A Digital Twin Simulator of a Pastillation Process with Applications to Automatic Control based on Computer Vision
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913748553302016 |
|---|---|
| author | González, Leonardo D. Pulsipher, Joshua L. Jiang, Shengli Soderstrom, Tyler Zavala, Victor M. |
| author_facet | González, Leonardo D. Pulsipher, Joshua L. Jiang, Shengli Soderstrom, Tyler Zavala, Victor M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16539 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Digital Twin Simulator of a Pastillation Process with Applications to Automatic Control based on Computer Vision González, Leonardo D. Pulsipher, Joshua L. Jiang, Shengli Soderstrom, Tyler Zavala, Victor M. Optimization and Control Robotics 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. |
| title | A Digital Twin Simulator of a Pastillation Process with Applications to Automatic Control based on Computer Vision |
| topic | Optimization and Control Robotics |
| url | https://arxiv.org/abs/2503.16539 |