Soft sensors with white- and black-box approaches for a wastewater treatment process

The increasing degradation of water resources makes it necessary to monitor and control process variables that may disturb the environment, but which may be very difficult to measure directly, either because there are no physical sensors available, or because these are too expensive. In this work, two soft sensors are proposed for monitoring concentrations of nitrate (NO) and ammonium (NH) ions, and of carbonaceous matter (CM) during nitrification of wastewater. One of them is based on reintegration of a process model to estimate NO and NH and on a feedforward neural network to estimate CM. The other estimator is based on Stacked Neural Networks (SNN), an approach that provides the predictor with robustness. After simulation, both soft sensors were implemented in an experimental unit using FIX MMI (Intellution, Inc) automation software as an interface between the process and MATLAB 5.1 (The Mathworks Inc.) software

Evolution of single-particle structure of silicon isotopes

New data on proton and neutron single-particle energies $E_{nlj}$ of Si isotopes with neutron number N from 12 to 28 as well as occupation probabilities $N_{nlj}$ of single-particle states of stable isotopes 28, 30Si near the Fermi energy were obtained by the joint evaluation of the stripping and pick-up reaction data and excited state decay schemes of neighboring nuclei. The evaluated data indicate the following features of single-particle structure evolution: persistence of Z = 14 subshell closure with N increase, the new magicity of the number N = 16, and the conservation of the magic properties of the number N = 20 in Si isotopic chain. The features were described by the dispersive optical model. The calculation also predicts the weakening of N = 28 shell closure and demonstrates evolution of a bubble-like structure of the proton density distributions in neutron-rich Si isotopes