The weight factors of error analysis in the numerical modeling of the mineral laboratory
Ge Sheng Li
The contaminations often occur in the busy sample preparation works of mineral laboratories. The SPI® is a measurement of the hardness of the iron ore from the perspective of semi-autogenous milling. The weights vary as the grind ability changes. The selection of the samples weights is a critical issue to decrease the data error. Error analysis is the study of quality and quantity of error that occurs. In numerical simulation of modeling of real experiments, error analysis is concerned with the changes in the parameter output of the model to the variety about a mean. For instance, the SAG Power Index is taken in an experiment modeled as a function of two variables z=f(w, s), W is the weight directed measured from the real scale, s is the SAG Power Index. The error analysis deals with the numerical errors in w and s (around mean values and) to error in z (around a mean). Error analysis involves both forward error analysis and backward error analysis. Forward error analysis comprises the analysis of a function which is an approximation (often a finite polynomial) to a function to determine the bounds on the error in the approximation; backward error analysis includes the analysis of the approximation function to determine the bounds on the parameters of the result.
The weights changes from 100g to 60kg, each group comprises 20 samples, compared with the standard weight, the conclusion is the average weight of sample is 20kg/each.
Ge Sheng Li
The contaminations often occur in the busy sample preparation works of mineral laboratories. The SPI® is a measurement of the hardness of the iron ore from the perspective of semi-autogenous milling. The weights vary as the grind ability changes. The selection of the samples weights is a critical issue to decrease the data error. Error analysis is the study of quality and quantity of error that occurs. In numerical simulation of modeling of real experiments, error analysis is concerned with the changes in the parameter output of the model to the variety about a mean. For instance, the SAG Power Index is taken in an experiment modeled as a function of two variables z=f(w, s), W is the weight directed measured from the real scale, s is the SAG Power Index. The error analysis deals with the numerical errors in w and s (around mean values and) to error in z (around a mean). Error analysis involves both forward error analysis and backward error analysis. Forward error analysis comprises the analysis of a function which is an approximation (often a finite polynomial) to a function to determine the bounds on the error in the approximation; backward error analysis includes the analysis of the approximation function to determine the bounds on the parameters of the result.
The weights changes from 100g to 60kg, each group comprises 20 samples, compared with the standard weight, the conclusion is the average weight of sample is 20kg/each.