Purpose of the present invention is exactly to have a large amount of human and material resources and the time of cost consumption that needs in order to overcome and to solve the Optimum Experiment process that has rubber milling of internal mixer now, and shortcoming and problem that the various optimum condition parameters of technological procedure can change along with the variation of each manufacturing parameter, a kind of relevant parameters that can gather under daily working condition in the production process of research and design just can carry out the calendering process Optimization Work automatically, come automatic analysis, revise technological procedure even there is which working condition variation to cause the variation of optimum process condition parameter also can optimize the optimum process condition that makes new advances, and a kind of rubber milling of internal mixer automatic process optimization monitoring system and automatic optimization method thereof that calendering process is produced by amended new technological procedure.
The present invention realizes by following structure technology scheme and method scheme: the structure of this rubber milling of internal mixer automatic process optimization monitoring system forms block diagram as shown in Figure 1, it is made of signal input apparatus, Computer Control Unit, signal output driving device, the common electrical connection of printer, wherein signal input apparatus comprises switching value input isolation circuit, analog input buffer circuit, instantaneous power Acquisition Circuit, Computer Control Unit comprises next bit computer CM-85 industrial computer, host computer IBM-PC industrial computer, the signal output driving device comprises switching value output driving circuit, analog output drive circuit, and its interconnected relationship is: the switching value input isolation circuit in the signal input apparatus is electrically connected mutually by switching value input signal cable and banbury; The analog input buffer circuit is electrically connected mutually by analog input holding wire and banbury; The instantaneous power Acquisition Circuit is electrically connected mutually by voltage and current signal line and banburying owner motor; Next bit computer CM-85 industrial computer in the Computer Control Unit is electrically connected with signal input apparatus mutually by switching value output signal line, analog quantity output signals line, the instantaneous power output signal line of signal input apparatus; Host computer IBM-PC industrial computer is electrically connected with next bit computer CM-85 industrial computer mutually by serial line interface COM1; Switching value output driving circuit in the signal output driving device is electrically connected with next bit computer mutually by switching value drive input signal line, and be electrically connected mutually by the driving mechanism of switching value drive output signal line with banbury, the analog output drive circuit is electrically connected with next bit computer mutually by analog quantity drive input signal line, and is electrically connected mutually by the executing agency of analog quantity drive output signal line with banbury; Printer is electrically connected mutually by print signal input line and host computer IBM-PC computer. Its action principle is: switching value, analog input buffer circuit input to switching value, the analog quantity input of CM-85 industrial computer with the working state signal (switching value, analog quantity) of banbury after isolation, and the instantaneous power Acquisition Circuit changes the power of banburying owner motor mixing process consumption into corresponding instantaneous power signal and is sent to CM-85 industrial computer simultaneously. CM-85 industrial computer is judged the mixer mixing process state according to these input signals, makes preliminary calculating, arrangement, and sends the data information of arrangement to IBM-PC industrial computer by serial port. IBM-PC industrial computer has very strong operational capability, it analyzes the data of each mixing process, add up, draw out the instantaneous power curve of mixing process, and calculating the side-play amount of control signal according to the setting value of technical recipe, the control side-play amount that will produce by serial port again sends CM-85 industrial computer to. CM-85 industrial computer produces corresponding switch controlling signal and analog control signal according to the control side-play amount, and pass to the signal output driving device by delivery outlet, after the signal output driving device amplifies switch, the isolation of analog control signal photoelectricity, switching value signal and analog signals that output has certain driving force are used for controlling executing agency, to reach the purpose of control mixer mixing process Automatic Optimal.
