Artificial Neural Network Modeling of the Deposition Rate of Lactose Powder in Spray Dryers

Samaneh Keshani, Wan Ramli Wan Daud, Meng Wai Woo, Meor Zainal Meor Talib, A. Luqman Chuah, A. R. Russly

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

A spray dryer is the ideal equipment for the production of food powders because it can easily impart well-defined end product characteristics such as moisture content, particle size, porosity, and bulk density. Wall deposition of particles in spray dryers is a key processing problem and an understanding of wall deposition can guide the selection of operating conditions to minimize this problem. The stickiness of powders causes the deposition of particles on the wall. Operating parameters such as inlet air temperature and feed flow rate affect the air temperature and humidity inside the dryer, which together with the addition of drying aids can affect the stickiness and moisture content of the product and hence its deposition on the wall. In this article, an artificial neural network (ANN) method was used to model the effects of inlet air temperature, feed flow rate, and maltodextrin ratio on wall deposition flux and moisture content of lactose-rich products. An 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 using the ANN and the measured data taken under the same conditions. The optimum condition found by the ANN for minimum moisture content and minimum wall deposition rate for lactose-rich feed was inlet air temperature of 140°C, feed rate of 23 mL/min, and maltodextrin ratio of 45%. The ANN technology has been shown to be an excellent investigative and predictive tool for spray drying of lactose-rich products.

Original languageEnglish
Pages (from-to)386-397
Number of pages12
JournalDrying Technology
Volume30
Issue number4
DOIs
Publication statusPublished - Mar 2012

Fingerprint

lactose
drying apparatus
Driers (materials)
Lactose
Deposition rates
Powders
sprayers
Air intakes
Neural networks
air intakes
Moisture
moisture content
products
drying
Flow rate
flow velocity
Temperature
Spray drying
Backpropagation algorithms
temperature

Keywords

  • Artificial neural network (ANN)
  • Deposition flux
  • Deposition rate
  • Glass transition temperature
  • Lactose
  • Maltodextrin ratio
  • Optimization
  • Spray drying

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Physical and Theoretical Chemistry

Cite this

Artificial Neural Network Modeling of the Deposition Rate of Lactose Powder in Spray Dryers. / Keshani, Samaneh; Wan Daud, Wan Ramli; Woo, Meng Wai; Meor Talib, Meor Zainal; Chuah, A. Luqman; Russly, A. R.

In: Drying Technology, Vol. 30, No. 4, 03.2012, p. 386-397.

Research output: Contribution to journalArticle

Keshani, Samaneh ; Wan Daud, Wan Ramli ; Woo, Meng Wai ; Meor Talib, Meor Zainal ; Chuah, A. Luqman ; Russly, A. R. / Artificial Neural Network Modeling of the Deposition Rate of Lactose Powder in Spray Dryers. In: Drying Technology. 2012 ; Vol. 30, No. 4. pp. 386-397.
@article{2b9cf294a07d48cb8b5be8e04ededef9,
title = "Artificial Neural Network Modeling of the Deposition Rate of Lactose Powder in Spray Dryers",
abstract = "A spray dryer is the ideal equipment for the production of food powders because it can easily impart well-defined end product characteristics such as moisture content, particle size, porosity, and bulk density. Wall deposition of particles in spray dryers is a key processing problem and an understanding of wall deposition can guide the selection of operating conditions to minimize this problem. The stickiness of powders causes the deposition of particles on the wall. Operating parameters such as inlet air temperature and feed flow rate affect the air temperature and humidity inside the dryer, which together with the addition of drying aids can affect the stickiness and moisture content of the product and hence its deposition on the wall. In this article, an artificial neural network (ANN) method was used to model the effects of inlet air temperature, feed flow rate, and maltodextrin ratio on wall deposition flux and moisture content of lactose-rich products. An 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 using the ANN and the measured data taken under the same conditions. The optimum condition found by the ANN for minimum moisture content and minimum wall deposition rate for lactose-rich feed was inlet air temperature of 140°C, feed rate of 23 mL/min, and maltodextrin ratio of 45{\%}. The ANN technology has been shown to be an excellent investigative and predictive tool for spray drying of lactose-rich products.",
keywords = "Artificial neural network (ANN), Deposition flux, Deposition rate, Glass transition temperature, Lactose, Maltodextrin ratio, Optimization, Spray drying",
author = "Samaneh Keshani and {Wan Daud}, {Wan Ramli} and Woo, {Meng Wai} and {Meor Talib}, {Meor Zainal} and Chuah, {A. Luqman} and Russly, {A. R.}",
year = "2012",
month = "3",
doi = "10.1080/07373937.2011.638228",
language = "English",
volume = "30",
pages = "386--397",
journal = "Drying Technology",
issn = "0737-3937",
publisher = "Taylor and Francis Ltd.",
number = "4",

