Neural network modeling of the photocatalytic degradation of 2,4-dihydroxybenzoic acid in aqueous solution

Artificial neural networks have been used for modeling the TiO$_2$ photocatalytic degradation of 2,4-dihydroxybenzoic acid, chosen as a model water contaminant, as a function of the concentrations of substrate and catalyst. The experimental design methodology was applied to the choice of an appropriate set of experiments well distributed in the experimental region (Doehlert uniform array). Contrary to a classical treatment of the data, based on apparent rate constants modeled by a quadratic polynomial function, neural network analysis of the same experimental data does not require the use of any kinetic or phenomenological equations and allows the simulation and the prediction of the pollutant degradation as a function of irradiation time, as well as prediction of reaction rates, under varying conditions within the experimental region

Detection of outliers in a gas centrifuge experimental data

Isotope separation with a gas centrifuge is a very complex process. Development and optimization of a gas centrifuge requires experimentation. These data contain experimental errors, and like other experimental data, there may be some gross errors, also known as outliers. The detection of outliers in gas centrifuge experimental data is quite complicated because there is not enough repetition for precise statistical determination and the physical equations may be applied only to control of the mass flow. Moreover, the concentrations are poorly predicted by phenomenological models. This paper presents the application of a three-layer feed-forward neural network to the detection of outliers in analysis of performed on a very extensive experiment

Semi-mechanistic Models for State-estimation - Soft Sensor for Polymer Melt Index Prediction

Nonlinear state estimation is a useful approach to the monitoring of industrial (polymerization) processes. This paper investigates how this approach can be followed to the development of a soft sensor of the product quality (melt index). The bottleneck of the successful application of advanced state estimation algorithms is the identification of models that can accurately describe the process