Main Modules (Instructor and Module)

Source: Industrial Ecology Teaching

1. Stefan & Zhi: Basic Principles of Dynamic Material Flow Analysis (Lecture)

2. Stefan & Zhi: Dynamic Stock Models (Lecture)

• Population Balance Models
• Age-Cohorts
• Lifetime Model

3. Stefan (Assistant: Huimei Li): Inflow-Driven Modeling (Coding)

a. Exercise: "Dynamic Model of the German Steel Cycle, 1800-2008"

The goals of this exercise are twofold:

• To develop a systems understanding regarding the development of flows and stocks in material cycles, using the example of the steel cycle in Germany.
• To estimate steel stocks using dynamic stock modelling.

Prerequisites: Calculus, Simple Differential Equations, Discrete and Continuous Random Variables, Convolution.
Level of Difficulty: (+++)

• IEooc_Methods3_Exercise1 (pdf)
• IEooc_Methods3_Exercise1 (Data, xlsx)

Sample solutions for this exercise are available:

• IEooc_Methods3_Exercise1_Solution (pdf)
• IEooc_Methods3_Exercise1_Solution (xlsx)

b. Jupyter Notebook: Tutorial on Inflow-Driven and Stock-Driven Modelling Using the dynamic_stock_model Class in Python (Chinese Steel Stock Example)

This workbook demonstrates how inflow-driven and stock-driven modelling can be implemented in Python using the dynamic_stock_model class.

Prerequisites: Calculus, Simple Differential Equations, Discrete and Continuous Random Variables, Convolution, Basic Programming and Data Visualization in Python.
Level of Difficulty: (+++)

Two versions of this notebook are available:

• IEooc_Methods3_Software1 (ODYM) (ipynb) | Run externally in nbviewer for use with the dynamic MFA library of the ODYM MFA software.
• IEooc_Methods3_Software1 (Old Dynamic MFA Class) (ipynb) | Run externally in nbviewer for use with the stand-alone dynamic MFA library that is no longer maintained.
• IEooc_Methods3_Software1 (Data File) for both versions of the notebook.

4. Zhi (Assistant: Zhaoxing Wang): Stock-Driven Modeling (Coding)

a. The Chinese Steel Cycle (Same Data as Module 3b)

5. Stefan (Assistant: Jiajia Li): Stock-Driven Modeling (Coding)

a. Jupyter Notebook: Tutorial on Stock-Driven Modelling for Material Stocks in Products Using the dynamic_stock_model Class in Python (Global Passenger Vehicle Fleet Example)

This workbook demonstrates how stock-driven modelling can be implemented in Python using the dynamic_stock_model class and applied to calculate material flows and stocks in products.

Prerequisites: Calculus, Simple Differential Equations, Discrete and Continuous Random Variables, Convolution, Basic Programming and Data Visualization in Python.
Level of Difficulty: (+++)

• IEooc_Methods3_Software2 (ipynb) | Run externally in nbviewer
• IEooc_Methods3_Software2 (Data File)

6. Zhi (Assistant: Jingyang Song): Example III: GloBus - Global Dynamic Building Sand Use Model

• GloBUS_Tutorials
• GloBUS