Calendering process Automatic Optimal monitoring system electric circuit schematic diagram such as Fig. 2, shown in Figure 3. Wherein Fig. 2 is signal input apparatus circuit theory diagrams and CM-85 industrial computer part, and Fig. 3 is signal output driving device circuit theory diagrams and IBM-PC industrial computer part. Signal input apparatus is in parallel to be electrically connected by instantaneous power Acquisition Circuit, 16 tunnel identical switching value input isolation circuit, 16 tunnel identical analog input buffer circuits and consists of. Wherein instantaneous power Acquisition Circuit schematic diagram as shown in Figure 4, LMDS Light Coupled Device TLP1, the transistor T2 Schmidt trigger IC1 that it forms by Power arithmetic device W, by infrarede emitting diode D1, infrared acceptor T1, monostable shaping circuit IC2, resistance R 1～R4, capacitor C 1 common connection in series-parallel electrical connection consist of; A certain way switch amount input isolation circuit schematic diagram as shown in Figure 5, the common connection in series-parallel electrical connection of the LMDS Light Coupled Device TLP2 that it is comprised of infrarede emitting diode D2, infrared acceptor T3, transistor T4, resistance R 5～R7 consists of; A certain road analog input buffer circuit as shown in Figure 6, it by Zener diode DW1～DW4, operational amplifier IC3～IC4, LMDS Light Coupled Device TLP3～TLP4, transistor T5, T6, resistance R 8～R15, capacitor C 2～C5, potentiometer RW1 is common and electric the connecting and composing of connecting. The simple fundamental diagram of said units circuit is as follows: its effect of instantaneous power Acquisition Circuit shown in Figure 4 is to convert the voltage of main motor, current signal the pulse signal of instantaneous power to by computing, exports after the isolation shaping. Its principle comes the voltage of autonomous motor, electric current to produce electric impulse signal through Power arithmetic device W, infrarede emitting diode D1 among the LMDS Light Coupled Device TLP1 just has corresponding pulse current to flow through, the colelctor electrode of infrared acceptor T1 produces corresponding high-low level to be changed, change and cause triode T2 colelctor electrode to produce corresponding high-low level, and remove to drive monostable circuit IC2 by the shaping of IC1, the pulse signal of IC2 output one fixed width. Its principle of switching value input isolation circuit shown in Figure 5 is when the on-the-spot switching value of industrial production is connected, infrared light diode D2 among the LMDS Light Coupled Device TLP2 just has an electric current to flow through, infrared acceptor T3 colelctor electrode level is just less than 0.7V, and this low level is so that triode T4 ends, output one high level signal. Its principle of analog input buffer circuit shown in Figure 6 is that circuit adopts two LMDS Light Coupled Device TLP3, TLP4 with push pull mode work, two inputs, output characteristics are synthesized, after amplifying, inverting amplifier IC3 changes the emitter current of transistor T 5, T6, and with current constant mode driving TLP3, TLP4, behind the electric current that the electric current of being exported by TLP3, TLP4 consists of via amplifier IC4---the voltage conversion circuit, export an analog voltage signal; The signal output driving device is in parallel to be electrically connected by 16 tunnel identical switching value output driving circuit, 16 tunnel identical analog output drive circuits and consists of, switching value input isolation circuit in its switching value output driving circuit and the signal input apparatus is identical, analog input buffer circuit in its analog output drive circuit and the signal input apparatus is just the same, their operation principle is also identical, different just its switching values that is input as computer export drives signal and analog quantity drives signal, and its output is used for driving executing agency.
Calendering process automatic optimization method scheme of the present invention is as follows: (1) is at first determined to optimize to investigate index, utilizes the automatic detection of calendering process Automatic Optimal monitoring system of the present invention to obtain this optimization and investigates index; (2) determine to optimize which factor, factor get different value, level should be able to divide automatically; (3) theoretical according to Orthogonal Experiment and Design, select with because of the corresponding orthogonal table of factor and number of levels, index is investigated in fixed optimization carries out main effect and calculates with reciprocation that (main effect reflection factor is to the Index Influence size, and the change amount of commonly using its response represents; Reciprocation represents the effect that each experimental factor cooperatively interacts and produces, thereby strengthens or weaken each experimental factor to the independent impact of index), thus find out each factor is investigated index to need optimization influence degree; (4) data obtained according to orthogonal test, adopt the variance analysis method of in the mathematical statistics experimental data being processed that the data that detect are carried out variance analysis, optimization is examined the influence degree (conclusion of this quantitative analysis can be more accurate than micro-judgment, concrete) of poor index to judge each factor. This rubber mixing-milling technique automatic optimization method, be further characterized in that except adopting instantaneous power, energy consumption, mixing time, elastomeric compound temperature be the factor, also can the section of employing mean power, the consumption of section energy, as the section such as the glue of reborning, throw to fill and expect and throw the oil plant section, throw to fill and expect discharge zone, throwing oil plant to discharge zone etc. and to have suffered the journey mean power be that factor is optimized. Level also can be defined as the interval with level except may be defined as certain numerical value, be convenient to add up tuning and calculate. Such as two levels, averaging is boundary, level 1 〉=mean value, level 2＜mean value, three levels, a symmetric interval centered by averaging are a level, be greater than or less than between this interval left and right region, be respectively in addition two levels, such as level 1≤(mean value-standard deviation), (mean value-standard deviation)＜level 2＜(mean value+standard deviation), level 3 〉=(mean value+standard deviation). The program flow diagram of this rubber mixing-milling technique automatic optimization method as shown in Figure 7. Its concrete calendering process Automatic Optimal process and step is as follows: (1) uses rubber milling of internal mixer automatic process optimization monitoring system of the present invention to detect image data. From lower top bolt close, the glue of reborning when floating weight is mentioned, material, rubber powder or reclaimed rubber and throw carbon black or non-carbon black filler after pressurize, pressurize after pressurizeing, sweep powder or propose the floating weight somersault behind the throwing oil plant, open at last lower top bolt, mention floating weight, the time of each test point of discharge end record, sizing material temperature, instantaneous power, accumulation consumed energy, each actual inventory, the relevant datas such as cooling water temperature when at every turn beginning mixing and discharge of ram piston pressure and rubber, filler, oil plant etc.; (2) confirmed test lot number (generally take 50 times or 100 times as a collection of, minimum must not be less than 20 times for a collection of) is input into calendering process Automatic Optimal monitoring system by the operator; (3) design experiment determines to optimize index, generally at first is the optimization that improves qualification rate, next optimization for shortening time, conserve energy (sometimes for improving qualification rate, also need time expand or increase energy consumption); (4) determine the factor of impact optimization index and the division of each factor different tests level according to the needs of fixed optimization index, test to two levels, generally average and be the boundary, a symmetric interval value centered by the test of three levels averaged is a level, greater than this interval or less than in addition two levels that are respectively of the value in two intervals about this interval; (5) adopt the variance analysis method of in the mathematical statistics experimental data being processed to carry out variance analysis and conspicuousness detection, factor of judgment is to optimizing the influence degree of index. Determine best factor level combination with the method for analysis of variance of orthogonal test; (6) carry out the check of variance analysis suitability: the test of normality, the independence test test for equal variances that comprise test data. If suitability is upchecked, i.e. the basic assumption of variance analysis is set up, and then the result of variance analysis can accept, and the calendering process Automatic Optimal is finished. This calendering process automatic optimization method implementation step and process procedures flow diagram are as shown in Figure 8.
