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相关概念视频

Portland Cement01:21

Portland Cement

567
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...
567
Strength and Heat of Hydration01:29

Strength and Heat of Hydration

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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.
The heat of hydration for each cement compound is significant; for instance, tricalcium aluminate (C3A) and...
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Design Example: Managing Concrete Workability01:14

Design Example: Managing Concrete Workability

275
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.
275
Clausius-Clapeyron Equation02:35

Clausius-Clapeyron Equation

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The equilibrium between a liquid and its vapor depends on the temperature of the system; a rise in temperature causes a corresponding rise in the vapor pressure of its liquid. The Clausius-Clapeyron equation gives the quantitative relation between a substance’s vapor pressure (P) and its temperature (T); it predicts the rate at which vapor pressure increases per unit increase in temperature.
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预测热制造中的能源需求:一个数据驱动的方法

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.

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概括

这项研究开发了一个深度学习框架,用于预测生生产的能源需求. 门式循环单元 (GRU) 模型实现了高精度,使工业运营和能源管理高效.

关键词:
门式经常性单位 (GRU)长时间短期记忆 (LSTM)深度学习是一种深度学习.预测能源消耗 预测能源消耗经常性的神经网络.时间序列预测时间序列预测

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科学领域:

  • 工业工程 工业工程 工业工程
  • 人工智能的人工智能
  • 能源管理 能源管理

背景情况:

  • 能源需求预测对于能源密集型行业的运营效率至关重要.
  • 快生产在管理波动的能源消耗方面面临着挑战.
  • 数据驱动的决策对于工业的可扩展性和可持续性至关重要.

研究的目的:

  • 开发和评估一个深度学习框架,用于快生产中的短期能源需求预测.
  • 整合时间和操作变量,以准确预测能源使用情况.
  • 提高运营效率并支持数据驱动的决策.

主要方法:

  • 利用一年的实际电力消耗数据.
  • 集成的时间 (负载配置,时间) 和操作 (生产代理,轮班) 变量.
  • 训练并比较长期短期记忆 (LSTM),门式循环单元 (GRU) 和Conv1D神经网络模型.

主要成果:

  • 门式循环单位 (GRU) 模型显示出最高的预测准确性.
  • 在测试组件上获得了2.18 kW的根平均平方误差 (RMSE),0.49 kW的平均绝对误差 (MAE) 和3.64%的对称平均绝对百分比误差 (SMAPE).
  • GRU模型提供了可靠的短期电力需求预测,分辨率为10分钟.

结论:

  • 深度学习,特别是GRU,是有效的能源需求预测在生制造业.
  • 准确的预测可以实现成本高效的调度,基础设施规划和容量管理.
  • 该框架支持在能源密集型工业过程中优化可持续性.