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DEPOSITION OF LACTOSE IN SPRAY DRYERS

机译:喷雾干燥机中乳糖的沉积

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Wall deposition is a key processing problem in spray dryers.Wall deposition of particles can be indirectly affects quality of the product through degradation of the deposited particles and the resulting contamination of the main product.Its understanding guides the selection of operating conditions of the spray dryers that minimize wall deposition and hence help to improve product quality.The stickiness of powders causes the deposition of particles on the wall.Operating parameters such as the inlet air temperature and feed flow rate affect the air temperature and humidity inside the dryer,which together with addition of drying aids can affect stickiness of the product and moisture content and hence its deposition on the wall.In this work,the artificial neural networks (ANN) method modeled the effects of the inlet air temperature,feed flow rate and maltodextrin ratio on wall deposition flux and moisture content of lactose-rich products.The ANN trained by back propagation algorithms was developed to predict two performance indices based on the three input variables.The results showed good agreement between predicted results by using ANN and the measured data taken under the same conditions.The ANN technology had shown to be an excellent investigative and predictive tool for spray drying process of lactose-rich products.
机译:壁沉积是喷雾干燥机的关键加工问题,颗粒的壁沉积可通过沉积颗粒的降解和对主要产品的污染而间接影响产品质量,其理解指导选择喷雾干燥机的工作条件粉末的粘性会导致颗粒在壁上的沉积。操作参数(如进风温度和进料流速)会影响干燥机内的空气温度和湿度,这与干燥机内部的温度和湿度有关。添加干燥助剂会影响产品的粘性和水分含量,从而影响产品在壁上的沉积。在本文中,人工神经网络(ANN)方法模拟了进气温度,进料流速和麦芽糊精比率对壁的影响。富含乳糖的产品的沉积通量和水分含量。开发了用于基于三个输入变量的两个性能指标的预测结果。结果表明,使用ANN进行的预测结果与在相同条件下获得的测量数据之间具有良好的一致性.ANN技术已证明是一种出色的喷雾干燥调查和预测工具富含乳糖的产品的加工过程。

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