def assets_to_factors_optim(self, gamma=0.5, A=None, activate_cons=True):
if A is None:
A = self.asset_to_factor_exp()
if activate_cons:
lb = np.zeros(self.n_factors)
ub = np.ones(self.n_factors)
bnds = tuple([(i, j) for i, j in zip(lb, ub)])
cons = lc(np.ones((1, len(lb))), np.ones(1), np.ones(1))
else:
bnds = None
cons = ()
w_ini = np.ones(self.n_factors) / self.n_factors
sol = minimize(self.assets_to_factors_obj,
w_ini,
args=(self.w_asset.values, gamma),
bounds=bnds,
constraints=cons,
method='SLSQP')
w_f = pd.Series(np.round(sol.x, 5), index=self.w_factor.index)
return w_f
def main():
# Create feedstock instances
manure = Feedstock("dairy", 200, 16.31, 0.65, 1500, 0.5)
silage = Feedstock("mais", 120, 81.10, 0.41, 130, 0.5)
silage.fshow_properties()
manure.fshow_properties()
# create mixed feedstock instances with averaged data
cn_mix = ad_util.fco_cn([manure.x, silage.x], [manure.cn, silage.cn])
bd_mix = ad_util.fco_bd([manure.x, silage.x], [manure.bd, silage.bd])
d_mix = ad_util.fco_d([manure.x, silage.x], [manure.d, silage.d])
bmp_mix = -ad_util.fblending([manure.x, silage.x], [manure.bmp, silage.bmp], [manure.cn, silage.cn], [manure.bd, silage.bd])
mix_fs = Feedstock("mix", bmp_mix, cn_mix, bd_mix, d_mix, 1)
mix_fs.fshow_properties()
# solve ode system with initial substrate composition
tspan = np.linspace(0, 35, 1000)
init_manure = manure.fsubstrate()
init_silage = silage.fsubstrate()
S0 = init_manure[0] + init_silage[0]
X0 = init_manure[1] + init_silage[1]
x_mix = mix_fs.fcodigestion(35, [km, Ks, kd])
S_mix = x_mix[0]
X_mix = x_mix[1]
M_mix = x_mix[2]
# obtain the optimized blending stat
print("\nFeedstock blending optimization has started...")
x_0 = [manure.x, silage.x]
bounds = bd([0, 0], [1, 1])
constr = lc([[manure.cn, silage.cn], [manure.bd, silage.bd], [1, 1]], [20, 0.6, 1], [40, 1, 1])
blend = minimize(ad_util.fblending, x_0, args=([manure.bmp, silage.bmp],\
[manure.cn, silage.cn],[manure.bd, silage.bd]), \
method='trust-constr', bounds=bounds, constraints=constr, tol=1e-9, \
options={'verbose': 1, 'disp': True})
print("\n")
manure.x = blend.x[0]
silage.x = blend.x[1]
manure.fshow_properties()
silage.fshow_properties()
# generate the new blended mixture instance
cn_mix = ad_util.fco_cn([manure.x, silage.x], [manure.cn, silage.cn])
bd_mix = ad_util.fco_bd([manure.x, silage.x], [manure.bd, silage.bd])
d_mix = ad_util.fco_d([manure.x, silage.x], [manure.d, silage.d])
bmp_mix = -ad_util.fblending([manure.x, silage.x], [manure.bmp, silage.bmp], [manure.cn, silage.cn], [manure.bd, silage.bd])
mix_new = Feedstock("mix", bmp_mix, cn_mix, bd_mix, d_mix, 1)
mix_new.fshow_properties()
# solve ode system with new substrate composition
x_blend = mix_new.fcodigestion(35, [km, Ks, kd])
S_blend = x_blend[0]
X_blend = x_blend[1]
M_blend = x_blend[2]
# visualization
plt.figure(1)
plt.title('Simulation at original blending stats (i=m=50%)')
plt.plot(tspan, S_mix, label='S')
plt.plot(tspan, X_mix, label='X')
plt.plot(tspan, M_mix, label='M')
plt.xlabel('time [d]')
plt.ylabel('concentration [kg/m3]')
plt.legend(loc = 'best')
plt.ylim([0, 1500])
plt.grid(True)
plt.figure(2)
newtitle = 'Simulation at new blending stats (i='+'{:.2f}'.format(blend.x[1])+' m='+'{:.2f}'.format(blend.x[0])+')'
plt.title(newtitle)
plt.plot(tspan, S_blend, label='S')
plt.plot(tspan, X_blend, label='X')
plt.plot(tspan, M_blend, label='M')
plt.xlabel('time [d]')
plt.ylabel('concentration [kg/m3]')
plt.legend(loc = 'best')
plt.ylim([0, 1500])
plt.grid(True)
plt.show()
return 0
def main():
# retrive solving information
tspan = np.linspace(0, 35, 1000)
init = feffluent(x_M, d_M, d_I)
S0 = init[0]
X0 = init[1]
# solve ode system with initial substrate composition
x_blend_0 = odeint(fsimulate, [S0, X0, 0], tspan, args=([km, Ks, kd], ))
S_blend_0 = x_blend_0[:, 0]
X_blend_0 = x_blend_0[:, 1]
M_blend_0 = x_blend_0[:, 2]
# obtain the optimized blending stat
x_0 = [x_M, x_I]
bounds = bd([0, 0], [1, 1])
constr = lc([[CN_M, CN_I], [BD_M, BD_I], [1, 1]], [20, 0.6, 1], [30, 1, 1])
blend = minimize(fblending, x_0, args=([BMPe_M, BMPe_I],[CN_M, CN_I],[BD_M, BD_I]), \
method='trust-constr', bounds=bounds, constraints=constr, tol=1e-9, \
options={'verbose': 1, 'disp': True})
# solve ode system with new substrate composition
init_n = feffluent(blend.x[0], d_M, d_I)
S0_n = init_n[0]
X0_n = init_n[1]
x_blend_1 = odeint(fsimulate, [S0_n, X0_n, 0],
tspan,
args=([km, Ks, kd], ))
S_blend_1 = x_blend_1[:, 0]
X_blend_1 = x_blend_1[:, 1]
M_blend_1 = x_blend_1[:, 2]
print(blend)
# visualization
plt.figure(1)
plt.title('Simulation at original blending stats (i=m=50%)')
plt.plot(tspan, S_blend_0, label='S')
plt.plot(tspan, X_blend_0, label='X')
plt.plot(tspan, M_blend_0, label='M')
plt.xlabel('time [d]')
plt.ylabel('concentration [kg/m3]')
plt.legend(loc='best')
plt.ylim([0, 1500])
plt.grid(True)
plt.figure(2)
newtitle = 'Simulation at new blending stats (i=' + '{:.2f}'.format(
blend.x[1]) + ' m=' + '{:.2f}'.format(blend.x[0]) + ')'
plt.title(newtitle)
plt.plot(tspan, S_blend_1, label='S')
plt.plot(tspan, X_blend_1, label='X')
plt.plot(tspan, M_blend_1, label='M')
plt.xlabel('time [d]')
plt.ylabel('concentration [kg/m3]')
plt.legend(loc='best')
plt.ylim([0, 1500])
plt.grid(True)
plt.show()
return 0