The world is using a lot of materials in the day-today life. This requires a lot of mining of the ores from the ground. As the amount of ore is remarkable, also the amount of waste rock and for that reason the amount of the tailings is huge. The water content of the tailings is a subject to decrease. One potential technology for that is a paste thickener. In this paper, a multivariate linear regression model using paste line pressure difference as an output variable is described. The model can be utilized for the development of a new control strategy. Another model was formed using rake torque as an output variable.

AbstractThe solids content of slurry is typically increased in thickeners. A clean overflow and maximum solids concentration in the underflow are the general targets. The flocculant rate and underflow rate are the two independent variables that are typically used for control. The dependent variables include rake torque, underflow density, overflow turbidity, solids interface level (bed depth), solids inventory (bed pressure), solids settling rate and underflow viscosity. The research problem in question is that the outgoing paste is sometimes difficult to pump. The phenomena leading to this situation are not well known. In the worst-case scenario these phenomena cause clogging in the piping. A data analysis has been done to find the variables that affect and correlate with the pumping problem. The scope of this study covers the measurements from the feed line, thickener and underflow. The goal is to gain better understanding of the phenomena after this phase. The data analysis was done using the paste line pressure difference as a response variable and by dividing the data collected from Yara’s Siilinjärvi mill into two parts: operation areas with high and low pressure difference. The analysis is focused on Thickener 1 due to better availability of measurements. The knowledge of the variables found to influence the pressure difference can be utilized in further development.