用数字孪生模拟结晶过程，实现视觉反馈的实时控制。
A Digital Twin Simulator of a Pastillation Process with Applications to Automatic Control based on Computer Vision
- 构建结晶过程数字孪生，生成真实热成像数据训练视觉传感器。
- 通过卷积神经网络输出温度与流量信号，实现动态调速稳定输出。
- 适用于工业结晶设备故障检测与自动控制，适合自动化工程师参考。
本文提出一种结晶过程的数字孪生仿真系统，可生成逼真的热成像数据，用于训练基于卷积神经网络（CNN）的计算机视觉软传感器。该传感器输出温度和产品流量信号，实现过程的实时监测与闭环反馈控制。结晶技术广泛应用于多个行业，面临旋转壳体堵塞位置实时识别、传送带速度与运行参数自动调节等挑战。所提仿真框架能准确模拟此类行为，生成可用于图像处理算法与控制架构对比测试的真实数据。案例研究涵盖不同设备尺寸、堵塞位置及持续时间，采用贝叶斯优化调参的反馈控制器，根据CNN输出信号动态调整传送带速度，以实现期望的工艺输出。
原文摘要 · Abstract (English)
We present a digital-twin simulator for a pastillation process. The simulation framework produces realistic thermal image data of the process that is used to train computer vision-based soft sensors based on convolutional neural networks (CNNs); the soft sensors produce output signals for temperature and product flow rate that enable real-time monitoring and feedback control. Pastillation technologies are high-throughput devices that are used in a broad range of industries; these processes face operational challenges such as real-time identification of clog locations (faults) in the rotating shell and the automatic, real-time adjustment of conveyor belt speed and operating conditions to stabilize output. The proposed simulator is able to capture this behavior and generates realistic data that can be used to benchmark different algorithms for image processing and different control architectures. We present a case study to illustrate the capabilities; the study explores behavior over a range of equipment sizes, clog locations, and clog duration. A feedback controller (tuned using Bayesian optimization) is used to adjust the conveyor belt speed based on the CNN output signal to achieve the desired process outputs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。