In-process adjustment to crushing systems

A method comprising:

• obtaining pre-crush particle image data captured before crushing a portion of particles with a crushing system;
• obtaining settings data indicating one or more settings of the crushing system;
• determining, from the pre-crush particle image data and the settings data and by a prediction model, predicted post-crush data that indicates predicted characteristics of the portion of particles at an output of the crushing system, wherein the prediction model is configured to receive both the pre-crush particle image data and the settings data as input and predict how the crushing system will process the portion of particles based on both the pre-crush particle data and the settings data;
• obtaining post-crush particle data indicating actual characteristics of the portion of particles at the output of the crushing system;
• comparing the predicted post-crush data with the post-crush particle data to generate training data;
• and
• training the prediction model with the training data.

A method comprising:

• obtaining pre-crush particle image data captured before crushing a portion of particles with a crushing system;
• obtaining settings data indicating one or more settings of the crushing system;
• determining, from the pre-crush particle image data and the settings data and by a prediction model, predicted post-crush data that indicates predicted post-crush characteristics of the portion of particles at an output of the crushing system, wherein the prediction model is configured to receive both the pre-crush particle image data and the settings data as input and predict how the crushing system will process the portion of particles based on both the pre-crush particle image data and the settings data;
• determining an error between the predicted characteristics of the portion of particles and target characteristics for crushed particles output by the crushed system;
• re-training the prediction model with training data generated based on the error;
• and
• generating, from the error between the predicted post-crush characteristics and the target characteristics and by a control model, control signals for altering operations of the crushing system, wherein the control model is configured to receive as input the error between the predicted post-crush characteristics of the portion of particles and target characteristics.

A system comprising:

• at least one processor;
• and a data store coupled to the at least one processor having instructions stored thereon which, when executed by the at least one processor, causes the at least one processor to perform operations comprising:
• obtaining pre-crush particle image data captured before crushing a portion of particles with a crushing system;
• obtaining settings data indicating one or more settings of the crushing system;
• determining, from the pre-crush particle image data and the settings data and by a prediction model, predicted post-crush data that indicates predicted characteristics of the portion of particles at an output of the crushing system, wherein the prediction model is configured to receive both the pre-crush particle image data and the settings data as input and predict how the crushing system will process the portion of particles based on both the pre-crush particle image data and the settings data;
• obtaining post-crush particle data indicating actual characteristics of the portion of particles at the output of the crushing system;
• comparing the predicted post-crush data with the post-crush particle data to generate training data;
• and
• training the prediction model with the training data.