Machine Learning-Based Soft Sensors for Vacuum Distillation Unit

TL;DR

This paper presents a comprehensive framework for developing machine learning-based soft sensors to predict product quality in petroleum distillation, addressing data preprocessing, model testing, and deployment challenges.

Contribution

It introduces a complete pipeline for soft sensor development using neural networks, from raw data preprocessing to model evaluation and deployment.

Findings

Enhanced data quality improves model accuracy.

Neural network models outperform traditional methods.

Framework facilitates real-time process optimization.

Abstract

Product quality assessment in the petroleum processing industry can be difficult and time-consuming, e.g. due to a manual collection of liquid samples from the plant and subsequent chemical laboratory analysis of the samples. The product quality is an important property that informs whether the products of the process are within the specifications. In particular, the delays caused by sample processing (collection, laboratory measurements, results analysis, reporting) can lead to detrimental economic effects. One of the strategies to deal with this problem is soft sensors. Soft sensors are a collection of models that can be used to predict and forecast some infrequently measured properties (such as laboratory measurements of petroleum products) based on more frequent measurements of quantities like temperature, pressure and flow rate provided by physical sensors. Soft sensors short-cut the pathway to obtain relevant information about the product quality, often providing measurements as frequently as every minute. One of the applications of soft sensors is for the real-time optimization of a chemical process by a targeted adaptation of operating parameters. Models used for soft sensors can have various forms, however, among the most common are those based on artificial neural networks (ANNs). While soft sensors can deal with some of the issues in the refinery processes, their development and deployment can pose other challenges that are addressed in this paper. Firstly, it is important to enhance the quality of both sets of data (laboratory measurements and physical sensors) in a data pre-processing stage (as described in Methodology section). Secondly, once the data sets are pre-processed, different models need to be tested against prediction error and the model's interpretability. In this work, we present a framework for soft sensor development from raw data to ready-to-use models.