The present invention compared with prior art has following advantage and beneficial effect: (1) native system adopts two computer division of dutys, the signals collecting of industry spot, signal controlling, selects the CM that control ability is strong, commercial performance is good-85 industrial computer. And the IBM that operational capability is strong, capacity is large, control ability is not good enough-PC bus industrial computer is adopted in the storage of mass data, calculating, model foundation etc. The calculating of whole system, analysis are carried out under off-line state, and system crash and the mistake in computation of having avoided the computing of separate unit machine excess load to cause are so that whole system has very high reliability and good working interface; (2) CM-85 industrial computer address, data, control three buses all adopt the photoelectricity isolation, so that can resist various normalities, common-mode interference in rugged environment; IBM-PC industrial computer adopts general PC bus, and passive mother board and corresponding functionalization template so that computer configuation is simple, is convenient to install, seals, is keeped in repair; (3) two computers adopt the serial asynchronous communication mode, so that the intercommunication resource is more reliable, and can two machines be placed on the different location according to on-the-spot installation situation, also can carry out the extra long distance communication; (4) the photoelectricity isolation is all adopted in all signal inputs of native system, output, so that the various interference of industry spot can not affect by holding wire the normal operation of computer; (5) enforcement of the present invention can be exempted the rubber prior art like that for carrying out the lot of experiments work that process optimization work will be carried out; (6) because traditional handicraft is checked its quality again after producing finished product glue, and the technology of the present invention adopts the calendering process procedure parameter for optimizing index, and can in time monitor, so that going out the front, product do not know its quality condition, and can automatically adjust in good time, take precautions against quality accident in possible trouble, so the present invention is more direct effectively; (7) whether using the technology of the present invention, can to grasp at any time real-time technological procedure be optimum process condition, and technological procedure is remained under the optimum process condition state; (8) use the technology of the present invention production control process, can make the sizing material of each mixing output be certified products, can greatly enhance productivity and product percent of pass, significant economic benefit and social benefit are arranged.
Embodiments of the present invention are as follows: the embodiment of the rubber milling of internal mixer automatic process optimization monitoring system among the present invention can be like this: (1) presses Fig. 2～circuit draw PCB shown in Figure 3, then screen components and parts and install and simple debugging, just can be made into signal input apparatus case and signal output driving box. In the present embodiment: the optional 6N136 type of TLP1 LMDS Light Coupled Device, the optional TTL117 type of TLP2 LMDS Light Coupled Device, the optional TLP521 type of TLP3～TLP4 LMDS Light Coupled Device, the optional 74LS132 of IC1, the optional 74LS221 of IC2, the optional LF356N of IC3, IC4; (2) then be electrically connected accordingly by the shown in Figure 1 and top described annexation of specification, just can realize this calendering process Automatic Optimal monitoring system; As long as the implementation of (3) the inventive method---calendering process automatic optimization method is with reference to program flow diagram shown in Figure 7, and by top specification described concrete calendering process automatic optimization method and step, just can realize the automatic excellent method of rubber milling of internal mixer technique of the present invention. The below is several specific embodiments of the inventive method:
Embodiment 1: improve the plasticity qualification rate. (1) use rubber milling of internal mixer automatic process optimization monitoring system of the present invention to collect the relevant parameters of 96 batches of elastomeric compounds; (2) learn that from the instantaneous power of off-take point this batch elastomeric compound plasticity (the given qualified index by traditional handicraft of user is 0.20 ± 0.04) qualification rate only has 75%, the target of Automatic Optimal is for improving its qualification rate; (3) analysis of problem: such as following table 1
