Design Guidelines for High Performance Industrial Furnaces - High Temperature Air Combustion: From Energy Conservation to Pollution Reduction - page 293

Signal
Generator

Mux

yout
To Workspace

Step Input 2

Door Open 1

0

0.00385
s+0.014

+

output

50

Step Input 1

Set point 2

COG

Disturbance model

-
-

0.0122
s +0.0175

+

+

+

-

-

3 PID

Controller

Saturation 2

Pressure
(mmH2O)

VVVF

Set point 1

0.0048

20.3095

Subsystem

s+0.0185

offset

Saturation 1

Damper

Door Open

du/dt

0

Switch F.F.

Step Input

Kalman Filter

Optimum compensation control

FIGURE 5.58 Flow of optimum compensation control.

are fed into a heating furnace on a desired timing basis. The method currently under
investigation is described below.
The schedule-free heating method refers to a method of realizing the need to
change operating conditions such as the target temperature, soaking level, etc. of a
discharged steel workpiece at a given time by using a continuous heating furnace,
as shown in Figure 5.59 . The control system incorporated in this method, which
processes the optimization task of minimizing the error between the set value and
the actual temperature measurement, is known as the “online schedule-free heating
optimum control system.”
The online tracking function estimates the data that cannot be directly measured
(temperature distribution in a heated steel workpiece and temperature distribution on
the furnace wall) of all the data required for the control system of a continuous heating
furnace. For example, the current temperature distribution data are estimated by giving
measurable data (furnace temperature, air-to-fuel ratio, etc.) as well as previous tem-
perature distribution data to the online model. Otherwise, if the temperature distribution
data can be partially measured with a thermocouple or a radiation thermometer, the
calculated estimate is corrected on the basis of the measured data.
The online optimum heat pattern calculation function is the core function of the
entire control system. This function processes the optimization of a proper evaluation
function at given control intervals by assuming the consequent furnace temperature
in the future as a first-order-lag function of the set value at each moment in the
control cycle and forecasting the temperature of a slab at the outlet by using the
online model. The initial set value of the group of furnace temperature set values at
given intervals obtained through the optimization process is selected as the optimum
set value and passed to the interface.