Viscometric characterization of fluid foods in tube flow | |
| Author | Prasad, Adhikari Benu |
| Call Number | AIT Thesis no. AE-97-16 |
| Subject(s) | Viscosimeter Food industry and trade |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering, School of Environment, Resources, and Development |
| Publisher | Asian Institute of Technology |
| Abstract | A tube flow viscometer was modified to operate both in transient and continuous flow conditions. It could continuously record the pressure drop and mass flow rate through a PC based data logging system. Experiments were performed with five fluid foods, both in transient and continuous flow modes, using different diameter stainless steel tubes to cover maximum possible shear rate range. All the fluid samples were very viscous and the flow was confined to laminar regime. Tests were also carried out in low shear rate range using a Brookfield viscometer. The fluids, without exception, were pseudoplastic in nature and best characterized with power law model in the form of wall shear stress vs. apparent wall shear rate. However, the power law parameters for Brookfield and tube viscometers were entirely different because of the high shear rate and wall slippage in the later case . Two empirical models were developed to correlate the Brookfield parameters with slip corrected flow parameters of tube viscometer. The pressure drop gradients calculated using the new models were comparable with the experimental ones. Finally, neural networks were applied to predict the pressure drop in the tube flow. Four network architectures, two each from NeuroShell 2 and WinNN 0.97, were trained and used to predict pressure drop gradients based on the mass flow rate without slip correction, Brookfield flow parameters ( m and n), mass density, tube diameter as inputs. The net predictions were very accurate and closely followed the experimental values. Generalized regression architecture from NeuroShell 2 and quickpropagation from winNN Q.97 were much faster to learn and their predictions were within 3% average absolute error range. |
| Year | 1997 |
| Type | Thesis |
| School | School of Environment, Resources, and Development (SERD) |
| Department | Department of Food, Agriculture and Natural Resources (Former title: Department of Food Agriculture, and BioResources (DFAB)) |
| Academic Program/FoS | Agricultural and Food Engineering (AE) |
| Chairperson(s) | Jindal, V. K. ; |
| Examination Committee(s) | Athapol Noomhorm ;Vincent, J. C.; |
| Scholarship Donor(s) | Keidanren (Federation of Japanese Business Organizations) ; |
| Degree | Thesis (M.Eng.) - Asian Institute of Technology, 1997 |