(EXPERIENCE OF USING SIMULATIONS TO IMPROVE EVALUATION ACCURACY OF FINAL PRODUCT QUALITY)
Fuel and energy resources are currently the main expense items at oil and gas refineries and petrochemical plants in Russia. Therefore, it is extremely important now to improve the efficiency of such plants while minimizing energy costs.
Soft sensors are widely used in control of continuous industrial processes. They act as customizing add-in for evaluating the finished product quality, and also integrate into advanced process control systems to improve their energy efficiency, which correlates with the main provisions of the Industry 4.0 concept. The main problems in the development of soft sensors are: noise (e. g. caused by inaccurate observations or faulty sensors), lack of various operation mode values for industrial units, and nonstationary nature of the process under investigation.
The article demonstrates the possibility to use the combined data (simulation and industrial) when setting-up soft sensors at poor data representativeness due to lack of information within the entire unit operation range. This approach enables improving the accuracy of soft sensors. The article presents soft sensors’ setup results based on the combined data to evaluate the quality of the final product of a mass-exchange process aimed at solving the training set poor representativeness issue. Synthesis of high-octane additive to gasoline (methyl tert-butyl ether) was analyzed as an example.
S.A. Shevlyagina, PhD in Engineering, winner of the International Young Scientists Awards “Oil and Gas Projects: A Glance into the Future”, Institute of Automation and Control Processes, Far Eastern Branch of Russian Academy of Sciences (Vladivostok, Russia), firstname.lastname@example.org
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