Prediction of cost and emission from Indian coal-fired power plants with CO2 capture and storage using artificial intelligence techniques
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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.
Keywordscarbon capture and storage power plants artificial intelligence genetic programming neuro fuzzy
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