swolfpy-project/swolfpy-inputdata

The swolfpy-inputdata module is responsible for storing, updating, and generating new uncertain input data for the process models in swolfpy-processmodels.

Input data for swolfpy's life-cycle process models (swolfpy_inputdata)

• Free software: GNU GENERAL PUBLIC LICENSE V2
• Website: https://swolfpy-project.github.io
• Documentation: https://swolfpy.readthedocs.io.
• Repository: https://github.com/SwolfPy-Project/swolfpy-inputdata

Features

• Input data for Life-cycle process models of swolfpy
  • Common data (e.g., molecular weights, heating values)
  • Material properties (46 common waste fractions; e.g., Food waste, Yard waste)
    • Chemical properties (e.g., carbon content, methane yield)
    • Physical properties (e.g., moisture content, density)
  • Material dependent process model inputs (e.g., separation efficiency for each waste fraction in the trommel)
  • Material indepent process model inputs
• Built-in Monte Carlo simulation

Description of columns in the csv file for input data

Field	Description
Category	Category of the input (e.g., energy recovery, post closure)
Dictonary_Name	Name of the dictionary and attribute (whitespace is not allowed)
Parameter Name	Short name of the parameter (whitespace is not allowed)
Parameter Description	Longer description of the parameter
Amount	Default value for the parameter
Unit	Unit of the parameter (e.g., MJ/Mg, kW, hours/day)
Uncertainty_type	0: Undefined, 2: Lognormal, 3: normal, 4: Uniform, 5: Triangular, 7: Discrete Uniform
Loc	Mean for lognormal and normal distribution
scale	Standard deviation for lognormal and normal distribution
shape	Shape parameter for Weibull, Gamma or Beta distributions
Minimum	Lower bound/minimum for lognormal, normal, uniform, triangular, and discrete uniform distributions
maximum	Upper bound/maximum for lognormal, normal, uniform, triangular, and discrete uniform distributions
Reference
Comment

Installation

1- Download and install miniconda from: https://docs.conda.io/en/latest/miniconda.html

2- Update conda in a terminal window or anaconda prompt:

conda update conda

3- Create a new environment for swolfpy:

conda create --name swolfpy python=3.9

4- Activate the environment:

conda activate swolfpy

5- Install swolfpy_inputdata in the environment:

pip install swolfpy_inputdata

6- Use in python (e.g., Landfill model):

import swolfpy_inputdata as spid
data = spid.LF_Input()
model.calc()
#Example: Returs the actk parameter in landfill
data.LF_gas['actk']
#Example: Returns input data in dataframe format
data.Data