Neural Computing in Product Formulation

TCE

The Chemical Educator

ISSN: 1430-4171 (electronic version)

Table of Contents

Abstract Volume 8 Issue 3 (2003) pp 211-218

Neural Computing in Product Formulation

Raymond C. Rowe and Elizabeth A. Colbourn*

PROFITS Group, School of Pharmacy, University of Bradford, Bradford, W Yorks BD7 1DP, UK and Intelligensys Ltd., Belasis Business Centre, Billingham, Teesside TS23 4EA, UK, colbourn@intelligensys.co.uk
Received January 2, 2003. Accepted March 4, 2003

Published online: 2 May 2003

Abstract. Artificial intelligence techniques increasingly are being used to improve product formulations by developing models that relate alterations in ingredients and processing conditions to changes in observed properties. From relatively few applications in the early to mid-1990s, the use of neural computing in its broadest sense is gaining acceptance worldwide in a number of industry sectors. The new generation of formulators can expect to use these techniques routinely, making it timely for educators to be aware of this emerging new field. This paper outlines the key concepts underlying neural networks, fuzzy logic, genetic algorithms, and neurofuzzy systems, and reviews how these technologies have been used, singly and in combination, to model and optimize formulations in areas like pigments and dyes, adhesives, paints and coatings, and oils and lubricants.

Key Words: Computers in Chemistry; computational chemistry; drugs/pharmaceuticals; dyes; plastics; statistics/data analysis

(*) Corresponding author. (E-mail: colbourn@intelligensys.co.uk)

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