Abstract
The grinding process is a typical complex nonlinear multivariable process with strongly coupling and large time delays. Based on the data-driven modeling theory, the integrated modeling and intelligent control method of grinding process is carried out in the paper, which includes the soft-sensor model of economic and technique indexes, the optimized set-point model utilizing case-based reasoning, and the self-tuning PID decoupling controller. For forecasting the key technology indicators (grinding granularity and mill discharge rate of grinding process), an adaptive soft-sensor modeling method based on wavelet neural network optimized by the improved shuffled frog leaping algorithm (ISFLA) is proposed. Then, a set point optimization control strategy of grinding process based on case-based reasoning (CBR) method is adopted to obtain the optimized velocity set-point of ore feed and pump water feed in the grinding process controlled loops. Finally, a self-tuning PID decoupling controller optimized is used to control the grinding process. Simulation results and industrial application experiments clearly show the feasibility and effectiveness of control methods and satisfy the real-time control requirements of the grinding process.
Highlights
Grinding process has complex production technique and many influencing factors, such as the characteristics of the ore fed into the circuit, the flow velocity of water fed into the loops, and the changes of the cyclone feed ore
Aiming at the grinding and classification process, the grinding granularity and grinding discharge ratio soft-sensor model is set up based on the wavelet neural network
The input-output data set is shown in Table 1 in order to train and test the improved shuffled frog leaping algorithm (ISFLA)-based Wavelet neural network (WNN) softsensor model
Summary
Grinding process has complex production technique and many influencing factors, such as the characteristics of the ore fed into the circuit (ore hardness, particle size distribution, mineral composition, or flow velocity), the flow velocity of water fed into the loops, and the changes of the cyclone feed ore. Reference [3] proposed a multivariable fuzzy supervisory control method composed by the fuzzy supervisor, loop precedent set-point model, and the particle size soft-sensor model. Reference [4] studied the grinding process with non-linear, multivariable, time varying parameters, boundary conditions, and fluctuations complex features and proposed an integrated intelligent model for dynamic simulating, of the grinding and classification process
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