AU2021100760A4 - Method and device for controlling cut tobacco drying parameters - Google Patents
Method and device for controlling cut tobacco drying parameters Download PDFInfo
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- AU2021100760A4 AU2021100760A4 AU2021100760A AU2021100760A AU2021100760A4 AU 2021100760 A4 AU2021100760 A4 AU 2021100760A4 AU 2021100760 A AU2021100760 A AU 2021100760A AU 2021100760 A AU2021100760 A AU 2021100760A AU 2021100760 A4 AU2021100760 A4 AU 2021100760A4
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0205—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system
- G05B13/024—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system in which a parameter or coefficient is automatically adjusted to optimise the performance
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- A—HUMAN NECESSITIES
- A24—TOBACCO; CIGARS; CIGARETTES; SIMULATED SMOKING DEVICES; SMOKERS' REQUISITES
- A24B—MANUFACTURE OR PREPARATION OF TOBACCO FOR SMOKING OR CHEWING; TOBACCO; SNUFF
- A24B3/00—Preparing tobacco in the factory
- A24B3/04—Humidifying or drying tobacco bunches or cut tobacco
-
- A—HUMAN NECESSITIES
- A24—TOBACCO; CIGARS; CIGARETTES; SIMULATED SMOKING DEVICES; SMOKERS' REQUISITES
- A24B—MANUFACTURE OR PREPARATION OF TOBACCO FOR SMOKING OR CHEWING; TOBACCO; SNUFF
- A24B9/00—Control of the moisture content of tobacco products, e.g. cigars, cigarettes, pipe tobacco
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
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- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
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- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Manufacture Of Tobacco Products (AREA)
Abstract
The present disclosure relates to a method and a device for controlling cut
tobacco drying parameters. The method comprises: obtaining a value of an HT inlet
moisture parameter; determining a value of at least one target parameter related to the
HT inlet moisture parameter according to the value of the HT inlet moisture parameter;
acquiring an actual parameter in each production process according to a sequence of
the production processes of tobacco, and comparing the actual parameter with the
value of the corresponding target parameter; and executing a normal production
process flow if a comparison result meets a preset condition. The present disclosure
may accurately control each target parameter, thereby improving the cut tobacco
quality.
DRAWINGS
1/3
120
obtaining a value of an HT inlet moisture parameter
determining a value of at least one target parameter
related to the HT inlet moisture parameter 140
according to the value of the HT inlet moisture
,parameter
acquiring an actual parameter in each production
process according to a sequence of the production 160
processes of tobacco, and comparing the actual
parameter with the value of the corresponding target
parameter
executing a normal production process flow ifa 180
comparison result meets a preset condition
Fig. 1
1.2
0
0
Wriableo
Fig. 2
Description
1/3
120 obtaining a value of an HT inlet moisture parameter
determining a value of at least one target parameter related to the HT inletmoisture parameter 140 according to the value of the HT inlet moisture ,parameter
acquiring an actual parameter in each production process according to a sequence ofthe production 160 processes of tobacco, and comparing the actual parameter with the value of the corresponding target parameter
executing a normal production process flow ifa 180 comparison result meets a preset condition
Fig. 1
1.2
0
0
Wriableo
Fig. 2
Technical Field The present disclosure relates to the field of tobacco industry, in particular to a method and a device for controlling cut tobacco drying parameters.
Background The cut tobacco preparation process is a core link of cigarette manufacturing,
provides cut tobacco products for the making and assembling procedure and is a direct
provider of cigarette sensory features including aroma quality, aroma quantity, etc.,
while the cut tobacco drying procedure is an important link of preparation process,
and parameters setting of cut tobacco drying plays a decisive role in aroma features of
cut tobacco and a main role in the filling effect of the cut tobacco.
At present, parameters of cut tobacco drying can only be manually set, and the
precision of the parameters cannot be accurately controlled, which seriously affects
the improvement of the cut tobacco quality.
Summary of the disclosure
The present disclosure provides a method and a device for controlling cut
tobacco drying parameters, which can solve the problem that at present the precision
of the related parameter of cut tobacco drying cannot be improved.
