Added Benefit With Zero Added Cost
AspenTech’s University Program offers
flexible, affordable software packages to enhance your chemical engineering
curriculum. The new computer-based teaching modules and Aspen Process
Controller software are included in the university package at no extra cost.
Solutions Specially Tailored for Universities
AspenTech has developed interactive computer-based teaching modules which can
be integrated into existing curricula. Modules created by experts were tested
out by recent graduates from leading universities. Feedback was used to enhance
the modules to ensure content is both challenging and informative.
Build theoretical and empirical model-based, multivariable controller applications
Modules walk students through the
configuration procedure in a step by step manner. They will use first principle
equations (mass, energy, and momentum) to generate working
Show how process outputs respond to changes in process inputs
Explore relationships by experimenting with changing set points for
input and output variables. Evaluate the performance of model predictive
control in the face of a strongly interacting multi-input multi output (MIMO)
Develop a better understanding of process behavior and performance
Show how the filter compares output measurements
from the plant with output predictions from the model in order to determine the
dynamic state of the plant and to predict future plant performance.
Optimize by setting targets, defining constraints, manipulating inputs and allowing trade-offs
See how the optimizer computes the best steady
state operating point for the plant, subject to constraints and economic tuning.
Show how trade-offs affect a process, and how to determine what to maximize and
where to minimize.
Learn how to develop a move plan
Address the question: How will you get there? Develop
a multiple-step manipulated variable move plan that takes the plant from its
current operating point to the optimal steady state with controller tuning.
Illustrate the advantages of MPC over PID controllers
Show that model predictive control is
suitable for processes with difficult dynamics, tightly constrained systems, and
strongly interacting multi-variable systems.