Hurley, JP
1994
Coal Ash Behavior and Management Tools
Erickson, T.A.; O'Leary, E.M.; Folkedahl, B.C.; Ramanathan, M.; Zygarlicke, C.J.; Steadman, E.N.; Hurley, J.P. and Benson, S.A.
The Impact of Ash Deposition on Coal-Fired Plants, Taylor & Francis, Inc., 1994. Funded by US Department of Energy, Electric Power Research Institute, Tow, Texaco, Shell, Union Electric, Kansas City Power and Light, Minnesota Power and Northern States Power.
Over the past five years, computer-based research tools have been increasingly applied in making important economic and operational decisions in the utility power industry. These tools-which include models, indices, databases, and data manipulation programs-are used by researchers, operators, and managers in the evaluation of coal utilization as an efficient and environmentally acceptable source of energy. Applicable tools that have been developed at, and are currently used by, the Energy & Environmental Research Center (EERC) include Partchar©, MINCLASS©, VISCAL©, MANAGER©, ATRAN, LEADER©, PHOEBE©, and PCQUEST©. These software applications range from databases for retrieving coal and coal product analysis, to computer codes to process coal and coal product analysis, to advanced models and indices to evaluate the operational impacts of specific systems.
1993
Predicting Ash Behavior in Utility Boilers
Benson, S.A.; Hurley, J.P.; Zygarlicke, C.J.; Steadman, E.N. and Erickson, T.A.
Energy & Fuels, 7 (6):746, 1993. Funded by US Department of Energy and ACERC.
In recent years, significant advances have been made in the development of methods to predict ash behavior in utility boilers. This paper provides an overview of methods used to assess and predict ash formation and deposition. These prediction methods are based on a detailed knowledge of ash formation and deposition mechanisms that has been obtained through bench, pilot, and field-testing and detailed coal and ash characterization. The paper describes advanced methods of coal and ash analyses and the advantages of these methods over conventional methods. The advanced coal characterization methods provide sufficient data to predict size and composition distribution of fly ash. The composition and size data are used as inputs to mechanistic models that ultimately predict deposition propensities in various locations of utility boilers. Advanced indices based on advanced coal analysis data have also been developed and are being applied to predict convective pass fouling tendencies.
1992
Predicting Ash Behavior in Utility Boilers: Assessment of Current Status
Benson, S.A.; Erickson, T.A.; Hurley, J.P.; Zygarlicke, C.J. and Steadman, E.N.
Electric Power Research Institute Conference on Coal Quality, San Diego, CA, August 1992, Funded by US Department of Energy, ABB-Combustion Engineering, Electric Power Research Institute and ACERC.
The development of effective methods to predict ash behavior in utility boilers requires detailed information on ash-forming constituents in the coal, ash formation mechanisms, and behavior of the ash species in combustion systems. This paper described the application of advanced methods to characterize coal and provides examples of two methods to predict ash behavior. The advanced methods of coal analysis provide quantitative information on the size, association, and abundance of ash-forming species in the coal. The methodologies include computer-controlled scanning electron microscopy (CCSEM) and chemical fractionation. The CCSEM technique determines the size, abundance, and composition of 2000 to 3000 mineral grains in coal. The chemical fractionation technique is used to determine the abundance of organically associated inorganic components in lignite and subbituminous coals. The information obtained from the advanced methods of analyses is used as input into computer codes to predict ash behavior. The two methods to predict ash behavior include a fouling index and a phenomenological/mechanistic model. The fouling index provides the ability to rank coals based on their potential to produce convective pass deposits and was developed using data obtained from advanced methods of analyses, a knowledge base of ash behavior, and full-scale utility boiler operational data. The phenomenological or mechanistic models are used to predict the particle-size and composition distribution of ash and deposition potential of the ash as a function of boiler geometry and operational conditions. These predictive techniques have been developed through the use of full-scale utility boiler experience and have been verified for selected systems; however, these techniques are limited to certain types of coals and to certain regions of the boiler.
