hipercap project: assessment of co2 capture technologies · –task 4.3 - assessment of capture...

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DNV GL © 2014 26.03.2015 SAFER, SMARTER, GREENER DNV GL © 2014 26.03.2015 Jock Brown HiPerCap Project: Assessment of CO2 Capture Technologies 1 Melbourne Workshop

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Page 1: HiPerCap Project: Assessment of CO2 Capture Technologies · –Task 4.3 - Assessment of capture technologies studied in WP1-3 (DNV GL, SINTEF, EDF, EON, AEE, GNF) – Not started

DNV GL © 2014 26.03.2015 SAFER, SMARTER, GREENER DNV GL © 2014

26.03.2015

Jock Brown

HiPerCap Project: Assessment of CO2 Capture Technologies

1

Melbourne Workshop

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DNV GL © 2014 26.03.2015

Global reach – local competence

2

400 offices

100 countries

16,000 employees

150 years

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DNV GL © 2014 26.03.2015

DNV GL Oil and Gas

5,500 exceptional

people who care about

making the industry

safer, smarter and

greener

Combining industry and

domain knowledge with

project and operational

expertise

A global network of

experts, working

together to solve local

customer challenges.

4

Service line hubs

39 Oil & Gas countries

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DNV GL © 2014 26.03.2015

HiPerCap Objectives

1. To develop high-potential novel and environmentally benign technologies and

processes for post-combustion CO2 capture leading to real breakthroughs.

2. To achieve 25% reduction in efficiency penalty compared to a demonstrated

state-of-the-art capture process

3. Deliver proof of concept for technologies

4. Develop a fair methodology for comparing capture technologies

5. Develop technology roadmaps for the two most promising technologies

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DNV GL © 2014 26.03.2015

HiPerCap Project

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DNV GL © 2014 26.03.2015

WP4 Overview

Activities:

– Task 4.1 – Establishment of Methodology (DNV GL, SINTEF, EDF, EON, AEE,

GNF)

– Task 4.2 - Data collection of capture technologies studied in WP1-3 (SINTEF,

DNV GL, EON)

– Task 4.3 - Assessment of capture technologies studied in WP1-3 (DNV GL,

SINTEF, EDF, EON, AEE, GNF) – Not started

– Task 4.4 - Guidelines for selection and benchmarking of two breakthrough

technologies to be studied in WP5 (EDF, DNV, SINTEF, TNO, EON, AEE, GNF) –

Not started

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DNV GL © 2014 26.03.2015

Assessment Methodology

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DNV GL © 2014 26.03.2015

The Final Assessment

Ultimately the impact of CCS on the COST of the product produced will be how

future CCS investment decisions are made

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DNV GL © 2014 26.03.2015

Scope of the assessment

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Capture Process

ConditioningPre-

treatmentCompression

ReferencePower Plant(modified)

Scope 5

flue gas

Treatedflue gas

Coal

Power(kWe)

Capture technology

treatedflue gas

ReferencePower Plant

Reference Coal fired power plant

flue gasCoal

Power (kWe)

Cooling Water

steam

condensate

CoolingWater

CapturedCO2

Overall comparison

On level of key indicators

the following performance

can be determined:

Indicator Energy

Indicator Environmental

Indicator Cost

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DNV GL © 2014 26.03.2015

WP4 Approach and Workflow

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Capture Process

Scope 1

T ConditioningPre-

treatmentCompression

ReferencePower Plant(modified)

Scope 4

Scope 5

flue gas

Treatedflue gas

CapturedCO2Coal

Power(kWe)

Scope 2

Capture technology

Scope 0

Scope 3 Scope 3

treatedflue gas

steam

condensate

CoolingWater

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DNV GL © 2014 26.03.2015

WP4 Approach and Workflow

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DNV GL © 2014 26.03.2015

WP4 Approach and Workflow

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DNV GL © 2014 26.03.2015

Reference Coal Fired Power Plant and State of the Art Capture

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DNV GL © 2014 26.03.2015

Reference Coal fired Power Plant

Updated EBTF Case

820MW Advanced supercritical (ASC) pulverised coal (approx. 600 °C/280 bar)

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DNV GL © 2014 26.03.2015

State of the Art Capture Technology

Criteria

– Technology needs to be installed on coal power plant

– Full set of data and details need to be publicly available

– The largest available reference should be used

– CESAR 1 case

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DNV GL © 2014 26.03.2015

Environmental

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Approach

Objective: To show that given the best available information, the capture

technology is environmentally benign.

Pass or Fail assessment

Traffic Light Assessment for each polluting component:

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DNV GL © 2014 26.03.2015

Categories

Air Pollution and Other Emissions

– Eg. SOx, NOx, PM, metals, acid and organic chemicals

Water

– Eg. Water consumed and produced, nutrients and organic pollutants in water

Materials of construction and consumed by process

– Eg. Metals for construction, sorbent materials, minerals, membranes, solvents

Wastes

– Eg. Soild and liquid waste such as reclaimer waste

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DNV GL © 2014 26.03.2015

Energy KPI

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DNV GL © 2014 26.03.2015

Background

Objective:

– Show that capture processes have reached goal of a reduction in energy

penalty by 25% compared to current state of the art technology.

Boundary conditions

– Minimal capture rate 85%

What to benchmark

– Impact of capture processes on the reference power plant output

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DNV GL © 2014 26.03.2015

Preferred energy KPI

Specific energy penalty of avoided CO2 (SEPAC) [MJe/kg CO2]

𝑺𝑬𝑷𝑨𝑪 =𝑷𝒓𝒆𝒇−𝑷

𝝓𝑪𝑶𝟐,𝒓𝒆𝒇− 𝝓𝑪𝑶𝟐

– P = net electric output of the power plant in MWe

– φCO2 = the emitted flow of CO2 in 𝑘𝑔𝐶𝑂2/𝑠

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DNV GL © 2014 26.03.2015

Cost

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DNV GL © 2014 26.03.2015

Approach

Objective: To show new technology is cost competitive with existing technologies

and costs have not been sacrificed in pursuit of a reduction in energy

consumption.

Pass or Fail assessment

Estimate CAPEX and OPEX (excluding Energy)

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DNV GL © 2014 26.03.2015

Uncertainty and Data Quality

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DNV GL © 2014 26.03.2015

Uncertainty

Two types of uncertainty:

Parameter Uncertainty

– Uncertainty in experimental measurements made

Model Uncertainty

– Uncertainty related to assumptions in model and physics behind the models

– Want to understand which assumptions the model is most sensitive to

– Aim to reduce the influence of assumptions made

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DNV GL © 2014 26.03.2015

Summary of Uncertainty Drivers

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Strength-of-

knowledge

Belief in

deviations from

assumptions

Sensitivity with

respect to

assumptions

Uncertainty

description

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DNV GL © 2014 26.03.2015

Uncertainty – A double edged sword

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Target

Concept Design Prototype manufacturing

Qualification phases

Serv

ice L

ife

Compliance

with target

Upper limitLifetime Probability

Density Distribution

Acceptance

Percentile

Lower limit

Testing Pilot

Perf

orm

ance

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DNV GL © 2014 26.03.2015

Seeing the Potential of Novel Technologies

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DNV GL © 2014 26.03.2015

SAFER, SMARTER, GREENER

www.dnvgl.com

Thanks very much

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Jock Brown

[email protected]

+47 907 35453

www.dnvgl.com/ccus