A method for controlling cut tobacco drying parameters includes:
obtaining a value of an HT inlet moisture parameter;
determining a value of at least one target parameter related to the HT inlet
moisture parameter according to the value of the HT inlet moisture parameter;
acquiring an value of actual parameters in each production process according to a
sequence of the production processes of tobacco, and comparing the actual values
with the that of corresponding target parameters; and
executing a normal production process flow if a comparison result meets a preset condition.
In one of the embodiments, the method further includes:
generating early warning information if the comparison result does not meet the
preset condition.
In one of the embodiments, determining a value of at least one target parameter
related to the HT inlet moisture parameter according to the value of the HT inlet
moisture parameter includes:
screening factors influencing the HT inlet moisture parameter on the basis of
production factor, and determining at least one target parameter related to the HT inlet
moisture parameter;
determining a calculation model between the HT inlet moisture parameter and
the at least one target parameter; and
calculating the value of the at least one target parameter on the basis of the value
of the HT inlet moisture parameter and the calculation model.
In one of the embodiments, prior to determining a calculation model between the
HT inlet moisture parameter and the at least one target parameter, the method further
includes:
nondimensionalizing the at least one target parameter; and
determining a calculation model between the HT inlet moisture parameter and
the at least one target parameter specifically includes:
determining a calculation model between the HT inlet moisture parameter and
the nondimensionalized at least one target parameter.
In one of the embodiments, the method further includes:
obtaining a value of a historical corresponding target parameter on the basis of
the value of the HT inlet moisture parameter; and
correcting the calculation model on the basis of the values of the actual parameter and the historical target parameter.
A device for controlling cut tobacco drying parameters includes:
an obtaining module, used for obtaining a value of an HT inlet moisture
parameter;
a determination module, used for determining a value of at least one target
parameter related to the HT inlet moisture parameter according to the value of the HT
inlet moisture parameter;
a comparison module, used for acquiring an actual parameter in each production
process according to a sequence of the production processes of tobacco, and
comparing the actual parameter with the value of the corresponding target parameter;
and
an execution module, used for executing a normal production process flow if a
comparison result meets a preset condition.
In one of the embodiments, the device further includes:
an early warning module, used for generating early warning information if the
comparison result does not meet the preset condition.
In one of the embodiments, the determination module is specifically used for:
screening factors influencing the HT inlet moisture parameter on the basis of a
production factor, and determining at least one target parameter related to the HT inlet
moisture parameter;
determining a calculation model between the HT inlet moisture parameter and
the at least one target parameter; and
calculating the value of the at least one target parameter on the basis of the value
of the HT inlet moisture parameter and the calculation model.
In one of the embodiments, the determination module is further used for:
nondimensionalizing the at least one target parameter; and determining a calculation model between the HT inlet moisture parameter and the at least one target parameter specifically includes: determining a calculation model between the HT inlet moisture parameter and the nondimensionalized at least one target parameter.
In one of the embodiments, the determination module is further used for:
nondimensionalizing the at least one target parameter; and
the determining a calculation model between the HT inlet moisture parameter
and the at least one target parameter specifically includes:
determining a calculation model between the HT inlet moisture parameter and
the at least one target parameter subjected to nondimensionalization. According to the present disclosure, values of other target parameters related to the HT inlet moisture parameter may be directly determined according to the value of the HT inlet moisture parameter required to be met by cut tobacco drying, such that each process in the cut tobacco production process is controlled, each target parameter is accurately controlled, and the cut tobacco quality is improved.
Brief Description of Figures
Fig. 1 shows a flow diagram of the method for controlling cut tobacco drying
parameters according to one embodiment of the present disclosure;
Fig. 2 shows a schematic diagram of the linear relation between correlation
coefficients and variables in Table 1 according to one embodiment of the present
disclosure;
Fig. 3 shows a normal frequency distribution chart of an HT inlet moisture
parameter according to one embodiment of the present disclosure;
Fig. 4 shows a schematic diagram of acquiring historical target parameters
according to the HT inlet moisture parameters according to one embodiment of the
present disclosure;
Fig. 5 shows a structural diagram of the device for controlling cut tobacco drying parameters according to one embodiment of the present disclosure.
