How should the proportional, integral, and derivative values for valve tuning be set in order to stabilize the temperature?

Temperature control is quite troublesome, as there is a significant lag in temperature changes... Our company also uses Allen-Bradley’s DCS system, but it’s not possible to achieve accurate control just by relying on a single set of data; various interference factors need to be taken into account... Of course, I hope that someone else can come up with a better control solution.

The key is to identify where the interfering factors originate. Control object: Have the processes and equipment, along with the instrumentation, handle it. Confounding variables: Add feedback or address the confounders at their root. Control system: PID tuned properly ； Adjust the secondary circuit first, then adjust the primary circuit ； Control valve: An improperly selected control valve, one with poor performance, or one that is faulty can also affect control.

Have the process department conduct tracking and analysis to summarize the on-site parameters. In short, it’s a good idea to listen to the opinions of those on-site. Many valves cannot always be used in automatic cascade control, as there will inevitably be deviations from the design.

1. Common mnemonic for PID control: To find the optimal parameter settings, start by checking values from low to high. Begin with the proportional term, then add the integral term, and finally the derivative term. If the curve oscillates frequently, increase the proportional gain; if the curve wanders around in large loops, reduce the proportional gain. If the curve takes a long time to return to its normal position, decrease the integration time. If the curve’s fluctuations have a long cycle, increase the integration time further. If the oscillation frequency is high, first reduce the derivative term. If there is significant error and slow response, increase the derivative time. An ideal curve should have two peaks, with the first peak being higher than the second, in a 4:1 ratio. Observe carefully, make adjustments as needed, and conduct thorough analysis – this way, the quality of tuning will be excellent. 2. For the engineering-based tuning of PID controller parameters, the following empirical values can be used as references for P.I.D parameters in various control systems: Temperature T: P=20~60%, T=180~600s, D=3-180s; Pressure P: P=30~70%, T=24~180s; Liquid level L: P=20~80%, T=60~300s; Flow rate L: P=40~100%, T=6~60s. 3. Principles and characteristics of PID control In practical engineering applications, the most widely used control laws are proportional, integral, and derivative control, commonly referred to as PID control, or PID regulation. The PID controller has been around for nearly 70 years, and thanks to its simple structure, good stability, reliable performance, and ease of adjustment, it has become one of the key technologies in industrial control. When the structure and parameters of the controlled object cannot be fully understood, or an accurate mathematical model is not available, and other techniques from control theory cannot be applied, the structure and parameters of the system controller must be determined based on experience and on-site tuning; in such cases, PID control technology is the most convenient to use. That is, when we do not fully understand a system and its controlled object, or when it is not possible to obtain the system parameters through effective measurement methods, PID control technology is the most suitable approach. There is PID control; in practice, there are also PI and PD control. A PID controller controls the system by calculating the control amount using proportion, integration, and differentiation based on the system’s error. Proportional (P) control Proportional control is the simplest form of control. The output of its controller is proportional to the input error signal. When only proportional control is used, there is a steady-state error in the system output. 　 Integral (I) control: In integral control, the output of the controller is proportional to the integral of the input error signal. For an automatic control system, if there is a steady-state error after it reaches steady state, then such a control system is referred to as a system with steady-state error, or simply a system with error. To eliminate steady-state error, an “integral term” must be introduced into the controller. The integral term represents the time-dependent integration of the error, and as time increases, this integral term grows. In this way, even if the error is small, the integral term increases over time; it drives the output of the controller to increase, thereby further reducing the steady-state error until it becomes zero. Therefore, a proportional-plus-integral (PI) controller enables the system to have no steady-state error after reaching steady state. Differential (D) control: In differential control, the output of the controller is proportional to the derivative of the input error signal (i.e., the rate of change of the error). Automatic control systems may experience oscillations or even instability during the process of correcting errors. The reason is the presence of components with large inertia or components that introduce delay, which have a role in suppressing errors; their changes always lag behind those of the errors. The solution is to make the change in the error-suppression effect \"proactive\", that is, when the error approaches zero, the error-suppression effect should be zero. In other words, it is often not sufficient to include only a “proportional” term in the controller; the function of this proportional term is merely to amplify the magnitude of the error. What is needed now is a “derivative” term, which can predict the trend of error changes. Thus, a controller with both proportional and derivative terms can bring the control action to eliminate the error to zero, or even to a negative value, thereby preventing severe overshoot of the controlled variable. Therefore, for controlled objects with high inertia or lag, a proportional-plus-differential (PD) controller can improve the dynamic characteristics of the system during the regulation process.

A common mnemonic for PID control: To find the optimal parameter settings, check values in ascending order; start with the proportional term, then add the integral term, and finally the derivative term. If the curve oscillates frequently, increase the proportional gain; if the curve wanders around in large swings, decrease the proportional gain. If the curve takes a long time to return to its normal position, reduce the integration time; if the curve’s fluctuations have long periods, extend the integration time further. If the oscillation frequency is high, first reduce the derivative term. If there is significant error and slow response, increase the derivative time. An ideal curve consists of two waves, with the first wave being higher than the second, in a 4:1 ratio.

Because the sensitive temperature point of this distillation tower lags by about ten minutes, the fluctuations are relatively large