Linear Approximation of Process Models

Key Idea

Process models usually exhibit some form of non-linearity due to the multiplication of an extensive flowrate and an intensive thermodynamic state variable, chemical kinetics, or various types of transport phenomenon. Near a steady-state, however, an approximate linear models often provide a 'good-enough' dynamical model for control design and analysis. Here we show how a linear approximation is constructed using a Taylor series expansion.

Why is this Important?

As we will learn later in this course, linear models are amenable to control design and analysis. For process systems that will be operated near a steady-state, linear models provide a useful framework for control design.

Procedure for a Single Variable System

We start with a process model that consists of a single first-order ordinary differential equation of the form


where $h$ is the state variable and $q$ is an input variable. Choose a nominal value for the process input $q$, we'll call this nominal value $\bar{q}$. The following procedure will produce a linear approximate valid near the steady-state.

Step 1. Find a Nominal Steady State Operating Condition

Find the steady-state value of $h$ by solving


where $\bar{q}$ is a nominal (i.e, typical, or desired value) of a manipulated process variable.

Step 2. Define deviation variables
\begin{eqnarray*} x & = & h-\bar{h}\\ u & = & q-\bar{q} \end{eqnarray*}
Step 3. Taylor Series Expansion

Compute the first terms in the Taylor series and evaluate at steady-state. The higher-order terms are not needed provided the lower-order terms are non-zero and the deviations are small. $$ f(\bar{h}+x,\bar{q}+u)\approx f(\bar{h},\bar{q})+\left.\frac{\partial f}{\partial h}\right|_{\bar{h},\bar{q}}x+\left.\frac{\partial f}{\partial q}\right|_{\bar{h},\bar{q}}u+\cdots $$

Step 4. Linear Approximation

The linear approximation is $$ \frac{dx}{dt}=\underbrace{\left.\frac{\partial f}{\partial h}\right|_{\bar{h},\bar{q}}}_{a}x+\underbrace{\left.\frac{\partial f}{\partial q}\right|_{\bar{h},\bar{q}}}_{b}u $$

Example: Gravity Drained Tank

A simple model for the liquid height in a gravity-drained tank with cross-sectional area $A$ is


where $q_{in}$ is a volumetric inflow and $C$ is a constant associated with the drain. This is a non-linear process model that can be written




Given a nominal inlet flowrate $\bar{q}_{in}$, the steady state value of $h$, that is $\bar{h}$, is found by solving the steady state equation


which gives


It's interesting to note the steady-state height of the liquid in a gravity-drained tank is proportional to the square of the nominal flowrate. A 50\% increase in flowrate more than doubles the liquid height.

Let $x$ and $u$ represent the deviations from steady-state

\begin{eqnarray*} x & = & h-\bar{h}\\ u & = & q-\bar{q} \end{eqnarray*}


\begin{eqnarray*} \frac{d(\bar{h}+x)}{dt} & = & \frac{1}{A}\left(\bar{q}_{in}+u-C\sqrt{\bar{h}+x}\right) \end{eqnarray*}

The Taylor series expansion

$$f(\bar{h}+x,\bar{q}_{in}+u)\approx f(\bar{h},\bar{q}_{in})+\left.\frac{\partial f}{\partial h}\right|_{\bar{h},\bar{q}}x+\left.\frac{\partial f}{\partial q}\right|_{\bar{h},\bar{q}}u+\frac{1}{2}\left.\frac{\partial^{2}f}{\partial h^{2}}\right|_{\bar{h}}x^{2}\cdots$$

For this example


An alternative form of the model is found by substituting the solution for $\bar{h}$. While these have somewhat different analytical expressions for a given application they will yield identical numerical results.


How well does this approximation work?

This question can be answered by comparing the results of two simulations. In the first case the simulation consists of integrating


as shown in the graph below.

where $A=1$, $C=2$, initial condition $h(0)=0$, and a constant input $q_{in}=\bar{q}_{in}=1$. For these parameter values, the approximate linear model for the deviation from steady-state is given by

\begin{eqnarray*} \frac{dx}{dt} & = & \left(-\frac{C^{2}}{2A\bar{q}_{in}}\right)x+\left(\frac{1}{A}\right)u\\ \\ & = & -2\,x+u \end{eqnarray*}

In terms of deviations from steady state, the input $u=q_{in}-\bar{q}_{in}=0$ and the initial conditionis $x(0)=h(0)-\bar{h}=-\bar{h}$. Plotting $h(t)$ and $x(t)+\bar{h}$ on the same axis produces the results shown in Figure \ref{fig:LinearApproximation}.

