What is a PID Temperature Control
PID temperature control is a loop control feature found on most process controllers to improve the accuracy of the process. PID temperature controllers work using a formula to calculate the difference between the desired temperature setpoint and current process temperature, then predicts how much power to use in subsequent process cycles to ensure the process temperature remains as close to the setpoint as possible by eliminating the impact of process environment changes.
PID temperature controllers differ from On/Off temperature controllers where 100% power is applied until the setpoint is reached, at which point the power is cut to 0% until the process temperature again falls below the setpoint. This leads to regular overshoots and lag which can affect the overall quality of the product.
Temperature controllers with PID are more effective at dealing with process disturbances, which can be something as seemingly innocuous as opening an oven door, but the change in temperature can then have an impact on the quality of the final product. If the PID temperature controller is tuned properly it will compensate for the disturbance and bring the process temperature back to the setpoint, but reduce power as temperature approaches the setpoint so that it doesn’t overshoot and risk damaging the product with too much heat.
The P, I & D
PID control belongs to the “optimal” category of control theory which specifies that a certain process variable is optimally achieved. For temperature controller PID, the optimal variable is maintaining the process temperature at the setpoint for the desired period of time, avoiding any severe changes from lag, overshoot or disturbances.
The three elements of the PID algorithm are the Proportional, the Integral, and the Derivative. These elements each relate to the variance in the process temperature versus the setpoint in a period of time.

Proportional – the variance between the setpoint and the current process temperature

Integral – the previous variance from the setpoint

Derivative – the predicted future variance based on previous and current variance