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Related Concept Videos

Portland Cement01:21

Portland Cement

221
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...
221
Hydration of Cement01:24

Hydration of Cement

237
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...
237
Fineness of Cement01:15

Fineness of Cement

131
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...
131
Porosity in Cement Paste01:18

Porosity in Cement Paste

140
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...
140
Types of Cement II01:22

Types of Cement II

109
Portland blast-furnace cement is made by blending Portland cement clinker with granulated blast-furnace slag, which accounts for 25 to 65 percent of the cement's weight. Despite its similarities to ordinary Portland (Type I) cement in terms of fineness and setting times, its early strength is lower, though it achieves comparable strength later on. It's particularly suited for mass concrete structures and marine environments due to its lower heat of hydration and superior sulfate...
109
Additives and Fillers in Concrete01:29

Additives and Fillers in Concrete

96
Additives and fillers are integral to enhancing the properties of concrete. Pozzolans and blast-furnace slag are additives or admixtures due to their reactions with calcium hydroxide released during cement hydration. Fillers, which are finely ground and similar in fineness to Portland cement, improve concrete attributes such as workability density, and reduce capillary bleeding or cracking. Some fillers possess hydraulic properties or participate in benign reactions within the cement paste.
The...
96

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Related Experiment Video

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Procedure to Evaluate the Efficiency of Flocculants for the Removal of Dispersed Particles from Plant Extracts
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Applying machine learning random forest (RF) method in predicting the cement products with a co-processing of input

Jin Hwi Kim1, Dong Hoon Lee2, Joseph Albert Mendoza3

  • 1Department of Civil and Environmental Engineering, Konkuk University, Seoul, 05029, Republic of Korea.

Environmental Research
|January 28, 2024
PubMed
Summary

Managing heavy metals in cement production is crucial. This study uses machine learning to predict cement

Keywords:
CementHeavy metalsInput materialsMachine learningRandom forest

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Area of Science:

  • Environmental Science
  • Materials Science
  • Chemical Engineering

Background:

  • Co-processing recycled waste in cement production utilizes alternative raw materials and fuels.
  • These alternative inputs can contain hazardous heavy metals, posing risks to human health and the environment.
  • Effective management and prediction of input material impacts are essential for hazard prevention.

Purpose of the Study:

  • To investigate the influence of heavy metal concentrations in input materials on final cement products.
  • To develop a predictive model for heavy metal concentrations in cement based on input material composition.
  • To assess the feasibility of controlling heavy metal levels in cement through careful selection of alternative raw materials and fuels.

Main Methods:

  • Analysis of six heavy metals in input raw materials and fuels from cement manufacturers (2016-2017).
  • Principal Component Analysis (PCA) to identify leading causes of heavy metal presence.
  • Random Forest (RF) ensemble model application for predicting cement heavy metal concentrations.

Main Results:

  • Lead (Pb) and Copper (Cu) concentrations in cement were significantly higher than other heavy metals (Cr, As, Cd, Hg).
  • The RF model demonstrated strong predictive performance for Cu (R²=0.71), Cd (R²=0.71), and Cr (R²=0.92).
  • PCA effectively profiled the influence of each input material's heavy metal concentration.

Conclusions:

  • The Random Forest model accurately predicts cement heavy metal concentrations based on input material composition.
  • This predictive capability allows for better management of alternative raw materials and fuels.
  • The study highlights the potential to control heavy metal levels in cement by managing input material quality.