Automated Mill Control using Vibration Signal Processing

Transcription

1 Abstract Automated Mill Control using Vibration Signal Processing Karl S. Gugel, Ph.D., President, Digital Control Lab, Inc. Rodney M. Moon II, Process Engineer, California Portland Cement Published IEEE Charleston World Cement Conference 2007 Historically many different signals have been employed in the quest for optimal automatic closed loop mill control. These include mill power, sound, elevator amps, bearing pressure and temperature as a few examples. However, within the last few years, a new technology is emerging that makes all these antiquated techniques obsolete. This new technology is mill control using vibration sensors, analog to digital converters and digital signal processing (DSP). This paper presents a brief but current overview of mill vibration analysis and control using signal processing techniques. It then also documents an actual before and after case comparing mill control using classical techniques versus automate loop control using mill vibration to instantaneously estimate and control mill fill level. It will be shown that significant improvements in material throughput, reduced kilo-watt per ton numbers, improved material quality can all be realized with vibration control. Introduction to Shell Based Vibration Monitoring Presently there are two main types of vibration monitoring systems. Those that measure from the shell of the mill and those that monitor from the bearing housing or some other fixed position. Beginning with shell based vibration systems, a standard approach is to measure the vibration directly off of the shell of the mill as shown in Figure Raw, Finish, Ball, Roller or Rod Mill Vibe sensor Cross Section of Mill at the Accelerometer Power Source RF Receiving Unit 4-20mA Output Used for Normal RS232, RS485 Operation Or Ethernet Used for Calibration Figure 1. Typical Shell Based RF Vibration Measurement with Mill Cross Section (shown left) This system is comprised of a low power transmitter containing an A/D converter and accelerometer that in turn communicates with a fixed unit nearby. Power can come from batteries, a mechanical fixed pendulum generator or even peltier junctions where the heat of the mill is used to generate electrical power. Modern systems also employ a simple tilt sensor in the transmitter such that the position of the mill is known at the time of sampling. This allows the unit to synchronize when it is the optimal time to sample during the revolution. A typical European system breaks up the revolution into 12 equally spaced arcs and samples the vibration at a point opposite to where the balls are impacting the mill. Here for diagram simplicity, we show the revolution (360 o ) being divided into 8 arcs (45 o each) for sampling. The balls are lifted by the mill via the arcs labeled 2-4. The point of impact is estimated to be between arcs 5 and 6. The idea point to obtain the fill level therefore turns out to be between arcs 1 and 2. The reason for this is that it is most desirable to sample the vibration where it is summed by the metal housing. i.e. You let the metal shell of the mill act as an integrator for vibration energy. If you sample in the regions nearby where balls strike the accelerometer, the reading is subject to large variations in signal energy due

2 to the direct proximity of a ball strike. For example, if the balls strike directly at the exact location of the sensor, we see a very strong vibration reading and if the strikes are a short distance away, we observe a much lower intensity signal. Therefore in a single sensor shell monitoring system, you must use a tilt or similar sensor to synchronize the data sampling such that vibration is sampled through arcs 1 and 2. Another approach is to simply have two vibrations sensors on the shell and take an instantaneous average of the two signals. See Figure 2. In each of these example rotations, the total relative distance of the two sensors from the point of impact is given as D1 + D2. Notice that D1+D2 is equivalent for each case and therefore the average vibration intensity obtained in each case is nearly identical. vibration sensors D1 D1 D1 D2 Note: D2 = 0 D1 = distance from Sensor1 to point of impact D2 = distance from Sensor2 to point of impact D1+D2 = mill circumference/2 D2 Figure 2. Three Different Sensor Alignment Examples for a Two Sensor Based Shell System Through extensive testing we have found that whether you synchronize a single sensor to sample only through an area opposite the impact zone or sum the instantaneous vibration from two sensors 180 degrees apart, minimal difference can be detected between the two signals. Both techniques produce similar results that are superior to traditional mic based fill level indicators. An example of this is a trend shown in Figure 3. obtained in 2001 at Cemex Tepeaca. Here our shell based system was specifically compared against what Cemex felt to be one of their finest microphone based fill level systems. A several month study was performed by Cemex corporate engineers which included a very detailed sensitivity analysis comparing the two signals. Cemex concluded that the vibration based system was 2.71 times more sensitive than sound when compared against mill feed gain and 2.64 times more sensitive when compared against mill returns gain performance.

