A NEW APPROACH TO MODELING AND CONTROL OF A FOOD EXTRUSION PROCESS USING ARTIFICIAL NEURAL NETWORK AND AN EXPERT SYSTEM

The paper presents a new approach to the modeling of the start‐up part of a food extrusion process. A neural network model is proposed and its parameters are determined. Simulation results with real data are also presented. The inputs and outputs of the model are among those used by the human operator during the start‐up process for control. An intelligent controller structure that uses an expert system and “delta‐variations” to modify inputs is also proposed.     

Influence of hydrophobic characteristic of organo-modified precursor on wettability of silica film

The objective of this study is to design new hybrid silica materials as templates with hydrophobic properties, prepared at room temperature by a base catalyzed sol–gel process. As silica sources, organoalkoxysilanes functionalized with short hydrophobic chains were used: tetraethylorthosilicate (TEOS), methyltriethoxysilane (MTES), vinyltriethoxysilane (VTES), octyltriethoxysilane (OTES) and isobutyltriethoxysilane (iTES). It was shown that hydrophobicity of the functionalized silica nanoparticles increased as a function of length of the aliphatic chains (MTES < iTES < OTES) or when, instead of a hydrophobic alkyl chains (substituting group of silica precursors), a monounsaturated group was used (VTES). It was observed that the samples responded in a specific way to each type (hydrophilic or hydrophobic) of the dropped liquid. Even though the experiments were limited to short hydrocarbon chains, they showed that there is a threshold to reach high hydrophobicity of the hybrid surface.