双胞胎AI:智能屏障流分离器与数字双胞胎和AI集成
Shohreh Kia1, Johannes B Mayer1, Erik Westphal2
1Institute for Software and Systems Engineering, Clausthal University of Technology, 38678 Clausthal-Zellerfeld, Germany.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
本研究介绍了一种智能系统,用于优化使用人工智能和数字双胞胎技术的屏障旋流分离器 (BECS). 该系统提高了分离质量,并减少了工业回收的能源使用.
科学领域:
- 材料科学与工程 材料科学与工程
- 人工智能的人工智能
- 工业自动化 工业自动化
背景情况:
- 工业分离过程,如使用屏障旋流分离器 (BECS) 的工业分离过程,在优化各种材料和操作条件的性能方面经常面临挑战.
- 在回收应用中,最大限度地提高分离效率,同时最大限度地降低能源消耗和确保运行安全至关重要.
研究的目的:
- 开发和验证一个全面的智能系统,以优化BECS性能.
- 利用人工智能,物联网和数字双胞胎技术来提高分离质量,能源效率和预测性维护.
主要方法:
- 该系统在BECS的工业数据上进行了训练和验证,并根据81个操作场景进行了验证.
- 使用YOLOv11n-seg的AI驱动的智能分离模块用于材料和形状分类.
- 开发了一个用于检测铁污染的热监测单元,与数字双胞胎 (Azure Digital Twins) 和PLC集成用于自主控制.
主要成果:
- 智能分离模块在材料分类方面达到0.838的平均平均精度 (mAP),在形状分类方面达到91.8%的精度.
- 热单元检测到铁污染,温度上升超过20°C,响应时间低于2.5秒.
- 综合系统证明了物理参数的自主调整,以实现最佳的分离和能源减少.
结论:
- 开发的智能系统有效优化了BECS性能,从而提高了分离质量和降低了能源消耗.
- 人工智能,物联网和数字双胞胎技术的整合为工业回收提供了可靠,可扩展的解决方案.
- 该系统增强了运营安全,并通过实时监控和自主控制实现了预测性维护.
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