The nature of the feed solution can be conducive to the conduct of the drying process by changing the operational conditions.
Among the controlled conditions, the most important thing is to decide the products moisture content of the feed solution, the rotational speed of the drum (i.e., residence time) and the temperature of the barrel wall.
The relationship of those three is mutual restraint.
And the temperature of calcine is one
of the most important parameters in the process control of rotary kiln.
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It is significant for ameliorating production quality, saving energy,
reducing operating costs to improve control precision of rotary kiln.
The production process of rotary kiln is a complicated physical
chemistry course of reaction and has characteristics of big inertia,
pure time-delay, nonlinearity and so on.
With the development of the Chinese cement manufacturing, the new kind cement rotary kiln has been widely used.
With the development of the Chinese cement manufacturing, the new kind cement rotary kiln has been widely used.
The Rotary kiln decomposition is the most important craft tache in the cement production line,
and its running status affects the output, quality, energy consumption
and environment pollution.
It is hard to describe exactly the production
processes in the cement rotary kiln, which include fuel combustion,
heat transfer, and chemical composition of clinker.
The most important
reason is that there are chemical reaction, physical reaction and
mineralogical reactions simultaneously during the complicated heat
transfer process.
Stable control of temperature in rotary kiln is
critically important.
The sintering process of the cement has the
characteristics of non-linearity, ultivariable, close coupling, large
time lag, and time varying.
This causes the kiln-status of the rotary
kiln system to be complex.
Just as what mentioned above, it is difficult
to describe the rotary kiln with an accurate mathematical model.
The
traditional control method is no longer suitable to control the rotary
kiln system.
Artificial neural networks are good at identifying and
controlling complex nonlinear systems.
As they are suitable for
multi-variable applications, they can easily identify the interactions
between the inputs and outputs.
It has been shown that a multilayer feed
forward neural network using deviation signals as inputs can identify
the complex and nonlinear dynamics of the cement rotary kiln with
adequate accuracy to design a controller.
