Abstract
Abstract The 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.
Highlights
The modern world is highly dependent on materials
The flocculant rate and underflow rate are the two independent variables that are typically used for control
The knowledge of the variables found to influence the pressure difference can be utilized in further development
Summary
The modern world is highly dependent on materials. The saying: “What you can’t grow, you need to dig” is true and. Different methods are used to control the paste thickener. Li and Wang [3] and Chai et al [4] propose an intelligent switching control which includes an underflow slurry flow-rate (USF) pre-setting unit and a fuzzy reasoning-based USF set-point compensator. Langlois and Cipriano [5] have introduced a dynamic simulator for the thickener which can be utilized to develop different kinds of control strategies. Concha and Sbarbaro [8] tested different thickener control strategies using a calibrated simulator, pointing out the weaknesses and giving hints for improving their performance. In the worst-case scenario these phenomena cause clogging in the piping. This leads to a need for extra maintenance and even stoppages, it would be beneficial to understand these phenomena better. Information about the performance optimization of paste thickening can be found for example in [9]
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