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#Simulation of a Gravity-Drained Tank

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy.integrate import odeint

# parameter values
qin = 1
A = 1
C = 2

# steady state
hbar = (qin/C)**2

# nonlinear simulation
def hdot(h,t):
    return (qin - C*np.sqrt(h))/A

t = np.linspace(0,5)
h = odeint(hdot,[0],t)

# linear approximation
a = -C**2/2/A/qin
b = 1/A

def xdot(x,t):
    return a*x + b*u

u = 0
x = odeint(xdot,[-hbar],t)

# visualization
plt.legend(['Nonlinear','Linear Approximation'],loc='lower right')
plt.xlabel('Time [min]')
plt.ylabel('Height [m]')
plt.title('Gravity-drained tank: Nonlinear model vs. linear approximation')


1. Suppose you have a tank with an cross-sectional area of 1 square meter, a steady inlet flow $\bar{q}_{in}=10$ liters/min, and observe a liquid height of 0.5 meters. What is the constant $C$? What is the characteristic time constant for the tank?

2. You have an elementary reaction

$$2{\cal A}\longrightarrow Products$$

carried out under isothermal conditions in a stirred tank of volume $V$. The reaction rate is given by the expression


and the inlet concentration to the tank is $C_{A,in}$ at flowrate $q$. Construct a linear approximation for the dynamics of this process in the neighborhood of a steady state.

Multivariable Systems

Key Idea

Most process models consist of more than a single state and a single input. Techniques for linearization extend naturally to multivariable systems. The most convenient mathematical tools involve some linear algebra.

Example: Gravity Drained Tanks

Here we develop a linear approximation to a process model for a system consisting of coupled gravity-drained tanks.

Gravity Drained Tanks

\begin{eqnarray*} A_{1}\frac{dh_{1}}{dt} & = & q_{in}-C_{1}\sqrt{h_{1}}\\ A_{2}\frac{dh_{2}}{dt} & = & q_{d}+C_{1}\sqrt{h_{1}}-C_{2}\sqrt{h_{2}} \end{eqnarray*}
Nominal Inputs
\begin{eqnarray*} \bar{q}_{in} & = & \bar{q}_{in}\\ \bar{q}_{d} & = & 0 \end{eqnarray*}
Steady State for nominal input
\begin{eqnarray*} \bar{h}_{1} & = & \frac{\bar{q}_{in}^{2}}{C_{1}^{2}}\\ \bar{h}_{2} & = & \frac{\bar{q}_{in}^{2}}{C_{2}^{2}} \end{eqnarray*}
Deviation Variables


\begin{eqnarray*} x_{1} & = & h_{1}-\bar{h}_{1}\\ x_{2} & = & h_{2}-\bar{h}_{2} \end{eqnarray*}

Control input $$u=q_{in}-\bar{q}_{in}$$

Disturbance input $$d=q_{d}-\bar{q}_{d}$$

Measured output $$y=h_{2}-\bar{h}_{2}$$

\begin{eqnarray*} \frac{dh_{1}}{dt} & = & \frac{1}{A_{1}}\left(q_{in}-C_{1}\sqrt{h_{1}}\right)=f_{1}(h_{1},h_{2},q_{in},q_{d})\\ \frac{dh_{2}}{dt} & = & \frac{1}{A_{2}}\left(q_{d}+C_{1}\sqrt{h_{1}}-C_{2}\sqrt{h_{2}}\right)=f_{2}(h_{1},h_{2},q_{in},q_{d}) \end{eqnarray*}

Taylor series expansion for the first tank

\begin{eqnarray*} \frac{dx_{1}}{dt} & = & f_{1}(\bar{h}_{1}+x_{1},\bar{h}_{2}+x_{2},\bar{q}_{in}+u,\bar{q}_{d}+d)\\ & \approx & \underbrace{f_{1}(\bar{h}_{1},\bar{h}_{2},\bar{q}_{in})}_{0}+\left.\frac{\partial f_{1}}{\partial h_{1}}\right|_{SS}x_{1}+\left.\frac{\partial f_{1}}{\partial h_{2}}\right|_{SS}x_{2}+\left.\frac{\partial f_{1}}{\partial q_{in}}\right|_{SS}u+\left.\frac{\partial f_{1}}{\partial q_{d}}\right|_{SS}d\\ & \approx & \left(-\frac{C_{1}}{2A_{1}\sqrt{\bar{h}_{1}}}\right)x_{1}+\left(0\right)x_{2}+\left(\frac{1}{A_{1}}\right)u+\left(0\right)d\\ & \approx & \left(-\frac{C_{1}^{2}}{2A_{1}\bar{q}_{in}}\right)x_{1}+\left(\frac{1}{A_{1}}\right)u \end{eqnarray*}