| The observed value number | Plasticity variable residual value | Average under the residual value | Standard error | The T value | The P value＞| the T| value |
| 96 batches |
Y1—0.20
|
-0.014
|
0.028
|
-5.634
|
0.0001
|
Table 1 Plays mistake namely is the ratio of average under the increment standard deviation of plasticity variate-value and the increment, and its general computing formula is:
The T value is a test statistics in order to check plasticity qualification rate effect after the optimization whether to significantly improve and to use. Its computing formula is:
Whether the P value is remarkable in order to differentiate the T test statistics, obeys T distribution inspection statistic with 95% confidence level, and its value is greater than | the probability critical value of T|, according to checking in the T distribution probability table; The plastic mean value of data is 0.183 by the gross, and is significantly less than normal than qualified index 0.20, thereby mixing total power consumption also may be less than normal by the gross; (4) optimization of off-take point, the gross energy that consumes during take discharge is factor A, and plasticity qualification rate PY1 is target, and the division of level is as follows:
level 1＜15.04KH, 2＞15.04KH under water, variance analysis such as following table 2:
| Soruces of variation | The free degree | Quadratic sum | All square | The F value | P value＞F |
|
A
|
1
|
0.8041
|
0.8041
|
4.40
|
0.0387
|
| Error |
94
|
17.1959
|
0.1829
| | |
| Total sum of squares |
95
|
18.0000
| | | |
Table 2 be to plasticity when varying level (scope), the analysis of variance table that refining glue energy is consumed. Wherein, the F value is in order to check varying level on an energy consumption impact significant test statistics whether, and its computing formula is:
(for level of signifiance a=0.05, factor A has appreciable impact to Py1, and level of signifiance a is in order to determine the reliability of assay, is a wrongheaded probability of permission of determining according to actual needs, generally all be decided to be 5% aborning), the Py1 of each level is listed in the table below 3:
| Level | The observed value number |
PY1
| Standard deviation |
|
1
|
73
|
0.6986
|
0.4620
|
|
2
|
23
|
0.9130
|
0.2881
|
The energy plasticity qualification rate Py1 that consumes high level 2 is significantly increased as seen from Table 3. The gross energy that consumes during each observed value discharge such as following table 4:
| The observed value number | Minimum | Maximum | On average | Standard error |
| 23 batches |
15.15
|
17.89
|
15.93
|
0.78
|
As seen from Table 4, can average 15.93 o'clock, discharge is more excellent; (5) throw the optimization of filler point, take the energy consumption of throwing filler point as factor A, plasticity Y1 is target, the division of level such as following table 5:
| Level |
1
|
2
|
3
|
| Value |
＜2
|
[2，3.587]
|
＞3.587
|
Variance analysis such as following table 6:
| Soruces of variation | The free degree | Quadratic sum | All square | The F value | P value＞F |
|
A
|
2
|
0.0080
|
0.0040
|
5.66
|
0.0049
|
| Error |
86
|
0.0611
|
0.0007
| | |
| Total sum of squares |
8.8
|
0.0691
| | | |
From table 5, as seen from Table 6, the energy consumption when throwing filler has appreciable impact to the Y1 value. The Y1 value of each level such as following table 7:
| Level | The observed value number | Average Y1 | Standard deviation |
|
1
|
24
|
0.170
|
0.022
|
|
2
|
44
|
0.192
|
0.028
|
|
3
|
21
|
0.180
|
0.028
|
As seen from Table 7, the average Y1 of level 2 is near qualified German-Chinese value 0.20 (intermediate value is a ranking test data placed in the middle in all test datas). Level 2 is respectively thrown energy consumption such as the following table 8 of filler point:
| The observed value number | Minimum | Maximum | On average | Standard error |
|
44
|
2.169
|
3.586
|
3.073
|
0.373
|
More excellent during average energy consumption 3.073 as seen from Table 8. (6) carry out analysis-by-synthesis: the energy consumption of off-take point and throwing filler point is Y1 and the PY1 of the observed value in above-mentioned optimization interval in the data by the gross, is summarized as follows table 9:
| The observed value number |
Y1
|
PY1
|
|
14
|
0.198
|
0.929
|
As seen from Table 9, Y1 and qualified intermediate value (0.20) are very approaching, so overall acceptability rate PY1 improves greatly; (7) carry out the suitability verification, because passing through, so the above results can be accepted; (8) if will further improve qualification rate, available optimum results produced certain batch (more than 20 times) and is optimized with further raising qualification rate again.