Detailed Description
For making the objectives, technical solutions and advantages of the present
disclosure clearer, the present disclosure will be described in further detail below in
conjunction with the accompanying drawings and embodiments. It should be
understood that the specific embodiments described herein are merely illustrative of
the present disclosure and are not intended to limit the present disclosure.
Fig. 1 shows a flow diagram of the method for controlling a cut tobacco drying
parameter of one embodiment. As shown in Fig. 1, the method includes:
step 120, obtaining a value of an HT inlet moisture parameter;
step 140, determining a value of at least one target parameter related to the HT
inlet moisture parameter according to the value of the HT inlet moisture parameter;
step 160, acquiring an actual parameter in each production process according to a
sequence of the production processes of tobacco, and comparing the actual parameter
with the value of the corresponding target parameter; and
step 180, executing a normal production process flow if a comparison result
meets a preset condition.
In some embodiments, values of other target parameters related to the HT inlet
moisture parameter are directly determined according to the value of the HT inlet
moisture parameter required to be met by cut tobacco drying, such that each process
in the cut tobacco production process is controlled, thereby, each target parameter is
accurately controlled, and the cut tobacco quality is improved.
In the cut tobacco production process, for the finally obtained cut tobacco, the
HT inlet moisture parameter determines the quality of the cut tobacco. Thus, for the
cut tobacco with corresponding quality, the value of the HT inlet moisture parameter
meeting the requirement is preferably set. HT is a tunnel-type damping machine, and moisture controlling before drying is mainly achieved by controlling the HT inlet moisture.
In at least one embodiment, step 140 that determining a value of at least one
target parameter related to the HT inlet moisture parameter according to the value of
the HT inlet moisture parameter includes:
screening factors influencing the HT inlet moisture parameter on the basis of
production factor, and determining at least one target parameter related to the HT inlet
moisture parameter;
determining a calculation model between the HT inlet moisture parameter and
the at least one target parameter; and
calculating the value of the at least one target parameter on the basis of the value
of the HT inlet moisture parameter and the calculation model.
In accordance with some embodiments, with process regulatory factors and
equipment factors are excluded, 14 target parameters of the production factor are
obtained in total by screening 26 factors influencing the cut tobacco drying inlet
moisture parameter in the whole process, and the 14 target parameters are related to
the cut tobacco drying inlet moisture parameter, the correlation analysis being as
follows in Table 1 below:
Table 1 correlation analysis between the 14 target parameters and the cut tobacco
drying inlet moisture parameter X4 X1 (moisture of X2 (moisture of (compensatio (H X3 (water Item loose moisture loose moisture n steam T .. adding amount). Tul regaining inlet) regaining outlet) opening ml_ degree) et R 0.190 0.528 0.541 -0.350 mo P <0.05 <0.01 <0.01 <0.01 ist ur Signi e) fican ce
X5 (time from vacuum moisture X6 (storage time X7 (feeding X8 (feeding regaling regaining to in temporary ne moisture) inlet moing outlet mosue loose moisture storage cabinet) moisture) regaining) R 0.613 0.221 0.408 0.511 P <0.01 <0.01 <0.01 Signi fican ** ** ** ce X9 X10 (feeding X11 (storage (compensation and moisture time in inet. steam opening discharging underground degree) opening degree) cabinet) moisture)
R -0.406 -0.018 0.618 0.998
P <0.01 <0.01 <0.01 Signi fican ** ** ** ce X14 X13 (hot air (compensation outlet moisture) steam opening degree) R 1.000 0.709 P <0.01 <0.01 Signi fican ** ** ce
In Table 1, X1 to X14 represent corresponding target parameters in sequence,
which may be seen specifically from Table 1.
