Prediction of final product properties after cocurrent spray drying

M. Kwapińska; I. Zbiciński

doi:10.1081/DRT-200065075

Prediction of final product properties after cocurrent spray drying

M. Kwapińska, I. Zbiciński

Lodz University of Technology

Research output: Contribution to journal › Article › peer-review

Abstract

The effect of drying and atomization conditions on the physical properties of powders for agglomerate-like materials and skin-forming material are studied in this article. A neural model is used for powder bulk and tapped density predictions.

Original language	English
Pages (from-to)	1653-1665
Number of pages	13
Journal	Drying Technology
Volume	23
Issue number	8
DOIs	https://doi.org/10.1081/DRT-200065075
Publication status	Published - 2005
Externally published	Yes

Keywords

Density
Neural network
Particle diameter
Powder
Principal component analysis

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1081/DRT-200065075

Cite this

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title = "Prediction of final product properties after cocurrent spray drying",

abstract = "The effect of drying and atomization conditions on the physical properties of powders for agglomerate-like materials and skin-forming material are studied in this article. A neural model is used for powder bulk and tapped density predictions.",

keywords = "Density, Neural network, Particle diameter, Powder, Principal component analysis",

author = "M. Kwapi{\'n}ska and I. Zbici{\'n}ski",

year = "2005",

doi = "10.1081/DRT-200065075",

language = "English",

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pages = "1653--1665",

journal = "Drying Technology",

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AU - Kwapińska, M.

AU - Zbiciński, I.

PY - 2005

Y1 - 2005

N2 - The effect of drying and atomization conditions on the physical properties of powders for agglomerate-like materials and skin-forming material are studied in this article. A neural model is used for powder bulk and tapped density predictions.

AB - The effect of drying and atomization conditions on the physical properties of powders for agglomerate-like materials and skin-forming material are studied in this article. A neural model is used for powder bulk and tapped density predictions.

KW - Density

KW - Neural network

KW - Particle diameter

KW - Powder

KW - Principal component analysis

UR - https://www.scopus.com/pages/publications/24344492752

U2 - 10.1081/DRT-200065075

DO - 10.1081/DRT-200065075

M3 - Article

AN - SCOPUS:24344492752

SN - 0737-3937

VL - 23

SP - 1653

EP - 1665

JO - Drying Technology

JF - Drying Technology

IS - 8

ER -

Prediction of final product properties after cocurrent spray drying

Abstract

Keywords

UN SDGs

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