As a result of good modeling capabilities, neural networks have been used extensively for a number of chemical engineering applications such as sensor data analysis, fault detection and nonlinear process identification. However, only in recent years, with the upsurge in the research on nonlinear control, its use in process control has been widespread. But there is almost no work which has tested the capacity of Artificial Neural Network (ANN) to model Reactive Distillation (RD). The superiority of ANN was tested for the synthesis of 2-methylpropylacetate from 2-methylpropanol and acetic acid by RD. The effects of various parameters like feed, temperature, reflux ratio, and boil-up ratio, each varied at three levels, were studied. At each combination, the composition of distillate and bottom was predicted by Aspen simulation. An appropriate ANN model was fitted using generated data. A three-layered feed-forward back-propagation model, with 3, 5 and 4 neurons in the layers, was fitted. Log-sig function was used in each layer. The model was found to have high R² (= 0.993) value and good predicting capability.

2-Methylpropylacetate is an industrial solvent widely used in the manufacture of paints and coatings as well as in many other branches of the chemical industry. Industrial synthesis of this ester is usually carried out in an equilibrium reactor, with the reaction products (water and 2-methylpropylacetate) and the rest of the reactants (acetic acid and 2-methylpropanol) being separated in a system of several rectification columns. Reactive Distillation (RD) can be used for such type of reactions (Hanika et al., 1999; and Quido et al., 2001).

Designing an RD column and maintaining an optimal operating condition are complicated because of the nonlinear interactions between the operating input and output variables. Methods such as fuzzy logic, Artificial Neural Network (ANN), and neuro-fuzzy are generally used to construct the knowledge database for crude distillation units (Leo et al., 2004). However, almost no work has been found which tests the utility of ANN in RD. Hence, it was thought desirable to work on ANN for RD. In this paper, attempts are made to test the utility of ANN for the synthesis of 2-methylpropylacetate from 2-methylpropanol and acetic acid by RD.

Chemical Engineering Journal, Artificial Neural Network (ANN), Aspen, Reactive Distillation (RD), Methylpropylacetate, Reflux Ratio, RR, Control Display Unit, CDU, Iso-Butyl Alcohol, IBOH, Mean Square Error, MSE, Mean Relative Error, MRE, Nonlinear Process Identification, Chemical Equilibrium.