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

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In this paper, we present our first results from an industrial application of model predictive control (MPC) with real-time steady-state target optimization (RTO) for control of an industrial spray dryer that produces enriched milk powder. The MPC algorithm is based on a continuous-time transfer function model identified from data and states estimated by a time-varying Kalman filter. The RTO layer utilizes the same linear model and a nonlinear economic objective function for calculation of the economically optimized targets. We demonstrate, by industrial application of the MPC, that this method provides significantly better control of the residual moisture content, increases the throughput and decreases the energy consumption compared to conventional PI-control. The MPC operates the spray dryer closer to the residual moisture constraint of the powder product. Thus, the same amount of feed produces more powder product by increasing the average water content. The value of this is 186,000 €/year. In addition, the energy savings account to 6,900 €/year.
Original languageEnglish
Title of host publicationProceedings of the 15th annual European Control Conference (ECC '16)
Number of pages7
PublisherIEEE
Publication date2016
Pages1038-1044
DOIs
Publication statusPublished - 2016
Event15th European Control Conference (ECC16) - Aalborg, Denmark
Duration: 29 Jun 20161 Jul 2016
Conference number: 16
http://www.ecc16.eu/call.shtml

Conference

Conference15th European Control Conference (ECC16)
Number16
CountryDenmark
CityAalborg
Period29/06/201601/07/2016
Internet address
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • Powders, Moisture, Feeds, Regulators, Optimization, Steady-state, Data models

ID: 128327141