Smart Manufacturing of Cement

Project Lead: University of Louisville
Partners: Argos

Member % Cost Share: 32%
CESMII % Cost Share: 68%
Duration: 24 Months

Problem Statement

Cement manufacturing is energy-intensive (5GJ/t) and comprises a significant portion of the energy footprint of the composite material. Incorporating modern monitoring, simulation and control systems will allow lower energy use, lower environmental impact and lower costs.

Project Goal

Using predictive process models, data analytics, sensors and machine learning, a Smart Manufacturing for cement control system platform will be developed in partnership with ARGOS USA cement in an effort to provide a more energy-efficient clinker production process with better quality control.

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Technical Approach

Lab and rotary cement kilns at the cement plant will be used as test beds to develop a sensor suite, predictive models and control system logic to characterize the product stream in the kilns. Additional systems states such as mass and air flow(s), along with rotational velocity will also be used to control the Kiln for optimal production quality and energy use using real-time predictive control.

Key Tasks & Milestones

  • Thermal Model of Typical Kiln and Sensor Deployment Plan
  • Scale Model of a Rotary Cement Kiln
  • Product Quality Assessment and Sampling
  • Multi-physics Modelling
  • Model-Based Control System Development and System Optimization
  • Economic Analysis of control strategies
  • Implementation & validation of limited version of control system at a cement plant

    Potential Impact

    Because energy (fuel) costs are a significant portion of the cost of the cement production, lowering firing temperatures and times will reduce cost and environmental impacts making this industry more viable through adoption of Smart Manufacturing technologies and processes.​


    • Contributions will be made to the SM Platform™ core technologies (specifically in the data acquisition for high temperature manufacturing, contextualization, and control).​
    • A multi-physics cement manufacturing model will be developed that will serve as a basis for process analytics and control for similar processes through the SM Platform.​
    • Building on the SM Platform core technologies,  data analytics and machine learning algorithms will be developed that can be used for process assessment and control across a wide range of high energy manufacturing processes.​

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