Hurley, JP
1994
Coal Ash Behavior and Management Tools
Erickson, T.A.; O'Leary, E.M.; Folkedahl, B.C.; Ramanathan, M.; Zygarlicke, C.J.; Steadman, E.N.; Hurley, J.P. and Benson, S.A.
The Impact of Ash Deposition on Coal-Fired Plants, Taylor & Francis, Inc., 1994. Funded by US Department of Energy, Electric Power Research Institute, Tow, Texaco, Shell, Union Electric, Kansas City Power and Light, Minnesota Power and Northern States Power.
Over the past five years, computer-based research tools have been increasingly applied in making important economic and operational decisions in the utility power industry. These tools-which include models, indices, databases, and data manipulation programs-are used by researchers, operators, and managers in the evaluation of coal utilization as an efficient and environmentally acceptable source of energy. Applicable tools that have been developed at, and are currently used by, the Energy & Environmental Research Center (EERC) include Partchar©, MINCLASS©, VISCAL©, MANAGER©, ATRAN, LEADER©, PHOEBE©, and PCQUEST©. These software applications range from databases for retrieving coal and coal product analysis, to computer codes to process coal and coal product analysis, to advanced models and indices to evaluate the operational impacts of specific systems.
1993
Predicting Ash Behavior in Utility Boilers
Benson, S.A.; Hurley, J.P.; Zygarlicke, C.J.; Steadman, E.N. and Erickson, T.A.
Energy & Fuels, 7 (6):746, 1993. Funded by US Department of Energy and ACERC.
In recent years, significant advances have been made in the development of methods to predict ash behavior in utility boilers. This paper provides an overview of methods used to assess and predict ash formation and deposition. These prediction methods are based on a detailed knowledge of ash formation and deposition mechanisms that has been obtained through bench, pilot, and field-testing and detailed coal and ash characterization. The paper describes advanced methods of coal and ash analyses and the advantages of these methods over conventional methods. The advanced coal characterization methods provide sufficient data to predict size and composition distribution of fly ash. The composition and size data are used as inputs to mechanistic models that ultimately predict deposition propensities in various locations of utility boilers. Advanced indices based on advanced coal analysis data have also been developed and are being applied to predict convective pass fouling tendencies.
1992
Predicting Ash Behavior in Utility Boilers: Assessment of Current Status
Benson, S.A.; Erickson, T.A.; Hurley, J.P.; Zygarlicke, C.J. and Steadman, E.N.
Electric Power Research Institute Conference on Coal Quality, San Diego, CA, August 1992, Funded by US Department of Energy, ABB-Combustion Engineering, Electric Power Research Institute and ACERC.
The development of effective methods to predict ash behavior in utility boilers requires detailed information on ash-forming constituents in the coal, ash formation mechanisms, and behavior of the ash species in combustion systems. This paper described the application of advanced methods to characterize coal and provides examples of two methods to predict ash behavior. The advanced methods of coal analysis provide quantitative information on the size, association, and abundance of ash-forming species in the coal. The methodologies include computer-controlled scanning electron microscopy (CCSEM) and chemical fractionation. The CCSEM technique determines the size, abundance, and composition of 2000 to 3000 mineral grains in coal. The chemical fractionation technique is used to determine the abundance of organically associated inorganic components in lignite and subbituminous coals. The information obtained from the advanced methods of analyses is used as input into computer codes to predict ash behavior. The two methods to predict ash behavior include a fouling index and a phenomenological/mechanistic model. The fouling index provides the ability to rank coals based on their potential to produce convective pass deposits and was developed using data obtained from advanced methods of analyses, a knowledge base of ash behavior, and full-scale utility boiler operational data. The phenomenological or mechanistic models are used to predict the particle-size and composition distribution of ash and deposition potential of the ash as a function of boiler geometry and operational conditions. These predictive techniques have been developed through the use of full-scale utility boiler experience and have been verified for selected systems; however, these techniques are limited to certain types of coals and to certain regions of the boiler.