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30 / March 2020 powderbulk.com that the speed reduction will place the effective air velocity close to but not below the material's minimum entrainment velocity. Process variations such as rate, ambient temperature, and material properties can mar- ginally increase or decrease the effective air velocity. If the system is operating too close to the material's minimum entrainment velocity and the effective air velocity dips, then a cycle of reduction begins. Figure 3's Zenz plot shows the relationship between the velocity and the resulting pressure drop per distance (mbar per meter) of the system. On the right-hand or "correct" side of each rate line, the pressure reduces to the minimum entrainment velocity; this is where the system should be. On the left-hand or "wrong" side of the minimization curve, lower velocity will cause a rise in pressure. Additional leakage and air compression cause yet more uncontrolled velocity reduction, and the cycle continues until the system is in distress. A system in distress will be unstable with large swings in pressure and effective air velocity. In the best-case scenario, the system will hit a high-pressure setpoint, and the process controls will stop the material feed so the system can recover. In a less-beneficial scenario, the pipe will become plugged with material due to the lack of effective air velocity and manual intervention will be required. Because this instability is detrimental to the performance of the system and process, manual speed reductions require that some amount of residual excess velocity remain. A more productive and beneficial method of velocity control uses an algorithm to direct the blower speed; this process is called dynamic velocity control. By taking into account real-time, real-world inputs about the system, such as pipe diameter, blower characteristics, and airlock characteristics, speed corrections have a tangible influ- ence over the effective air velocity. The algorithm also has performance input, such as the system's operating pressure, which helps the pneumatic conveying system adapt to changing process conditions. Dynamic velocity control is different from a proportional-integral- derivative (PID) control loop, which uses a process condition setpoint to modulate a device. The pressure minimum curve excludes typical control mechanisms because the system could be operating on either side (left or right) of the minimum; in this case, the blower might slow down when it should be speeding up or vice versa. An algorithm will relate the blower speed and perfor- mance to the effect on velocity as the pressure changes. When a velocity is operating below the minimum, the blower will speed up, and when a velocity is greater than the minimum, the blower will slow down. The dynamic velocity control method allows the system to operate at or just below the material's minimum entrainment veloc- requirements. The compression volume is inherently less with reduced air velocity, but the pressure is also decreased from less resistance in the conveying line, as shown in the Zenz diagram in Figure 3. Putting velocity control to work If the benefits of velocity control in a dilute-phase sys- tem are accepted as significant, then examining the methods of accomplishing this task is a worthwhile activity. An obvious feature to add to a blower is a VFD, which can be directed to change the system's blower speed. The system can be designed in the tradi- tional way with conservatism and safety factors (extra velocity or motor power) employed. Then, at the point of implementation, the speed can be reduced to accom- plish some intended goal related to the benefits. Speed reduction can be a manual process or automated in the form of an algorithm. Manual speed changes can normally have a noticeable impact on the system's performance with- out adding significant, undue risk. Considering the system's design safety factors related to determining effective air velocity, a 5 percent reduction in speed is a reasonable change that can be tested and proven in a production environment. The assumed risk is