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Generation of Power Plant Artificial Immune System Using the Partition of the Universe Approach

Ghassan Al-Sinbol(1), Mario George Perhinschi(2*)

(1) Department of Mechanical and Aerospace Engineering, West Virginia University, United States
(2) Department of Mechanical and Aerospace Engineering, West Virginia University, United States
(*) Corresponding author


DOI: https://doi.org/10.15866/ireaco.v9i1.8170

Abstract


The process of generating an artificial immune system for a complex power plant using an alternative method is presented in this paper. As compared to the clustering approach, the proposed partition of the universe approach is less computationally intensive and facilitates the use of full-dimensional self for system abnormal condition detection. In conjunction with a positive selection-type detection algorithm, it does not require explicit non-self generation. Using full-dimensional self prevents the hiding of non-self regions within self projections. These advantages are illustrated using the self for an acid gas removal unit built with the partition of the universe approach to detect the occurrence of several abnormal conditions. Excellent detection performance is obtained in terms of detection time, false alarms, and detection rate. Therefore, the proposed approach can potentially represent an effective and efficient tool for complex system abnormal condition monitoring.
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Keywords


Artificial Immune System; Power Plant Monitoring and Control; Artificial Intelligence Techniques

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References


B. Rukes, R. Taud, Status and perspectives of fossil power generation plants, Energy, Vol. 29 (Issue 12-15): 1853-1874, 2004.
http://dx.doi.org/10.1016/j.energy.2004.03.053

W. L. Luyben, B.D. Tyreus, and M.L. Luyben, Plantwide Process Control (McGraw-Hill, New York, 1998).
http://dx.doi.org/10.1002/aic.690431205

G. P. Rangaiah, V. Kariwala, Plantwide Control: Recent Developments and Applications (John Wiley and Sons Ltd, 2012),
http://dx.doi.org/10.1002/9781119968962.ch1

D. Dasgupta (Ed.), Artificial Immune Systems and Their Applications (Springer Verlag, 1999).
http://dx.doi.org/10.1007/978-3-642-59901-9

E. Hart, J. Timmis, Application areas of AIS: The past, the present and the future, Applied Soft Computing 8 (1): 191-201, 2008.
http://dx.doi.org/10.1016/j.asoc.2006.12.004

D. Dasgupta, L. F. Nino, Immunological Computation – Theory and Applications (CRC Press, Auerbach Publications, Taylor & Francis Group, 2009).

M. G. Perhinschi, G. Al-Sinbol, D. Bhattacharyya, F. Lima, G. Mirlekar, and R. Turton, Development of an Immunity-based Framework for Power Plant Monitoring and Control, Advanced Chemical Engineering Research, Vol. 4 (Issue. 1): 15-28, September 2015
http://dx.doi.org/10.12783/acer.2015.0401.02

F. Esponda, S. Forrest, and P. Helman, A Formal Framework for Positive and Negative Detection Schemes, IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 34 (Issue 1): 357-373, 2004.
http://dx.doi.org/10.1109/tsmcb.2003.817026

M. G. Perhinschi, H. Moncayo, D. Al Azzawi, and I. Moguel, Generation of Artificial Immune System Antibodies Using Raw Data and Cluster Set Union, IC: International Journal of Immune Computation, Vol. 2 (No. 1): 1-15, March, 2014.

F. A. Gonzalez, D. Dasgupta, Anomaly Detection Using Real-valued Negative Selection, Genetic Programming and Evolvable Machines, Vol 4 (No. 4): 383-403, 2003.
http://dx.doi.org/10.1023/a:1026195112518

H. Moncayo, I. Moguel, M. G. Perhinschi, A. Perez, D. Al Azzawi, and A. Togayev, Structured Non-Self Approach for Aircraft Failure Identification within an Immunity-based Fault Tolerance Architecture”, accepted for publication in The Aeronautical Journal, Oct. 2015.
http://dx.doi.org/10.1017/aer.2016.15

D. Al Azzawi, M. G. Perhinschi, and H. Moncayo, Artificial Dendritic Cell Mechanism for Aircraft Immunity-based Failure Detection and Identification, AIAA Journal of Aerospace Information Systems, Vol. 11 (No. 7): 467-481.
http://dx.doi.org/10.2514/1.i010214

