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Physics-informed machine learning surrogate models: Enhancing data-driven forecasting for digital twins in mineral processing
Minerals Engineering ( IF 4.9 ) Pub Date : 2025-05-16 , DOI: 10.1016/j.mineng.2025.109424
Mikko Seppi, Joonas Linnosmaa, Akhtar Zeb
Minerals Engineering ( IF 4.9 ) Pub Date : 2025-05-16 , DOI: 10.1016/j.mineng.2025.109424
Mikko Seppi, Joonas Linnosmaa, Akhtar Zeb
In mineral processing, optimal operation and process control are essential. Accurate forecasting models are needed, and while data-driven models for multistep forecasting have shown promise, the potential for incorporating physics-based information to enhance accuracy was investigated in this paper. The objective was to determine accuracy improvement by adding physics information to the baseline data-driven models. Three purely data-driven multivariate multistep forecasting models (CNN, GRU, LSTM) from previous research were selected and their architectures recreated for comparison. Pre-processing and feature engineering methods were harmonised to form baseline models, which were validated on the same physical flotation process. Physics information was added using two different physics-guided loss functions that utilised mass balance, resulting in surrogate models based on multivariate multistep forecasting and physics-informed machine learning (PIML). These models were trained and tested on the same dataset to compare performance with baseline models and among different algorithms. Key findings indicated that all the PIML models outperformed their data-driven counterparts. The largest improvements in the NRMSE and NMAE were observed in the LSTM models, while the CNN models showed the lowest improvement. Error distributions across the forecasting horizons improved for all the models, and a loss function utilising a mean absolute error performed better than a mean-squared error across all models. In conclusion, incorporating physics information into multivariate multistep forecasting surrogates generally enhanced forecasting accuracy. However, the extent of improvement varied, and the decision to integrate physics should be made on a case-by-case basis, considering the need for domain knowledge and the increased time and resource requirements for PIML model development.
中文翻译:
基于物理学的机器学习代理模型:增强矿物加工中数字孪生的数据驱动预测
在矿物加工中,最佳作和过程控制至关重要。需要准确的预测模型,虽然用于多步预测的数据驱动模型已显示出前景,但本文研究了整合基于物理的信息以提高准确性的潜力。目标是通过将物理信息添加到基线数据驱动模型来确定准确性的提高。从以前的研究中选择了三个纯数据驱动的多变量多步骤预测模型 (CNN、GRU、LSTM),并重新创建它们的架构以进行比较。将前处理和特征工程方法协调一致,形成基线模型,这些模型在相同的物理浮选工艺上进行了验证。使用两种不同的物理引导损失函数添加物理信息,这些函数利用质量平衡,从而产生基于多变量多步预测和物理知情机器学习 (PIML) 的代理模型。这些模型在同一数据集上进行了训练和测试,以将性能与基线模型和不同算法进行比较。主要研究结果表明,所有 PIML 模型的性能都优于数据驱动的模型。在 LSTM 模型中观察到 NRMSE 和 NMAE 的改进最大,而 CNN 模型的改进最低。所有模型的预测范围内的误差分布都得到了改善,并且使用平均绝对误差的损失函数在所有模型中的表现优于均方误差。总之,将物理信息纳入多元多步预测代理通常会提高预测准确性。 然而,改进的程度各不相同,考虑到对领域知识的需求以及 PIML 模型开发时间和资源需求的增加,应根据具体情况做出集成物理场的决定。
更新日期:2025-05-16
中文翻译:

基于物理学的机器学习代理模型:增强矿物加工中数字孪生的数据驱动预测
在矿物加工中,最佳作和过程控制至关重要。需要准确的预测模型,虽然用于多步预测的数据驱动模型已显示出前景,但本文研究了整合基于物理的信息以提高准确性的潜力。目标是通过将物理信息添加到基线数据驱动模型来确定准确性的提高。从以前的研究中选择了三个纯数据驱动的多变量多步骤预测模型 (CNN、GRU、LSTM),并重新创建它们的架构以进行比较。将前处理和特征工程方法协调一致,形成基线模型,这些模型在相同的物理浮选工艺上进行了验证。使用两种不同的物理引导损失函数添加物理信息,这些函数利用质量平衡,从而产生基于多变量多步预测和物理知情机器学习 (PIML) 的代理模型。这些模型在同一数据集上进行了训练和测试,以将性能与基线模型和不同算法进行比较。主要研究结果表明,所有 PIML 模型的性能都优于数据驱动的模型。在 LSTM 模型中观察到 NRMSE 和 NMAE 的改进最大,而 CNN 模型的改进最低。所有模型的预测范围内的误差分布都得到了改善,并且使用平均绝对误差的损失函数在所有模型中的表现优于均方误差。总之,将物理信息纳入多元多步预测代理通常会提高预测准确性。 然而,改进的程度各不相同,考虑到对领域知识的需求以及 PIML 模型开发时间和资源需求的增加,应根据具体情况做出集成物理场的决定。