Soft-Sensor Estimation of an Apatite Thickener Operation at the Siilinjärvi Concentrator

Remes, Antti; Aaltonen, J.; Koivo, H.N.

doi:10.3182/20091014-3-cl-4011.00031

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Langlois and Cipriano [5] have introduced a dynamic simulator for the thickener which can be utilized to develop different kinds of control strategies. Remes, Aaltonen and Koivo [6] have developed a Kalman filter based soft sensor to monitor the thickener operation to reduce process disturbances. Bergh, Ojeda and Torres [7] have introduced the expert control tuning of an industrial thickener.…”

Section: Introductionmentioning

confidence: 99%

Data analysis of a paste thickener

et al. 2020

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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.

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Section: Introductionmentioning

confidence: 99%

Data analysis of a paste thickener

et al. 2020

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Performance optimization of paste thickening

Kosonen¹,

Kauvosaari²,

Gao³

et al. 2017

Proceedings of the 20th International Seminar on Paste and Thickened Tailings

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Traditionally, thickener controls are implemented as single loop controllers in plant control systems. Based on our experiences from numerous thickener installations, this approach has several drawbacks, leading easily to poor process control and sub-optimal process performance. Especially challenging applications are paste thickeners, where the requirement is to achieve consistently high underflow density and high system availability without suffering process upsets and mechanical failures. The main reason for the poor performance of the traditional, single loop Proportional Integral (PI) controllers is their inability to handle the slow response dynamics and cross action between controlled variables. This makes tuning of the PI-loops challenging, and compromises have to be made between system robustness and the desired speed of response. In many cases, these controllers are run either totally or partially in manual mode, and operator intervention is needed to maintain process stability and the desired operation point in varying running situations. This paper discusses the optimizing control solution for paste thickeners. Initially, the instrumentation needed is briefly discussed. After this, an optimizing control solution based on multivariable model predictive control (MPC) is introduced, and the results from applying this control strategy to a production scale concentrate thickener are presented. Finally, a simulated example shows how the same control strategy improves the performance of paste thickener when compared to traditional control strategies. It is shown that by using an advanced, model based control strategy, the thickener can consistently run with higher and more stable underflow density, so that the required level of overflow clarity is also obtained.

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Soft-Sensor Estimation of an Apatite Thickener Operation at the Siilinjärvi Concentrator

Cited by 2 publications

References 6 publications

Data analysis of a paste thickener

Data analysis of a paste thickener

Performance optimization of paste thickening

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