Fig. 2 shows a schematic diagram of the linear relation between correlation
coefficients and variables in Table 1. Based on Table 2, it can be seen that the above
14 target parameters have a correlation with the HT inlet moisture parameter.
In some embodiments, prior to determining a calculation model between the HT
inlet moisture parameter and the at least one target parameter, the method further
includes: nondimensionalizing the at least one target parameter; and determining a calculation model between the HT inlet moisture parameter and the at least one target parameter specifically includes, determining a calculation model between the HT inlet moisture parameter and the nondimensionalized at least one target parameter.
For the above 14 target parameters, 7 target parameters thereof need to be
nondimensionalized. Specifically, the effectiveness and accuracy of a data sample as
the basis of data analysis need to be fully guaranteed. Meanwhile, many influence
factors exist, units need to be unified to guarantee the physical significance of the
model, and therefore the target parameters thereof need to be nondimensionalized.
Specific details are as follows:
dimensionless moisture content = original moisture content/(30%);
dimensionless water adding amount of loose moisture regaining = original water
adding amount of loose moisture regaining/(620 L);
dimensionless compensation steam opening degree = original compensation
steam opening degree/(80%);
dimensionless time from vacuum moisture regaining to loose moisture regaining
= original time from vacuum moisture regaining to loose moisture regaining/(50 m);
dimensionless storage time in a temporary storage cabinet = original storage time
in a temporary storage cabinet/(175 m);
dimensionless feeding and moisture discharging opening degree = original
feeding and moisture discharging opening degree/(50%);
dimensionless storage time in an underground cabinet = original storage time in
an underground cabinet/(1250 m);
the above are dimensionless conversion formulas and conversion data
corresponding to the target parameters.
In some embodiments, when the calculation model between the HT inlet
moisture parameter and the nondimensionalized at least one target parameter is
determined, the HT inlet moisture parameter needs to be subjected to normal
distribution inspection to guarantee that data analysis has practical significance and
facilitates later correlation analysis. Fig. 3 shows a normal frequency distribution
chart of the HT inlet moisture parameter, the histogram shows that the HT inlet
moisture parameter conforms to normal distribution, and by means of Lilliefors
inspection, the HT inlet moisture parameter conforms to normal distribution with a
mean value of 21.9 and a standard deviation of 0.11032. In conclusion, the HT inlet
moisture parameter conforms to the normal distribution by means of inspection, such
that the correlation analysis on the cut tobacco drying inlet moisture is feasible.
Reference may be made in particular to the following Table 2 and Table 3.
Table 2 Kolmogorov-Smimov test of Single sample VAR00001 N 96 Normal parametera"b Average 21.9023 Standard deviation Extremest difference Absolute 0.087 Positive 0.087 Negative -.053 Test statistical data 0.087 Progressive significance (Double tail) 0.072° a. Distribution is tested as normal. b. Calculation from data c. Lilliefors Significant Correction
Table 3 Normality test Kolmogorov-Smimova Shapiro-Wilk Statistical data df Significance Statistical data df Significance VAROOOO1 0.087 96 0.072 0.984 96 0.319 a. Lilliefors Significant Correction
For guaranteeing the accuracy of prediction, two qualified modeling methods
(BP Neural Network, Multiple Regression) are subjected to a comparative test.
Multiple Regression:
The equation is:
Y = 0.306 * 1 + 0.037 * 3 + 0.001 * 4 + 0.002 * 5 + 0.002 * 6 - 0.065 * 7
+ 0.004 * 9 - 0.001 * 11 + 0.006 * 12 + 0.004 * 14 + 0.583.
A prediction error is 0.085%.
Neural Network:
Equation selection: BP neural network
A prediction error is 0.046%.
Due to the fact that the neural network has features of self-learning and
self-optimization, the neural network is selected in some embodiments to serve as a
calculation model.
In at least one embodiment, the method further includes:
obtaining a value of a corresponding historical target parameter on the basis of
the value of the HT inlet moisture parameter; and
correcting the calculation model on the basis of the values of the actual
parameter and the historical target parameter.
