Fuzzy association rule-based set-point adaptive optimization and control for the flotation process
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ORIGINAL ARTICLE
Fuzzy association rule-based set-point adaptive optimization and control for the flotation process Mingxi Ai1 • Yongfang Xie1 • Shiwen Xie1
•
Jin Zhang1 • Weihua Gui1
Received: 19 May 2019 / Accepted: 17 February 2020 Ó Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Froth flotation is a complicated process which is difficult to establish its first-principle model. Due to the fluctuations in the grade of raw ore, adaptively adjusting the set-points is extremely important in the flotation process. The inappropriate setpoints easily lead to the instability of the process. This paper presents a fuzzy association rule-based set-point adaptive optimization and control strategy for the antimony flotation process without knowing the system model. Firstly, a fuzzy neural network is constructed as a soft-sensor to estimate the feed grade online because of the lack of efficient measurement equipment. Then, fuzzy association rule is used to mine the hidden relationship between the feed grade with reagent dosages and the optimal set-points. Through data mining from the quantitative database, the fuzzy inference system generates the optimal set-points. To implement satisfactory tracking performance, predictive controller is used to compute the control inputs. Because the system dynamics is unknown, long short-term memory network model is established to predict the future behaviors of the process. Finally, simulations and experiments are carried out to demonstrate the effectiveness of the proposed strategy. Compared to the manual manipulation, which is widely used in flotation processes, our control strategy achieves a better control performance, and the concentrate grades are more in line with the process requirement. Keywords Fuzzy neural network Fuzzy association rule Set-point optimization Predictive control Flotation process
1 Introduction A general industrial process control scheme usually involves an optimization module which generates the setpoints of the operational indices that maximize the economic performance function, and a controller which tracks the set-points [1–3]. A case in point is the froth flotation which is an important mineral concentration technique. The flotation is a complicated process with nonlinear dynamic behavior and uncertainty. The grade of the raw ore and prices of the reagents vary with the market, such that the set-points for the lower-level controller should be adjusted corresponding to the feed conditions. The setpoints of the operational indices are related to the optimal operation of the flotation process. Studies [1–3] pointed out & Shiwen Xie [email protected] 1
School of Automation, Central South University, Changsha 410083, Hunan, China
that predictive models of the operational indices are important to calculate the optimal set-points. For the flotation processes, however, it is difficult to build its firstprinciple model, resulting in the challenges in the optimization of the set-points.
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