Practice ArticleDemonstration of leapfrogging for implementing nonlinear model predictive control on a heat exchanger

- Nonlinear control requires a model, often developed with nonlinear regression.
- Model predictive control (MPC) needs to optimize the time-sequence of future manipulated variable inputs to the model.
- This work uses a novel Leapfrogging optimizer for both applications, and
- Demonstrates nonlinear MPC on a pilot-scale heat exchanger.

This work reveals the applicability of a relatively new optimization technique, Leapfrogging, for both nonlinear regression modeling and a methodology for nonlinear model-predictive control. Both are relatively simple, yet effective. The application on a nonlinear, pilot-scale, shell-and-tube heat exchanger reveals practicability of the techniques.