When the calculation model between the HT inlet moisture parameter and the at
least one nondimensionalized target parameter is determined, for guaranteeing that
data analysis has practical significance and facilitates later correlation analysis, the
HT inlet moisture parameter y may undergo a normal distribution test.
Fig. 4 shows a schematic diagram of acquiring a historical target parameter
according to the HT inlet moisture parameter. As shown in Fig. 4, according to the HT
inlet moisture parameter value of 21.9 and the water adding amount is estimated at
520, and preset moisture values include a leaf moistening inlet moisture value of 22.2, a feeding outlet moisture value of 22.8, and a loosening outlet moisture value of 20.5.
Meanwhile, historical batch data may be accessed for production reference according
to the HT inlet moisture parameter value of 21.9, for example, approximate data of
historical cut tobacco drying inlet moisture are listed in batches by inputting a set
moisture requirement of cut tobacco drying inlet in the Fig. 4.
According to above embodiments, the calculation model is corrected on the basis
of the values of the actual parameter and the historical target parameter. Regardless of
the neural network or multiple regression, the embodiments may be used for
correcting the calculation model.
In some embodiments, for step 180, it needs to illustrate that there are a plurality
of production processes for cut tobacco. For each production process, when a certain
process flow is finished, the actual parameter of the process flow is directly acquired,
and the actual parameter is compared with a corresponding target parameter. If the
comparison result meets the preset condition, the next process flow may be executed.
The preset condition may be that an absolute value of a difference between the two is
smaller than a preset value or other conditions.
On the basis of step 160, if the comparison result does not meet the preset
condition, the early warning information is generated. Technical personnel may
process the information in time after the early warning information is generated.
In some embodiments, for each process flow, the actual parameter and the
corresponding target parameter are checked.
Based on some embodiments, it can be seen that the parameters of the next
procedure is predicted in advance, which facilitates the intervention in production in
advance, and changes an original passive production mode.
Furthermore, historical approximate data can be called out for reference by
production personnel, so as to prevent abnormal batches from being generated; and in at least one embodiment, the neural network is used to construct the calculation model, which has a self-learning function and then has a function of improving prediction precision through self-learning.
Fig. 5 shows a structural diagram of a device for controlling a cut tobacco drying
parameter. As shown in Fig. 5, the device includes:
an obtaining module 520, used for obtaining a value of an HT inlet moisture
parameter;
a determination module 540, used for determining a value of at least one target
parameter related to the HT inlet moisture parameter according to the value of the HT
inlet moisture parameter;
a comparison module 560, used for acquiring an actual parameter in each
production process according to a sequence of the production processes of tobacco,
and comparing the actual parameter with the value of the corresponding target
parameter; and
an execution module 580, used for executing a normal production process flow if
a comparison result meets a preset condition.
According to the present disclosure, values of other target parameters related to
the HT inlet moisture parameter may be directly determined according to the value of
the HT inlet moisture parameter required to be met by cut tobacco drying, such that
each process in the cut tobacco production process is controlled, each target parameter
is accurately controlled, and the cut tobacco quality is improved.
In one implementation mode of this embodiment, the device further includes:
an early warning module, used for generating early warning information if the
comparison result does not meet the preset condition.
In one implementation mode of this embodiment, the determination module 540
is specifically used for: screening factors influencing the HT inlet moisture parameter on the basis of a production factor, and determining at least one target parameter related to the HT inlet moisture parameter; determining a calculation model between the HT inlet moisture parameter and the at least one target parameter; and calculating the value of the at least one target parameter on the basis of the value of the HT inlet moisture parameter and the calculation model.
In one implementation mode of this embodiment, the determination module is
further used for:
nondimensionalizing the at least one target parameter; and
determining a calculation model between the HT inlet moisture parameter and
the at least one target parameter specifically includes:
determining a calculation model between the HT inlet moisture parameter and
the nondimensionalized at least one target parameter.
