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
概括
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.
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.
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