In the process industry, optimizing production lines for long-term efficiency requires real-time monitoring and analysis of operation states to fine-tune production line parameters. However, the complexity in operational logic and the intricate coupling of production process parameters make it difficult to develop an accurate mathematical model for the entire process, thus hindering the deployment of efficient optimization mechanisms. In view of these difficulties, we propose to deploy a digital twin of the production line by digitally abstracting its physical layout and operational logic. By iteratively mapping the real-world data reflecting equipment operation status and product quality inspection in the digital twin, we adopt a quality prediction model for production process based on self-attention-enabled temporal convolutional neural networks. This model enables the data-driven state evolution of the digital twin. The digital twin takes a role of aggregating the information of actual operating conditions and the results of quality-sensitive analysis, which facilitates the optimization of process production quality with virtual-reality evolution under multi-dimensional constraints. Leveraging the digital twin model as an information-flow carrier, we extract temporal features from key process indicators and establish a production process quality prediction model based on the proposed composite neural network. Our operation experiments on a specific tobacco shredding line demonstrate that the proposed digital twin-based production process optimization method fosters seamless integration between virtual and real production lines. This integration achieves an average operating status prediction accuracy of over 98\% and near-optimal production process control.

翻译：在流程工业中，优化生产线以实现长期效率需要对运行状态进行实时监测与分析，从而对生产线参数进行精细调整。然而，由于运行逻辑的复杂性以及生产过程参数的紧密耦合，难以建立对整个生产流程的精确数学模型，因而阻碍了高效优化机制的部署。针对这些困难，我们提出通过数字化抽象生产线的物理布局和运行逻辑来部署其数字孪生。通过将反映设备运行状态和产品质量检验的真实数据在数字孪生中迭代映射，我们采用一种基于自注意力机制的时序卷积神经网络的质量预测模型。该模型实现了数字孪生的数据驱动状态演化。数字孪生负责聚合实际运行状况信息和质量敏感性分析结果，从而在多维约束下通过虚实演化优化流程生产质量。利用该数字孪生模型作为信息流载体，我们从关键工艺指标中提取时序特征，并基于所提出的复合神经网络建立了生产过程质量预测模型。我们在特定烟草制丝线上的操作实验表明，所提出的基于数字孪生的生产过程优化方法促进了虚实生产线的无缝集成。该集成实现了超过98%的平均运行状态预测精度，并实现了近似最优的生产过程控制。