Optimal Design of Mixer for Mixing Equipment

碩士 === 國立高雄應用科技大學 === 機械工程系 === 106 === Mixing equipment is one of the equipment commonly used in the food industry. When the product is a mixture of solid and liquid, the density and viscosity of the solid and liquid vary greatly. It is difficult to maintain the product evenly during the stirring p...

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Bibliographic Details
Main Authors: Wu,Wei-Ting, 吳威廷
Other Authors: Huang,Shyh-Chour
Format: Others
Language:zh-TW
Published: 2018
Online Access:http://ndltd.ncl.edu.tw/handle/nccgey
Description
Summary:碩士 === 國立高雄應用科技大學 === 機械工程系 === 106 === Mixing equipment is one of the equipment commonly used in the food industry. When the product is a mixture of solid and liquid, the density and viscosity of the solid and liquid vary greatly. It is difficult to maintain the product evenly during the stirring process, which often affects the quality of the mixed product. At present, the mixing equipment mainly relies on experience design, and the stirring performance is difficult to predict. How to achieve uniform mixing is a difficult problem to be overcome in the design of the mixing equipment. In this study, the stirring blade model was established by Solidworks software, and the ANSYS FLUENT software was used to simulate the flow field change of solid and liquid mixing to analyze the solid volume concentration. The design parameters of the mixing equipment are the stirring blade rotation speed, the stirring blade pitch, the stirring blade radius and the stirring blade width. With the Taguchi method, the design parameters with better quality are obtained with the aim of small characteristics. The simulation results show that the optimal parameters are a rotational speed of 50 rpm, a blade pitch of 46.7 cm, a blade radius of 21 cm, and a blade width of 9 cm. The main factor affecting the concentration value is the contribution of the blade pitch 51.14%. The contribution of the rotational speed is 42.29%, which is the secondary influence factor. Finally, the S/N ratio was calculated by the best level combination A_3 B_2 C_1 D_2, and the benefit evaluation results were reduced by 28% before and after the improvement of the process. Confirmation results show that the ability index is increased by 1.18 times.