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Samantha needs the most precise flow control method possible
Crestar Hotel’s ability to be profitable is based on the experience they create for their guests. The goal is to become that guests’ preferred stay choice. A lot of planning goes into each of the details that comprise this experience and the maintenance of a comforting environment is certainly one of them. So, the need to address any issues with its ability to maintain a comfortable environment at a precise temperature range within its walls is paramount.
As a result, Samantha’s only option is to use the nano PLC’s Proportional, Integral, Derivative (PID) method for controlling the flow of chilled air through the building.
PID control will ensure that the system will react quickly to disturbances and hold the flow rate to a setpoint with little to no error, and without oscillations.
Failure to apply precise control will lead to mechanical issues and adversely affect the hotel stay experience. If the flow rate in Crestar’s chiller system is too low, the building will not supply enough coolant throughout the building. And, if the flow rate is extremely low, the chiller itself may start to freeze leading to a costly mechanical failure.
PID, or proportional, integral, derivative control is the most commonly used control method when precision and accuracy are critical.
It works by summing scalar multiples of the error, integral of error, and derivative of error and outputting that summation as a reference to the system, or control device.
Each fundamental part of the function is used for a different reason, and adjustments can be made to the individual components to manipulate time dependent system responses like response time, decrease oscillations, reduce overshoot, etc.
Proportional control only relies on the error between the setpoint and the feedback from the system. The larger the error, the larger the increase in output.
The proportional gain, Kp scales the error that is fed back to the system. Getting the correct amount of proportional gain is critical because if there is too much of it, the system will overshoot and oscillate.
The integral component integrates or sums, the error over time.
An error can be positive or negative, therefore, even a small error term will slowly add up overtime, increasing or decreasing the output to the system until there is zero steady state error.
The integral gain, Ki is a multiplier to scale the amount of error that is summed together every cycle of the function block. Increasing the integral gain will increase the amount of correction to the point where the response may be undesirable.
Therefore, the standard is to use a small amount of gain to slowly reach a zero steady state error.
The derivative factor is proportional to the derivative of the error, or the rate of change of the error. Increasing the derivative gain,
Kd will cause the system to react more to changes in the error. Therefore, if there is noise on the feedback signal, the system will be highly sensitive to the fluctuations.
For this reason, derivative gain is not typically used.
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