Data-based Design of Inferential Sensors for Petrochemical Industry

@article{Mojto2021DatabasedDO,
  title={Data-based Design of Inferential Sensors for Petrochemical Industry},
  author={Martin Mojto and Karol Lubusk{\'y} and Miroslav Fikar and Radoslav Paulen},
  journal={Comput. Chem. Eng.},
  year={2021},
  volume={153},
  pages={107437}
}

Design of Multi-model Linear Inferential Sensors with SVM-based Switching Logic

This work introduces a novel SVM-based model training coupled with switching logic identification and proposes a direct optimization of data labelling for data labeling in the multi-model linear inferential sensors.

The efficiency of soft sensors modelling in advanced control systems in oil refinery through the application of hybrid intelligent data mining techniques

It was indicated from this study result that the ANFIS model is able to manage the complex data to predict two important parameters of light naphtha (API and RVP) compared to the simple regression model.

Developing Soft Sensors Based on Data-Driven Approach

  • Jialin Liu
  • Computer Science
    2010 International Conference on Technologies and Applications of Artificial Intelligence
  • 2010
The input sensors are validated before performing a prediction and the deterioration of the prediction performance due to the failed sensors can be removed by the sensor validation approach.

ANALYSIS AND DETECTION OF OUTLIERS AND SYSTEMATIC ERRORS IN INDUSTRIAL PLANT DATA

The methodology employed involves an approach based on statistical analysis, first-principle equations, neural network models, and a composite of these to detect outliers and systematic errors in industrial process data.

Data-driven Soft Sensors in the process industry

Smart process analytics for predictive modeling

...