1. Introduction
(Background or
Motivation)
BIOGAS INFRASTRUCTURE DESIGN WITH OBJECT ORIENTED PROGRAMMING (OOP)
Steve JH Lee1, Alex Dowling1, Kibaek Kim2, Victor M. Zavala1
1Department of Chemical and Biological Engineering, UW-Madison, 2 Mathematics and Computer Science Division, Argonne National Laboratory
Method,
Approach or
Research
Conclusions
Acknowledgements
Results
Next Steps
Hypothesis or Goal
Discussion
Methods
Goals
Introduction Results Discussion and Conclusions
• Large scale production of manure in America’s dairy farms, when left
unprocessed, can release significant amounts of methane during
decomposition.
 NREL1 reports that 5% of total gas used for electricity can be produced
from processing biowaste.
• Digester technologies can capture
methane from waste sources.
• Fundamental questions arise when
designing the infrastructure necessary for
regional biowaste processing.
 How do we balance project priorities
(health, emissions, cost)?
 What are the Trade-offs and Limiting
conditions 2?
• Technical languages such as Julia, allow one to develop the code structure
necessary to assess these considering factors using Wisconsin biogas
infrastructure data.
1. Understand and translate the mathematical models representing the
infrastructure components, constraints, and variables.
2. Design code utilizing functions and custom data types, which can produce
optimized solutions at various input combinations with flexibility.
3. Develop capability to graphically visualize solution networks.
(3) Define variables, constraints and objectives
 Variables
- Saved Emissions
- Transportation Emissions
- Operating Cost
etc
 Objectives
- Minimize total costs?
- Maximize biogas production?
How will the optimization
outcomes vary with the
stakeholder involved in the
project?
• A minimum stakeholder CO2 value of $100/ton is necessary for the project
to initiate (Table 1).
 Else, the utopia point is when no facilities are constructed- and that in
which no waste is processed.
• Solutions generated by the optimization model are reasonable: EX: More
processing facilities are constructed for a stakeholder assigning a high dollar
value to saved CO2 emissions in order to maximize CO2 capture (Figure 1).
• The optimization code written in OOP readily generates solutions for any
defined Cost (Transportation, Operating, Investment) vs Emissions (Saved,
Total, Transportation, Waste) objective pair.
 Either numerical or graphically displayed solutions can be used to
observe trade-offs and other comparisons for decision-making scenarios.
• What additional considerations must be taken into account to design a more
comprehensive infrastructure model?
Future Work
Acknowledgements
1 NREL (2014). Energy Analysis: Biogas Potential in the United States. Retrieved
from http://www.nrel.gov/docs/fy14osti/60178.pdf
2 Zavala, M. V. (2015). Multi-Objective, Multi-Stakeholder Optimization.
Table 1: Optimized solution values for major project variables (“utopia points”) for each
Stakeholder CO2 value. The objective function has been set to minimizing Investment cost and
Total emissions
Stakeholder Value of
CO2 Emissions
($/ton-CO2)
Total Costs
[Transportation,
Operating, Investment], $
Saved Emissions
(tons-CO2)
Net Project Revenue
($/yr)
Number of
Constructed
Processing Facilities
0.1 0 0 0 0
1 0 0 0 0
10 0 0 0 0
100 18,808,867 635211 7,518,291 10
1000 18,945,698 635211 7,381,460 10
10000 20,131,285 635211 6,195,873 10
100000 22,259,488 638066 4,067,670 12
1000000 26,605,644 638066 -278,468 18
(7) Map solutions for visual
assessment of results
Figure2: Plot of net revenue for stakeholders with varying CO2 values. The
objective function has been set to minimizing investment costs.
Log of Stakeholder CO2 Value ($/ton-CO2)
TotalProjectNetRevenue($)
• Lat-Long coordinates of
upstream (dairy farm) and
downstream (processing facility)
locations
• Size of dairy farms (cow-heads)
• Available digester technologies
• Conversion parameters for
manure  methane  electricity
(1) Import necessary data and parameters:
Simulate biogas
infrastructure by
translating
necessary models
into Julia code
(2) Import model
(4) Define stakeholders
Assign input data
(inputs, outputs) into
individual data classes.
Formulate a code
framework to accept
flexible inputs.
 Package code into
functions as much as
possible to reduce
computational
overhead.
(5) Apply OOP
principles
EX: What is the net
revenue for each
stakeholder when we want
to minimize costs and net
emissions?
(6) Consider scenarios
Figure 1: Map showing dairy farms (red) and waste processing facilities with digester technologies
(green). Size of red plots represent the farm’s cow-head.
Stakeholder CO2 Value = 1000$/ton Stakeholder CO2 Value = 100,000$/ton
• Implement a more complex, stochastic, multi-stakeholder
formulation using the CVaR method:
 Instead of solving for the “utopia point” in the model for each
individual stakeholder, solve for an optimized solution which
compromises over a set of stakeholders.
 How do the dissatisfactions change as we vary CVaR from
0 to 1?
 Consider what Cost-Emissions objective pair might be most
meaningful in testing the CVaR method.
The Biogas Model
Emissions Costs Transportation/Trips
Biogas Produced
Electricity Revenues
Facility and Waste
Optimization Objectives
• Minimize Emissions
• Maximize Economics