November 17, 2015

Applications of Produced Water in Biodiesel Production and

Distribution

Soumya Yadala,

Selen Cremaschi, PhD

The University of Tulsa

2015 International Petroleum Environmental Conference

Denver, CO

November 17-19, 2015

2

Presentation outline

Soumya Yadala, Selen Cremaschi 2015 IPEC

Algae based-Biodiesel

Biodiesel Production

Results

Mathematical Modeling

Future directions

Research Objective

Questions?

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2

3

4

5

6

7

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Motivation – Produced water

Soumya Yadala, Selen Cremaschi 2015 IPEC

Salty water trapped in the reservoir rock and brought up along with oil or gas during production

It can contain very minor amounts of chemicals, oil, and metals

These waters exist under high pressures and temperatures

Scientists recently were successful in conducting the first pilot-scale test of algae growth using water from an oil-production well in Jal, New Mexico

However, these large quantities of saline water have great potential value for algal biofuel production

Every year in the United States about 800 billion gallons of produced water is brought to the surface along with oil and gas and about 98% of this water is routinely disposed as a waste product

Introduction Methodology Results ConclusionObjectives

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Advantages

Soumya Yadala, Selen Cremaschi 2015 IPEC

Higher oil yieldAlgae biofuel

Grown on non-arable land and using produced water

No sulfur, non-toxic & biodegradable

Food vs. fuel

Higher growth rates & productivities

CO2 capture

Introduction Methodology Results ConclusionObjectives

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Soumya Yadala, Selen Cremaschi 2015 IPEC

Challenges

Algae biofuel

High capital, operating & production costs

Difficulty in scaling up

Variety of algae strains

Lack of optimal design of cultivation systems

Relatively new technology

Introduction Methodology Results ConclusionObjectives

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Biodiesel Production

Soumya Yadala, Selen Cremaschi 2015 IPEC

Selection of Algae Species

Selection of Location

Algae Cultivation

Harvesting

Drying

Extraction

Varying oil content and specific growth rates

Influences climatic conditions and sunlight

Introduction Methodology Results ConclusionObjectives

Transesterification

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Research Objective

Soumya Yadala, Selen Cremaschi 2015 IPEC

Methodology Results ConclusionObjectivesIntroduction

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Optimization

Soumya Yadala, Selen Cremaschi 2015 IPEC

Optimization focuses on finding the best solution from a set of available alternatives subject to constraints.

Alternatives Objective Function

Decision Variables Constraints

Optimization

Results ConclusionMethodologyIntroduction Objectives

Objective functionTo minimize the production, operating and transportation costs of biodiesel

Optimal algae cultivation systems Reliable supply chain network flow topology

of production and distribution centers

Alternatives Algae Species – I. galbana Cultivation Units – Raceway Ponds Geographical Locations – USA Routes Means of Transportation – Trucks, Rails, Barges, and Pipelines

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Alternatives - Locations

Soumya Yadala, Selen Cremaschi 2015 IPEC

Results ConclusionMethodologyIntroduction Objectives

SupplyTexas

Mississippi

Alabama

Kentucky

Georgia

Oklahoma

Virginia

Arizona

North Carolina

South Carolina

PortHouston

Gulf of Mississippi

Mobile

Paducah

Savannah

Tulsa

Norfolk

Phoenix

Wilmington

Charleston

ExtractionHouston

Gulf of Mississippi

Mobile

Paducah

Savannah

Tulsa

Norfolk

Phoenix

Wilmington

Charleston

Houston

Los Angeles

Philadelphia

Chicago

Toledo

TransesterificationHouston

Gulf of Mississippi

Mobile

Paducah

Savannah

Tulsa

Norfolk

Phoenix

Wilmington

Charleston

Houston

Los Angeles

Philadelphia

Chicago

Toledo

DemandHouston

Los Angeles

Philadelphia

Chicago

Toledo

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Alternatives – Means of Transportation

Soumya Yadala, Selen Cremaschi 2015 IPEC

Results ConclusionMethodologyIntroduction Objectives

Supply Port

Extraction

Esterification

Demand

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Alternatives - Routes

Soumya Yadala, Selen Cremaschi 2015 IPEC

Results ConclusionMethodologyIntroduction Objectives

Supply Port Extraction Transesterification Demand

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Decision Variables

Soumya Yadala, Selen Cremaschi 2015 IPEC

Alternatives Objective Function

Decision Variables Constraints

Optimization

Results ConclusionMethodologyIntroduction Objectives

Demand

DAProduced(Sloc), NPond(Sloc),TSApond(Sloc)

Supply

PortDATransported(Ploc) DAAvailable(Ploc)

ExtractionDATransported(Eloc) AOProduced(Eloc)

TransesterificationAOTransported(Tloc) BDProduced(Tloc)

TransportTruck(Sloc,Ploc), NTruck(Sloc,Ploc)

TransportTruck,Rail,Barge(Ploc,Eloc), NTruck,Rail,Barge(Ploc,Eloc)

TransportTruck,Rail,Barge(Eloc,Tloc), NTruck,Rail,Barge(Eloc,Tloc)

TransportTruck,Rail,Barge,Pipeline(Tloc,Dloc), NTruck,Rail,Barge,Pipeline(Tloc,Dloc)

