Theoretical Models of Chemical Processes and Control Strategies

Theoretical Models

Developed using chemistry, physics, and biology principles for physical insight and wide application.

Empirical Models

Derived from experimental data fitting, easier to develop but lack extrapolation capability.

Semi-empirical Models

Blend of theoretical and empirical models, incorporating knowledge for wider application.

CSTR Blending System

Control objective: blend inlet streams for desired outlet composition.

Controlled Variable

Variable to maintain at a set value, like composition in a blending system.

Manipulated Variable

Variable adjusted to control the system, e.g., flow rate in a blending process.

Disturbance Variable

Variable affecting the system but not directly controlled, like changing inlet composition.

Overall Balance; Steady-state

Equation summing all input and output rates at a constant state.

Degrees of Freedom Analysis

Ensures the model has a unique solution by balancing unknown variables and equations.

Laplace Transforms

Mathematical tool converting functions of time to functions of complex frequency.

Transfer Function Models

Mathematical representation of system dynamics in the frequency domain.

Conservation Laws

Principles like mass, component, and energy conservation in process modeling.

Linearization of Non-linear Models

Process of approximating non-linear models to linear ones for easier analysis.

Dynamic Behavior of 1st Order and 2nd Order Processes

Understanding how systems respond to input changes, crucial in process control.

Block Diagram Reduction Rule

Technique simplifying complex control system block diagrams for analysis.

Magnitude

deviation between the values of the setpoint

Duration

the length of time than an error condition existed

Rate of Change

the length of time that an error condition has existed

Continuous time control system

all signals are continuous

Discrete Time Control System

exists one or more discrete