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This nonlinear model database is an attempt to create a collaborative environment where process models can be documented and shared. As collaborators submit models the database will serve as a valuable starting point for the development of more sophisticated models for simulation, estimation, and control.
The database consists of nonlinear models that include chemical reactors, binary distillation columns, and simple mechanical systems. The complexity of the models range from a simple ODE model with 1 input and 1 state to a large DAE model with 2 inputs and 125 states. Most of the models are taken from published articles. Currently, all the models are written in MATLAB. Each one has a step response driver where the model response is computed with a MATLAB integrator (currently ODE15s).
Model 1 - CSTR
The CSTR model with A->B exothermic reaction is the most popular model in the database. It is a standard model that has been used in reaction engineering textbooks, simulation and control research, and demonstrations for industrial software.
The model has 2 states: the concentration of A and the temperature of the reaction vessel liquid. The manipulated variable is the jacket water temperature. At a jacket temperature of 305K, the reactor model has an oscillatory response. The oscillations are characterized by reaction run-away with a temperature spike. When the concentration drops to a low value, the reactor cools until the concentration builds and there is another run-away reaction.
Model 3 - CSTR
Models 1-5, 10 are all variations of the CSTR model. Model 3 in particular, has a reaction intermediary (B). There is an additional equation and variable to account for the intermediate reaction step.
Model 8 - Binary Distillation Column with 30 trays (cyclohexane n-heptane)
Distillation column models are generally good test models for nonlinear model reduction and identification. The concentrations at each stage or tray are highly correlated. The dynamics of the distillation process can be described by a relatively few number of underlying dynamic states. A couple papers have been published with this model as an example application. One in particular is:
Hahn, J. and T.F. Edgar, An improved method for nonlinear model reduction using balancing of empirical gramians, Computers and Chemical Engineering, 26, pp. 1379-1397, (2002)
This plot shows the system response after a step change in the reflux ratio from 3.0 to 1.5. Each trajectory represents the mole fraction of cyclohexane at each tray. The top reflux material becomes less pure (more n-heptane) due to the increased draw from the top of the column.
Model 12 - Cruise Control with Disturbance
This simple mechanical model is of an object that is seeking to maintain constant speed while subject to disturbances. In this case, the disturbance is the incline or decline angle.
Model 16 - Gravity Drained Water Tank
This gravity drained water tank was a control experiment for Tom Edgar's undergraduate control course. The students had to perform experiments to determine the process time constant and tune a PID controller. The model gave excellent predictions of level (or volume) and was used to demonstrate the advantage of model predictive control (MPC) over PID control for level control.
The trend shows the inlet valve 80% open for 60 seconds. The volume reaches 1400 mL before the inlet value is shut and the tank drains.
Model 21 - Human Blood Glucose Model for Insulin Control - Type I Diabetes
This model is combined from two related papers:
S. M. Lynch and B. W. Bequette, Estimation based Model Predictive Control of Blood Glucose in Type I Diabetes: A Simulation Study, Proc. 27th IEEE Northeast Bioengineering Conference, IEEE, 2001. and S. M. Lynch and B. W. Bequette,
Model Predictive Control of Blood Glucose in type I Diabetics using Subcutaneous
Glucose Measurements, Proc. ACC, Anchorage, AK, 2002. It is a simple 3 state model that
effectively describes blood glucose and insulin dynamics. The 3 states are
plasma glucose concentration (mmol/L), plasma insulin concentration (mU/L) in
remote compartment, and plasma insulin concentration (mU/L). The principal
disturbance variable is the glucose input.
Model 22 - Yeast Fermentation Bioreactor
Zoltan Nagy contributed this model of a continuous plug flow fermentation reactor. Oxygen solubility is a function of the minerals that are present in solution.
Thanks for visiting. You can make this database better by submitting your own models or improvements to the current set. Contact me: john@hedengren.net John Hedengren's Research Page |
MATLAB Nonlinear Model
Library
