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

Figure 2: 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)

To

tal P

roje

ct

Net

Reven

ue (

$)

• 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