Simultaneous denitrification and organics removal by denitrifying bacteria inoculum in a multistage biofilm process for treating desulfuration and denitration wastewater.

This study aimed to treat real wastewater from the desulfuration and denitration process in a petrochemical plant with high-strength nitrogen (TN≈200 mg/L, > 90% nitrate), sulfate (2.7%) and extremely low-strength organics (COD < 30 mg/L). Heterotrophic denitrification of multistage anoxic and oxic biofilm (MAOB) process in three tanks using facultative denitrifying bacteria inoculum was developed to simultaneously achieve desirable effluent nitrogen and organics at different hydraulic retention time (HRT) and carbon to nitrogen (C/N) mass ratios.

Kernel Recursive Least-Squares Temporal Difference Algorithms with Sparsification and Regularization.

By combining with sparse kernel methods, least-squares temporal difference (LSTD) algorithms can construct the feature dictionary automatically and obtain a better generalization ability. However, the previous kernel-based LSTD algorithms do not consider regularization and their sparsification processes are batch or offline, which hinder their widespread applications in online learning problems. In this paper, we combine the following five techniques and propose two novel kernel recursive LSTD algorithms: (i) online sparsification, which can cope with unknown state regions and be used for online learning, (ii) L 2 and L 1 regularization, which can avoid overfitting and eliminate the influence of noise, (iii) recursive least squares, which can eliminate matrix-inversion operations and reduce computational complexity, (iv) a sliding-window approach, which can avoid caching all history samples and reduce the computational cost, and (v) the fixed-point subiteration and online pruning, which can make L 1 regularization easy to implement.

Feature selection with neighborhood entropy-based cooperative game theory.

Feature selection plays an important role in machine learning and data mining. In recent years, various feature measurements have been proposed to select significant features from high-dimensional datasets. However, most traditional feature selection methods will ignore some features which have strong classification ability as a group but are weak as individuals.