Method for generating trained prediction model that predicts amount of dross generated in melting furnace, method for predicting amount of dross generated in melting furnace, and computer program

A method of predicting an amount of dross occurring in a melting furnace, comprising:

• a step of receiving, as inputs at run time, input data containing control pattern candidates, process pattern candidates, and one or more process target parameters indicating process fundamental information to be set for every single charge spanning from a loading of raw materials to a completion of melting;
• and
• a step of inputting the input data to a prediction model and outputting a predicted amount of dross generation for every single charge, wherein,
• the prediction model is a trained model that has been learned by using a learning data set generated by n-dimensional features that are extracted from one or more process state parameters of different attributes;
• each of the one or more process state parameters is defined by a continuous aggregate of chronological data that is acquired for every single charge based on an output from one of a variety of sensors provided in the melting furnace;
• and
• the learning data set contains one or more process target parameters encompassing a data range of the process target parameter or parameters contained in the input data.

A computer program, stored on a non-transitory computer readable storage medium, for causing a computer to execute:

• a step of acquiring a prediction model to predict an amount of dross occurring in energy efficiency of a melting furnace;
• a step of receiving input data containing control pattern candidates, process pattern candidates, and one or more process target parameters indicating process fundamental information to be set for every single charge spanning from a loading of raw materials to a completion of melting;
• and
• a step of inputting the input data to the prediction model and outputting a predicted amount of dross generation for every single charge, wherein,
• the prediction model is a trained model that has been learned by using a learning data set generated by n-dimensional features that are extracted from one or more process state parameters of different attributes;
• each of the one or more process state parameters is defined by a continuous aggregate of chronological data that is acquired for every single charge based on an output from one of a variety of sensors provided in the melting furnace;
• and
• the learning data set contains one or more process target parameters encompassing a data range of the process target parameter or parameters contained in the input data.