## Tank Blending

The objective of this case study is to create a dynamic model based on a physics-based derivation from species and energy balance equations. A mixing tank has a liquid inlet stream and outlet stream. The tank is well mixed so the concentration and temperature are assumed to be the same throughout the reactor.

The exercise involves starting with species and energy balance equations and deriving the dynamic concentration and temperature response. The derivation of the concentration response is developed and the temperature response is left as an exercise. Assume a constant volume *V* of 100 m^{3} and an inlet flow rate `\dot V` of 100 m^{3}/hr.

#### Species Balance

The first objective is to predict the concentration of *A* over a simulation time horizon. A species balance is created by relating the accumulation, inlet, and outlet terms of the number of moles *n* of species *A*. The accumulation of *A*, *d(n _{A})/dt*, in a control volume is calculated by inlet, outlet, reaction generation, and reaction consumption rates.

$$\frac{dn_A}{dt} = \sum \dot n_{A_{in}} - \sum \dot n_{A_{out}} + \sum \dot n_{A_{gen}} - \sum \dot n_{A_{cons}}$$

The molar amount, n_{A} is often measured as a concentration, *c _{A}*. In this application there are no reaction terms so the species balance can be simplified.

$$\frac{dc_A V}{dt} = c_{A_{in}} \dot V_{in} - c_{A_{out}} \dot V_{out}$$

import matplotlib.pyplot as plt

from scipy.integrate import odeint

# define mixing model

def mixer(x,t,Tf,Caf):

# Inputs (2):

# Tf = Feed Temperature (K)

# Caf = Feed Concentration (mol/m^3)

# States (2):

# Concentration of A (mol/m^3)

Ca = x[0]

# Parameters:

# Volumetric Flowrate (m^3/hr)

q = 100

# Volume of CSTR (m^3)

V = 100

# Calculate concentration derivative

dCadt = q/V*(Caf - Ca)

return dCadt

# Initial Condition

Ca0 = 0.0

# Feed Temperature (K)

Tf = 300

# Feed Concentration (mol/m^3)

Caf = 1

# Time Interval (min)

t = np.linspace(0,10,100)

# Simulate mixer

Ca = odeint(mixer,Ca0,t,args=(Tf,Caf))

# Construct results and save data file

# Column 1 = time

# Column 2 = concentration

data = np.vstack((t,Ca.T)) # vertical stack

data = data.T # transpose data

np.savetxt('data.txt',data,delimiter=',')

# Plot the results

plt.plot(t,Ca,'r-',linewidth=3)

plt.ylabel('Ca (mol/L)')

plt.legend(['Concentration'],loc='best')

plt.xlabel('Time (hr)')

plt.show()

#### Exercise: Energy Balance

An energy balance for this application starts with the balance equation for enthalpy, *h*. Enthalpy is related to temperature as *m c _{p} (T-T_{ref})* where c

_{p}is the heat capacity. With a constant reference temperature (T

_{ref}), this reduces to the following.

$$m\,c_p\frac{dT}{dt} = \sum \dot m_{in} c_p \left( T_{in} - T_{ref} \right) - \sum \dot m_{out} c_p \left( T_{out} - T_{ref} \right) + Q + W_s$$

There is no heat input `Q`, shaft work `W_s`, or reaction. Reduce this energy balance by eliminating any terms and simplifying the expression. Implement the additional energy balance equation and simulate a feed temperature change from 350 K to 300 K. The tank fluid is initially at 350 K. The liquid heat capacity is constant and does not depend on the concentration of *A*.

#### Energy Balance Solution

#### Exercise: Implement PID Control

Implement a PID controller that maintains the outlet concentration of *A* in the mixer by automatically adjusting the feed concentration. Assume that the concentration is continuously measured and that the controller should be designed to follow set point changes to 1.5 mol/L and then down to 1.0 mol/L. The maximum feed concentration is 2.0 mol/L and the minimum is 0.0 mol/L.