Viscosity virtual sensor to control combustion in fossil fuel power plants

Thermo-electrical power plants utilize fossil fuel oil to transform the calorific power of fuel into electric power. An optimal combustion in the boiler requires the fuel oil to be in its best conditions. One of fuel's most important properties to consider is viscosity. Viscosity has influence on the optimal combustion between fuel and air. Hardware viscosity meters for fuel oils are expensive and unreliable to operate in power plant conditions. Chemical laboratory measures viscosity accurately with special apparatus, but they cannot be used in a real time process. This paper describes the development of a virtual sensor that estimates fuel oil viscosity in the combustion process of a power plant. A virtual sensor or soft sensor is a computer program that estimates the value of a certain variable based on related measurements and a model of the process where the variable participates. In this project, a probabilistic model is constructed using automatic learning algorithms with historical data and experts' advice. The learning and validation experiments are described and discussed. The virtual sensor is installed in the Tuxpan Power Plant in Veracruz, Mexico.     

Optimization of Sesame Oil Extraction by Screw-Pressing at Low Temperature

Box-Behnken designs were used to optimize a process for sesame oil extraction by screw-pressing at low temperature (50 °C). Experimental designs included seed moisture content (SMC), pressing speed (PS), and restriction die (RD) as the main processing parameters. Extractions at pilot plant scale showed a peak in oil recovery (OR, 71.1 ± 2.80%) at 12.3% SMC, 4 mm RD, and 20 rpm PS. Theoretical models were scanned against experimental data in order to scale up the proposed oil extraction process to industrial scale. A fitted model for OR showed a maximum predicted value similar to the highest experimental value (74.4 ± 1.23%) under the following conditions: 8.03% SMC, 10 mm RD, and 20 rpm PS. Chemical quality parameters of oils obtained at both pilot and industrial scales were in the ranges stated in Codex (FAO/WHO) standards for non-refined sesame oil.     

User encoding for clustering in very sparse recommender systems tasks

Multimedia Tools and Applications - Recommender Systems are a very useful tool which let companies and service providers focus in the preferences of their customers, helping them to avoid an...     

Neuro-semantic prediction of user decisions to contribute content to online social networks

Neural Computing and Applications - Understanding at microscopic level the generation of contents in an online social network (OSN) is highly desirable for an improved management of the OSN and the...