Intelligent control method for aluminium oxide chamotte sintering rotary kiln

Abstract

The present invention relates to the automatic control of industrial thermal process and is especially the intelligent control process of sintering alumina clinker in rotary kiln. The present invention features that the alumina clinker sintering process is controlled by means of industrial control computer adopting control software with intelligent prediction, coordination, adaptation, multiple-target decision and multimode control technology. The control system has satisfactory effects of improving combustion status, maintaining stable kiln operation and ensuring clinker quality. The present invention can raise clinker quality and yield in the rotary kiln, reduce environmental pollution and lower coal consumption.

Description

Technical field [0001] An intelligent control method for an alumina clinker sintering rotary kiln relates to an automatic control of an industrial thermal process, in particular to an intelligent control method for the production process of an alumina clinker sintering rotary kiln. Background technique [0002] The alumina production process mainly includes Bayer method, sintering method and Bayer and sintering combined method according to the characteristics of ore processing. The sintering production process in the sintering method or combined method is specially designed for processing low-grade bauxite and Bayer process red mud, mainly including clinker firing, clinker dissolution, semen preparation, decomposition and evaporation, etc. Production processes. Among them, clinker sintering is the key process of alumina production by sintering method and mixed method (combined method). Clinker sintering production is organized around the rotary kiln. [0003] During the c...

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Problems solved by technology

[0009] Some large alumina plants in China have done some meaningful practice in the application of automatic control technology for clinker kilns, but most of the clinker kilns are currently under manual or semi-automatic control, which leads to unstable production and low equipment operation rate and low production capacity, unstable clinker quality, unsatisfactory dust collection effect, etc.

Because the traditional temperature measuring thermocouple is difficult to install and apply due to the rotation of the kiln body, there are currently two main methods for measuring the temperature of the firing zone: one is to install a non-contact infrared or optical fiber colorimetric measurement method in front of the rotary kiln head. The thermometer measures the temperature of the firing zone, but actually can only detect the temperature of the material between the firing zone and the cooling zone, and the interference of smoke and dust in the kiln causes a large measurement error; another method is to use computer digital image processing technology Combining with the operating experience of the fire watchers, the fire watch image signal captured by the camera is quantified, and the flame temperature level information is extracted, which is used as the indirect measurement information of the firing zone temperature. This method is greatly affected by the kiln condition change

[0010] In addition, in terms of control methods, the combination of fuzzy control and PID control is mainly used to realize the single-loop control of the firing zone temperature. Heuristic knowledge, in terms of control performance, it is characterized by poor anti-interference ability, slow adjustment speed, and large overshoot. When the production boundary conditions such as fuel or slurry composition or other process conditions fluctuate, the practicability and adaptability are poor. Only in the kiln Only when the environment is relatively stable can it be put into operation, and the automatic operation rate is low

[0011] Due to the large volume of the clinker kiln, the complex physical and chemical reaction process and the complex heat transfer process, the operating conditions and working conditions vary greatly, such as the kiln lining, the thickness of the kiln skin, the flow rate of raw slurry, moisture, composition, and fuel coal quality. ;Due to serious interference and severe coupling between control variables, nonlinearity, time variation, large inertia, and large time lag, it is difficult to establish a mathematical model of a certain input versus output; control variables such as coal feeder speed, smoke exhauster damper opening Severe coupling between speed, feeding amount, kiln speed, etc.; control methods based on mathematical models or with constant gain such as PID and MPC control methods have poor adaptability

The rotary kiln process has limited detection methods, serious measurement interference, large time lag, nonlinear control problems, and it is difficult to achieve automatic control, which largely depends on manual skills and experience

It is a challenge for a control system based on conventional detection methods to replace manual fire inspection.

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