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.

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 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.

In the milling circuit with an electromagnetic mill, a pneumatic system is used to transport the material and to maintain several parameters of the process. The pneumatic system contains three air inlets with controllable flaps and each inlet stream serves a different purpose, however, they are strongly physically coupled through the rest of the installation. Thus, position of each control flap strongly and nonlinearly affects the operating point of each inlet stream. Dynamics of air velocity within the main inlet pipe with respect to the setpoint of the main flap's position has been identified for multiple operating points of the installation, and the estimated models were checked and assessed. The resultant models may then be used to tune controller parameters for subsequent operating points. Identification and verification of the derived models was performed on data measured at an experimental milling installation.

The moisture of bulk material has a significant impact on the energetic efficiency of dry grinding, resultant particle size distribution and particle shape, and conditions of powder transport. This research aims to develop computer vision and thermovision techniques for the on-site estimation of moisture content in copper ore, for use, e.g., in dry grinding installations. The influence of particle size on the results of moisture estimation is also studied. The tested granular material was copper ore of particle size 0–2 mm and relative moisture content of 0.5–11%. Both vision and thermovision images were taken at standard and macro scales. The results suggest that median-intensity vision images monotonically reflect copper ore moisture in the range of about 0.5–5%. Suitable models were identified and cross-validated here. In contrary, thermograms should not be analyzed simply for their mean temperature but treated with computer vision processing algorithms.

The objective of the research is to develop a cheap and reliable technique for online measurement of loose material granularity and flow. Such data is very important to efficiently control industrial milling in order to optimize energy consumption and product quality. For the measurement system to meet industrial requirements concerning harsh environmental operating, like dust and attrition, the measurement should be contactless. A review of existing techniques of granularity and flow measurement is presented in this paper. Then, vibration sensing is proposed as the one meeting all requirements. Appropriate laboratory rig was assembled and the material was accordingly prepared for experiments. A signal processing algorithm extending the idea of spectrum subtraction has been developed. It is demonstrated in the paper that information about the grain size and material flow is stored in the signal and can be used for control.