Yue Heng

Multivariable intelligent decoupling control and its applications

Many complex industrial processes are multivariable, strongly coupled, nonlinear and time-variant, and conventional decoupling based control techniques will not work. This paper discusses multivariable intelligent decoupling control algorithms, and develops the three-level multivariable intelligent decoupling technique which can be easily realized in distributed control systems. Finally, examples of successful applications of the multivariable intelligent decoupling control technique in complex industrial processes are given.Abstract: Many complex industrial processes are multivariable, strongly coupled, nonlinear and time-variant, control systems based on conventional decoupling techniques cannot work. This paper discussed multivariable intelligent decoupling control algorithms, and developed the three-leveled multivariable intelligent decoupling technique which can be easily realized in Distributed Control Systems (DCS). Finally, examples of successful applications of multivariable intelligent decoupling control technique in complex industrial processes are introduced.

Multi-variable Control for HPAL Using Nonlinear Multivariable Decoupling PID Control based on Predictive Model

Abstract High pressure acid leaching process is the use of strong acid slurry in the high temperature & pressure conditions, and acid leaching to useful metal ions process. It is a typical multiple-input multiple-output system. The efficiency of acid leaching is sensitively influenced by temperature, pressure and level of the autoclave, and in general the single loop PI method is always adopted for designing each loop controller. But due to the coupling effects among those variables, the method cannot control the temperature, pressure and level stable, which result in the decrease of efficiency of acid leaching. By analyzing high pressure acid leaching characteristics a nonlinear decoupling predictive control approach is employed based on mechanistic models established. The method has been successful applied to a real large-scale abroad nickel hydroxide cobalt smelt plant. The results demonstrate that the method can effectively and steadily control the temperature, pressure and level of the autoclave at their targets nearby, and improve the efficiency of acid leaching.

Notice of Retraction Based on Spectral Information Fusion Soft Senser Modeling of Wet Ball Mill Load

Ball mill load measurement is essential to control and operational optimization for the wet grinding process, which affects the production efficiency and energy consumption. A frequency spectral information fusion soft sensor model is proposed to estimation the mill load in the paper. PCA is used to extract the feature of frequency spectrum to deal with many, noise and collinear variables. FFT is used to estimate the power spectral density(PSD)of the vibration and acoustic signal. PLS were combined with PCA scores inputs to develop mill load. Principal component numbers are selected by an optimize model. A case study shows that the proposed frequency spectral information fusion soft sensor model is effective, and produces better predictive performance than single sensor model.