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.
Samaneh Keshani Wan Ramli Wan Daud Nourouzi,M.M Meng Wai Woo Meor Zainal Meor Talib A.Luqman Chuah Russly A.R.
Department of Chemical and Process Engineering,Faculty of Engineering,University Kebangsaan Malaysia Department of Chemical and Environmental Engineering,Faculty of Engineering,University Putra Malaysi Department of Chemical Engineering Monash University,Clayton Campus,Victoria,Australia Department of Food Technology,Faculty of Food Science and Technology,University Putra Malaysia,Serda