Accurate estimation of multiple quality variables is critical for building industrial soft sensor models, which have long been confronted with data efficiency and negative transfer issues. Methods sharing backbone parameters among tasks address the data efficiency issue; however, they still fail to mitigate the negative transfer problem. To address this issue, a balanced Mixture-of-Experts (BMoE) is proposed in this work, which consists of a multi-gate mixture of experts (MMoE) module and a task gradient balancing (TGB) module. The MoE module aims to portray task relationships, while the TGB module balances the gradients among tasks dynamically. Both of them cooperate to mitigate the negative transfer problem. Experiments on the typical sulfur recovery unit demonstrate that BMoE models task relationship and balances the training process effectively, and achieves better performance than baseline models significantly.

翻译：对多个质量变量的精确估计对于构建工业软测量模型至关重要，然而该问题长期面临数据效率低下和负迁移的挑战。现有方法通过共享骨干网络参数提升数据效率，但仍未能有效缓解负迁移问题。为此，本文提出均衡混合专家（BMoE）模型，该模型包含多门控混合专家（MMoE）模块与任务梯度均衡（TGB）模块。其中混合专家模块旨在刻画任务关联性，而任务梯度均衡模块则动态平衡各任务间的梯度。两个模块协同作用以缓解负迁移问题。在典型硫磺回收装置上的实验表明，BMoE能够有效建模任务关系并均衡训练过程，其性能显著优于基线模型。