The levels of ΣPAHs and the total toxic benzo[a]pyrene equivalent (ranging from 6.41 to 72.54 μg/kg) might exert adverse biological effects. Shotgun metagenomic sequencing was employed to investigate the indigenous microbial community structure and function. The results revealed that Proteobacteria and Actinobacteria were the most abundant phyla, and Nocardioides and Microbacterium were the important genera. Based on COG and KEGG annotations, the highly abundant functional classes were identified, and these functions were involved in allowing microorganisms to adapt to the pressure from contaminants. Five petroleum hydrocarbon degradation-related genes were annotated, revealing the distribution of degrading microorganisms. This work facilitates the understanding of the composition, source, and potential ecological impacts of residual PAHs and n-alkanes in historically contaminated soil.