Embodiment 2: raise the efficiency and save energy consumption. (1) use rubber milling of internal mixer automatic process optimization monitoring system of the present invention to gather the relevant data of 166 batches of elastomeric compounds; (2) adopt the two horizontal factorial tests of three factors. The time that factor A, B, C represent respectively and throw the filler point, throw oil plant point and off-take point, the division of level following 10:
| Factor |
A
|
B
|
C
|
| Level |
| |
1
|
2
|
1
|
2
|
1
|
2
|
| Value (second) |
≤16
|
＞16
|
≤84
|
＞84
|
≤166
|
＞166
|
(3) to power consumption statistical analysis such as following table 11～table 14:
Table 11
| Soruces of variation | The free degree | Quadratic sum | All square | The F value | P value＞F |
| The model error total sum of squares |
7
158
165
|
8.7411
51.3715
60.1126
|
1.2487
0.3251
|
3.84
|
0.0007
|
Table 12
| Soruces of variation | The free degree | Class1 SS | All square | The F value | P value＞F |
|
A
B
A＊B
C
A＊B
B＊C
A＊B＊C
|
1
1
1
1
1
1
1
|
0.5531
2.0493
5.3969
0.0047
0.0021
0.0000
0.7349
|
0.5531
2.0493
5.3969
0.0047
0.0021
0.0000
0.7349
|
1.70
6.30
16.60
0.01
0.01
2.26
2.26
|
0.1940
0.0131
0.0001
0.9048
0.9935
0.1347
0.1347
|
Table 13
| Soruces of variation | The free degree | Type 3SS | All square | The F value | P value＞F |
|
A
B
A＊B
C
A＊C
B＊C
A＊B＊C
|
1
1
1
1
1
1
1
|
0.9756
1.9367
2.1780
0.0642
0.1142
0.0531
0.7349
|
0.9756
1.9367
2.1780
0.0642
0.1142
0.0531
0.7349
|
3.00
5.96
6.70
0.14
0.35
0.16
2.26
|
0.0852
0.0158
0.0105
0.7068
0.5543
0.6866
0.1347
|
Table 14
| The A factor | The B factor | The C factor | The observed value number | Average power consumption | Standard deviation |
|
1
1
1
1
2
2
2
2
|
1
1
2
2
1
1
2
2
|
1
2
1
2
1
2
1
2
|
96
8
6
9
11
11
2
23
|
22.46
22.60
21.87
21.67
22.47
22.33
22.21
22.64
|
0.49
0.57
0.62
0.49
0.93
0.25
0.16
0.77
|
As seen from Table 11, factor is significant on the impact of index. His-and-hers watches 13, table 14, it is 0.05 o'clock choosing level of signifiance a value, can think that the reciprocation of B factor and A factor is significant, wherein Class1 SS and type 3SS represent respectively the first kind and quadratic sum corresponding to the 3rd class estimable function, and power consumption situation such as the table 14 of each horizontal combination. See that from the power consumption angle A factor level 1, B factor level 2 make up relative energy-saving with factor level combination and A factor level 1, the B factor level 2 of C factor level 1 with the factor level of C factor level 2, are respectively 21.87KWH and 21.67KWH; (4) to statistical analysis consuming time such as following table 15～table 18:
Table 15
| Soruces of variation | The free degree | Quadratic sum | All square | The F value | P value＞F |
| The model error total sum of squares |
7
158
165
|
2483.98
933.46
3417.45
|
354.85
5.91
|
60.06
|
0.0001
|
Table 16
| Soruces of variation | The free degree | Class1 SS | All square | The F value | P value＞F |
|
A
B
A＊B
C
A＊B
B＊C
A＊B＊C
|
1
1
1
1
1
1
1
|
1078.80
733.44
342.44
220.98
24.29
69.42
24.60
|
1078.80
733.44
342.44
220.98
14.29
69.42
24.60
|
182.60
124.14
57.96
37.40
2.42
11.75
4.16
|
0.0001
0.0001
0.0001
0.0001
0.1218
0.0008
0.1430
|
Table 17
| Soruces of variation | The free degree | Type 3SS | All square | The F value | P value＞F |
|
A
B
A＊B
C
A＊B
B＊C
A＊B＊C
|
1
1
1
1
1
1
1
|
72.90
114.42
67.84
324.86
38.34
87.84
24.60
|
72.90
114.42
67.84
324.86
38.34
87.84
|
12.34
19.37
11.48
54.99
6.49
14.87
4.16
|
0.0006
0.0001
0.0009
0.0001
0.0118
0.0002
0.0430
|
Table 18
| The A factor | The B factor | The C factor | The observed value number | On average consuming time | Standard deviation |
|
1
1
1
1
2
2
2
2
|
1
1
2
2
1
1
2
2
|
1
2
1
2
1
2
1
2
|
96
8
6
9
11
11
2
23
|
174.52
176.50
174.00
178.33
174.27
176.91
175.50
185.78
|
1.90
1.07
0.00
1.66
4.73
1.14
2.12
3.80
|
From table 15～as seen from Table 17 A, B, C three factors and reciprocation thereof highly significant all, and from the situation consuming time of each horizontal combination of table 18 visible consuming time the shortest be horizontal combination 1-2-1, power consumption time that little horizontal combination namely; (5) in order to select optimum horizontal combination, should be handled as follows (namely operating with certain horizontal combination).
