Soft-Sensor for Class Prediction of the Percentage of Pentanes in Butane at a Debutanizer Column

Sensors

Eva Portillo¹  Itziar Landa-Torres²  Lucía Orbe²  Iratxe Niño-Adan³  Diana Manjarres³ 

[1] Department of Automatic Control and Systems Engineering, Faculty of Engineering, University of the Basque Country UPV/EHU, 48013 Bilbao, Spain;Petronor Innovación S.L, 48550 Muskiz, Spain;TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain;

关键词: pentanes;    classification;    autoML;    soft-sensor;    normalisation;    feature weighting;   

DOI  :  10.3390/s21123991

来源: DOAJ

【 摘 要 】

Refineries are complex industrial systems that transform crude oil into more valuable subproducts. Due to the advances in sensors, easily measurable variables are continuously monitored and several data-driven soft-sensors are proposed to control the distillation process and the quality of the resultant subproducts. However, data preprocessing and soft-sensor modelling are still complex and time-consuming tasks that are expected to be automatised in the context of Industry 4.0. Although recently several automated learning (autoML) approaches have been proposed, these rely on model configuration and hyper-parameters optimisation. This paper advances the state-of-the-art by proposing an autoML approach that selects, among different normalisation and feature weighting preprocessing techniques and various well-known Machine Learning (ML) algorithms, the best configuration to create a reliable soft-sensor for the problem at hand. As proven in this research, each normalisation method transforms a given dataset differently, which ultimately affects the ML algorithm performance. The presented autoML approach considers the features preprocessing importance, including it, and the algorithm selection and configuration, as a fundamental stage of the methodology. The proposed autoML approach is applied to real data from a refinery in the Basque Country to create a soft-sensor in order to complement the operators’ decision-making that, based on the operational variables of a distillation process, detects 400 min in advance with $98.925\%$ precision if the resultant product does not reach the quality standards.

【 授权许可】

Unknown