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Greybox model for multistage spray drying plants constrained to small datasets
Author(s) -
Miklos Robert,
Petersen Lars Norbert,
Poulsen Niels Kjølstad,
Utzen Christer,
Jørgensen John Bagterp,
Niemann Hans Henrik
Publication year - 2021
Publication title -
advanced control for applications: engineering and industrial systems
Language(s) - English
Resource type - Journals
ISSN - 2578-0727
DOI - 10.1002/adc2.61
Subject(s) - subspace topology , experimental data , spray drying , multivariable calculus , process (computing) , computer science , production (economics) , identification (biology) , data mining , mathematics , artificial intelligence , engineering , statistics , control engineering , botany , chemical engineering , biology , economics , macroeconomics , operating system
Dynamic models of spray drying plants are required for many multivariable control strategies for spray dryers, for example, for model predictive control. Often, the model and its parameters are determined by fitting the model to experimental data. When the experimental data is generated, the experiments disturb normal production and introduce production losses. These losses increase the expenses for determining the model. Therefore, the experimentation time must be limited, which then limits the amount of data available for the model fitting. The limited amount of data restricts the complexity of the spray dryer model. In this article, we propose a greybox model with less complexity than the available models in the literature. The greybox model is derived by combining the process structure, physical relations and data from experimental data. The model is validated with experimental data from an MSD20 pilot plant. Further, the greybox model is compared to a model estimated by subspace identification. The comparison shows that the greybox model has a higher fit to the validation data than the subspace method.