A Feature Importance Analysis for Soft-Sensing-Based Predictions in a Chemical Sulphonation Process
| Autor: | Per Olav Hansen, Asmund Hugo, An Ngoc Lam, Espen Martinsen, Øystein Haugen, Brice Morin, Enrique Garcia-Ceja |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Computer Science - Machine Learning 0209 industrial biotechnology chemical Computer science Decision tree 02 engineering and technology computer.software_genre Machine Learning (cs.LG) 020901 industrial engineering & automation feature selection Linear regression 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Range (statistics) FOS: Electrical engineering electronic engineering information engineering Electrical Engineering and Systems Science - Signal Processing sulphonation Computer Sciences Regression analysis prediction Random forest Variable (computer science) machine learning Datavetenskap (datalogi) Metric (mathematics) 020201 artificial intelligence & image processing Data mining computer |
| Zdroj: | ICPS |
| Popis: | 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. Comment: Accepted for: 3rd IEEE International Conference on Industrial Cyber-Physical Systems (ICPS 2020) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|