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Automatic control of the crushing process (1)
The crushing process is a process in which the material mass is reduced, and is the first stage of processing the ore material. The crushing process is a very complicated process of dimensional change of the material block, which is related to the material resistance strength, hardness, toughness, shape, size, humidity, density and uniformity, as well as the interaction and distribution of the material block at the moment of breaking. The dimensional changes in the crushing process are complicated.
Due to the complexity of the crushing process, it is difficult to accurately detect and control the process parameters in the crushing process. The crushing process control is mainly to adjust the feeding amount of the crusher and the size of the discharge port to maintain the load balance between coarse crushing, medium crushing and fine crushing and the continuity of production, improve the efficiency of the whole crushing operation, and reduce the final product granularity. Reduce energy consumption and improve economic efficiency. Multi-stage (coarse, medium crush, fine crush) crusher control is a complex control system that also includes interlocking and control of auxiliary equipment. Firstly, the automatic control system of the single-stage crushing unit is adopted, and the entire crushing system control is realized on this basis. Although there are many introductions to the control of crushers at home and abroad, such as control methods based on electricity current or power, unit power consumption, material level, etc.; in fact, more crusher load control is used.
The crusher control system can be a conventional instrument control system or a computer control system. Due to the large lag of the crushing process and the changing nature of the ore, the fuzzy controller (Fyzzy Controler) was introduced into the automatic control system of the crusher, and satisfactory results were obtained.
This chapter focuses on two systems for crusher load control: one is a control system consisting of conventional instruments; the other is a control system with fuzzy controllers. Usually for presentation purposes only sequence controller; for detecting, outside exclude mixing means endanger the safety devices ore metal objects, like the ore car illustrative only, as the application and design reference.
First, the sequential control of the crushing process
The continuity of the crushing operation requires a certain order of operation between the start and stop of each equipment, otherwise it will cause the crusher to clog and interrupt production, and even cause serious equipment accidents. Therefore, in order to control the equipment between the crushing operations, a corresponding logic control system is designed. It can use logic control systems composed of logic devices such as relays, magnetic components, and semiconductors. However, due to the shortcomings of such logic devices, such as low anti-interference ability, poor stability, and easy damage, they are gradually replaced by more reliable controllers such as programmable controllers. The programmable controller overcomes the shortcomings of the above logic devices, and is flexible and convenient to operate and reliable in operation. For different applications, no major changes are required in the hardware, mainly to modify the programming software, which is very convenient to use. The sequence controller should meet the following requirements:
(1) Each piece of equipment must be started at a certain time interval in the order specified by the process. The starting sequence is opposite to the running direction of the ore material, and the crusher is usually started first.
(2) Each device can be started separately or in groups.
(3) When the production of the crushing is stopped, the order of stopping is opposite to the order of starting.
(4) When a certain equipment in the crushing system is forced to stop, in order to avoid problems such as blockage, all other equipment supplying the equipment and mineral materials must also stop, but its subsequent equipment can not stop.
(5) In order to improve the on-site environment, the dust removal equipment is first started, and when the system is parked, it is necessary to stop at the end.
For example, the starting process of the three-stage crushing process flow chart shown in Figure 1 can be followed by the following sequence: fine crusher → medium crusher → coarse crusher → 5 # belt conveyor → 4 # belt conveyor → vibrating screen → 2 # , 3 #带机→1 #带机→é€çŸ¿æœº. Each link should also provide the time required for the start-up.
Stop process: feed machine → 1 # belt machine → 2 # , 3 # belt machine → vibrating screen → 4 # belt machine → 5 # belt machine → coarse crusher → medium crusher → fine crusher.
The automatic control of the mine-distributing trolley is to distribute the qualified products of the belt conveyor to the grinding powder ore bin to the various mine bins. In order to prevent the ore from being segregated in the mine, and to ensure uniformity of the ore-feeding grain size, it is usually controlled automatically according to the timing and order of the number of powder mines. This system is part of a sequential control system.
Interlocking control of other equipment, such as the ore mine ore level to the minimum, in order to retain a small amount of ore to protect the mining machine, from the direct collision of ore, should stop mining to the mine. [next]
Second, the crusher load control system and self-organizing fuzzy ( Fuzzy ) controller
During the crushing process, the load of the crusher is affected by the random variation of the ore supply, ore properties, etc., and the hysteresis is large. The application of the PID regulation system often fails to obtain good results. The application of fuzzy control theory can solve the above problems better and obtain better results.
