Allan Kern
Allan Kern, P.E., is owner, APC Performance LLC. He has over 30 years of advanced process control (APC) experience and has authored numerous papers on cost-effective APC solutions. He is the inventor of an inherently adaptive control algorithm and a model-less method of multivariable control. He is a 1981 Chemical Engineering graduate of the University of Wyoming and has professional engineering licenses in Control System Engineering and Chemical Engineering.
Articles by Allan Kern
Understanding the critical role of metrics for advanced process controls
Advanced process controls (APC) requires appropriate metrics, or key performance indicators (KPIs) to ensure safe, efficient operations. Alarms, relief valves, safety systems and APC need monitoring. Learn five good APC metrics, the missing metric and four recommended practices for APC metrics.
Understanding APC limits and targets
Process automation and optimization need to use automated multivariable control so it evolves into an industry core-competency, providing low cost, low maintenance and high agility with long life-cycle technology instead of manual multivariable control. Understand limits and targets of advanced process control.
Understanding the matrix for APC improvements
More agile tools are helping multivariable control become more effective for advanced process control and other applications than traditional model-based control (MPC) and real-time optimization. Process engineers, operations personnel, DCS and APC engineers, and other process operation and optimization stakeholders benefit from understanding the matrix.
Multivariable control as a core competency
A healthy multivariable advanced process control (APC) layer should be a core-competency of the process industries.
Changes in store for advanced process control
In advanced process control (APC), closed-loop multivariable control brings the inherent benefits of greater timeliness and consistency, fewer alarms and constraint violations and more effective process optimization. Multivariable control is core to most process operations. See “What is ‘real-time’ optimization?”
Feedforward: Not as popular as expected, again
Feedforward and feedback: Multivariable control, like single-loop control, can be accomplished primarily with feedback control and selective (not wholesale) use of feedforward.
Understanding multivariable control: The missing metric
Advanced process control: An improved understanding of the role of multivariable control in industrial process operations will lead to more cost-effective solutions and engage a wider circle of people in the process automation enterprise.
Advanced process control: More answers
After a webcast on “Advanced process control: Past, present and future,” more answers to audience questions are provided by the webcast speaker covering advanced process control (APC) to help with process optimization.
Operation-driven matrix design
A multivariable control matrix has manipulated variables (MVs) on one axis, controlled variables (CVs) on the other axis, and models in the matrix that indicate a relationship between that MV/CV pair. Effective multivariable controllers use the right models for various control and optimization needs.
Advantages of RPC and limits of model-based control
Part 3: Rate-predictive control, an alternative algorithm to proportional-integral-derivative (PID) and model-based control, can provide single-loop control. See three RPC advantages and two model-based control limitations.