Robust estimation procedures make it possible to reduce the leverage of outliers on the measurement results. Application of the least median of squares (LMS) method to determine the calibration curves of measuring instruments involves the need to evaluate the obtained results. This paper proposes the method of estimating the variance of the LMS regression coefficients, which allows the evaluation of the quality of the set regression coefficients. The concept of an a priori estimation consists in performing an estimation before the measurement experiment. The a posteriori estimation uses the data from the previously conducted measurement experiment, thus making it possible to obtain much more accurate results compared with the a priori method. Simulation tests were carried out in order to compare the estimation results with the ones obtained in the course of the simulated experiment. The obtained results confirm that the proposed concepts are correct.

Moisture of bulk material has a significant impact on energetic efficiency of dry grinding, resultant particle size distribution and particle shape, and conditions of powder transport. As a consequence, moisture needs to be measured or estimated (modelled) in many points. This research investigates mutual relations between material moisture and particle classification process in a grinding installation. The experimental setup involves an inertial-impingement classifier and cyclone being part of dry grinding circuit with electromagnetic mill and recycle of coarse particles. The tested granular material is copper ore of particle size 0–1.25 mm and relative moisture content 0.5–5%, fed to the installation at various rates. Higher moisture of input material is found to change the operation of the classifier. Computed correlation coefficients show increased content of fine particles in lower product of classification. Additionally, drying of lower and upper classification products with respect to moisture of input material is modelled. Straight line models with and without saturation are estimated with recursive least squares method accounting for measurement errors in both predictor and response variables. These simple models are intended for use in automatic control system of the grinding installation.

The process of loose materials grinding is directly dependent on the type of feed and its physical parameters resulting from presence of water in the material. The grinding process in the electromagnetic mill installation is carried out on dry material, using pneumatic transport and classification system with recycle. Preliminary analyses have shown the possibility to significantly improve the product quality and efficiency of the milling process by appropriate control of the moisture content of the milled material. The main objective of the research is to develop a model describing the changes of moisture content of bulk material in the installation of the electromagnetic mill. This paper presents the results of preliminary studies on models describing the drying process in the path of material classification and its separation from the transport air.

The article presents results of research on evaluation of the quality of material obtained in the milling and classification installation with the electromagnetic mill. Several methods of indirect on-line measurements in the process of separating the recycle stream from the final product stream were investigated. The methods discussed in the paper are moisture content measurements of the milled material using impedance and near infrared absorption methods as well as vibration-based measurements of granularity and flow rate of the material. The experiments performed on the classification system of electromagnetic mill circuit show that indirect methods of moisture measurements provide satisfactory results, whereas vibration-based method fails in this case. The grains of copper ore examined in the experiments (present in the classification circuit during normal operation of the installation) are too small and lightweight and the signal-to-noise-ratio of the recorded acceleration signal is too low for the vibration method to be used.

This paper presents a machine vision method for detection and classification of copper ore grains. We proposed a new method that combines both seeded regions growing segmentation and edge detection, where region growing is limited only to grain boundaries. First, a 2D Fast Fourier Transform (2DFFT) and Gray-Level Co-occurrence Matrix (GLCM) are calculated to improve the detection results and processing time by eliminating poor quality samples. Next, detection of copper ore grains is performed, based on region growing, improved by the first and second derivatives with a modified Niblack’s theory and a threshold selection method. Finally, all the detected grains are characterized by a set of shape features, which are used to classify the grains into separate fractions. The efficiency of the algorithm was evaluated with real copper ore samples of known granularity. The proposed method generates information on different granularity fractions at a time with a number of grain shape features.

Moisture measurement of bulk materials is important factor considering optimization of the milling process. Moisture measurement of loose material is difficult due to its inhomogeneities. Review of existing techniques for moisture measurement of bulk material proved that universal methods for moisture measurement of bulk material are not currently available. The distinctive construction of the electromagnetic mill requires the use of dedicated measuring devices. The object of the research is to develop a low-cost and reliable transducer for moisture measurement in an electromagnetic mill. The article presents the results of research on impedance methods of measurement. There is also described innovative design of the dedicated transducer. Examination of the transducer has been made after installing it in the screw feeder of the electromagnetic mill. The resulting measuring range, accuracy and reliability presented in this paper confirm the possibility of developing cheap and reliable devices for moisture measurement of bulk material in the installation of electromagnetic mill.