In one implementation mode of this embodiment, the determination module is
further used for:
nondimensionalizing the at least one target parameter; and
the determining a calculation model between the HT inlet moisture parameter
and the at least one target parameter specifically includes:
determining a calculation model between the HT inlet moisture parameter and
the at least one target parameter subjected to nondimensionalization.
In some embodiments, the implementation modes of the above device are
identical to the embodiments of the above method, which may be seen specifically
from the content in the embodiments of above method, and will not elaborated
specifically hereinafter.
The various technical features of the embodiments described above may be arbitrarily combined, and not all possible combinations of the various technical features in the embodiments described above are described for brevity of description, however, to the extent that there is no contradiction in the combination of the technical features, the combination shall be considered to fall within the scope of the description.
The embodiments described above give only a few embodiments of the present
disclosure, which are specific and detailed in description, but cannot be construed as
limiting the scope of the disclosure patent accordingly. It should be noted that several
modifications and improvements may also be made to those of ordinary skill in the art
without departing from the concept of the present disclosure, which all fall within the
scope of protection of the present disclosure. Therefore, the scope of protection of the
disclosure patent shall be subject to by the claims appended hereto.
Claims (9)
1. A method for controlling cut tobacco drying parameters, comprising:
obtaining a value of an HT inlet moisture parameter;
determining a value of at least one target parameter related to the HT inlet moisture
parameter according to the value of the HT inlet moisture parameter;
acquiring an actual parameter in each production process according to a sequence of
the production processes of tobacco, and comparing the actual parameter with the
value of the corresponding target parameter; and
executing a normal production process flow if a comparison result meets a preset
condition.
2. The method of claim 1, wherein, the method further comprises:
generating an early warning information if the comparison result does not meet the
preset condition.
3. The method of claim 1, wherein, determining a value of at least one target
parameter related to the HT inlet moisture parameter according to the value of the HT
inlet moisture parameter comprises:
screening factors influencing the HT inlet moisture parameter on the basis of
production factor, and determining at least one target parameter related to the HT inlet
moisture parameter;
determining a calculation model between the HT inlet moisture parameter and the at
least one target parameter; and
calculating the value of the at least one target parameter on the basis of the value of
the HT inlet moisture parameter and the calculation model.
4. The method of claim 3, wherein, prior to determining a calculation model
between the HT inlet moisture parameter and the at least one target parameter, the
method further comprises:
nondimensionalizing the at least one target parameter; and
determining a calculation model between the HT inlet moisture parameter and the at
least one target parameter specifically comprises:
determining a calculation model between the HT inlet moisture parameter and the
nondimensionalized at least one target parameter.
5. The method of claim 3, wherein, the method further comprises:
obtaining a value of a corresponding historical target parameter on the basis of the
value of the HT inlet moisture parameter; and
correcting the calculation model on the basis of the values of the actual parameter and
the historical target parameter.
6. A device for controlling cut tobacco drying parameters, comprising:
an obtaining module, used for obtaining a value of an HT inlet moisture parameter;
a determination module, used for determining a value of at least one target parameter
related to the HT inlet moisture parameter according to the value of the HT inlet
moisture parameter;
a comparison module, used for acquiring an actual parameter in each production
process according to a sequence of the production processes of tobacco, and
comparing the actual parameter with the value of the corresponding target parameter;
and
an execution module, used for executing a normal production process flow if a
comparison result meets a preset condition.
7. The device of claim 6, wherein, the device further comprises:
an early warning module, used for generating early warning information if the
comparison result does not meet the preset condition.
8. The device of claim 6, wherein, the determination module is specifically used for:
screening factors influencing the HT inlet moisture parameter on the basis of a
production factor, and determining at least one target parameter related to the HT inlet
moisture parameter;
determining a calculation model between the HT inlet moisture parameter and the at
least one target parameter; and
calculating the value of the at least one target parameter on the basis of the value of
the HT inlet moisture parameter and the calculation model.
9. The device of claim 8, wherein, the determination module is further used for:
nondimensionalizing the at least one target parameter; and
determining a calculation model between the HT inlet moisture parameter and the
nondimensionalized at least one target parameter.
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