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使用增量深度极端学习机器预测炉中温度分布
Manli Lv1,2, Jianping Zhao1, Shengxian Cao2
1College of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China.
本研究引入了一种增量深度极端学习机器 (IDELM) 模型,用于准确预测炉温度. 数据驱动的方法实现了不到11%的误差的预测准确度,优化了工业流程.
科学领域:
- 工程 工程师 工程师 工程师
- 计算科学 计算科学
背景情况:
- 准确的炉温度预测对于优化工业流程和确保安全至关重要.
- 传统方法可能缺乏动态运行条件所需的效率和准确性.
研究的目的:
- 开发一个数据驱动的模型来预测炉温分布.
- 为了提高预测准确度,使用增量深度极端学习机器 (IDELM) 算法.
主要方法:
- 进行计算流体动力学 (CFD) 模拟以生成基线温度数据.
- 采用K-means集群和随机抽样来对数据进行分类并减少计算负载.
- 为每个工作条件子类构建了一个基于深度信念网络 (IDBN) 的新型增量模型.
主要成果:
- 拟议的基于IDELM的模型显示了对炉温的强有力的预测能力.
- 该模型在不同的工作条件下实现了对称平均绝对百分比误差低于11%.
- 基于递增的数据重建方法提高了模型效率.
结论:
- 开发的数据驱动的IDELM模型为实时炉温预测提供了一个有前途的解决方案.
- 这种方法提高了运营效率,并可能降低工业炉的能源消耗.
- 该方法为复杂的热系统中类似的预测建模任务提供了一个可扩展的框架.
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