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SMART MANUFACTURING OF CEMENT

PROJECT LEAD

University of Louisville

PARTNERS

Argos

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.

TECHNICAL APPROACH

  • Create data acquisition tools, asset templates and predictive tools for the air separation units and demonstrate their application in an integrated environment.

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KEY TASKS/MILESTONES:

  • Thermal Model of Typical Kiln and Sensor Deployment Plan (M4)
  • Scale Model of a Rotary Cement Kiln (M12)
  • Product Quality Assessment and Sampling (M12)
  • Multi-physics Modelling (M18)
  • Model-Based Control System Development and System Optimization (M18)
  • Economic Analysis of control strategies (M22)
  • Implementation & validation of limited version of control system at a cement plant (M24)

POTENTIAL IMPACT:

  • Contributions will be made to the SM Platform™ core technologies

BENEFIT TO CESMII:

  • 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. (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

PROJECT COST SHARE AND DURATION:

Project Duration: 24 months, CESMII Cost Share: 68%, Member Cost Share: 32%









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LOCATION

Headquarters: Los Angeles, California
(888) 720-8096 | info@cesmii.org