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Related Concept Videos

Portland Cement01:21

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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The hydration of cement is an exothermic reaction in which heat is generated as cement hydrates. This heat of hydration is critical to cement's strength development. The rate at which this heat is generated affects the temperature rise, with a majority of the heat being released early in the hydration process, half within the first three days, and about 75% within the first week.
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This example deals with managing the workability of concrete for a raft foundation project under hot weather conditions. Workability is crucial for ensuring the concrete is easy to place, compact, and finish. In this scenario, a slump test — a common method to measure the workability of fresh concrete — initially indicated low workability. This was attributed to the rapid water loss from the concrete mix, exacerbated by the high temperatures causing the course aggregates to heat up.
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Forecasting Energy Demand in Quicklime Manufacturing: A Data-Driven Approach.

Jersson X Leon-Medina1,2, John Erick Fonseca Gonzalez2, Nataly Yohana Callejas Rodriguez3

  • 1Grupo de Investigación en Biochar, Suelo y Cambio Climático (Pyrosfera), Suministros Mineros e Industriales de Colombia LTDA-SUMININCO LTDA, Km1 vía Nobsa-Duitama Vereda Guaquida, Nobsa 152280, Colombia.

Sensors (Basel, Switzerland)
|December 31, 2025
PubMed
Summary
This summary is machine-generated.

This study developed a deep learning framework to forecast energy demand for quicklime production. The Gated Recurrent Unit (GRU) model achieved high accuracy, enabling efficient industrial operations and energy management.

Keywords:
Gated Recurrent Unit (GRU)Long Short-Term Memory (LSTM)deep learningenergy consumption predictionrecurrent neural networktime series forecasting

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Area of Science:

  • Industrial Engineering
  • Artificial Intelligence
  • Energy Management

Background:

  • Energy demand forecasting is crucial for operational efficiency in energy-intensive industries.
  • Quicklime production faces challenges in managing fluctuating energy consumption.
  • Data-driven decision-making is essential for industrial scalability and sustainability.

Purpose of the Study:

  • To develop and evaluate a deep learning framework for short-term energy demand forecasting in quicklime production.
  • To integrate temporal and operational variables for accurate energy usage prediction.
  • To enhance operational efficiency and support data-driven decision-making.

Main Methods:

  • Utilized one year of real electricity consumption data.
  • Integrated temporal (load profile, time) and operational (production proxies, shifts) variables.
  • Trained and compared Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Conv1D neural network models.

Main Results:

  • The Gated Recurrent Unit (GRU) model demonstrated the highest predictive accuracy.
  • Achieved a Root Mean Square Error (RMSE) of 2.18 kW, Mean Absolute Error (MAE) of 0.49 kW, and Symmetric Mean Absolute Percentage Error (SMAPE) of 3.64% on the test set.
  • GRU model provided reliable short-term power demand forecasts with 10-min resolution.

Conclusions:

  • Deep learning, particularly GRU, is effective for energy demand forecasting in quicklime manufacturing.
  • Accurate forecasts enable cost-efficient scheduling, infrastructure planning, and capacity management.
  • The framework supports sustainability optimization in energy-intensive industrial processes.