(187b) Comparing Numerical Optimization on Neural Networks Versus Multivariate Predictive Models

Multivariate analysis (MVA) is a proven method for analyzing and interpreting large volumes of industrial data. MVA identifies the most significant correlations between input and quality variables, and the resulting prediction models can be leveraged to guide processes to desired outcomes. MVA models can be further exploited through the application of numerical optimization, where desired quality variables are achieved from an optimal combination of inputs, while maintaining the historical correlation structure of the data and relevant constraints.

MVA methods, such as projection onto latent structures (PLS), have been successfully used in optimization formulations owing to the common use of derivative-based algorithms. But with the rising popularity of black-box methods for prediction purposes (in particular, neural networks (NN)), it is now of interest to apply numerical optimization to these methods.

The black-box nature of NN models raises some concerns surrounding the ability to find a feasible solution path and final solution. As such, the focus of this preliminary research is to assess the suitability of NN models for the application of numerical optimization, using an industrial polyolefin dataset. This dataset is used to build both a PLS model and a NN model to predict the same multiple quality variables. The PLS model is built directly from the raw input and quality data, whereas the neural network uses the PCA scores as a dimensionality-reduction preprocessing step prior to training. The two models are then formulated into optimization problems in order to solve for an optimal set of inputs based on a set of targeted quality variables.

The presentation will provide an overview of the concepts of MVA and how numerical optimization is applied to PLS models, followed by a discussion on how the PLS and NN models are trained with the dataset and their role in the optimization framework. Next, a comparison of the results of the model predictions and optimization cases will be presented. Finally, a discussion of the challenges encountered and next steps to further this research by moving forward with a dataset with more nonlinearities will be presented.