D. Al Azzawi, M. G. Perhinschi, H. Moncayo, and A. Perez, A Dendritic Cell Mechanism for Detection, Identification, and Evaluation of Aircraft Failures, J. of Control Eng. Practice, Vol. 41, 134-148.
http://dx.doi.org/10.1016/j.conengprac.2015.04.010

T. Stibor, P. Mohr, and J. Timmis, Is negative selection appropriate for anomaly detection? Genetic and Evolutionary Computation Conference (GECCO '05), pp. 569–576, IEEE Computer Society Press, June 2005.
http://dx.doi.org/10.1145/1068009.1068061

Z. Ji, D. Dasgupta, Applicability Issues of the Real-valued Negative Selection Algorithms, Genetic and Evolutionary Computation Conference, 111-118, July 2006.
http://dx.doi.org/10.1145/1143997.1144017

H. Moncayo, M. G. Perhinschi, and J. Davis, Aircraft Failure Detection and Identification Using an Immunological Hierarchical Multi-self Strategy, AIAA Journal of Guidance, Control, and Dynamics, Vol. 33 (No. 4): 1105-1114, 2010.
http://dx.doi.org/10.2514/1.47445

M. G. Perhinschi, H. Moncayo, and D. Al Azzawi, Integrated Immunity-Based Framework for Aircraft Abnormal Conditions Management, AIAA Journal of Aircraft, Vol. 51 (Issue 6): 1726-1739.
http://dx.doi.org/10.2514/1.c032381

C. A. Janeway, P. Travers, M. Walport, M., and M. J. Shlomchik, Immunobiology: The Immune System in Health and Disease (6th ed., Garland Science, New York, 2005)

G. W. Rowe, The Theoretical Models in Biology (Oxford Univ. Press, Oxford, 1994).

C. S. William, C. A. Nelson, R. D. Newberry, D. M. Kranz, J. H. Russell and D. Y. Loh, Positive and negative selection of an antigen receptor on T cells in transgenic mice, Nature, 336(6194), pp. 73-76.
http://dx.doi.org/10.1038/336073a0

C. Elkan, Using the Triangle Inequality to Accelerate k-Means, Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), pp. 147-153, AAAI Press, Menlo Park, CA, 2003

J. Davis, M. G. Perhinschi, H. Moncayo, Evolutionary Algorithm for Artificial Immune System-Based Failure Detector Generation and Optimization, AIAA Journal of Guidance, Control, and Dynamics, Vol. 33, No. 2, pp. 302-320, Mar.-Apr. 2010
http://dx.doi.org/10.2514/1.46126

D. Bhattacharyya, R. Turton, and S. E. Zitney, Steady-state simulation and optimization of an integrated gasification combined cycle power plant with CO2 capture. IndEngChem Res 50(3), pp. 1674-1690, 2010.
http://dx.doi.org/10.1021/ie101502d

F.V. Lima, D. Bhattacharyya, R. Turton, P. Mahapatra, and S. E. Zitney, Control of integrated gasification combined cycle power plants with CO2 capture, The Impact of Control Technology, (2nd edition, T. Samad and A.M. Annaswamy (eds.), IEEE Control Systems Society, 2014).

L. Wiley, Using Dynamic Simulation to Drive Process Design, Control, and Optimization, 2015 AlChE Annual Meeting, Salt Lake City, UT, Nov. 8-13, 2015.

Simsci-Esscor, Dynamic Simulation Suite User Guide, available at http://www.simsci esscor.com, [accessed Sept. 2015].

S.E. Zitney, E.A. Liese, P. Mahapatra, R. Turton, D. Bhattacharyya, G. Provost, AVESTAR Center: Dynamic Simulation-Based Collaboration Toward Achieving Operational Excellence for IGCC Plants with Carbon Capture, Proc. of the 29th Annual International Pittsburgh Coal Conference, Pittsburgh, PA, 2012.

NRCCE, Advanced Virtual Energy Simulation Training and Research Center (AVESTAR®), NRCCE, 2015. available at: http://nrcce.wvu.edu/promotion/as/ [accessed: 21- Sep- 2015].


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