A Feature Importance Analysis for Soft-Sensing-Based Predictions in a Chemical Sulphonation Process

TL;DR

This study analyzes feature importance in predicting product quality in a chemical sulphonation process using soft-sensing, revealing that only the top three variables are needed for effective predictions, with random forests performing best overall.

Contribution

It introduces a feature importance analysis for soft-sensing in chemical processes, identifying key variables for accurate product quality prediction.

Findings

Top 3 variables suffice for satisfactory predictions.

Random forest outperforms other models with all variables.

Feature importance ranking guides efficient model design.

Abstract

In this paper we present the results of a feature importance analysis of a chemical sulphonation process. The task consists of predicting the neutralization number (NT), which is a metric that characterizes the product quality of active detergents. The prediction is based on a dataset of environmental measurements, sampled from an industrial chemical process. We used a soft-sensing approach, that is, predicting a variable of interest based on other process variables, instead of directly sensing the variable of interest. Reasons for doing so range from expensive sensory hardware to harsh environments, e.g., inside a chemical reactor. The aim of this study was to explore and detect which variables are the most relevant for predicting product quality, and to what degree of precision. We trained regression models based on linear regression, regression tree and random forest. A random forest model was used to rank the predictor variables by importance. Then, we trained the models in a forward-selection style by adding one feature at a time, starting with the most important one. Our results show that it is sufficient to use the top 3 important variables, out of the 8 variables, to achieve satisfactory prediction results. On the other hand, Random Forest obtained the best result when trained with all variables.