ISA Interchange

AutoQuiz: Best Strategy for Finding Efficient Operating Points with Complex Shifting Process Conditions

Written by Joel Don | May 12, 2017 1:00:24 PM

AutoQuiz is edited by Joel Don, ISA's social media community manager.

Today's automation industry quiz question comes from the ISA Certified Automation Professional certification program. ISA CAP certification provides a non-biased, third-party, objective assessment and confirmation of an automation professional's skills. The CAP exam is focused on direction, definition, design, development/application, deployment, documentation, and support of systems, software, and equipment used in control systems, manufacturing information systems, systems integration, and operational consulting. Click this link for information about the CAP program. The following question comes from the CAP study guide, Performance Domain II, Definition: Identify customer requirements and complete high-level analysis of the best way to meet those requirements.

What is required to find the MOST efficient operating points that are highly dependent on complex shifting process conditions, such as equipment fouling?

a) real-time optimization with a detailed process model
b) tactical schedulers with business economics
c) linear programs with detailed process economics
d) model predictive controls with a constraint pusher

 

Real-time optimization uses a reconciled high fidelity process model with the equipment details, such as heat transfer coefficients and physical properties of the components.

Tactical schedulers do not have process knowledge built in and may request operating points that are not achievable or advisable based on equipment and process constraints.

A linear program assumes fixed economic relationships and does not deal with stationary behavior shifts and nonlinear process behavior.

A model predictive control with a constraint pusher is only able to do a simple maximization on minimization of a process variable such as feed flow.

The correct answer is A, real-time optimization with a detailed process model.