具有知识转移的多变量协作建模及其在铁漂浮等级软传感中的应用
概括
本研究介绍了一种知识转移方法,有效地利用旧数据用于新的铁漂浮模型,在有限的新数据下提高准确性. 多变量方法通过包括生产参数来提高尾矿等级软传感器性能.
科学领域:
- 矿物加工 矿物加工
- 在工业运营中的数据科学.
- 机器学习用于流程优化
背景情况:
- 由于设备和原材料的更新,铁漂浮生产阶段经常发生变化.
- 现有的运行条件预测模型随着阶段变化而变得过时,浪费了宝贵的历史数据.
- 在小型当前数据集上构建的数据驱动模型缺乏准确性.
研究的目的:
- 提出一种知识转移方法,在更新的生产阶段利用历史数据.
- 为了能够快速建立准确的预测模型,并尽可能少地收集新数据.
- 通过结合生产过程参数来提高尾矿等级软传感器的准确性.
主要方法:
- 采用知识转移方法,将现有模型调整为新的生产阶段.
- 为软传感器开发引入了一个多变量协作建模策略.
- 该方法将历史数据集与有限的当前阶段数据集集成为模型构建.
主要成果:
- 建议的知识转移方法有效地利用过时的数据集来适应新的操作条件.
- 快速建立准确的预测模型,减少了对广泛的新数据收集的需求.
- 多变量方法显著提高了尾矿等级软传感器的准确性.
结论:
- 知识转移是更新预测模型在动态工业过程中的一个可行的策略,如铁漂浮.
- 将生产过程参数集成到软传感器模型中,提高了它们的预测能力.
- 该方法通过实验验证和工业应用证明了其实际有效性.
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