3 Feed Returns Shell Vibration (Unfiltered) Electronic Ear Figure 3. Inlet Shell Vibration versus Microphone Fill Level Monitoring Vibration Monitoring Off of the Bearing Housing Because the vibration generated by the balls impacting the shell wall is so immense, the bearing housing can also be used to monitor the fill level in a similar manner as in the shell based system. This was discovered three years ago on an installation in Australian Cement. While the mill was in operation, the author placed his hand on the bearing housing and surrounding mill structure and found a significant amount of vibration that later was found to go away when the mill was stopped. At this installation, we got approval to compare the fill level signal taken off of the bearing housing cover and compare it to one obtained from the inlet shell. See Figure 4. for the sensor location. What we observed is that the two signals were nearly identical but with the bearing housing being slightly less noisy. We attributed this to the fact that the bearing housing cover acts as an even greater integrator than the shell based sensor because it is a pick-up point where all the vibration energy on one side of the mill is summed. On the opposite side of the ball impact on the shell, the signal is still susceptible to how close the balls impact the shell near the sensor. This is a very random process. In some instances a large mass of balls may strike the shell directly across the sensors but in other cases they may collide with each other and less than impact the inner shell surface. The amount of balls striking the shell and the proximity of the strike therefore effects the power of the signal sampled by the vibration sensor. This turns out to be a more noisy signal when you measure vibration from just across the other side of the mill. Whereas on the bearing housing this effect is less pronounced because you are a much farther distance away from the mill. You see a more complete picture of all the vibration created by all the balls striking the mill surface.

4 Figure 4. Typical Bearing Housing Vibration Sensor Placement In addition to observing less noise on the signal at the bearing housing, we also found the signal to be slightly attenuated in power when compared to the one taken off of the shell. This again is due to the fact that the sensor is farther from the vibration source (point of impact). However, because we had no power supply constraint, as is the case of a shell based system, the system was re-designed such that it was ten th times more sensitive than the original shell based hardware. Thus we can now detect changes to 1/10 of a percent in fill level and accuracy down to +/ ma on the 4-20mA fill level output. With further bearing housing vibration signal analysis testing, we found that the vibration monitored on the inlet housing corresponds to the fill level in the inlet end of the mill or inlet chamber in the case of a two chamber mill. The vibration taken off of the outlet bearing housing can be used to indicate the amount of material at the outlet end of the mill (single compartment mill) or outlet chamber (two compartment mill). Some companies then use the inlet signal as the primary signal for auto loop control while others use the outlet side. We have found that most prefer the inlet because this signal has the fastest response. When this signal is used for auto control, benefits can be observed in increased material throughput, reduced kilowatts per ton of material produced and significant improvements in material quality. An actual case study is now presented to illustrate these points. Actual Case Study Cement Plant, 2004 thru Circuit Configuration Two single compartment mills were tested where each mill feeds two first generation separators. This testing involved 2 mills and 4 separators. Each mill discharges into two bucket elevators which in turn discharge into their respective separator. The mill dimensions are 13 by 21 and the separators are 16 in diameter. The vibration control sensors were mounted on the feed trunions as shown earlier in this paper. The vibration signal was inserted into a PID in the same place where the mill kilowatt variable was used before. However the PID was changed to react proportionally to this new more responsive signal. This new PID block then generated a total feed set point that was broken up by a fixed constant percentage to generate a target for each mill feeder.

5 System Control Under Manual Operation and Auto Control Using Kilowatts & Vibration Beginning with manual operation, this control scheme requires constant operator attention to attempt to optimize the variables associated with the mill. In this scenario, the moving loads in the mill and separators are left out of process control. These factors can be moving in different time intervals which make them hard to react to even with a good data historian and an operator s full attention. This leads to conservative decisions with respect to feed tonnage in order to greatly ease mill operation. Basically the operator is afraid to push the mill anywhere near its true optimal operation. See Figure 5 as an example. Here a 100 kilowatt difference between the high and low mill power can be observed for the same throughput tonnage. With this shift in mill load we can expect more variation in the cement than the case where the power is stable (flat). In addition, there was an unexpected drop in the feeders, observed at 13:00 hours, which then caused the mill power to significantly drop and consequently significantly increase the load in the bucket elevator and separator. The point being that the operator missed this activity or chose to take no action. After this time, the mill also begins to load and unload as observed in the kilowatts. This in turn causes the bucket elevators and separator to act in an equally cyclical manner such that the mill is not being operated in an optimal manner. We will further discuss and illustrate the idea of a mill optimally running shortly. Figure 5. Mill Operation under Manual Control Figure 6 is an illustration of the mill being controlled via a PID with mill kilowatts as the control variable. The theory of this control is that once the mill exceeds a maximum kilowatt it can be controlled automatically. Similarly if the mill power is under the kilowatt set point, the feed is decreased. Hence the control loop reacts inversely to achieve the desired set point. One of the drawbacks of this operation is that it requires an operator to monitor the system until the load has been reached and then you switch to automatic control. This is due to the fact that the mill must go through a power curve where it ramps up to a peak value initially as the mill fills from empty and then drops down as material causes the balls to move towards the chamber s center of mass line. Hence the optimal zone is on the right side of this curve. If you tried this optimal point in the control system at start-up, you won t pass this point and the mill will empty. This control scheme also has a wide span of operating values for the kilowatt variable due to the nature of the signal. It is typical to see the control system swing between two points. Unfortunately you cannot

