How Deep Learning is Used to Determine Woodyard Chip Size Webinar
The greatest expense to fully integrated papermills are the raw material wood chips that make up the slurry that later becomes paper on the reel of the machine. Random and off-line sampling of the chips provides less than .001% classification of the material. Chips that are off specification in size, wrong species or contaminated with bark and other foreign matter can greatly impact the subsequent paper making process. Additionally, vendors or internal suppliers that sell or provide these chips to the mill cannot be held to any penalty or feedback loop for supplying chips outside set quality standards. This paper discusses how camera-based imaging with deep learning (artificial intelligence) is used to provide real-time classification of wood chip thickness on the conveyer line. Results are reviewed from a mill installation utilizing a multiple bin chip thickness distribution.
Who Should Watch:
- Process Engineers
- Asset Lead and Mill Manager
- Pulp and Paper
- Any manufacturer with random size raw material input
- Describe real world applications in the pulp and paper industry for image based deep learning
- Explain the type and magnitude of variances in the wood yard and its effect on the final product at the paper machine reel
Brian Mock is the owner and President of Event Capture Systems located in Charlotte, North Carolina. He completed his undergraduate studies at NC State with a BS in Civil Engineering and Master’s Degree in Civil and Textile Engineering. He has been working in the paper industry for over 25 years in the field of camera-based quality control technologies. He currently serves as the Vice President of the Process Control Division for TAPPI and has several published papers as well as 5 US Patents. He also enjoys working closely with students and faculty at the NC State Pulp and Paper School’s pilot paper machine to ensure their students are actively exposed to quality control technologies during their college experience.