Online method to determine quality standards for incoming wood chips to a papermill

A computing device for monitoring and determining a quality level of incoming wood chips from one or more sources in absence of off-line inspection, the computing device comprising:

• a memory;
• and
• at least one processor configured for:
• receiving real-time visual data from an online camera system configured to capture images on-line and in real-time of incoming wood chips associated with an paper manufacturing process;
• determining online and in real-time an indication of quality level associated with the incoming wood chips based on the real-time visual data, and deep learning using an artificial neural network configured to learn an algorithm while processing additional image data;
• and
• transmitting, to at least one of a graphical user interface (GUI) and a computer log, the indication of quality level and at least one timestamp associated with the real-time visual data.

A non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium storing instructions to be implemented on at least one computing device including at least one processor, the instructions when executed by the at least one processor cause the at least one computing device to perform a method of determining a quality level of incoming wood chips from one or more sources in absence of off-line inspection, the method comprising:

• receiving real-time visual data from a online camera system configured to capture images on-line and in real-time of incoming wood chips associated with a paper manufacturing process;
• determining online and in real-time an indication of quality level associated with the incoming wood chips based on the real-time visual data and deep learning using an artificial neural network configured to learn an algorithm while processing additional image data;
• and
• transmitting, to at least one of a graphical user interface (GUI) and a computer log, the indication of quality level and at least one timestamp associated with the real-time visual data.