Development of genetic programming based softsensor model for styrene polymerization process and its application in model based control

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Ghugare:2016:ICC,

• author = "Suhas B. Ghugare and Sanjeev S. Tambe",
• booktitle = "2016 Indian Control Conference (ICC)",
• title = "Development of genetic programming based softsensor model for styrene polymerization process and its application in model based control",
• year = "2016",
• pages = "238--244",
• abstract = "In recent years, soft sensors have been established as a valuable alternative to the traditional hardware sensors for the acquisition of critical information regarding difficult-to-measure process variables and/or parameters in chemical process monitoring and control. Soft-sensors can also be modified as a novel process identification tool for process monitoring and model based control. Often, in polymer industries the main polymerization reaction is highly nonlinear and complex to model accurately by the conventional first principle's approach. In such cases, genetic programming (GP) - a novel artificial intelligence-based exclusively data-driven modelling technique - can be employed for process identification. In this work GP-based soft sensors have been developed for a continuous styrene polymerization reactor. The resulting GP-based models (soft sensor) showed high prediction and generalisation performances. The best performing model was successfully used in designing a model predictive control (MPC) scheme for the polymerization reactor.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/INDIANCC.2016.7441134",
• month = jan,
• notes = "Also known as \cite{7441134}",

}

Genetic Programming entries for Suhas B Ghugare Sanjeev S Tambe

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