chalida u-tapao steven a. gabriel, christopher peot and mark ramirez

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Identification of optimal strategies for energy management and reducing carbon dioxide emission at

the Blue Plains Advanced Wastewater Treatment Plant (AWTP)

Chalida U-tapao Chalida U-tapao Steven A. Gabriel, Christopher Peot and Mark RamirezSteven A. Gabriel, Christopher Peot and Mark Ramirez

Dept. of Civil & Env. Engineering, University of Maryland, Dept. of Civil & Env. Engineering, University of Maryland, College Park, MarylandCollege Park, Maryland

District of Columbia Water and Sewer Authority, Washington DCDistrict of Columbia Water and Sewer Authority, Washington DC13 November 200913 November 2009

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Outline

•Overview of energy, wastewater treatment process and objective of this research

•Flowchart of modeling decisions/processes (the Blue Plains AWTP is case study)

•Ongoing work

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(Source: EIA, Energy perspective , June 2009)

U.S. Primary Energy Overview

• Imports fill the gap between U.S. energy use and production• Petroleum is the major imported fuel

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U.S. Energy Consumption by Energy Source, 2008

(Source: EIA, Renewable Energy Consumption and Electricity 2008 Statistics)

Pretoleum, 37%

Coal, 23%

Natural gas, 24%Nuclear Electric

Power, 9%

Renewable Energy, 7%

Wind7%

Geothermal5%

Hydropower34%

Biomass53%

Solar1%

• More renewable energy will decrease imported petroleum, coal and natural gas

• Many renewable energy sources can be selected

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Wastewater Treatment Process

(Source: DC Water and Sewer Authority)

• Contaminated substances are separated in solid form

• Almost all solids are biomass

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Biosolids is a Significant Renewable Energy Source

(Source: DC Water and Sewer Authority)

• Biosolids is biomass that is renewable energy source

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A Huge Plant Such as The Blue Plains AWTP Has Great Potential to Produce Renewable Energy

(Source: DC Water and Sewer Authority)

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Objectives of this research

• Find optimal strategies for energy management • Use energy sources that can reduce the carbon footprint at the Blue Plains AWTP

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SEWAGE

WASA Operations

BiosolidsBiogas

Land application

Electricity

Use at WASA

Outside sales

Investment $

Other clean energywind, solar, etc

Outside salestransp.indust.

Operations/Investments

IdigesterIwind

Isolar

PB 1-PB

PE,W

1- PE,W

electric power grid/market

Flowchart

PB=% of sewage to be converted to biosolids

PE,W=% of power from methane to be used at WASA

PG,W=% of methane to be used at WASA

PG,W

natural gas grid/market

Methane

Use at WASA

carbon allowance market

$$

$IWASA

1-PG,W

10

330 MGD

GHG (CO2)

GHG (CO2)

Biosolids

1,163 tons/day

GHG

(CO2 CH4 ,N2O)

Odor

736,087 kWH/day

The Blue Plains AWTP operating process

(Source: Gabriel, S.A., et al., Statistical Modeling to Forecast Odor Levels of Biosolids Applied to Reuse Sites. Journal of Environmental Engineering, 2006).

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The Average Amount of Biosolids Per Day for the Blue Plains AWTP

-

200

400

600

800

1,000

1,200

1,400

Jun-08 Jul-08 Sep-08 Oct-08 Dec-08 Feb-09 Mar-09 May-09 Jul-09

months (2008-2009)

amou

nt o

f bi

osol

ids

per

day/

(ton

s)

Average 1,163 tons per day

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The Average Amount of Organic Dry Matter of Biosolids Per Day for the Blue Plains AWTP

-

50

100

150

200

250

300

Jun-08 Jul-08 Sep-08 Oct-08 Dec-08 Feb-09 Mar-09 May-09 Jul-09

months (2008-2009)

orga

nic

dry

mat

ter

of b

ioso

lids

pe

r da

y/(t

ons)

Average 239 tons per day

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The Anaerobic Biodegradation Production Process

Active biomass + C-substance CH4 + CO2 + stabilized biomass + H2O

Biogas composition

Methane gas 55-65%

Carbon dioxide 30-40%

Water vapor, traces of H2S and H2 0-5%

(Source: Appels, L., et al., Principles and potential of the anaerobic digestion of waste-activated sludge).

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ODS : organic dry solids of the sludge (wt%)

Biogas is 0.4 x 239 x 1000 = 95.6 x 103 cubic meters per day

Methane is 60 % = 57.3 x 103 cubic meters per day

Carbon dioxide is 35% = 33.5 x 103 cubic meters per day

The Relation Between Biogas Production and Retention Time

(Source: Appels, L., et al., Principles and potential of the anaerobic digestion of waste-activated sludge).

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Historic Daily Power Consumption Data for The Blue Plains AWTP

600

650

700

750

800

850

900

Jan Feb Mar Apil May Jun Jul Aug Sep Oct Nov Dec

x 103 kWH

2008

2007

2006

Average 2008 = 736 x 103 kilowatt hours per day

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From Methane Gas to Electricity

• Methane 1 ft3 = 1,028 BTU• 3,412 BTU methane = 1 kWH• Blue Plains AWTP will have almost 534 x 103 kilowatt

hours per day from methane gas( Source: http://tonto.eia.doe.gov/kids/goodstuff.cfm?page=about_energy_conversion_calculator-basics)

• Plant needs 736 x 103 kilowatt hours per day (not enough)

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Other Renewable Options are at Blue Plains

• Methane from biosolids generate electricity that is not enough for the Blue Plains AWTP operations

(Need 736 x 103 kilowatt hours per day but it is able to

generate only 534 x 103 kilowatt hours per day )

• Other options more than methane or electricity is to invest in renewable energy source (e.g., wind, solar, hydropower and geothermal)

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Prediction of Carbon Dioxide (CO2) Credits

• 0.8 tons CO2 credits per dry ton biosolids

(Brown, S., H. Gough, and et al., Green Aspects of Biosolids Processing and Use 2009)

• 0.8 x 1,163 tons biosolids per day

• CO2 credits are 930 tons per day

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Financial Benefit from CO2 Credits

Market Volume (MtCO2) Value( US$ million)

RGGI 27.4 108.9

Transaction Volume and Value, Global Carbon Market, 2008

Source: Ecosystem Marketplace, New Carbon Finance

108.9/27.4 = $ 3.94 per ton CO2

• The Blue Plains AWTP

$3.94 x 930 tons per day = $ 3,692 per day

= $ 1.3 million per year

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Summary

• Biosolids from the Blue Plains AWTP is a significant renewable energy source. It has a high efficiency to generate methane and electricity

Methane is 57.4x103 cubic meters per day Electricity is 534.5x103 kilowatt hours per day

• CO2 credits is 930 tons CO2 per day • Financial benefit from CO2 credits is about $ 3,692 per day

• Methane for transportation grid

• Selling Electricity to Grid Electric Power

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Ongoing Work

• Build a multiobjective optimization model in order to make best decisions to:– minimize DCWASA’s CO2 footprint

– minimize energy usage– minimize costs– other considerations (as appropriate)

• Will consider both investment decisions as well as operational ones