Process, quality, and energy optimizationPQ-005

Long-duration adaptive raw-mix control

Heidelberg Materials Hellas · Industrial raw mill; site not stated, Greece · more than 14000 operating hours; 2024 publication

Use this record when

The decision this case can inform

Use this record when raw-material dynamics and laboratory sampling intervals make fixed-gain raw-mix control unreliable over long operating periods.

Evidence scope

Peer-reviewed industrial implementation exceeding 14,000 hours. It reports achieved variability and model-identification limits, not a simple before/after saving.

Source-supported facts

What the public record actually establishes

4 sourced points
  1. F1

    The industrial line included a 400 t/h vertical raw mill and a kiln rated at 4,400 t/day.

  2. F2

    Alternative-fuel ash required frequent changes to the LSF target, while laboratory sampling occurred at one- or two-hour intervals.

  3. F3

    Adaptive gain scheduling used dynamic models, robustness criteria, and simulation before and during industrial use.

  4. F4

    Over more than 14,000 hours, LSF standard deviation ranged from 1.5 to 3; only 10.7% of dynamic datasets were sufficiently informative for parameter estimation.

Structured interpretation

Facts and reported results are kept separate from the lesson a plant may choose to test.

01

Operating context

Raw-material and process dynamics changed over time, challenging fixed-gain raw-mix control.

02

Intervention or finding

Adaptive gain scheduling used industrial data, dynamic models, robustness criteria, and simulators.

03

Documented result

Implementation exceeded 14000 operating hours; LSF standard deviation ranged 1.5-3 and other moduli were close to analytical reproducibility.

04

Plant interpretation

Evaluate control over long periods and record laboratory reproducibility so measurement noise is not mistaken for process variation.

05

Transfer boundary

Site unnamed; result is achieved variability rather than a simple before/after percentage.

Before applying the lesson

Questions to verify at your plant

These are decision checks, not operating instructions. Resolve them through local risk assessment, technical review, and authorization.

  1. 01

    Is laboratory reproducibility known so measurement noise is not mistaken for process instability?

  2. 02

    Does the data contain enough process excitation to identify model changes without destabilizing production?

  3. 03

    How are delayed laboratory results aligned with transport and residence time through the process?

  4. 04

    What limits, fallback gains, and review rules apply when the adaptive estimate becomes weak or implausible?