}

TY - JOUR

T1 - Artificial Neural Network Modeling of the Deposition Rate of Lactose Powder in Spray Dryers

AU - Keshani, Samaneh

AU - Wan Daud, Wan Ramli

AU - Woo, Meng Wai

AU - Meor Talib, Meor Zainal

AU - Chuah, A. Luqman

AU - Russly, A. R.

PY - 2012/3

Y1 - 2012/3

N2 - A spray dryer is the ideal equipment for the production of food powders because it can easily impart well-defined end product characteristics such as moisture content, particle size, porosity, and bulk density. Wall deposition of particles in spray dryers is a key processing problem and an understanding of wall deposition can guide the selection of operating conditions to minimize this problem. The stickiness of powders causes the deposition of particles on the wall. Operating parameters such as inlet air temperature and feed flow rate affect the air temperature and humidity inside the dryer, which together with the addition of drying aids can affect the stickiness and moisture content of the product and hence its deposition on the wall. In this article, an artificial neural network (ANN) method was used to model the effects of inlet air temperature, feed flow rate, and maltodextrin ratio on wall deposition flux and moisture content of lactose-rich products. An 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 using the ANN and the measured data taken under the same conditions. The optimum condition found by the ANN for minimum moisture content and minimum wall deposition rate for lactose-rich feed was inlet air temperature of 140°C, feed rate of 23 mL/min, and maltodextrin ratio of 45%. The ANN technology has been shown to be an excellent investigative and predictive tool for spray drying of lactose-rich products.

AB - A spray dryer is the ideal equipment for the production of food powders because it can easily impart well-defined end product characteristics such as moisture content, particle size, porosity, and bulk density. Wall deposition of particles in spray dryers is a key processing problem and an understanding of wall deposition can guide the selection of operating conditions to minimize this problem. The stickiness of powders causes the deposition of particles on the wall. Operating parameters such as inlet air temperature and feed flow rate affect the air temperature and humidity inside the dryer, which together with the addition of drying aids can affect the stickiness and moisture content of the product and hence its deposition on the wall. In this article, an artificial neural network (ANN) method was used to model the effects of inlet air temperature, feed flow rate, and maltodextrin ratio on wall deposition flux and moisture content of lactose-rich products. An 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 using the ANN and the measured data taken under the same conditions. The optimum condition found by the ANN for minimum moisture content and minimum wall deposition rate for lactose-rich feed was inlet air temperature of 140°C, feed rate of 23 mL/min, and maltodextrin ratio of 45%. The ANN technology has been shown to be an excellent investigative and predictive tool for spray drying of lactose-rich products.

KW - Artificial neural network (ANN)

KW - Deposition flux

KW - Deposition rate

KW - Glass transition temperature

KW - Lactose

KW - Maltodextrin ratio

KW - Optimization

KW - Spray drying

UR - http://www.scopus.com/inward/record.url?scp=84856841080&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84856841080&partnerID=8YFLogxK

U2 - 10.1080/07373937.2011.638228

DO - 10.1080/07373937.2011.638228

M3 - Article

AN - SCOPUS:84856841080

VL - 30

SP - 386

EP - 397

JO - Drying Technology

JF - Drying Technology

SN - 0737-3937

IS - 4

ER -