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Related Concept Videos

Fineness of Cement01:15

Fineness of Cement

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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.
Direct...
190
Porosity in Cement Paste01:18

Porosity in Cement Paste

205
The porosity of concrete is a measure of the void spaces within its structure. These spaces impact its strength and durability significantly. When water and cement interact, a chemical reaction called hydration creates a semi-solid paste. This paste includes combined water, making up approximately 23% of the cement's dry mass, and gel water, which fills minuscule voids known as gel pores, accounting for about 28% of the cement gel volume.
The balance of water to cement in the mix is...
205
Hydration of Cement01:24

Hydration of Cement

350
Hydration of cement is a chemical reaction between cement particles and water. This process occurs primarily through two mechanisms: through-solution and topochemical. In the through-solution process, anhydrous compounds dissolve into their constituents, hydrates form in the solution, and then precipitate from the supersaturated solution. The topochemical process involves solid-state reactions at the cement particle surface. The through-solution process dominates the topochemical process at the...
350
Design Example: Aggregate Gradation01:24

Design Example: Aggregate Gradation

144
The right type and quality of aggregates are crucial for concrete as they significantly influence its properties, mix proportions, and cost-effectiveness. If different sources are available for sand, the commonly used fine aggregate in concrete, the selection of sand is primarily based on its gradation.
The grading, or particle-size distribution, of sand is determined using sieve analysis, with standard sizes ranging from 150 μm to 10 mm (ASTM No. 100 sieve to 3⁄8 in. sieve). Sand is...
144
Effects of Air-entrainment in Concrete01:28

Effects of Air-entrainment in Concrete

128
Air entrainment in concrete significantly enhances the material's durability, especially in environments subjected to freeze-thaw cycles. Introducing small air bubbles into the concrete mix acts as internal voids that accommodate the expansion of water when it freezes, thereby alleviating internal stress and preventing structural cracks. This function is crucial in climates with significant freezing and thawing, as it protects the concrete from repeated stresses that could lead to premature...
128
Porosity and Absorption of Aggregate01:20

Porosity and Absorption of Aggregate

364
Aggregates contain pores of varying sizes; while some are completely enclosed within the particles, others open onto the surface, allowing water to penetrate. The porosity of aggregates is a major factor contributing to the overall porosity of concrete, given that aggregates constitute about three-quarters of concrete's volume.
When all pores in an aggregate are filled with water, the aggregate is considered saturated and surface-dry. If left in dry air, water will evaporate until the...
364

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A spatio-temporal data decoupling convolution network model for specific surface area prediction in cement grind

Xiaochen Hao1, Gaolu Huang1, Ze Li1

  • 1Yanshan University, Qinhuangdao, China.

ISA Transactions
|November 13, 2022
PubMed
Summary
This summary is machine-generated.

Accurate prediction of cement specific surface area is crucial for production efficiency. A novel spatio-temporal decoupling convolution neural network (STG-DCNN) model effectively addresses challenges in cement grinding for improved quality prediction.

Keywords:
Convolutional neural networkFeature extractionIndustrial mapPrediction of cement qualitySpatio-temporal decoupling

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Area of Science:

  • Materials Science and Engineering
  • Chemical Engineering
  • Artificial Intelligence in Manufacturing

Background:

  • Specific surface area is a key indicator of cement product quality, impacting production scheduling, energy efficiency, and performance.
  • The cement grinding process presents significant challenges for accurate quality prediction due to non-linearity, uncertainty, dynamic delays, and multi-scale variations.
  • Existing soft-sensing models struggle to effectively capture the complex dynamics of cement grinding for reliable specific surface area prediction.

Purpose of the Study:

  • To develop an accurate soft-sensing model for predicting the specific surface area of cement.
  • To overcome the limitations of existing models in handling the complex, dynamic nature of the cement grinding process.
  • To improve cement quality management through enhanced prediction capabilities.

Main Methods:

  • Proposed a spatio-temporal decoupling convolution neural network (STG-DCNN) model designed to extract and fuse features from both temporal and spatial dimensions.
  • Constructed temporal and spatial series maps using production variables data, reflecting the mechanism of cement grinding.
  • Employed a sliding window technique to align time scales in the temporal series map and establish variable coupling relationships within the spatial series map.

Main Results:

  • The STG-DCNN model demonstrated high prediction accuracy for specific surface area.
  • Experimental results validated the robustness and superiority of the proposed method compared to existing approaches.
  • The model was successfully implemented and tested on a real-world cement grinding quality management database.

Conclusions:

  • The STG-DCNN model offers an effective solution for accurate specific surface area prediction in the cement industry.
  • The spatio-temporal decoupling approach successfully addresses the complexities of the cement grinding process.
  • This advancement contributes to optimized production scheduling, energy conservation, and improved cement performance.