School of Chemical Engineering and Advanced Materials, Newcastle University. School of Engineering, University of Hull

Transcription

1 Fei Li 1, Jie Zhang 1, Meihong Wang 2, Eni Oko 2 1 School of Chemical Engineering and Advanced Materials, Newcastle University 2 School of Engineering, University of Hull

2 Introduction to carbon capture and storage Modelling of carbon capture processes Bootstrap aggregated neural networks Neural network modelling results Conclusions

3 Carbon Oxide (CO 2 ) 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 CO 2 emission, consisting of three major methods: Post-combustion system Pre-combustion system Oxyfuel combustion system

4 Advantages: Available for retrofitting existing power generation plants. Capture CO 2 of low partial pressure in flue gas. Disadvantages: Large energy requirement for absorbent generation. Thus optimisation and control of CCS systems is very important.

5

6 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.

7 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

8 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, ) Forward selection and backward elimination based selective combination (Ahmad and Zhang, Neurocomputing, 2009, )

9 The standard error of the ith predicted value is estimated as e n 1 { b [ y( xi ; W ) y( xi ; )] n 1 b 1 n 1/ 2 b where y(x ; i.) = y( x b i; W ) / n 1 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(x ; i.) 1.96 e. 2 }

10 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 (u 1 ), CO 2 concentration in flue gas (u 2 ), pressure of flue gas (u 3 ), temperature of flue gas (u 4 ), lean solvent flow rate (u 5 ), MEA concentration (u 6 ), lean solvent temperature (u 7 ). o Output: CO 2 capture level (y). o Model form: y=f(u 1, u 2,,u 7 ) o 59 samples for training, 11 samples for testing and 18 samples for validation

11 Dynamic models o Input variables: inlet gas flow rate (u 1 ), CO 2 concentration in inlet gas flue (u 2 ), inlet gas temperature (u 3 ), inlet gas pressure (u 4 ), MEA circulation rate (u 5 ), lean loading (u 6 ), lean solution temperature (u 7 ) and reboiler temperature (u 8 ). o Output variables: CO 2 capture level, CO 2 production rate o First order nonlinear dynamic model One-step-ahead prediction: ŷ(t) = f[y(t-1), u 1 (t-1), u 2 (t-1),, u 8 (t-1)] Multi-step-ahead prediction: ŷ(t) = f[ŷ (t-1), u 1 (t-1), u 2 (t-1),, u 8 (t-1)] o 438 samples for training, 95 samples for testing. Run 7 and Run 2 are used as validation data.

12 Static model Mean squared errors (MSE) of individual neural network and aggregated neural networks on unseen validation data 0.8 mse(validation) network NO. 0.2 mse(validation) number of networks

13 Static model prediction for CO 2 capture level on unseen validation data 105 o:actual values; +:predictions; --:95% confidence bounds CO2 capture level (%) samples

14 Dynamic model MSE of CO 2 production rate for individual neural networks (left) and aggregated neural networks (right) MSE (training & testing) MSE (training & testing) MSE (validation) Neural network numbers MSE (validation) Number of neural networks

15 o Prediction of CO 2 production rate :process; --:one-step-ahead prediction CO2 production rate time(s)

16 :process; --:multi-step-ahead prediction 60-steps-ahead prediction 0.06 CO2 production rate(kg/s) time(s)

17 o Prediction of CO 2 capture level 99 -:process; --:one-step-ahead prediction CO2 capture level(%) time(s)

18 99 -:process; --:multi-step-ahead prediction steps-ahead prediction CO2 capture level(%) time(s)

19 Neural network models for CO 2 capture level and CO 2 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.

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

21