Model Predictive Control for Blending Processes in Cement Plants

Zhanhao Zhang, Marcus Krogh Nielsen, Guruprasath Muralidharan, Steen Hørsholt, John Bagterp Jørgensen*

*Corresponding author for this work

Research output: Contribution to journalConference articleResearchpeer-review

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Abstract

In this paper, we discuss model predictive control applied to blending processes. Blending processes are ubiquitous in the chemical process industries since reactants usually need to be mixed before entering a reactor. Many times, the blending is trivial as pure streams of reactants are mixed. We consider non-trivial blending problems in which non-pure streams with the reactants are to be mixed. The motivating example is the blending problem that occurs in cement production. The raw mix for the cement kiln must have a specified chemical composition. This composition is obtained by mixing piles with different chemical compositions and economic value such that the raw mix meets specifications in the cheapest possible way. We formulate the blending problem as a nonlinear optimization problem that can be approximated well as a convex quadratic optimization problem. We implement the corresponding nonlinear and linear model predictive controllers (NMPC, LMPC) using a continuous-time transfer function description that is realized as a discrete-time linear state space model. The controller obtains feedback by combining regularly sampled online measurements and irregularly sampled laboratory measurements using a time variant dynamic Kalman filter with memory. Numerical simulations demonstrate that the NMPC and LMPC have similar performance.

Original languageEnglish
Book seriesIFAC-PapersOnLine
Volume55
Issue number7
Pages (from-to)483-488
ISSN2405-8963
DOIs
Publication statusPublished - 2022
Event13th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems - Busan, Korea, Republic of
Duration: 14 Jun 202217 Jun 2022

Conference

Conference13th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems
Country/TerritoryKorea, Republic of
CityBusan
Period14/06/202217/06/2022

Keywords

  • Advanced Process Control
  • Blending
  • Cement Processes
  • Model Predictive Control

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