Application of Artificial Neural Networks for Optimizing Operating Conditions of a Chemical Process

Considering that there are a significantly large number of variables and several steps, in a chemical process, modeling of such a method is non-trivial. Moreover, the process has a non-linear and transience dependence on the conditions. Artificial Neural Network (ANN) and Deep Learning methods based on ANNs have been widely recognized as one of the most influential modeling and learning techniques for nonlinear systems. The salient feature of ANN-based modeling is that the detailed mathematical information about the process steps is not pre-determined or fed to the system; instead, the learning takes place by using the training examples. Therefore, it is not surprising that ANN-based modeling is used for predicting the outcomes of chemical processes. Experimental data will be required to train the model and also to validate the predictions.