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相关概念视频

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
Design Example: Aggregate Gradation01:24

Design Example: Aggregate Gradation

94
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...
94
Design Example: Managing Concrete Workability01:14

Design Example: Managing Concrete Workability

81
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.
81
Portland Cement01:21

Portland Cement

219
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...
219
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

54
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
54
Production Efficiency01:01

Production Efficiency

16.8K
Net production efficiency (NPE) is the efficiency at which organisms assimilate energy into biomass for the next trophic level. Due to low metabolic rates and less energy spent on thermoregulatory processes, the NPE of ectotherms (cold-blooded animals) is 10 times higher than endotherms (warm-blooded animals).
16.8K

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相关实验视频

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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在水泥生产中,在用户定义的优化目标函数中的数据驱动人工智能模型.

Othonas Manis1, Michalis Skoumperdis2, Christos Kioroglou2

  • 1Titan Cement Group, 11143 Athens, Greece.

Sensors (Basel, Switzerland)
|February 24, 2024
PubMed
概括
此摘要是机器生成的。

这项研究优化了使用机器学习 (ML) 和微分演化 (DE) 的水泥厂运营. 德成功地提高了水泥厂和的性能,降低了目标功能价值.

关键词:
一个水泥炉的炉.一个水泥厂的水泥厂.集群集成是指集群集成.不同的进化是不同的进化.功能选择 功能选择关键绩效指标关键绩效指标机器学习是机器学习.优化的优化优化优化.

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科学领域:

  • 工业工程 工业工程 工业工程
  • 化学工程是化学工程的重要组成部分.
  • 人工智能的人工智能

背景情况:

  • 水泥行业的能源密集度很高,需要运营优化.
  • 分析了希腊水泥行业现有工厂运营情况.
  • 机器学习 (ML) 模型被用来处理复杂的工业数据.

研究的目的:

  • 为了提高水泥厂的磨坊和炉单元的运营效率.
  • 使用人工智能 (AI) 模型开发优化策略.
  • 为了使专业用户能够根据工厂变量定义和优化目标函数.

主要方法:

  • 使用ML模型计算非操纵变量,选择具有最小规范化根平均平方误差 (NRMSE) 的变量.
  • 如果数据分布发生变化,人工智能模型需要重新训练以确保准确性.
  • 差异演变 (DE) 方法用于优化用户定义的目标函数.

主要成果:

  • DE方法成功优化了目标函数,该函数是选定变量的线性组合.
  • 使用DE的优化使得水泥厂和炉的运行性能得到改善.
  • 与工厂当前的运行值相比,实现了较低的目标功能值.

结论:

  • 集成ML和DE提供了一种可靠的方法来优化能源密集型工业流程,如水泥生产.
  • 用户定义的目标函数和约束允许定制优化策略.
  • 拟议的方法证明了在水泥行业提高效率和降低运营成本的巨大潜力.