Abstract
Spinning cone column as a distillation tower has various applications in food industry. It has a complex geometric structure which makes the modeling of liquid and gas regimes in the column rather difficult.In the last decade, artificial neural networks havebeen used in various industries considerably. Unlike empirical correlation, neural network does not requirephysical mechanism which occurs in column. Therefore, in this research the effect of tray speed, pressure drop, cone spacing, and flooding for both small and large scales operation has been examined using artificial neural network. Furthermore, variation of gas and liquid flow rate in an industrial scale spinning cone column has also been evaluated. To obtain this objective, multilayer perceptron structure and Levenberg-Marquart training algorithm has been utilized. The findings of this study reveal that the predictions of this work are much accurate than those obtained from the existing empirical correlation. There also exists a good compatibility between the pressure drop values predicted from the present study and the experimental data in both dry and wet state (normalized bias = 0.00232, mean squared error = 0.0021, and root mean squared error = 0.0021). From the scheme adopted in this work, the spinning cone column capacity at different operating conditions could be estimated more accurately than the exiting correlations.
Highlights
Premature deterioration of concrete structures mainly occurs due to the ingress of chloride ions and carbon dioxide [1]
Some authors argue that those compounds which are used to protect concrete reinforcement are not effective [10] when the concrete is kept immersed in NaCl solution, whereas others report that some compounds are effective in reducing corrosion rate of steel rebar in concrete contaminated with chlorides [22, 23]
It is understandable from the polarization data that at very low concentration (50 ppm), sodium nitrite acted as a corrosion antagonist, i.e., the corrosion rate of steel rebar treated with nitrite was greater than that of the rebar immersed in concrete pore solution (CPS) without nitrite
Summary
Premature deterioration of concrete structures mainly occurs due to the ingress of chloride ions and carbon dioxide [1]. Some authors argue that those compounds which are used to protect concrete reinforcement are not effective [10] when the concrete is kept immersed in NaCl solution, whereas others report that some compounds are effective in reducing corrosion rate of steel rebar in concrete contaminated with chlorides [22, 23]. The present investigation aims to explain the behaviour of these chemical species on steel rebar in contaminated concrete pore solution. Chemicals such as trisodium citrate (98%), sodium chloride (> 99.9%), NaNO2 (EMSURE®) and zinc acetate (> 99%) were purchased from Merck Millipore. To investigate the surface morphological changes on the steel rod dipped in simulated concrete pore solution [46, 47], microscopic studies were performed using Leica Stereo Microscope (S8ACO)
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