Neural networks for modelling of chemical reaction systems with complex kinetics: Oxidation of 2-octanol with nitric acid

Eugeniusz Molga , B.A.A. Van Woezik , K. Roel Westerterp


Application of neural networks to model the conversion rates of a heterogeneous oxidation reaction has been investigated - oxidation of 2-octanol with nitric acid has been considered as a case study. Due to a more complex and unknown kinetics of the investigated reaction the proposed approach based on application of neural networks is an efficient and accurate tool to solve modelling problems. The elaborated hybrid model as well as the modelling procedure have been described in detail. Learning data used to train the networks have been extracted from the experimental results obtained in an extensive investigation programme performed with a RC1 Mettler-Toledo reaction calorimeter. The influence of different operating conditions on the accuracy and flexibility of the obtained results has been investigated and discussed. It has been found that with the proposed approach the behaviour of a semi-batch reactor, i.e. its concentration and heat flow time profiles, can be predicted successfully within a singular series of experiments; however, the generalisation of the neural network approach to all series of experiments was impossible.
Author Eugeniusz Molga (FCPE / DPKT)
Eugeniusz Molga,,
- Department of Process Kinetics and Thermodynamics
, B.A.A. Van Woezik - [University of Twente (UT)
Administrator unit: 'COP'
B.A.A. Van Woezik,,
- Universiteit Twente
, K. Roel Westerterp - University of Twente
K. Roel Westerterp,,
Journal seriesChemical Engineering and Processing : Process Intensification, ISSN 0255-2701
Issue year2000
Keywords in Englishneural networks, reaction kinetics, liquid-liquid oxidation
ASJC Classification1500 General Chemical Engineering; 1508 Process Chemistry and Technology; 1600 General Chemistry; 2102 Energy Engineering and Power Technology; 2209 Industrial and Manufacturing Engineering
Languageen angielski
Molga E. (i in.) - Neural networks for...pdf 187.14 KB
Score (nominal)30
Score sourcejournalList
Publication indicators Scopus Citations = 27; WoS Citations = 23; GS Citations = 25.0; Scopus SNIP (Source Normalised Impact per Paper): 2000 = 0.655; WoS Impact Factor: 2006 = 1.129 (2) - 2007=1.474 (5)
Citation count*25 (2014-12-22)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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