Abstract
This paper proposes a hybrid process modeling and optimization formalism integrating artificial neural networks (ANNs) and genetic algorithms (GAs). The resultant ANN-GA strategy has the advantage that it allows process modeling and optimization exclusively on the basis of process input-output data. In the hybrid strategy, first an ANN-based process model is developed from the input-output process data. Next, the input space of the model representing process input variables is optimized using GAs, with a view to simultaneously maximize multiple process output variables. The GAs are stochastic optimization methods possessing certain unique advantages over the commonly used gradient-based deterministic algorithms. The efficacy of the hybrid formalism has been evaluated for modeling and optimizing the zeolite (TS- 1)-catalyzed benzene hydroxylation to phenol reaction whereby several sets of optimized operating conditions have been obtained. A few optimized solutions have also been subjected to the experimental verification, and the results obtained thereby matched the GA-maximized values of the three reaction output variables with a good accuracy.
Original language | English (US) |
---|---|
Pages (from-to) | 2159-2169 |
Number of pages | 11 |
Journal | Industrial and Engineering Chemistry Research |
Volume | 41 |
Issue number | 9 |
DOIs | |
State | Published - May 1 2002 |
ASJC Scopus subject areas
- General Chemistry
- General Chemical Engineering
- Industrial and Manufacturing Engineering