Prediction of cost and emission from Indian coal-fired power plants with CO₂ capture and storage using artificial intelligence techniques

Abstract

Coal-fired power plants are one of the most important targets with respect to reduction of CO₂ emissions. The reasons for this are that coal-fired power plants offer localized large point sources (LPS) of CO₂ and that the Indian power sector contributes to roughly half of all-India CO₂ emissions. CO₂ capture and storage (CCS) can be implemented in these power plants for long-term decarbonisation of the Indian economy. In this paper, two artificial intelligence (AI) techniques—adaptive network based fuzzy inference system (ANFIS) and multi gene genetic programming (MGGP) are used to model Indian coal-fired power plants with CO₂ capture. The data set of 75 power plants take the plant size, the capture type, the load and the CO₂ emission as the input and the COE and annual CO₂ emissions as the output. It is found that MGGP is more suited to these applications with an R² value of more than 99% between the predicted and actual values, as against the ~96% correlation for the ANFIS approach. MGGP also gives the traditionally expected results in sensitivity analysis, which ANFIS fails to give. Several other parameters in the base plant and CO₂ capture unit may be included in similar studies to give a more accurate result. This is because MGGP gives a better perspective toward qualitative data, such as capture type, as compared to ANFIS.

Keywords

carbon capture and storage / power plants / artificial intelligence / genetic programming / neuro fuzzy

References

[1] Sathaye J, Shukla P R. Methods and models for costing carbon mitigation. Annual Review of Environment and Resources, 2013, 38(1): 137–168

[2] Sharma S K, Choudhury A, Sarkar P, Biswas S, Singh A, Dadhich P K, Singh A K, Majumdar S, Bhatia A, Mohini M, Kumar R. Greenhouse gas inventory estimates for India. Current Science, 2011, 101(3): 405–415

[3] Goel M. Recent approaches in CO₂ fixation research in India and future perspectives towards zero emission coal based power generation. Current Science, 2009, 97(11): 1625–1633

[4] IPCC. Special Report on Carbon Dioxide Capture and Storage. Cambridge: Cambridge University Press, 2005, https://www.ipcc.ch/report/srccs/

[5] Haszeldine R S. Carbon capture and storage: how green can black be? Science, 2009, 325(5948): 1647–1652

[6] Singh U. Carbon capture and storage: an effective way to mitigate global warming. Current Science, 2013, 105(7): 914–922

[7] Rubin E S, Davison J E, Herzog H J. The cost of CO₂ capture and storage. International Journal of Greenhouse Gas Control, 2015, 40: 378–400

[8] Shukla P, Dhar S, Pathak M, Mahadevia D, Garg A. Pathways to deep decarbonization in India. SDSN-IDDRI, 2015, http://deepdecarbonization.org/wp-content/uploads/2015/09/DDPP_IND.pdf

[9] Rao A B, Rubin E S. A technical, economic, and environmental assessment of amine-based CO₂ capture technology for power plant greenhouse gas control. Environmental Science & Technology, 2002, 36(20): 4467–4475

[10] Rubin E S, Chen C, Rao A B. Cost and performance of fossil fuel power plants with CO₂ capture and storage. Energy Policy, 2007, 35(9): 4444–4454

[11] Singh U, Rao A B. Estimating the environmental implications of implementing carbon capture and storage in Indian coal power plants. In: Proceedings on Advances in Green Energy (ICAGE).IEEE, 2014, 226–232

[12] Singh R, Kainthola A, Singh T N. Estimation of elastic constant of rocks using an ANFIS approach. Applied Soft Computing, 2012, 12(1): 40–45

[13] Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 1985, SMC-15 (1): 116–132

[14] Sugeno M, Kang G T. Structure identification of fuzzy model. Fuzzy Sets and Systems, 1988, 28(1): 15–33

[15] Jang J S. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 1993, 23(3): 665–685

[16] Sahu M, Singh P, Mahapatra S S, Khatua K K. Prediction of entrance length for low Reynolds number flow in pipe using neuro-fuzzy inference system. Expert Systems with Applications, 2012, 39(4): 4545–4557

[17] Koza J R. Genetic Programming: on the Programming of Computers by Means of Natural Selection. Boston: MIT Press, 1992

[18] Garg A, Vijayaraghavan V, Mahapatra S S, Tai K, Wong C H. Performance evaluation of microbial fuel cell by artificial intelligence methods. Expert Systems with Applications, 2014, 41(4): 1389–1399

[19] Garg A, Garg A, Tai K. A multi-gene genetic programming model for estimating stress-dependent soil water retention curves. Computational Geosciences, 2014, 18(1): 45–56

[20] Searson D P. GPTIPS 2: An open-source software platform for symbolic data mining. In: Gandomi A.H., Alavi A.H. and Ryan, C. (eds.), Handbook of Genetic Programming Applications. Springer, Switzerland. 2015

[21] CEA. CO₂ baseline database for the Indian power sector, Central Electricity Authority, Ministry of Power, Government of India, New Delhi. 2016, http://www.cea.nic.in/reports/others/thermal/tpece/cdm_co2/user_guide_ver11.pdf

[22] Berkenpas M B, Frey H C, Fry J J, Kalagnanam J, Rubin E S. Integrated environmental control model technical documentation. Center for Energy and Environmental Studies. Carnegie Mellon University, USA. 1999, https://www.cmu.edu/epp/iecm/IECM_Publications/1999ra%20Berkenpas%20et%20al,%20IECM%20Tech.pdf

[23] Singh U, Rao A B. Integrating SO₂ and NO_x control systems in Indian coal-fired power plants. Decision, 2015, 42(2): 191–209

[24] Rao A B, Rubin E S. Identifying cost-effective CO₂ control levels for amine-based CO₂ capture systems. Industrial & Engineering Chemistry Research, 2006, 45(8): 2421–2429

[25] Tan R R, Ng D K, Foo D C, Aviso K B. Crisp and fuzzy integer programming models for optimal carbon sequestration retrofit in the power sector. Chemical Engineering Research & Design, 2010, 88(12): 1580–1588

RIGHTS & PERMISSIONS

2017 Higher Education Press and Springer-Verlag Berlin Heidelberg