efficient way of calculating Monte Carlo results for different impact assessment methods in Brightway - brightway

Question

I am trying to do a comparative monte carlo calculation with brightway2 using different impact assessment methods. I thought about using the switch_method method to be more efficient, since the technosphere matrix is the same for a given iteration. However, I am getting an assertion error. A code to reproduce it could be something like this
import brighway as bw
bw.projects.set_current('ei35') # project with ecoinvent 3.5
db = bw.Database("ei_35cutoff")
# select two different transport activities to compare
activity_name = 'transport, freight, lorry >32 metric ton, EURO4'
for activity in bw.Database("ei_35cutoff"):
if activity['name'] == activity_name:
truckE4 = bw.Database("ei_35cutoff").get(activity['code'])
print(truckE4['name'])
break
activity_name = 'transport, freight, lorry >32 metric ton, EURO6'
for activity in bw.Database("ei_35cutoff"):
if activity['name'] == activity_name:
truckE6 = bw.Database("ei_35cutoff").get(activity['code'])
print(truckE6['name'])
break
demands = [{truckE4: 1}, {truckE6: 1}]
# impact assessment method:
recipe_midpoint=[method for method in bw.methods.keys()
if method[0]=="ReCiPe Midpoint (H)"]
mc_mm = bw.MonteCarloLCA(demands[0], recipe_midpoint[0])
next(mc_mm)
If I try switch method I get the assertion error.
mc_mm.switch_method(recipe_midpoint[1])
assert mc_mm.method==recipe_midpoint[1]
mc_mm.redo_lcia()
next(mc_mm)
Am I doing something wrong here?

Answer 1

I usually store characterization factor matrices in a temporary dict and multiply these cfs with the LCI resulting from MonteCarloLCA directly.
import brightway2 as bw
import numpy as np
# Generate objects for analysis
bw.projects.set_current("my_mcs")
my_db = bw.Database('db')
my_act = my_db.random()
my_demand = {my_act:1}
my_methods = [bw.methods.random() for _ in range(2)]
I wrote this simple function to get characterization factor matrices for the product system I will generate in the MonteCarloLCA. It uses a temporara "sacrificial LCA" object that will have the same A and B matrices as the MonteCarloLCA.
This may seem like a waste of time, but it is only done once, and will make MonteCarlo quicker and simpler.
def get_C_matrices(demand, list_of_methods):
""" Return a dict with {method tuple:cf_matrix} for a list of methods
Uses a "sacrificial LCA" with exactly the same demand as will be used
in the MonteCarloLCA
"""
C_matrices = {}
sacrificial_LCA = bw.LCA(demand)
sacrificial_LCA.lci()
for method in list_of_methods:
sacrificial_LCA.switch_method(method)
C_matrices[method] = sacrificial_LCA.characterization_matrix
return C_matrices
Then:
# Create array that will store mc results.
# Shape is (number of methods, number of iteration)
my_iterations = 10
mc_scores = np.empty(shape=[len(my_methods), my_iterations])
# Instantiate MonteCarloLCA object
my_mc = bw.MonteCarloLCA(my_demand)
# Get characterization factor matrices
my_C_matrices = get_C_matrices(my_demand, my_methods)
# Generate results
for iteration in range(my_iterations):
lci = next(my_mc)
for i, m in enumerate(my_methods):
mc_scores[i, iteration] = (my_C_matrices[m]*my_mc.inventory).sum()
All your results are in mc_scores. Each row corresponds to a method, each column to an MC iteration.

Answer 2

Not very elegant, but try this:
iterations = 10
simulations = []
for _ in range(iterations):
mc_mm = MonteCarloLCA(demands[0], recipe_midpoint[0])
next(mc_mm)
mcresults = []
for i in demands:
print(i)
for m in recipe_midpoint[0:3]:
mc_mm.switch_method(m)
print(mc_mm.method)
mc_mm.redo_lcia(i)
print(mc_mm.score)
mcresults.append(mc_mm.score)
simulations.append(mcresults)
CC_truckE4 = [i[1] for i in simulations] # Climate Change, truck E4
CC_truckE6 = [i[1+3] for i in simulations] # Climate Change, truck E6
from matplotlib import pyplot as plt
plt.plot(CC_truckE4 , CC_truckE6, 'o')
If you then make a test and do twice the simulation for the same demand vector, by setting demands = [{truckE4: 1}, {truckE4: 1}] and plot the result you should get a straight line. This means that you are doing dependent sampling and re-using the same tech matrix for each demand vector and for each LCIA. I am not 100% sure of this but I hope it answers your question.