.. General

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

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• Free software: GNU GENERAL PUBLIC LICENSE V2

• Documentation: https://swolfpy.readthedocs.io.

• Repository: https://bitbucket.org/msm_sardar/swolfpy-inputdata

• Other links:

  • https://go.ncsu.edu/swolfpy
  • https://jwlevis.wixsite.com/swolf

| 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

.. list-table:: Description of columns in the csv file for input data :widths: auto :header-rows: 1

• • 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

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=3.7

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

.. endInstallation

======= History

0.1.0 (2020-05-06)

• First release on PyPI. Data for the Life-cycle process models include: LF, WTE, Composting, AD, SS_MRF, reprocessing and Collection.