Suppose that univariate data are drawn from a mixture of two distributions that are equal up to a shift parameter. Such a model is known to be nonidentifiable from a nonparametric viewpoint. However, if we assume that the unknown mixed distribution is symmetric, we obtain the identifiability of this model, which is then defined by four unknown parameters: the mixing proportion, two location parameters and the cumulative distribution function of the symmetric mixed distribution. We propose estimators for these four parameters when no training data is available. Our estimators are shown to be strongly consistent under mild regularity assumptions and their convergence rates are studied. Their finite-sample properties are illustrated by a Monte Carlo study and our method is applied to real data.Comment: Published at http://dx.doi.org/10.1214/009053606000000353 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

This study aims to understand the mechanisms of nitrite appearance during wastewater denitrification by biofilters, focusing on the role of the carbon source. Experiments were carried out at lab-scale (batch tests) and full-scale plant (Parisian plant, capacities of 240,000 m(3) day(-1)). Results showed that the nature of the carbon source affects nitrite accumulation rates. This accumulation is low, 0.05 to 0.10 g N-NO2(-) per g N-NO3(-) eliminated, for alcohols such as methanol, ethanol, or glycerol. The utilization of glycerol leads to fungal development causing clogging of the biofilters. This fungal growth and consequent clogging exclude this carbon source, with little nitrite accumulation, as carbon source for denitrification. Whatever the carbon source, the C/N ratio in the biofilter plays a major role in the appearance of residual nitrite; an optimal C/N ratio from 3.0 to 3.2 allows a complete denitrification without any nitrite accumulation.

No abstract

No abstract

Due to severe mathematical modeling and calibration difficulties open-loop feedforward control is mainly employed today for wastewater denitrification, which is a key ecological issue. In order to improve the resulting poor performances a new model-free control setting and its corresponding "intelligent" controller are introduced. The pitfall of regulating two output variables via a single input variable is overcome by introducing also an open-loop knowledgebased control deduced from the plant behavior. Several convincing computer simulations are presented and discussed.