Figure 1 Three-stage crushing process flow chart
1. Self-organizing fuzzy controller and its working principle
The structure of a simple fuzzy controller is shown in the solid line of Figure 2. It is an ordinary two-input-single-output fuzzy controller whose control rules are summarized by summing up a large number of operators' knowledge and operating experience on the control object. The GE, GE, and GU in the figure are the deviation, the variation of the deviation, and the proportionality of the control amount, respectively. In the fuzzy calculation, the deviation e and the deviation change e are converted into deviation and deviation variation fuzzy sets E and E by fuzzy conversion. The E and E and the fuzzy relation matrix R are combined to obtain the control quantity Fuzzy set U, and the control quantity U is obtained by decision, and then the proportional quantity is obtained by multiplying the proportional quantity to obtain the determined control quantity U i . E, E, U, and U correspond to different combinations of deviations and deviations in the universe, resulting in a control table.
The control table obtained by the above method is applied to the system, and the effect is poor. Because the system has more random operating conditions, the lag is greater. To this end, a self-organizing fuzzy controller that can automatically correct and improve the fuzzy control rules is designed. The performance test, control quantity correction and control rule correction are added to the structure, as shown by the dotted line in Figure 2. The hardware structure of this system is shown in Figure 3. After the current transformer detects the corresponding load current of the drive motor, it is converted into a corresponding DC voltage by the rectification filter to supply the ADA converter. The digital quantity Y output by the ADA is sent to the Z-80II type single board machine. The computer compares it with the given value S, and finds that the deviation e is sent from the entrance of the organization fuzzy controller. The self-organized fuzzy control table is used to obtain the thyristor firing angle a value of the feeder to trigger the power supply of the feeder. Control the silicon, output the corresponding voltage to adjust the feed amount of the feeder, to achieve the purpose of stabilizing the load of the drive motor of the crusher.
2. Modify the control table by self-organizing the fuzzy controller
The steps of the system using this method are as follows:
(1) Performance measurement Take “deviation†(e(nT)=Y n -S), and “deviation variation†(e(nT)=e n -e n-1 ) is two parameters to measure the output characteristics and Deviation of the desired characteristics is shown in Figure 4. The correction amount P(nT) required for the output characteristics is calculated by the same method as the fuzzy control table. The "deviation" e(nT), "deviation change" e(nT), and the correction amount P(nT) are divided into the following levels: [next]
Figure 2 Â Self-organizing fuzzy controller
Figure 3 Â Modified system hardware structure diagram
Figure 4 Â Performance measurement curve
e(nT): -6,-5,-4,-3,-2,-1,-0, +0,1,2,3,4,5,6;
e(nT): -6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6;
P(nt): -6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6;
The magnitude of the correction amount P(nT) is determined according to whether e(nT) is at or above a given value, and e(nt) is a trend toward a given value or away from a given value. This gives a calibration table as shown in Table 1.
(2) Control quantity correction system is a system with single input and single output with pure hysteresis. The control quantity correction should be (the problem of lag is not considered)
Table 1 Â Calibration table
e(nT)
P(nT)=r(nT)
e(nT)
Leaving (or towards) a given value change
Directional (or away) change in setpoint
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
give
set
value
Take
under
-6
-5
-4
-3
-2
-1
-0
6
6
6
6
6
5
4
6
6
6
6
5
4
3
6
6
6
5
5
4
3
6
6
6
5
4
2
1
6
6
5
4
4
1
0
6
6
5
4
3
1
0
6
5
4
4
2
1
0
0
3
3
3
1
0
0
0
2
2
2
0
0
0
0
2
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
give
set
value
Take
on
+0
1
2
3
4
5
6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
-1
-1
-2
0
0
0
0
-2
-2
-2
0
0
0
-1
-3
-3
-3
0
0
-1
-2
-3
-4
-5
-6
0
-1
-3
-4
-5
-6
-6
0
-1
-4
-4
-5
-6
-6
-1
-2
-4
-5
-6
-6
-6
-2
-3
-5
-5
-6
-6
-6
-3
-4
-5
-6
-6
-6
-6
-4
-5
-6
-6
-6
-6
-6
r(nT)=KP(nT)
Here, since the control amount and the output are both normalized, the coefficient K=1, so
r(nT)=P(nT)