Nitrogen resource recovery from mature leachate via heat extraction technology: An engineering project application.

A large pool of ammonia in mature leachate is challenging to treat with a membrane bioreactor system to meet the discharge Standard for Pollution Control on the Landfill Site of Municipal Solid Waste in China (GB 16889-2008) without external carbon source addition. In this study, an engineering leachate treatment project with a scale of 2,000 m3/d was operated to evaluate the ammonia heat extraction system (AHES), which contains preheat, decomposition, steam-stripping, ammonia recovery, and centrifuge dewatering. The operation results showed that NH3-N concentrations of raw leachate and treated effluent from an ammonia heat extraction system (AHES) were 1,305-2,485 mg/L and 207-541 mg/L, respectively. The ratio of COD/NH3-N increased from 1.40-1.84 to 7.69-28.00. Nitrogen was recovered in the form of NH4HCO3 by the ammonia recovery tower with the introduction of CO2, wherein the mature leachate can offer 37% CO2 consumption. The unit consumptions of steam and power were 8.0% and 2.66 kWh/m3 respectively, and the total operation cost of AHES was 2.06 USD per cubic metre of leachate. These results confirm that heat extraction is an efficient and cost-effective technology for the recovery of nitrogen resource from mature leachate.

Extracting Common Mode Errors of Regional GNSS Position Time Series in the Presence of Missing Data by Variational Bayesian Principal Component Analysis.

Removal of the common mode error (CME) is very important for the investigation of global navigation satellite systems' (GNSS) error and the estimation of an accurate GNSS velocity field for geodynamic applications. The commonly used spatiotemporal filtering methods normally process the evenly spaced time series without missing data. In this article, we present the variational Bayesian principal component analysis (VBPCA) to estimate and extract CME from the incomplete GNSS position time series. The VBPCA method can naturally handle missing data in the Bayesian framework and utilizes the variational expectation-maximization iterative algorithm to search each principal subspace. Moreover, it could automatically select the optimal number of principal components for data reconstruction and avoid the overfitting problem. To evaluate the performance of the VBPCA algorithm for extracting CME, 44 continuous GNSS stations located in Southern California were selected. Compared to previous approaches, VBPCA could achieve better performance with lower CME relative errors when more missing data exists. Since the first principal component (PC) extracted by VBPCA is remarkably larger than the other components, and its corresponding spatial response presents nearly uniform distribution, we only use the first PC and its eigenvector to reconstruct the CME for each station. After filtering out CME, the interstation correlation coefficients are significantly reduced from 0.43, 0.46, and 0.38 to 0.11, 0.10, and 0.08, for the north, east, and up (NEU) components, respectively. The root mean square (RMS) values of the residual time series and the colored noise amplitudes for the NEU components are also greatly suppressed, with average reductions of 27.11%, 28.15%, and 23.28% for the former, and 49.90%, 54.56%, and 49.75% for the latter. Moreover, the velocity estimates are more reliable and precise after removing CME, with average uncertainty reductions of 51.95%, 57.31%, and 49.92% for the NEU components, respectively. All these results indicate that the VBPCA method is an alternative and efficient way to extract CME from regional GNSS position time series in the presence of missing data. Further work is still required to consider the effect of formal errors on the CME extraction during the VBPCA implementation.