6 decrease this span. This effect can be observed in the large swings in the mill kilowatt signal shown in Figure 6. When the mill is loaded, the shifts in kilowatts aren t very dramatic and tuning has to accommodate a slow change. However in a rapid mill load change, the control system can t react fast enough. In the event of a large feed disturbance, the system can make its way back past the maximum kilowatts set point and the mill begins to empty itself. Because of these different rates of response to a changing load it is very difficult to tune the control settings in the PID. You undershoot and overshoot your optimal target point of operation. Figure 6. Mill Operation under kw Control Unlike kw control, vibration control showed a much better ability to adjust to varying mill loading conditions. See Figure 7. The system could also be placed in automatic control immediately upon mill start-up. We found that the vibration signal reacts both to fresh feed and circulating load. Through historian data it was observed that fresh feed had the most dramatic effect of the two material inputs. We also found that the smaller span and a faster signal response allowed the PID parameters to be shortened such that the control system made changes faster than in the kw control case. The vibration control methodology relies on having appropriately placed clamps to optimize performance. In the case of an accidental feed starvation where the appropriate tonnage clamps have been made, the recovery rate by the PID signal is much faster and thus there were fewer instances of over loading of the mill. This system requires far less monitoring of the mill by the operators and can be tuned on day to day or shift to shift basis. You tweak your control variables and then observe the effect over several hours of operation. This is a much less frequent tuning of parameters when compared to manual operation where you are constantly adjusting parameters. Under vibration control, it can be observed that the mill kw, bucket elevator and separator signals are far more stable (flat) as illustrated in Figure 7. Thus it is possible to push the mill to higher throughput levels than was achievable under the two previous control schemes. This is due to the much faster response time of the vibration fill level to changes in feed when compared to mill kilowatts. See Figure 8. The

7 signal response and magnitude of change of the vibration signal allows the controller to return quicker to a steady state operating point. Figure 7. Mill Operation under Vibration Control Figure 8. Mill Vibration Fill Level Response vs. Mill kw Response

8 Results of Running Under Vibration Control A comparison of results compiled from running for several months under vibration control versus mill kilowatt control is now presented. Beginning with kilowatts per ton consumed, it was found that vibration control yielded an average KPT drop of 6.2 %. This drop is illustrated in Figure 9. Figure 9. Mill kw Trends Using kw and Vibration Auto Control Interesting changes were also observed in the lab results of material sampled under vibration control versus those obtained during kw control. These are now presented in Table 1. Table Average & Blaine Results of Vibration Auto Control Laboratory Measurement Product A Product B Change in -325 Average +0.25% +0.70% Change in -325 Standard Deviation -60.0% +19% Change in Blaine Average -4.60% -5.40% Change in Blaine Standard Deviation -20.0% +12.6% Silo Core Strength Standard Deviation -37 % -28 % Both cements showed increases in the -325 mesh, however it was very similar to the original value. The reduction of the Blaine should account for less over-grinding and the improvement in the kilowatt hour per ton. Cement B showed an increase in the deviation. It is an idea that the over grinding of the cement lead to a higher frequency but in a less efficient manner. The improvements of average values exceeded the changes in the deviation. Ultimately the cement had improvements in the strength and the deviation of the core strengths.

9 Conclusions Fill level based upon mill vibration, is a fast repeatable precise signal. We have found it to be less noisy and more indicative of true mill fill level when it was taken off of the bearing housing than on the actual shell of the mill. This is due to the integration of total grinding energy observed at the bearing housing versus a point source measurement taken on the shell. In addition, because this measurement can be taken from a fixed point, the shell based system s low power supply constraint is not applicable to hardware mounted on the bearing housing. The system can be made much more sensitive and at a lower overall hardware cost. When using vibration for control, we have observed flat stable process signals such as mill, elevator and separator power. This increased stability allows an operator to comfortably increase the throughput target for the mill while operating at a lower power point on the mill kw power curve. Hence we can produce more material for less power and thereby significantly decrease historical average kw hour per ton numbers. Additional benefits are that as the mill becomes more consistent in operation (stable), improvements can be observed in -325 and Blaine cement averages as well as improvements in standard deviations of these signals. Basically the more stable your process becomes, the more material for less power can be produced and with high quality levels.