### Process Control Quiz Screencasts

Choose from the list of screencasts below. Each screencast has at least one interactive quiz during the video. The description above each video provides a brief summary.

- Blending Process: Dynamic Modeling
- Blending Process: Steady-State
- Blending Process: Deviation Variables
- Blending Process: Introduction to Linearization
- Blending Process: Linearization Example
- Blending Process: Introduction to Laplace Transform
- Blending Process: Laplace Transformation Example
- Blending Process: State Space
- Blending Process: Laplace Transform and State Space Quiz
- Blending Process: Closing the Loop
- Blending Process: Closed-Loop Response
- Blending Process: Closed-Loop Stability
- Blending Process: Direct Synthesis
- Blending Process: Introduction to Ratio Control
- Blending Process: Ratio Control Example
- Blending Process: Relative Gain Array

**Description**: Builds a dynamic model of the blending process using mass balances. This case study was inspired by the Blending Process example in Chapter 2 of “Process Dynamics and Control,” Seborg, Edgar, Mellichamp, Doyle, 3rd Edition, Wiley, 2011.

**Description**: Develops the mass balances for a blending tank at steady-state.

**Description**: Writes the mass balances for a blending process in terms of deviation variables.

**Description**: Presents the concept of linearization using a first-order Taylor series approximation, and demonstrates this concept on the blending process. The example used here is the volume in the tank, which is described by a linear differential equation.

**Description**: Demonstrates linearization using the blending process. A nonlinear differential equation is considered here, which describes the mass fraction in the tank.

**Description**: Presents the concept of the Laplace transform, and demonstrates the method on the blending process. The time-varying volume in the tank is calculated, for the case when a step change is applied to the inlet flow.

**Description**: Demonstrates the Laplace transfer method to the blending process. The time-varying mass fraction in the tank is calculated, for the case when a step change is applied to the inlet flow.

**Description**: The linear approximate model of the blending process is rewritten in state space form. The dynamic states are the deviations in volume and in mass fraction. The inputs are the flow rates of the inlet and outlet streams.

**Description**: Looks at the blending process and how to combine the LaPlace transform and state space into a single model.

**Description**: The basic principle of feedback is introduced, in which the process input is automatically and continuously adjusted based on the measured process output. A new closed loop dynamic system is obtained, in which the new input is the setpoint value for the output.

**Description**: The closed loop response of the blending process is calculated, for a step change in the process setpoint. A proportional controller is used, and the dependence of the response on the control gain is discussed.

**Description**: The concept of stability is presented, requiring a bounded output for any bounded input. The stability of the closed loop blending process is calculated for a proportional control law. The root locus plot is introduced to visualize the dependence of stability on controller gain.

**Description**: The feedback control law is calculated using the direct synthesis method, to achieve a desired closed-loop behavior for good setpoint tracking. Given a model of the process, the control law is calculated. For the blending process, this gives a proportional-integral controller.

**Description**: The concept of ratio control is introduced, as a type of feedforward control to keep two streams in a fixed ratio. The block diagram is introduced, and the overall transfer function is derived.

**Description**: The transfer function for the blending process is derived, using a proportional controller for the feedback block.

**Description**: The relative gain array is calculated for a multi-input, multi-output blending process. A best pairing of inputs and outputs is identified, as well as significant interaction terms between the two loops.