One, power consumption is compared as follows table 19
| Process | The observed value number | On average | Standard deviation | Standard error | Variance | The T value | The P value＞| T| |
|
1-2-1
|
6
|
21.87
|
0.62
|
0.25
| Equate |
0.6641
|
0.5230
|
|
1-2-2
|
9
|
21.67
|
0.49
|
0.16
| Unequal |
0.6964
|
0.4985
|
Ho: variance equates, F '=1.56, the free degree=(5,8) (P value＞F ')=0.5477
By table 19 comparative result as can be known, no matter whether the variance of two processing equates that its average there is no marked difference, and through check, the variance of two processing there is no marked difference.
Two, the table 20 that is compared as follows consuming time
| Process | The observed value number | On average | Standard deviation | Standard error | Variance | The T value | The P value＞| T| |
|
1-2-1
|
6
|
174.00
|
0.00
|
0.00
| Equate |
-7.84
|
0.0001
|
|
1-2-2
|
9
|
178.33
|
1.66
|
0.55
| Unequal |
-6.32
|
0.0000
|
From table 20 as seen, no matter whether the variance of two processing equates that its average all has marked difference; (6) in sum, it is best processing as can be known 1-2-1, to the throwing filler point of 6 observed values processing 1-2-1, time average such as the following table 21 of throwing oil plant point and off-take point:
| | Minimum | Maximum | On average |
| Throw filler point (second) and throw oil plant point (second) off-take point (second) |
15
87
164
|
15
93
165
|
15
90
164.8
|
Thereby the time controlling value that is optimized is: 15 seconds throwing fillers, 90 seconds throwing oil plants, 165 seconds discharges. Optimize rules by this and produce comparable former traditional handicraft (average wastage in bulk or weight energy 22.15KWH, 176.5 seconds total times) is produced, every crowd of elastomeric compound conserve energy 0.28KWH, during joint 11.5 seconds; (7) model is carried out the suitability verification and pass through, above-mentioned optimum results can be accepted.
Embodiment 3: optimize calendering process, shorten mixing time, reduce energy consumption. (1) use mixer mixing process Automatic Optimal monitoring system of the present invention to gather the relevant parameters of 64 batches of elastomeric compounds; (2) adopt each processing of two factors, two levels to repeat 8 times factorial test. Factor A, B represent respectively the time of throwing oil plant point and off-take point, its horizontal division such as following table 22:
| Factor |
A
|
B
|
| Level |
| |
1
|
2
|
1
|
2
|
| Value (second) |
≤71
|
＞71
|
≤156
|
＞156
|
| Power consumption (KWH) |
16.10
|
18.23
| | |
(3) carry out horizontal division take the time as factor and process, so optimize take energy as index the result of its variance analysis such as following table 23:
| Soruces of variation | The free degree | Quadratic sum | All square | The F value | P value＞F |
| A B A*B error total sum of squares |
1
1
1
28
31
|
36.15
8.89
2.69
96.85
144.57
|
36.15
8.89
2.69
96.85
144.57
|
10.45
2.57
0.78
|
0.0031
0.1200
0.3857
|
By table 22, to only have factor A as seen from Table 23 be significant, sees the power consumption situation of each level of factor A again, flat 1 less energy intensive of visible factor A water intaking, thus the throwing oil plant time that is optimized is 61 seconds; (4) carry out the verification of model suitability and pass through, model hypothesis is set up; (5) test of adopting again two horizontal repeat numbers not wait, factor A, B represent respectively energy consumption and the instantaneous power of discharge time of refueling time. Horizontal division such as following table 24:
| Factor |
A
|
B
|
| Level |
| |
1
|
2
|
1
|
2
|
| Value (KWH) |
≤8.632
|
＞8.632
|
≤0.484
|
＞0.484
|
(6) owing to carrying out the division of factor A, B with energy, so be optimized its results of analysis of variance such as following table 25～table 27 take the time as index:
Table 25
| Soruces of variation | The free degree | Quadratic sum | All square | The F value | P value＞F |
| The model error total sum of squares |
3
158
161
|
21068.47
53602.53
74670.99
|
7022.82
339.26
|
20.70
|
0.0001
|
Table 26
| Soruces of variation | The free degree | Class1 SS | All square | The F value | P value＞F |
|
A
B
A＊B
|
1
1
1
|
13710.70
6916.99
440.78
|
13710.70
6916.99
440.78
|
40.41
20.39
1.30
|
0.0001
0.0001
0.2561
|
Table 27
| Soruces of variation | The free degree | Type 3SS | All square | The F value | P value＞F |
|
A
B
A＊B
|
1
1
1
|
443.89
1943.4
440.78
|
443.89
1943.4
440.78
|
1.31
5.73
1.30
|
0.2544
0.0179
0.2561
|
From table 25～as seen from Table 27, A, all there were significant differences for each leveled time of B factor, but its reciprocation is not remarkable. Energy when the result who optimizes is oiling is 9.59KWH, and the instantaneous power during discharge is 0.42KWH.
