Accurate prediction of mill load parameters is crucial to improving grinding efficiency and saving energy. Traditional prediction models have challenges such as poor interpretability, low prediction efficiency and differences in data distribution. This study innovatively proposed a multi-task prediction model that integrates physical information and domain adaptation. By constructing a physical-data-driven hybrid model, the physical relationship between mill load parameters is embedded into the model as prior knowledge to improve the prediction accuracy of the model. At the same time, multi-task learning is used to predict the material-to-ball volume ratio and the pulp density at the same time, which improves efficiency and reduces repetitive work. The domain adaptation method is introduced to ensure that the model maintains stable prediction performance when the data distribution changes. Laboratory ball mill data verification shows that the proposed model not only improves the prediction accuracy, but also adapts well to variable working conditions, showing significant superiority.

中文翻译：

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使用物理信息和域自适应进行磨机载荷参数预测的多任务模型：使用实验室球磨机进行验证


准确预测磨机负载参数对于提高研磨效率和节约能源至关重要。传统的预测模型存在可解释性差、预测效率低、数据分布差异等挑战。本研究创新性地提出了一种融合物理信息和领域适应的多任务预测模型。通过构建物理数据驱动的混合模型，将磨机载荷参数之间的物理关系作为先验知识嵌入到模型中，以提高模型的预测精度。同时，采用多任务学习，同时预测料球体积比和纸浆密度，提高效率，减少重复性工作。引入域自适应方法，以保证模型在数据分布变化时保持稳定的预测性能。实验室球磨机数据验证表明，所提模型不仅提高了预测精度，而且对可变工况的适应性较强，显示出显著的优越性。