Process, quality, and energy optimizationPQ-003

Advanced control on a kiln and grate cooler

Industrial cement producer · Site confidential, Italy · 2020 data; 2023 publication

Use this record when

The decision this case can inform

Use this record when designing governance and acceptance criteria for advanced kiln-and-cooler control over sustained operation, not just a short optimization trial.

Evidence scope

Peer-reviewed industrial case with more than two years of operation and KPI/service-factor reporting. The plant is confidential and raw data are not supplied.

Source-supported facts

What the public record actually establishes

4 sourced points
  1. F1

    The controller used plant step tests, separate operating models, sensor redundancy, and bad-data handling across the kiln and cooler.

  2. F2

    Reported cumulative kiln fuel saving was about 4.6%, with mean NOx about 15% lower and NOx standard deviation about 32% lower.

  3. F3

    Cooler tertiary-air temperature increased from 891.88°C to 935.18°C, while high second-chamber pressure time fell from 3.44% to 0.38%.

  4. F4

    The study reports about 10% yearly cooler-electricity saving and controller service factor above 94% after more than two years.

Structured interpretation

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

01

Operating context

A cement kiln and grate cooler required stable multivariable control and lower energy and emissions variability.

02

Intervention or finding

Peer-reviewed study implemented model-based APC on kiln and cooler with KPI and service-factor evaluation.

03

Documented result

About 4.6% cumulative kiln fuel saving, 15% lower mean NOx, 32% lower NOx standard deviation, about 10% mean cooler electricity saving, and service factor above 94% after more than two years.

04

Plant interpretation

Govern APC with validated sensors, constraints, credible baselines, service-factor tracking, operator ownership, and model maintenance.

05

Transfer boundary

Plant is unnamed; results depend on configuration, baseline, constraints, and controller use.

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 the baseline normalized for feed, fuel, clinker chemistry, production rate, equipment condition, and ambient effects?

  2. 02

    Are sensor validation, constraints, overrides, and degraded modes tested before closed-loop use?

  3. 03

    Will service factor, operator interventions, quality, emissions, refractory risk, and energy be reviewed together?

  4. 04

    Who owns model maintenance when equipment, fuels, raw mix, or operating objectives change?