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Objective Function

Alternatives Objective Function

Decision Variables Constraints

Optimization

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0 1

1

n

TransExtPond

nTransExtPond

OpCt

LandCt

WaterCt

ElecticCt

MARRTranportCtCpCtZ

Min

Soumya Yadala, Selen Cremaschi 2015 IPEC

Results ConclusionMethodologyIntroduction Objectives

1%2% 3%4%

8%

70%

3%

1%

1% 1% 0%5%

1%Site Preparation

Pond levees

Paddle wheel

Harvesting

Flocculation

Extraction

Water & nutrient supply

Waste treatment

Buildings, roads, drainage

Electric Supply and distribution

Instumentation and machinery

Engineering and contingency

18%

20%

44%

4%

12% 3%

Nutrients

Chemicals

Labor

Maintenance and repair

Operating supplies

Taxes and insurance

Capital Costs Operating Costs

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Constraints

Alternatives Objective Function

Decision Variables Constraints

Optimization

Results ConclusionMethodologyIntroduction Objectives

Sloc,PlocTruckSlocproducedpond TransportDAn

DAProduced (Sloc), NPond(Sloc)Supply

TransportTruck(Sloc,Ploc)

PlocSloc,Ploc dtransporteTruck DATransport

PortDATransported (Ploc) DAAvailable (Ploc)

TransportTruck,Rail,Barge(Ploc,Eloc)

ElocdtransportePloc

BargePloc

RailPloc

Truck DATransportTransportTransportPloc,ElocPloc,ElocPloc,Eloc

Soumya Yadala, Selen Cremaschi 2015 IPEC

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Constraints

Alternatives Objective Function

Decision Variables Constraints

Optimization

Results ConclusionMethodologyIntroduction Objectives

ExtractionDATransported(Eloc) AOProduced(Eloc)

TransportTruck,Rail,Barge(Eloc,Tloc)

speciesnextracctio

Produced

Elocdtransporte OCη

AODA Eloc

Tloc

BargeTloc

RailTloc

TruckElocproduced Eloc,TlocEloc,TlocEloc,TlocTransportTransportTransportAO

TlocdtransporteEloc

BargeEloc

RailEloc

Truck AOTransportTransportTransportEloc,TlocEloc,TlocEloc,Tloc

TransesterificationAOTransported(Tloc) BDProduced(Tloc)

TransportTruck,Rail,Barge,Pipeline(Tloc,Dloc)

lipid

BDificationtransester

Produced

Tlocdtransporte

MWMW

η

BDAO Tloc

3

Soumya Yadala, Selen Cremaschi 2015 IPEC

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Constraints

Alternatives Objective Function

Decision Variables Constraints

Optimization

Results ConclusionMethodologyIntroduction Objectives

Soumya Yadala, Selen Cremaschi 2015 IPEC

TransesterificationAOTransported(Tloc) BDProduced(Tloc)

TransportTruck,Rail,Barge,Pipeline(Tloc,Dloc)

DlocPipe

DlocBarge

DlocRail

DlocTruckTlocproduced

Tloc,DlocTloc,Dloc

Tloc,DlocTloc,Dloc

TransportTransport

TransportTransportBD

DlocDloc

PipeDloc

Barge

DlocRail

DlocTruck

DemandTransportTransport

TransportTransport

Tloc,DlocTloc,Dloc

Tloc,DlocTloc,Dloc

Alternatives Objective Function

Decision Variables Constraints

Optimization GAMS(General Algebraic Modeling System) BARON

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Results

Soumya Yadala, Selen Cremaschi 2015 IPEC

ConclusionIntroduction Objectives Methodology Results

Supply Port Extraction Transesterification Demand

Texas

Mississippi

Alabama

Kentucky

Georgia

Houston

Gulf of MS

Mobile

Savannah

Houston

Mobile

Savannah

Houston

Los Angeles

Philadelphia

Chicago

Toledo

Gulf of MS

Houston

Gulf of MS

Philadelphia

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Results

Soumya Yadala, Selen Cremaschi 2015 IPEC

ConclusionIntroduction Objectives Methodology Results

Production Costs TransportCt

CpCtPond

OpCtPond

WaterCt

CpCtTrans

OpCtTrans

CpCtExt

OpCtExt

LandCt

ElectricCtRaceway Pond Dimensions = Channel Depth = 1 m

Pond width = 3.5 m Pond length = 300 m

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Results

Soumya Yadala, Selen Cremaschi 2015 IPEC

ConclusionIntroduction Objectives Methodology Results

Supply

Texas

Mississippi

Alabama

Georgia

x

x

x

x

8.5000E6

8.5200E6

8.8200E6

1.0665E7

= 8.5000E5 ha

= 8.5200E5 ha

= 8.8200E5 ha

= 1.0665E6 ha

NPond(Sloc) NTruck(Sloc,Ploc)

3.7686E5

7.5971E6

3.5821E5

4.3563E5

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Conclusions

Soumya Yadala, Selen Cremaschi 2015 IPEC

Model the dynamic behavior of algae biomass cultivation using HYSYS simulation software

Model the network flow topology of algae oil distribution in the United states

Future directions

A mathematical framework is developed to estimate the best combination of algae species, geographical location, and raceway pond geometry by combining experimentally validated temperature, irradiance, and algae growth models with optimization

Methodology Results ConclusionIntroduction Objectives

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Acknowledgement

Soumya Yadala, Selen Cremaschi 2015 IPEC

Department of Chemical Engineering, The University of Tulsa

TUPSE Research Group

Methodology Results ConclusionIntroduction Objectives

IPEC

November17, 2015

Soumya Yadala, Selen Cremaschi 2015 IPEC

THANK YOU

Questions???