In the coal-based combustion and gasification processes, the mineral matter contained in the coal (predominantly oxides), is left as an incombustible residue, termed ash. Commonly, ash deposits are formed on the heat ...In the coal-based combustion and gasification processes, the mineral matter contained in the coal (predominantly oxides), is left as an incombustible residue, termed ash. Commonly, ash deposits are formed on the heat absorbing surfaces of the exposed equipment of the combustion/gasification processes. These deposits lead to the occurrence of slagging or fouling and. consequently, reduced process efficiency. The ash fusion temperatures (AFTs) signify the temperature range over which the ash deposits are formed on the heat absorbing surfaces of the process equipment. Thus, for designing and operating the coal-based processes, it is important to have mathematical models predicting accurately the four types of AFTs namely initial deformation temperature, softening temperature, hemispherical temperature, and flow temperature. Several linear/nonlinear models with varying prediction accuracies and complexities are available for the AFT prediction. Their principal drawback is their applicability to the coals originating from a limited number of geographical regions. Accordingly, this study presents computational intelligenee (CI) based nonlinear models to predict the four AFTs using the oxide composition of the coal ash as the model input. The CI methods used in the modeling are genetic programming (GP), artificial neural networks, and support vector regression. The no table features of this study are that the models with a better AFT prediction and generalization performanee, a wider application potential, and reduced complexity, have been developed. Among the Ci-based models, GP and MLP based models have yielded overall improved performanee in predicting all four AFTs.展开更多
In India coal combustion is the single largest source of emission of mercury which is a widespread persistent global toxicant,travelling across international borders through air and water.As a party to the Minamata co...In India coal combustion is the single largest source of emission of mercury which is a widespread persistent global toxicant,travelling across international borders through air and water.As a party to the Minamata convention,India aims to monitor and reduce Hg emissions and stricter norms are introduced for mercury emissions from power plants(30μg/Nm 3 for flue gas in stack).This paper presents the results obtained during the experimental studies performed on mercury emissions at four coal-fired and one lignite-fired power plants in India.The mercury concentration in the feed coal varied between 0.12-0.27 mg/Kg.In the mercury mass balance,significant proportion of feed coal mercury has been found to be associated with fly ash,whereas bottom ash contained very low mercury.80%-90%of mercury was released to air through stack gas.However,for circulating fluidised bed boiler burning lignite,about 64.8%of feed mercury was found to get captured in the fly ash and only 32.4%was released to air.The mercury emission factor was found to lie in the range of 4.7-15.7 mg/GJ.展开更多
文摘In the coal-based combustion and gasification processes, the mineral matter contained in the coal (predominantly oxides), is left as an incombustible residue, termed ash. Commonly, ash deposits are formed on the heat absorbing surfaces of the exposed equipment of the combustion/gasification processes. These deposits lead to the occurrence of slagging or fouling and. consequently, reduced process efficiency. The ash fusion temperatures (AFTs) signify the temperature range over which the ash deposits are formed on the heat absorbing surfaces of the process equipment. Thus, for designing and operating the coal-based processes, it is important to have mathematical models predicting accurately the four types of AFTs namely initial deformation temperature, softening temperature, hemispherical temperature, and flow temperature. Several linear/nonlinear models with varying prediction accuracies and complexities are available for the AFT prediction. Their principal drawback is their applicability to the coals originating from a limited number of geographical regions. Accordingly, this study presents computational intelligenee (CI) based nonlinear models to predict the four AFTs using the oxide composition of the coal ash as the model input. The CI methods used in the modeling are genetic programming (GP), artificial neural networks, and support vector regression. The no table features of this study are that the models with a better AFT prediction and generalization performanee, a wider application potential, and reduced complexity, have been developed. Among the Ci-based models, GP and MLP based models have yielded overall improved performanee in predicting all four AFTs.
文摘In India coal combustion is the single largest source of emission of mercury which is a widespread persistent global toxicant,travelling across international borders through air and water.As a party to the Minamata convention,India aims to monitor and reduce Hg emissions and stricter norms are introduced for mercury emissions from power plants(30μg/Nm 3 for flue gas in stack).This paper presents the results obtained during the experimental studies performed on mercury emissions at four coal-fired and one lignite-fired power plants in India.The mercury concentration in the feed coal varied between 0.12-0.27 mg/Kg.In the mercury mass balance,significant proportion of feed coal mercury has been found to be associated with fly ash,whereas bottom ash contained very low mercury.80%-90%of mercury was released to air through stack gas.However,for circulating fluidised bed boiler burning lignite,about 64.8%of feed mercury was found to get captured in the fly ash and only 32.4%was released to air.The mercury emission factor was found to lie in the range of 4.7-15.7 mg/GJ.
