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} }
Figures and Tables from this paper
One Citation
Design of Multi-model Linear Inferential Sensors with SVM-based Switching Logic
- Computer ScienceArXiv
- 2022
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.
51 References
The efficiency of soft sensors modelling in advanced control systems in oil refinery through the application of hybrid intelligent data mining techniques
- Computer ScienceJournal of Physics: Conference Series
- 2020
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.
Design of inferential sensors in the process industry: A review of Bayesian methods
- Computer Science
- 2012
Input selection methods for data-driven Soft sensors design: Application to an industrial process
- Computer ScienceInf. Sci.
- 2020
The use of first principles model for evaluation of adaptive soft sensor for multicomponent distillation unit
- EngineeringChemical Engineering Research and Design
- 2019
Developing Soft Sensors Based on Data-Driven Approach
- Computer Science2010 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.
Soft sensors design in a petrochemical process using an Evolutionary Algorithm
- Computer Science
- 2019
ANALYSIS AND DETECTION OF OUTLIERS AND SYSTEMATIC ERRORS IN INDUSTRIAL PLANT DATA
- Computer Science
- 2007
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.