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main.py
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main.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Jul 1 11:35:26 2020
@author: 1052668570
"""
import os
import matplotlib.pyplot as plt
import time
import numpy as np
from Biorrefineria import Biorrfineria
from Proceso import ProcesoDA, ProcesoMEC
from Variable import Variable
np.random.seed(101)
wd = os.getcwd()
main_folder = 'OOP_TEST'
simulation = "test1" # Change this for every new run
path = wd + '\\' + main_folder + '\\' + simulation
if not os.path.exists(path):
os.makedirs(path)
# =============================================================================
# Initialize instances
# =============================================================================
def main(dias=3600, noise=False, failures=False, ontology=False):
# DA Variables definition
dil_da = Variable(name='da_dil', vals=0.6, units="$d^{-1}$",
desc="Tasa de dilución: Entrada(DA)")
agv_in_da = Variable(name='da_agv_in', vals=100.0, units="$mmol L^{-1}$",
desc="Concentración de acetato: Entrada(DA)")
dqo_in = Variable(name='da_dqo_in', vals=25.0, units="$gd^{-1}}$",
desc="Demanda Quimica de Oxígeno: Entrada(DA)")
biomasa = Variable(name='da_biomasa', vals=24.8, units="$X_{1}(gL^{-1})$",
desc="Biomasa: Salida(DA)")
dqo_out = Variable(name='da_dqo_out', vals=12.5, units="$gL^{-1}}$",
desc="Demanda Quimica de Oxígeno: Salida(DA)")
agv_out = Variable(name='da_agv_out', vals=42.0, units="$mmol L^{-1}$",
desc="Concentración de acetato: Salida(DA)")
# MEC Variables definition
agv_in_mec = Variable(name='mec_agv_in', vals=0.0, units="$gL^{-1}$",
desc="Concentración de acetato: Entrada(MEC)")
dil_mec = Variable(name='mec_dil', vals=1.5, units="$d^{-1}$",
desc="Tasa de dilución: Entrada(MEC)")
eapp = Variable(name='mec_eapp', vals=0.5, units="$V$",
desc="Voltaje: Entrada(MEC)")
ace_out = Variable(name='mec_ace_out', vals=2000.0, units="$gL^{-1}$",
desc="Concentración de acetato: Salida(DA)")
xa = Variable(name='mec_xa', vals=1.0, units="$X_{a} (mgL^{-1})$",
desc="Biomasa Anodofílicas: Salida(MEC)")
xm = Variable(name='mec_xm', vals=50.0, units="$X_{m} (mgL^{-1})$",
desc="Biomasa Metanogénicas: Salida(MEC)")
xh = Variable(name='mec_xh', vals=10.0, units="$X_{h} (mgL^{-1})$",
desc="Biomasa Hidrogenotropicas: Salida(MEC)")
mox = Variable(name='mec_mox', vals=100.0, units="$L^{-1}$",
desc="Medidor de oxidación: Salida(MEC)")
imec = Variable(name='mec_imec', vals=0.0, units="I_{MEC} (A)$",
desc="Corriente: Salida(MEC)")
qh2 = Variable(name='mec_qh2', vals=0.0, units="Q_{H_{2}} ($Ld^{-1})$",
desc="Flujo de Hidrógeno: Salida(MEC)")
# Create a biorefinery
bio = Biorrfineria('bio1')
bio.set_time(dias) # Set simulation days
# Create Process and add variables
da = ProcesoDA('da')
da.set_input_vars([dil_da, agv_in_da, dqo_in])
da.set_output_vars([biomasa, dqo_out, agv_out])
mec = ProcesoMEC('mec')
mec.set_input_vars([agv_in_mec, dil_mec, eapp])
mec.set_output_vars([ace_out, xa, xm, xh, mox, imec, qh2])
# Manually initialize DA input variables
da.input_vars[0].initialize_var(bio.time, noise=noise, sd=0.08)
da.input_vars[1].initialize_var(bio.time, noise=noise, sd=1)
da.input_vars[2].initialize_var(bio.time, noise=noise, sd=1)
