# Double Tank (GEKKO)

This page contains a solution of the Double Tank in GEKKO Python format. The GEKKO package is available with pip install gekko. The Python code uses orthogonal collocation and a simultaneous optimization method. The integral is converted to a differential equation through differentiation and definition of a new variable x3.

```import numpy as np
import matplotlib.pyplot as plt
from gekko import GEKKO

m = GEKKO() # create GEKKO model

# Add 0.01 as first step
# 0,0.01,0.1,0.2,0.3,...9.9,10.0)
m.time = np.insert(np.linspace(0,10,201),1,0.01)

# change solver options
m.solver_options = ['minlp_gap_tol 0.001',\
'minlp_maximum_iterations 10000',\
'minlp_max_iter_with_int_sol 100',\
'minlp_branch_method 1',\
'minlp_integer_tol 0.001',\
'minlp_integer_leaves 0',\
'minlp_maximum_iterations 200']

k1 = 2
k2 = 3

last = m.Param(np.zeros(202))
last.value[-1] = 1

sigma=m.MV(value=1,lb=1,ub=2,integer=True)
x1 = m.Var(value=2)
x2 = m.Var(value=2)
x3 = m.Var(value=0)
sigma.STATUS = 1

m.Obj(last*x3)

m.Equations([x1.dt() == sigma - m.sqrt(x1),\
x2.dt() == m.sqrt(x1) - m.sqrt(x2),\
x3.dt() == k1*(x2-k2)**2])

m.options.IMODE = 6
m.options.NODES = 3
m.options.SOLVER = 1
m.options.MV_TYPE = 0
m.solve()

plt.figure(1)
plt.step(m.time,sigma.value,'r-',label=r'\$\sigma\$ (1/2)')
plt.plot(m.time,x1.value,'k-',label=r'\$x_1\$')
plt.plot(m.time,x2.value,'g-',label=r'\$x_2\$')
plt.xlabel('Time')
plt.ylabel('Variables')
plt.legend(loc='best')
plt.show()```

## Results with APOPT (MINLP)

An MINLP solution is calculated with APOPT with an objective function value of 4.767757.