PyMLCA

Software for Generalized Matrix-based LCA and Reliability Based LCA

Shinsuke Sakai

Emeritus Professor, The University of Tokyo, Japan
Visiting Professor, Yokohama National University, Japan

Overview

This package provide the function for general-purpose matrix-based LCA analysis and LCA based on reliability design. Algorithm for sensitivity analysis using perturbation method is based on the theory shown by Sakai and Yokoyama[1]. Should any required packages be missing during execution, please install them accordingly.

[1]Shinsuke Sakai and Koji Yokoyama. Formulation of sensitivity analysis in life cycle assessment using a perturbation method. Clean technologies and environmental policy, Vol. 4, No. 2, pp. 72–78, 2002.

Procedure

1. Install this package using pip command.
2. Create a folder to store inventory data. As an example, the folder named 'SandwichPackage' is already created.
3. Save the inventory data to be analyzed in that folder.
4. Import PyMLCA module using 'from MLCArel import PyMLCA as pm' command.
5. Create an instance to manage the analysis using 'dp=pm.DesignProcess()' command.
6. From here on, use the created instance to perform the intended analysis.

Operation check

The following describes the method for checking when using the inventory data in the SandwichPackage folder. Sample data for SandwichPackaged are provided in the site.

First, create an instance and define the inventory data folder.

from MLCArel import PyMLCA as pm
dp=pm.DesignProcess()
path='./SandwichPackage'
dp.SetDfFromPath(path)

Next, perform a matrix-based LCA.

solution,surplusFlow,loadValue=dp.SimpleAnalysis()

Confirm the created coefficient matrix.

print(dp.rbld.mlca.coefficientMat)

The expected output would be as follows.

Display the solution of the process values.

print(solution)

The expected output would be as follows.

[ 0.202 0.1 10.2 0.1 ]

Display the names of the environmental impacts and their solutions.

flowName,b,loadName=dp.GetName()
print('Names of the environmental impacts=',loadName)
print('Their solutions=',loadValue)

The expected output would be as follows.

Names of the environmental impacts= ['SolidWaste', 'CO2']
Their solutions= [22.52 30.6 ]

Calculation of sensitivity matrix.

i=1
print('Calculation of sensitivity matrix for environmental load:',loadName[i])
print(dp.rbld.mlca.Smat(i))

The expected output would be as follows.

Calculation of sensitivity matrix for environmental load: CO2
[[-1.98039216 0.98039216 1. -0. ]
[-0. -1. -0. 1. ]
[ 1.98039216 0.01960784 -2. -0. ]
[-0. -0. -0. -1. ]]