Addition of anaerobic ammonium oxidation bacteria to lower running cost during the membrane bioreactor process treating sewage.

Reducing energy consumption or running cost associated with the membrane bioreactor (MBR) process is a serious challenge that needs to be addressed in treating sewage. The addition of anaerobic ammonium oxidation bacteria (AnAOB) to a running MBR has the potential to lower the aeration rate, thus decreasing the running cost in treating sewage. The results obtained showed that owing to addition of AnAOB, TN and NH4+-N removal rates increased by 9.8% and 1.13%, respectively, while the aeration rate decreased by 50%. Additionally, high throughput sequencing and isotope experiments showed that both AnAOB and heterotrophic denitrification bacteria could survive simultaneously and play an important role in nitrogen removal, with AnAOB having a significantly greater contribution. It can be concluded that the addition of AnAOB reduced the running cost of MBR in treating sewage.

Pilot Scale Application of a Ceramic Membrane Bioreactor for Treating High-Salinity Oil Production Wastewater.

The offshore oil extraction process generates copious amounts of high-salinity oil-bearing wastewater; at present, treating such wastewater in an efficient and low-consumption manner is a major challenge. In this study, a flat ceramic membrane bioreactor (C-MBR) process combining aerobic microbial treatment technology and ceramic membrane filtration technology was used to treat oil-bearing wastewater. The pilot test results demonstrated the remarkable performance of the combined sequential batch reactor (SBR) and C-MBR process, wherein the chemical oxygen demand (COD) and ammonia nitrogen (NH4+-N) removal rates reached 93% and 98.9%, respectively. Microbial analysis indicated that the symbiosis between Marinobacterium, Marinobacter, and Nitrosomonas might have contributed to simultaneously removing NH4+-N and reducing COD, and the increased enrichment of Nitrosomonas significantly improved the nitrogen removal efficiency. Cleaning ceramic membranes with NaClO solution reduces membrane contamination and membrane cleaning frequency. The combined SBR and C-MBR process is an economical and feasible solution for treating high-salinity oil-bearing wastewater. Based on the pilot application study, the capital expenditure for operating the full-scale combined SBR and C-MBR process was estimated to be 251,717 USD/year, and the unit wastewater treatment cost was 0.21 USD/m3, which saved 62.5% of the energy cost compared to the conventional MBR process.

Forecasting of landslide displacements using a chaos theory based wavelet analysis-Volterra filter model.

Landslide displacement time series can directly reflects landslide deformation and stability characteristics. Hence, forecasting of the non-linear and non-stationary displacement time series is necessary and significant for early warning of landslide failure. Traditionally, conventional machine learning methods are adopted as forecasting models, these forecasting models mainly determine the input and output variables experientially and does not address the non-stationary characteristics of displacement time series. However, it is difficult for these conventional machine learning methods to obtain appropriate input-output variables, to determine appropriate model parameters and to acquire satisfied prediction performance. To deal with these drawbacks, this study proposes the wavelet analysis (WA) to decompose the displacement time series into low- and high-frequency components to address the non-stationary characteristics; then proposes thee chaos theory to obtain appropriate input-output variables of forecasting models, and finally proposes Volterra filter model to construct the forecasting model. The GPS monitoring cumulative displacement time series, recorded on the Shuping and Baijiabao landslides, distance measuring equipment monitoring displacements on the Xintan landslide in Three Gorges Reservoir area of China, are used as test data of the proposed chaotic WA-Volterra model. The chaotic WA-support vector machine (SVM) model and single chaotic Volterra model without WA method, are used as comparisons. The results show that there are chaos characteristics in the GPS monitoring displacement time series, the non-stationary characteristics of landslide displacements are captured well by the WA method, and the model input-output variables are selected suitably using chaos theory. Furthermore, the chaotic WA-Volterra model has higher prediction accuracy than the chaotic WA-SVM and single chaotic Volterra models.