Taylor series expansion for the second tank

\begin{eqnarray*} \frac{dx_{2}}{dt} & = & f_{2}(\bar{h}_{1}+x_{1},\bar{h}_{2}+x_{2},\bar{q}_{in}+u,\bar{q}_{d}+d)\\ & \approx & \underbrace{f_{2}(\bar{h}_{1},\bar{h}_{2},\bar{q}_{in})}_{0}+\left.\frac{\partial f_{2}}{\partial h_{1}}\right|_{SS}x_{1}+\left.\frac{\partial f_{2}}{\partial h_{2}}\right|_{SS}x_{2}+\left.\frac{\partial f_{2}}{\partial q_{in}}\right|_{SS}u+\left.\frac{\partial f_{2}}{\partial q_{d}}\right|_{SS}d\\ & \approx & \left(\frac{C_{1}}{2A_{2}\sqrt{\bar{h}_{1}}}\right)x_{1}+\left(-\frac{C_{2}}{2A_{2}\sqrt{\bar{h}_{2}}}\right)x_{2}+\left(0\right)u+\left(\frac{1}{A_{2}}\right)d\\ & \approx & \left(\frac{C_{1}^{2}}{2A_{2}\bar{q}_{in}}\right)x_{1}+\left(-\frac{C_{2}^{2}}{2A_{2}\bar{q}_{in}}\right)x_{2}+\left(\frac{1}{A_{2}}\right)d \end{eqnarray*}

Measured outputs

\begin{eqnarray*} y & = & h_{2}-\bar{h}_{2}\\ & = & x_{2} \end{eqnarray*}
Summary of the linear model using matrix/vector notation
\begin{eqnarray*} \frac{d}{dt}\left[\begin{array}{c} x_{1}\\ x_{2} \end{array}\right] & = & \left[\begin{array}{cc} -\frac{C_{1}^{2}}{2A_{1}\bar{q}_{in}} & 0\\ \frac{C_{1}^{2}}{2A_{2}\bar{q}_{in}} & -\frac{C_{2}^{2}}{2A_{2}\bar{q}_{in}} \end{array}\right]\left[\begin{array}{c} x_{1}\\ x_{2} \end{array}\right]+\left[\begin{array}{c} \frac{1}{A_{1}}\\ 0 \end{array}\right]u+\left[\begin{array}{c} 0\\ \frac{1}{A_{2}} \end{array}\right]d\\ y & = & \left[\begin{array}{cc} 0 & 1\end{array}\right]\left[\begin{array}{c} x_{1}\\ x_{2} \end{array}\right]+\left[\begin{array}{c} 0\end{array}\right]u+\left[\begin{array}{c} 0\end{array}\right]d \end{eqnarray*}


1. First Order Reaction in a CSTR

A first-order reaction


takes place in an isothermal CSTR with constant volume $V$, volumetric flowrate in and out the same value $q$, reaction rate constant $k$, and feed concentration $c_{AF}$.

  • Write the material balance equation for this system as a first-order ordinary differential equation.
  • Algebraically determine the steady-state solution to this equation.
  • For what values of $q$, $k$, $V$, and $c_{AF}$ is this stable?
  • For a particular reaction $k$ = 2 1/min. We run this reaction in a vessel of volume $V$ =10 liters, volumetric flowrate $q$ = 50 liters/min. The desired exit concentrationof $A$ is $0.1$ gmol/liter. Assume that we can manipulate $c_{AF}$, the concentration of $A$ entering the CSTR, with proportional control, in order to control the concentration of $A$ exiting the reactor. Substitute the appropriate control law into the mathematical model and rearrange it to get a model of the same form as part (a).
  • Determine the steady-state solution to this equation. If we set $K_{c}=100$, what is the absolute value of the offset between the exit concentration of $A$ and the desired exit concentration of $A$?
2. Two Gravity Drained Tanks in Series

Consider the two-tank model. Assuming the tanks are identical, and using the same parameter values as used for the single gravity-drained tank, compute values for all coefficients appearing in the multivariable linear model. Is the system stable? What makes you think so?

3. Isothermal CSTR with second-order reaction

Consider an isothermal CSTR with a single reaction $$A\longrightarrow\mbox{Products}$$ whose reaction rate is second-order. Assume constant volume, $V$, and density, $\rho$, and a time-dependent volumetric flow rate $q(t)$. The input to the reactor has concentration $c_{A,in}$.

  • Write the modeling equation for this system.
  • Assume that the state variable is the concentration of $A$ in the output, $c_{A}$, and the flow rate $q$ is an input variable. There is a steady-state operating point corresponding to $q=1$ L/min. Other parameters are $k=1$ L/mol/min, $V=2$ L, $c_{A,in}=2$ mol/L. Find the linearized model for $\frac{dc'_{A}}{dt}$ valid in the neighborhood of this steady state.
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