Fei Li1, Jie Zhang1, Meihong Wang2, Eni Oko2

1School of Chemical Engineering and Advanced Materials, Newcastle University

2School of Engineering, University of Hull

Introduction to carbon capture and storage

Modelling of carbon capture processes

Bootstrap aggregated neural networks

Neural network modelling results

Conclusions

Carbon Oxide (CO2) emission, mainly from coal-fired

power plants, has drawn more attentions in public.

Carbon Capture and Storage (CCS) is an effective

and efficient technique to reduce CO2 emission,

consisting of three major methods:

Post-combustion system

Pre-combustion system

Oxyfuel combustion system

• Advantages:

• Available for retrofitting existing power generation plants.

• Capture CO2 of low partial pressure in flue gas.

• Disadvantages:

• Large energy requirement for absorbent generation.

• Thus optimisation and control of CCS systems is

very important.

• Mechanistic modelling

• Developed based on material and energy balance, and chemical reactions

• In the form of differential and algebraic equations and usually implemented in

Aspens HYSIS, gPROMS, etc.

• Time consuming in development and implementation, may not be suitable for real

time optimisation

• Data driven “black box” models

• Developed from process operation data using statistical and computational

intelligence techniques such as artificial neural networks (ANN), etc.

• Easy to develop and implement, suitable to real time optimisation applications

• However, a single neural network model can lack reliability when applied to

unseen data.

Improve model robustness by combining multiple imperfect neural network models

These neural network models can be developed on different parts of the data set and/or trained from different initial weights

X Y

Building a stacked neural network model ◦ Data are re-sampled using bootstrap re-sampling to

form several data sets ◦ A neural network model is developed on each data set ◦ These networks are combined through PCR (principal

component regression) or through simple average

Other advanced approaches ◦ Bayesian selective combination (Ahmad and Zhang,

Neural Computing & Applications, 2005, 78-87) ◦ Data fusion based approach (Ahmad and Zhang,

Computers & Chemical Engineering, 2006, 295-308) ◦ Forward selection and backward elimination based

selective combination (Ahmad and Zhang, Neurocomputing, 2009, 1198-1204 )

The standard error of the ith predicted value is estimated as

where y(xi; .) = and n is the number of neural networks.

Assuming that the individual network prediction errors are normally distributed, the 95% prediction confidence bounds can be calculated as y(xi; .) 1.96e.

2/12

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• Data generation

o gPROMS simulated static and dynamic process operation data

were generated by University of Hull

• Static models

o Inputs: inlet flue gas flow rate (u1), CO2 concentration in flue gas (u2),

pressure of flue gas (u3), temperature of flue gas (u4), lean solvent

flow rate (u5), MEA concentration (u6), lean solvent temperature (u7).

o Output: CO2 capture level (y).

o Model form: y=f(u1, u2,…,u7)

o 59 samples for training, 11 samples for testing and 18 samples for

validation

• Dynamic models

o Input variables: inlet gas flow rate (u1), CO2 concentration in inlet gas flue

(u2), inlet gas temperature (u3), inlet gas pressure (u4), MEA circulation rate (u5),

lean loading (u6), lean solution temperature (u7) and reboiler temperature (u8).

o Output variables: CO2 capture level, CO2 production rate

o First order nonlinear dynamic model

• One-step-ahead prediction: ŷ(t) = f[y(t-1), u1(t-1), u2(t-1), …, u8(t-1)]

• Multi-step-ahead prediction: ŷ(t) = f[ŷ (t-1), u1(t-1), u2(t-1), …, u8(t-1)]

o 438 samples for training, 95 samples for testing. Run 7 and

Run 2 are used as validation data.

• Static model Mean squared errors (MSE) of individual neural network and aggregated

neural networks on unseen validation data

0 5 10 15 20 25 30 350

0.2

0.4

0.6

0.8

mse(v

alid

ation)

network NO.

0 5 10 15 20 25 30 350

0.05

0.1

0.15

0.2

mse(v

alid

ation)

number of networks

Static model prediction for CO2 capture level on unseen validation data

0 2 4 6 8 10 12 14 16 1855

60

65

70

75

80

85

90

95

100

105

samples

CO

2 c

aptu

re level (%

)

o:actual values; +:predictions; --:95% confidence bounds

• Dynamic model MSE of CO2 production rate for individual neural networks (left) and

aggregated neural networks (right)

0 5 10 15 20 25 30 350

0.2

0.4

0.6

0.8

MS

E (

train

ing &

testing)

0 5 10 15 20 25 30 350

1

2

3

4

MS

E (

validation)

Neural network numbers

0 5 10 15 20 25 30 350

0.2

0.4

0.6

0.8

MS

E (

train

ing &

testing)

0 5 10 15 20 25 30 350

0.5

1

1.5

MS

E (

validation)

Number of neural networks

o Prediction of CO2 production rate

0 50 100 150 200 250 300 350 400 450 5000

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

time(s)

CO

2 p

roduction r

ate

-:process; --:one-step-ahead prediction

0 50 100 150 200 250 300 350 400 450 5000

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

time(s)

CO

2 p

roduction r

ate

(kg/s

)

-:process; --:multi-step-ahead prediction

60-steps-ahead prediction

o Prediction of CO2 capture level

0 100 200 300 400 500 60097.2

97.4

97.6

97.8

98

98.2

98.4

98.6

98.8

99

time(s)

CO

2 c

aptu

re level(%

)

-:process; --:one-step-ahead prediction

0 100 200 300 400 500 60097.2

97.4

97.6

97.8

98

98.2

98.4

98.6

98.8

99

time(s)

CO

2 c

aptu

re level(%

)

-:process; --:multi-step-ahead prediction

82-steps-ahead prediction

Neural network models for CO2 capture level and

CO2 production rate are developed and they give

accurate predictions.

Combining multiple neural networks gives more

accurate and reliable predictions.

These models can be effectively used in real time

optimisation and control, which is currently under

study.

EU under the project R&D in Coal-fired Supercritical Power Plant with Post-combustion Carbon Capture using Process Systems Engineering techniques (R-D-CSPP-PSE) (Project no. PIRSES-GA-2013-612230)