For all manufacturers who want to be invincible, it is an indispensable part of their daily operations to respond to increasingly discerning customers, increasingly severe environmental pressures and regulatory control, and constantly improve product production. The goal of factory managers is to produce high-quality products in the most efficient way to achieve the best benefits.

Under the guidance of this goal, with the advancement of science and technology, a large-scale manual to automatic process control transition has appeared in many industrial sectors, and this is also a long-term trend.

Effective automatic control strategies can reduce waste, maximize output, reduce labor requirements and improve product quality, thereby improving the efficiency of the production process.

This article describes the application of automated process control strategies in the grinding process after being used in particle size detection technology. For solid-state processors, granularity is usually a key variable and has a direct impact on plant and production tasks. Therefore, it is very common to control the granularity by manual methods. However, for fully automatic control, serial or online analysis is more advantageous. This article provides two industry case studies to illustrate the broad application and effectiveness of the strategies described in this article. They all use a laser diffraction online particle size analyzer (Malvin Insitec system), through which the continuous data stream generated drives the work of the entire control loop.

Laser diffraction

Laser diffraction is an industry-recognized effective method for detecting particle size. It is applied to modern instruments and meets industry standards for safety, reliability, and low maintenance. The key to this technology is the fast detection speed and very little response time in the detection and control process. Malvern Insitec is a fully automatic system that integrates tandem, online and inline solutions, suitable for both wet and dry processes. It also integrates all the functions of laser diffraction technology of related instruments in the industry.

Development of new particle size control strategy in cement grinding

Process flow chart of cement grinding in the commercial sector. Before installing the automatic control system, the granularity of the products in the process was manually controlled. For example, the speed variable of the sorter is controlled based on the manually analyzed data. This is a very basic case. Different operators have different views on choosing the best strategy; as a result, they have chosen different methods of operation. In addition, other work will let the operator put the process control on hold, so that sometimes, the process control will "do nothing". The result is unstable output, the root cause of which is the change in raw material supply and the inability to correct these manual control changes in a timely manner.

The difference in particle size distribution in the grinding feed is caused by the difference in raw material grading and sorting, which is inevitable in transportation and storage. These problems are difficult to eliminate, so it is essential to eliminate their effects during the production process. Detailed grinding data analysis explains the reasons for these changes. Although these data come from the constant speed feed test, the flow rate of raw materials back to the mill will vary greatly, sometimes reaching several tons per hour in ten minutes. Under this unstable raw material flow rate, the fluctuation is also accompanied by the change of the product particle diameter. The faster flow rate of raw materials is related to the smaller diameter of finely cut particles in the sorter. The above findings come from the design of the Heyd sorter, which is used to separate products and recycle gas streams.

The airflow speed through this sorter is a function of the pressure drop in the device and cannot be directly controlled (see Figure 3). When the pressure is reduced and the airflow velocity is high, the velocity in the separation zone will also be very high. As the pressure drop increases, the load increases, the air velocity in the machine decreases, and the diameter of the finely cut particles becomes smaller. This discovery allows us to formulate corresponding process control strategies through this effect, which is expected to be realized in the future.

The product particles in the grinding process can be controlled by changing the speed of the sorter (traditional control method) or changing the feed speed of the sorter (indirect method). The change in feed rate affects the pressure drop in the machine and changes the separation characteristics. Please refer to Figure 4, which is a trend graph of an automatic control strategy experiment using online particle data.

Here, the speed of the sorting machine can be changed automatically to meet the requirements of particle size, but it can only be achieved under some specific conditions set by the experiment. The feeding speed can be operated in real time to achieve the following goal: the detection of "too fine" particles reduces the feeding speed of the grinder and vice versa. The response time of this method is very short, we can clearly see in the start data. After 10 to 15 minutes of start-up or specification change, the grinder will gradually stabilize. If there is no human interference, it will always maintain a stable working state.

Obviously, this is a successful automatic control, which can bring huge variable cost benefits. Labor savings, reduced waste, increased output, faster specification changes, and faster startup speeds can all offset the initial capital expenditures associated with online analyzers. Our installation experience in many factories shows that the payback period for such projects is very short, as long as six months.

Control of grinding and filling amount in aluminum powder production

In the production of aluminum powder, because the density of aluminum is extremely low, the control of the amount of aluminum powder grinding and loading has been difficult to solve. Traditional grinding control techniques, such as weight cell and sonic detection, are not viable alternatives. Figure 5 shows the aluminum grinding process that produces coarse sand and flakes into powder. Before installing the online particle size analyzer, the finished product samples are tested every 4 hours. Online testing before installing the automation system shows that these manual testing control methods are flawed.

The diameter of the product particles varies greatly; for example, the average Dv (50) of a batch is 23.5 μm, and the maximum value reaches 37 μm. Generally speaking, these changes are caused by transmission. It can be detected by a method of measuring the light passing through the sample of the laser source to the detector, and the load index is obtained. In this case, unstable operation is the cause of frequent overfilling of the grinder; at the same time, the material flow into the sorter is uneven and the separation effect is also one of the reasons.

Using the same control strategy as cement grinding, the level of process operation will be greatly improved. In order to cope with the change of particle size, the operation of the sorter speed and grinding feed speed can put the diameter of the final product under stricter control. The decline in transmission illustrates the increase in effective output, which means that the project has brought economic benefits. The particle size distribution of the final product is narrower, and the control of the production process is easier and more effective.

Optimize process control

Although the concept of online analysis applied to automatic control is more and more accepted by people, only the combination of stable technology and reasonable control strategy can ensure the best completion of the task. The case in this article demonstrates a method that is quite effective and widely used for grinding control.

Online particle size analyzers, such as Malvern Insitec, based on particle diameter measurement technology, provide a continuous and accurate data stream required for automatic control. With these data, coupled with effective control methods, you can certainly improve process efficiency and save variable costs.

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