Embodiment 4: two indexs (plasticity, dispersiveness) are optimized. (1) uses rubber milling of internal mixer automatic process optimization monitoring system, in GK-270 type banbury, gather the parameters such as temperature, time, instantaneous power and energy consumption of tread rubber mixing process more than 50 batches; (2) plasticity and the dispersed result who detects inputted native system; (3) find mix quality plasticity and the dispersed mixing process parameter that appreciable impact is arranged by correlation analysis: the Automatic Optimal monitoring system, the gross energy (N) of instantaneous power (P) and consumption is to plastic sex conspicuousness when the elastomeric compound temperature (K) in the time of will analyzing mixing process total time (T), discharge, discharge. Basic statistics amount such as following table 28 that above-mentioned four factors are added up by 50 car glue:
| | Minimum of a value | Maximum | Mean value | Standard deviation |
| T (second) K (℃) P (KWH) N (KWH) |
144
147
0.585
17.81
|
201
176
0.637
23.14
|
171
167
0.630
21.09
|
5.84
5.11
0.0165
0.8390
|
Above-mentioned four factors are divided into Three regions, A1≤(average-standard deviation) according to mean value and the standard deviation of every factor, (average-standard deviation)＜A2＜(average+standard deviation), A3 〉=(average+standard deviation) are defined as this Three regions three levels such as the following table 29 of factor:
| Factor level |
A1
|
A2
|
A3
|
| T (second) K (℃) P (KWH) N (KWH) |
T1≤165
K1≤162
P1≤0.6134
N1≤20.5
|
165＜T2＜177
162＜K2＜172
0.6134＜P2＜0.6466
20.5＜N2＜21.9
|
T3≥177
K3≥172
P3≥0.6466
N3≥21.9
|
Four factors are carried out result such as the following table 30 of variance analysis and significance test:
| Factor | Soruces of variation | The free degree | Quadratic sum | All square | The F value | Critical value | Conspicuousness |
|
0.05
|
0.01
|
|
T
| The process errors total sum of squares |
2
97
99
|
0.00053
0.01583
0.01638
|
0.00027
0.00016
|
1.62
|
3.09
|
4.82
| |
|
K
| The process errors total sum of squares |
2
97
99
|
0.00076
0.01562
0.01638
|
0.00038
0.00016
|
2.37
|
3.09
|
4.82
| |
|
P
| The process errors total sum of squares |
2
97
99
|
0.01177
0.00443
0.01620
|
0.005887
0.000046
|
129.8
|
3.09
|
4.82
|
＊＊
|
|
N
| The process errors total sum of squares |
2
97
99
|
0.00231
0.01407
0.01683
|
0.00115
0.00015
|
7.96
|
3.09
|
4.82
|
＊＊
|
Instantaneous power when total time and temperature do not have total energy consumption and discharge to plastic impact as known from Table 30 is remarkable, will control in other words section's plasticity of elastomeric compound, instantaneous power when key is to control well the total power consumption of mixing process and discharge; Instantaneous power (P) when elastomeric compound temperature (K), discharge when (b) the Automatic Optimal monitoring system will be analyzed mixing process total time (T), discharge, consume total energy 1 amount (N) and disperse sex conspicuousness to elastomeric compound: the basic statistics amount of four factors such as following table 31:
| Factor | Minimum of a value | Maximum | Mean value | Standard deviation |
| T (second) K (℃) P (KWH) N (KWH) |
156
161
0.617
19.07
|
180
173
0.662
22.43
|
170
168
0.633
21.14
|
5
3
0.0124
0.7130
|
Mean value and standard deviation according to every factor are divided into Three regions, A1≤(average-standard deviation), (average-standard deviation)＜A2＜(average+standard deviation), A3 〉=(average+standard deviation), this Three regions is defined as three levels of factor, and they are such as following table 32:
Four factors are carried out result such as the following table 33 of variance analysis and significance test:
| Factor | Soruces of variation | The free degree | Quadratic sum | All square | The F value | Critical value | Conspicuousness |
|
0.05
|
0.01
|
|
T
| The process errors total sum of squares |
2
33
35
|
6.7649
23.7351
30.5
|
3.3824
0.7193
|
4.70
|
3.28
|
5.31
|
＊
|
|
K
| The process errors total sum of squares |
2
33
35
|
2.9087
27.5913
30.5
|
1.4544
0.8361
|
1.74
|
3.28
|
5.30
| |
|
P
| The process errors total sum of squares |
2
33
35
|
5.5337
24.9164
30.5
|
2.7669
0.7566
|
3.66
|
3.28
|
5.30
|
＊＊
|
|
N
| The process errors total sum of squares |
2
33
35
|
8.2130
22.2871
30.5
|
4.1065
0.6754
|
6.08
|
3.28
|
5.30
|
＊
|
