(397e) Integrating Dcs and MPC Into Problem-Based Undergraduate Chemical Engineering Education

This paper describes a Lamar University Chemical Engineering Department initiative to incorporate modern manufacturing tools including an industrial Distributed Control System (DCS), into a problem-based learning (PBL) educational program. A Honeywell Experion DCS system including simulated Honeywell C200 controllers is stimulated using process values obtained from AspenTech's HYSYS dynamic process simulation cases. The loop is closed between the DCS and dynamic process simulation using Microsoft's automation technology and OPC data transfer protocol. This environment allows us to incorporate realistic modern manufacturing tools for learning Advanced Regulatory Control, Model Predictive Control (MPC), and DCS elements such as data storage and data analysis.

As part of this initiative, we plan to adapt a senior undergraduate second control class (CHEN 4332, Process Control II) which covers elements of Distributed Control System, Advanced PID Control, and MPC and a senior Advanced Analysis class (CHEN 4350) in which students learn data screening and validation in the chemical process industries. Students in the advanced analysis class use historized data of process streams around a de-ethanizer to practice and learn data screening techniques. They then validate the screened data using process simulation tools. Students in the second process control class tune traditional PID and advanced regulatory control structures and are introduced to industrial MPC using software for multivariable constraint control developed at Lamar University. The Lamar MPC software includes three separate programs: (1) Model Identification (2) Controller Configuration (3) Controller Simulation consistent with the industrial steps used to implement this advanced control technology. In future implementations of this lab the students will connect their controller to the DCS system to control a dynamically simulated chemical process.

Tests were used as evaluation instruments to assess student understanding of the data analysis and MPC modules. Then, surveys were performed to assess student satisfaction.