In [1]:

    

import numpy as np
import pandas as pd
import math
import cmath
from scipy.optimize import root
from scipy.integrate import odeint
from __future__ import division
from scipy import *
from pylab import *
import matplotlib.pyplot as plt
%matplotlib inline


    

In [2]:

    

xo = 0.5
gamma = 0.8
yr = 0.1 #kg kgCO2 −1
TAO = 15


    

In [3]:

    

# en la matriz parametros se define cada conbinación (xk, A) como una fila de la matriz
parametros = np.array([[0.1, 1],[0.3, 2],[0.4, 4]])
parametros


    

    
Out[3]:





array([[ 0.1,  1. ],
       [ 0.3,  2. ],
       [ 0.4,  4. ]])

In [4]:

    

def intervalosExtraccion(tao, xk, A):
    tao1 = (xo - xk) / (gamma * A * yr)
    tao2 = tao1 + xk / (gamma * A * yr) * np.log(xk / xo + (1 - xk / xo) * np.exp(xo / xk * A))
    zk = xk / (A * xo) * np.log((xo * np.exp(gamma * A * yr / xk * (tao - tao1)) - xk) / (xo - xk))

    return tao1, tao2, zk


    

In [5]:

    

def rendimiento(tao, xk, A):
    
    tao1, tao2, zk = intervalosExtraccion(tao, xk, A)
    
    if tao <= tao1 and tao < tao2:
        e = gamma * yr* tao * (1- np.exp(- A))
        #print("tao < tao1 and tao < tao2")
        return e
    if tao > tao1 and tao <= tao2:
        zk = xk / (A * xo) * np.log((xo * np.exp(gamma * A * yr / xk * (tao - tao1)) - xk) / (xo - xk))
        e = gamma * yr * (tao - tao1 * np.exp(- A * (1 - zk)))
        #print("tao >= tao1 and tao < tao2")
        return e
    if tao > tao2:
        e = xo - xk / A * np.log(1 + xk / xo * (np.exp(xo / xk * A) - 1) * np.exp(gamma * A * yr / xk * (tao1 - tao)))
        #print("tao >= tao2")
        return e


    

In [6]:

    

caso1 = [[rendimiento(tao, xk, A) for tao in np.linspace(0,TAO)] for xk, A in parametros]
caso1


    

    




/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:4: RuntimeWarning: invalid value encountered in log






    
Out[6]:





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  0.49990700403098148]]

In [9]:

    

Tao = np.linspace(0,TAO)

plt.plot(Tao, caso1[2])
plt.title("Modelo Lack")
plt.xlabel(" $tao $ ")
plt.ylabel("Rendimiento e")


    

    
Out[9]:





<matplotlib.text.Text at 0x7f0789130320>






    

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