  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.
