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Fineness of Cement

Fineness of Cement

The fineness of cement directly influences the rate of hydration, as the hydration begins at the surface of the cement particles. In addition to hydration, the fineness of cement is vital for various properties of concrete including workability, gypsum requirement, and long-term behavior. The fineness of cement is represented in terms of the specific surface of cement which is typically measured in square meters per kilogram, with several methods available for this determination.
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Portland Cement

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Portland cement is the essential binding ingredient in concrete, made from finely ground materials including lime, iron, silica, and alumina. Lime is derived primarily from limestone, marble, marl, seashells, and clays, which also supply iron and alumina, while silica is sourced from sand, chalk, and bauxite. Contemporary manufacturing of Portland cement is a significant source of carbon dioxide emissions, prompting research into reducing its content in concrete through alternative...

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Related Experiment Video

Updated: Jul 2, 2025

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

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Data-Driven AI Models within a User-Defined Optimization Objective Function in Cement Production.

Othonas Manis¹, Michalis Skoumperdis², Christos Kioroglou²

¹Titan Cement Group, 11143 Athens, Greece.

Sensors (Basel, Switzerland)

|February 24, 2024

Summary

This summary is machine-generated.

This study optimizes cement plant operations using machine learning (ML) and differential evolution (DE). DE successfully improved cement mill and kiln performance, reducing the objective function value.

Keywords:

cement kiln cement mill clustering differential evolution feature selection key performance indicator machine learning optimization

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Area of Science:

Industrial Engineering
Chemical Engineering
Artificial Intelligence

Background:

The cement industry is highly energy-intensive, necessitating operational optimization.
Existing plant operations in Greece's cement industry were analyzed.
Machine learning (ML) models were employed to process complex industrial data.

Purpose of the Study:

To enhance the operational efficiency of a cement plant's mill and kiln unit.
To develop an optimization strategy using artificial intelligence (AI) models.
To enable expert users to define and optimize objective functions based on plant variables.

Main Methods:

Non-manipulated variables were computed using ML models, selecting those with minimum normalized root mean square error (NRMSE).
AI models require retraining if data distribution changes to ensure accuracy.
The differential evolution (DE) method was used to optimize a user-defined objective function.

Main Results:

The DE method successfully optimized the objective function, which was a linear combination of selected variables.
Optimization using DE led to improved operational performance in both the cement mill and kiln.
A lower objective function value was achieved compared to the plant's current operational values.

Conclusions:

The integration of ML and DE offers a robust method for optimizing energy-intensive industrial processes like cement production.
User-defined objective functions and constraints allow for tailored optimization strategies.
The proposed methodology demonstrates significant potential for improving efficiency and reducing operational costs in the cement industry.