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https://www.selleckchem.com/products/iacs-010759-iacs-10759.html This work focused on the experimental validation of software sensors with a view to improving on-line anaerobic digester monitoring. Based on cheaply available measurements such as conductivity, temperature, pH, redox potential, total suspended solids concentration and digester inflows and outflows, an intelligent estimator was built to reproduce the evolutions of key components such as volatile fatty acid, carbonate and alkalinity concentrations, as well as biogas composition (methane and carbon dioxide). The proposed solution considers a principal component pre-processing of the data selected as inputs of a radial basis function neural network (RBF-ANN) structure, using a particular sequential learning algorithm. Process dynamics were also taken into account, introducing a moving horizon version of this network (MH-RBF-ANN). Experimental results demonstrated the capacity of the MH-RBF-ANN to correctly predict the key-component evolutions and to improve the estimation accuracy, compared to the classical RBF-ANN.Absorption spectra within the infrared (IR) range of frequencies for nitrosamines in water are calculated using density function theory (DFT). Calculated in this study, are the IR spectra of C2H6N2O, C4H10N2O, C6H14N2O, C4H8N2O, C3H8N2O, and C8H18N2O. DFT calculated absorption spectra corresponding to vibration excited states of these molecules in continuous water background can be correlated with additional information obtained from laboratory measurements. The DFT software Gaussian was used for the calculations of excited states presented here. This case study provides proof of concept, viz., that such DFT calculated spectra can be used for their practical detection in environmental samples. Thus, DFT calculated spectra may be used to construct templates, for spectral-feature comparison, and thus detection of spectral-signature features associated with target materials.In this study, iron ore

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