Advanced Industrial Process Control and Optimization Techniques

Anti-Surge Control

Prevents the compressor from surging while maintaining operation as close to the limit as possible. This ensures that pumping valves operate with the minimum necessary movement, avoiding a loss of compressor performance.

Non-Linear Level Monitoring

Standard linear PID algorithms are often insufficient for non-linear processes. This control method utilizes specific programs that match the capacity of a given vessel to minimize variations in inflow or outflow when flow discrepancies occur.

Distillation Column Control

For distillation columns, the following variables are managed:

• Composition of impurities
• Temperatures of the rectification and stripping zones
• Head flow and temperature
• Bottom energy input
• Reflux-to-distillate ratio

Calculating Control Parameters

Control parameters are calculated based on variables that best represent the current state of the process.

Temperature Control Offset by Pressure

Because boiling point temperatures change with pressure, a reference pressure is used to maintain composition control. If the actual pressure deviates from the reference, the temperature is compensated accordingly.

Multivariable Control

A set of techniques designed to drive the process toward an economic optimum using specific simplifications. This is the most common approach in the refining industry.

Process Modeling

A representation that allows for process analysis without the need for physical experimentation. This model utilizes matrix-based principles:

• Linearity: If a manipulated variable changes by a factor of 3, the effect on the controlled variable is proportional.
• Superposition: The effect of two consecutive changes in a manipulated variable is the sum of the individual movements at the time they occur.

Linear Programming and Constraints

This approach seeks to achieve steady-state setpoints while ensuring all variables remain within specified limits. It identifies potential solutions and provides criteria for selecting the optimal one.

Multivariable Predictive Controller Modules

• Prediction Module: Calculates future values of each Controlled Variable (CV) based on historical data, variable changes, mathematical models, and the time required to reach a steady state.
• Optimization Module: Uses linear programming techniques, steady-state values from the prediction module, and Manipulated Variable (MV) costs.
• Control Module: Utilizes the optimization output, predictions, and control matrix to calculate the necessary movements of the MVs.