# Initialize outputs variables without noise
da.output_vars[0].initialize_var(bio.time)
da.output_vars[1].initialize_var(bio.time)
da.output_vars[2].initialize_var(bio.time)
# Manually initialize MEC input variables
mec.input_vars[0].initialize_var(bio.time, noise=False)
mec.input_vars[1].initialize_var(bio.time, noise=noise, sd=0.08)
mec.input_vars[2].initialize_var(bio.time, noise=noise, sd=0.04)
# Initialize outputs variables without noise
mec.output_vars[0].initialize_var(bio.time)
mec.output_vars[1].initialize_var(bio.time)
mec.output_vars[2].initialize_var(bio.time)
mec.output_vars[3].initialize_var(bio.time)
mec.output_vars[4].initialize_var(bio.time)
mec.output_vars[5].initialize_var(bio.time)
mec.output_vars[6].initialize_var(bio.time)
# mec.input_vars[2].plot()
# Initilize the output matrix for the simulation
da.initialize_outputs(bio.time)
mec.initialize_outputs(bio.time)
# Add processes to bio
bio.add_proceso([da, mec])
da_acc, mec_acc = bio.simulate(noise=noise,
failures=failures,
ontology=ontology,
batch_size=24)
return bio, da_acc, mec_acc
# bio, da_acc, mec_acc = main(dias=360, noise=True, failures=True)
times = np.zeros(1)
for i in range(1):
start_time = time.time()
bio, da_acc, mec_acc = main(dias=3600,
noise=False,
failures=False,
ontology=False)
end_time = time.time()
times[i] = end_time - start_time
print("--- %s seconds ---" % (times[i]))
print("--- %s min ---" % ((times[i])/60))
df_da, df_mec = bio.save_data(path)
# matlab_run = np.array([355.931666 ,
# 314.129656 ,
# 304.245377 ,
# 310.033032 ,
# 313.979148 ,
# 315.724433 ,
# 311.426489 ,
# 313.131862 ,
# 315.687116 ,
# 314.887149]
# )
# import pandas as pd
# times_df = pd.DataFrame(data=times, columns=['python_run_time_seg'])
# times_df['matlab_run_time_seg'] = matlab_run
# print(times_df.to_latex())
# da_values_avg_df = pd.DataFrame(data=np.zeros((3,3)),
# columns=['variable', 'python_avg', 'matlab_avg'])
# da_values_avg_df['variable'] = ['biomasa', 'dqo_out', 'agv_out']
# da_values_avg_df['python_avg'] = bio.procesos['da'].sim_outputs.mean(axis=1)
# da_values_avg_df['matlab_avg'] = [59.9596, 14.2128, 140.9430]
# print(da_values_avg_df.to_latex())
# mec_values_avg_df = pd.DataFrame(data=np.zeros((7,3)),
# columns=['variable', 'python_avg', 'matlab_avg'])
# mec_values_avg_df['variable'] = ['agv_out', 'xa', 'xm', 'xh', 'mox', 'imec', 'qh2']
# mec_values_avg_df['python_avg'] = bio.procesos['mec'].sim_outputs.mean(axis=1)
# mec_values_avg_df['matlab_avg'] = [5.6907e+03, 732.0475, 0.0266, 0.0114, 0.1013, 0.0462, 0.4554]
# print(mec_values_avg_df.to_latex())
# plt.figure()
# plt.plot(da_acc, '.-', label='DA', c='red')
# plt.plot(mec_acc, '.-', label='MEC', c='blue')
# plt.legend()
print(df_mec.tail(2).T)
for var in bio.procesos['da'].variables:
var.plot(limits=False)
for var in bio.procesos['mec'].variables:
var.plot()
# df_da, df_mec = bio.save_data(path)
# plt.figure(figsize=(12, 6))
# plt.scatter(range(len(df_da)), df_da['da_dqo_out'].values, s=1, alpha=0.3,
# c=df_da['labels'], cmap='viridis_r', marker='o')
# plt.grid()
# plt.figure(figsize=(12, 6))
# plt.scatter(range(len(df_mec)), df_mec['mec_qh2'].values, s=1, alpha=0.3,
# c=df_mec['labels'], cmap='viridis_r', marker='o')
# plt.grid()
bio.procesos['da'].sim_outputs[:, -2]