Industrial application of model predictive control to a milk powder spray drying plant

Petersen, Lars Norbert; Poulsen, Niels Kjølstad; Niemann, Hans Henrik; Utzen, Christer; Jørgensen, John Bagterp

doi:10.1109/ecc.2016.7810426

Cited by 7 publications

(4 citation statements)

References 18 publications

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“…These bounds guarantee identifiability of the greybox model, see the above section. The signs of the gains are known a priori 4,19 …”

Section: Parameter Estimationmentioning

confidence: 99%

“…Many types of liquid can be dried into powder for example milk, lithium, and pharmaceutical liquids 1‐3 . As a powder, the material gains several advantages.…”

Section: Introductionmentioning

confidence: 99%

“…For heating the air, a high amount of energy is consumed. For example, an industrial milk powder plant consumes 8 MW to produce 7.5 tons/hour powder 4 . Such large volume production can highly benefit from initiatives to increase energy efficiency or tightening the quality control 5 …”

Section: Introductionmentioning

confidence: 99%

“…Traditionally, a PID controller regulates the powder quality for spray dryers 3,6,7 . However, the highly cross‐coupled spray dryers limit the performance of the single‐loop control.…”

Section: Introductionmentioning

confidence: 99%

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Greybox model for multistage spray drying plants constrained to small datasets

et al. 2020

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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.

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“…These bounds guarantee identifiability of the greybox model, see the above section. The signs of the gains are known a priori 4,19 …”

Section: Parameter Estimationmentioning

confidence: 99%

“…Many types of liquid can be dried into powder for example milk, lithium, and pharmaceutical liquids 1‐3 . As a powder, the material gains several advantages.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Traditionally, a PID controller regulates the powder quality for spray dryers 3,6,7 . However, the highly cross‐coupled spray dryers limit the performance of the single‐loop control.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations