A System of Multistep Self-Learning Forecast and its Application on Oil Refining Industry

Jing Chen

doi:10.4028/www.scientific.net/AMR.605-607.1675

Paper Titles

Parameterization of Emotion Expression through Robot’s Body Language
p.1656

The Effect of Hexa-Orientation Press Control System Development on Diamond Material Grade
p.1661

Multi-Echelon Integrated Inventory Control Strategy of Equipment Spare Parts
p.1665

Research on Vacuum Consumable Arc Remelting Furnace Control System with Drop Short Pulses Testing
p.1670

A System of Multistep Self-Learning Forecast and its Application on Oil Refining Industry
p.1675

Application of Critical-Siphon Theory to Fastest Deadlock Controller for a Class of Flexible Manufacturing Systems
p.1679

Remote Monitoring System on Mining Visualization
p.1683

The Analysis of Soft Starting System of Brushless Excitation Synchronous Motor
p.1687

Based on Adaptive Control in the Process of the Liquid Level Instrumentation Control System Design
p.1692

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 605-607A System of Multistep Self-Learning Forecast and...

A System of Multistep Self-Learning Forecast and its Application on Oil Refining Industry

Abstract:

The catalytic cracking unit of oil refining industry, is a complex process characterized by its prolonged time delay and strong coupling ability.This system adopts the method of modern control theory, artificial intelligence combined effectively production techniques, researchs the problem of the multistep self-learning experience forecast online by computers and its applications in oil refinery. The system guides and optimizes the operations of the production process based on its memory, comparisons and analysis. The system is very effective to increase yield; It has been applied and popularized in 20 large-scale or middle-scale oil refining corporations.

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Periodical:

Advanced Materials Research (Volumes 605-607)

Pages:

1675-1678

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.605-607.1675

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Online since:

December 2012

Authors:

Jing Chen

Keywords:

Catalytic Cracking, Forecast, Operation Guidance, Self-Learning

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