From table 33 as can be known, total energy consumption is the most remarkable on the impact of dispersiveness, secondly, the impact of instantaneous power is also remarkable when total time and discharge, and the temperature effect of elastomeric compound is least remarkable, therefore will control dispersiveness, main is the control gross energy, secondly is control off-take point instantaneous power and total time. (4) optimization of parameter (time, instantaneous power, energy) during discharge: (a) for the optimization of elastomeric compound index of plasticity, consider the plastic factor of impact when mainly being discharge instantaneous power and total energy consumption, and the impact of time is not remarkable, so obtain under the prerequisite of optimal value in front two factors, the time is got minimum of a value; Instantaneous power during discharge (P) is to plastic optimization: the calculating arrangement of the parameter values such as the plasticity under three levels of instantaneous power and consuming time, power consumption is listed in the table below 34:
| The level of P | Gather lot number | The average of P | The plasticity average | The plasticity standard deviation | The N average | The T average |
|
A1
A2
A3
|
17
69
14
|
0.599
0.630
0.660
|
0.35
0.33
0.31
|
0.0062
0.0065
0.0084
|
21.39
21.13
20.54
|
173
171
168
|
Because the plastic acceptability limit of this glue is 0.28～0.36, average is 0.32, the horizontal A3 of gained P, and namely during instantaneous power 0.66KWH, plasticity is near average, and it is minimum to consume energy, and is consuming time the shortest; Total power consumption (N) is to plastic optimization: the parameter value calculation such as the plasticity under three levels of N and consuming time, consumption are put in order be listed in the table below 35 equally:
| The level of N | Gather lot number | The average of N | The plasticity average | The plasticity standard deviation | The T average |
|
A1
A2
A3
|
16
70
14
|
19.86
21.09
22.53
|
0.32
0.34
0.34
|
0.0114
0.0119
0.0127
|
172
170
173
|
From table 35 as seen, gross energy N gets the A1 level, and the plasticity average just overlaps with qualified average. Comprehensive P and N are to plasticity optimum results such as following table 36:
| Factor | Level | The P average | The N average | The T average | The plasticity average |
|
P
N
|
A3
A1
|
0.66
0.64
|
20.15
19.86
|
168
172
|
0.31
0.32
|
| Synthesis result |
0.65kWH
|
20KWH
| 170 seconds | |
(b) with respect to the optimization of the dispersed index of elastomeric compound, because time (T) and instantaneous power (P) when the dispersed factor of impact is discharge, the gross energy (N) that consumes, so this three factor is optimized: to the optimization of time T, by three levels of time T the time consumption and energy consumption of dispersiveness is put in order and to be listed in the table below 37:
| The level of T | Gather lot number | The average of T | Dispersed average | Dispersed standard deviation | The average of N |
|
A1
A2
A3
|
6
28
2
|
162
170
175
|
5
4
3.5
|
0.8576
0.8450
0.8839
|
20.73
21.21
21.39
|
From table 37 as seen, total time is under horizontal A1, and is dispersed maximum, and it is minimum to consume energy, and the time is also the shortest; The optimization of the instantaneous power during to discharge (P): three levels by P are put in order as being listed in the table below 38 consuming time, the power consumption of dispersiveness:
| The level of P | Gather lot number | The average of P | Dispersed average | Dispersed standard deviation | The T average | The N average |
|
A1
A2
A3
|
6
26
4
|
0.6138
0.6330
0.6537
|
4.25
3.75
5
|
0.6892
0.8724
0.0897
|
166
171
166
|
21.045
21.333
20.125
|
From table 38 as seen, instantaneous power (P), when horizontal A3 average is 0.6537KWH, dispersed maximum, and consuming time, power consumption economizes most; Optimization to gross energy (N): three levels by N are put in order and are listed in the table below 39 consuming time, the power consumption of dispersiveness:
| The level of N | Gather lot number | The average of N | Dispersed average | Dispersed standard deviation | The average of T |
|
A1
A2
A3
|
5
25
6
|
19.784
21.193
22.141
|
5.25
4.00
3.75
|
1.0062
0.8369
0.5342
|
168
170
170
|
From table 39 as seen, during total power consumption minimum (average 19.784KWH), dispersed average is maximum, and consuming time also short. T, P, N are to optimum results such as the following table 40 of dispersiveness:
| Factor | Level | Average | Dispersed average | The N average | The T average | The P average |
|
T
P
N
|
A1
A3
A1
|
162
0.6537
19.748
|
5
5
5.25
|
20.73
20.125
19.748
|
162
166
168
|
0.632
0.6537
0.656
|
| Synthesis result | |
20.2
|
165
|
0.65
|
(c) according to plasticity and dispersed optimum results arrangement for index are listed in the table below 41:
| Optimize index | Instantaneous power during discharge | The discharge time | Total power consumption |
| Dispersed plasticity |
0.65KWH
0.65KWH
| 165 seconds 170 seconds |
20.2KWH
20.0KWH
|
From table 41 as seen, respectively with different indexs, basic identical to the result that related factors is optimized.
Four factors are carried out result such as the following table 33 of variance analysis and significance test: