Erickson, TA
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.
1991
Interaction of Sodium, Sulfur, and Silica During Coal Combustion
Erickson, T.A.; Ludlow, D.K. and Benson, S.A.
Energy & Fuels, 5:539-547, 1991. Funded by Pittsburgh Energy Technology Center.
The interaction of sodium, sulfur, and silica at conditions typical in a pulverized coal furnace was investigated by using both model mixtures and a synthetic coal. The model mixtures consisted of selected inorganic constituents that were well mixed in proportions typically found in low-rank coal. The synthetic coal consisted of a furfuryl alcohol polymer with appropriate amounts of sodium, sulfur, and silica to duplicate the characteristics of low-rank coal. The model mixtures and synthetic coal were burned in a laminar flow (drop-tube) furnace at 900º, 1100º, 1300º, and 1500º C and residence times of 0.1, 0.5, 1.5, and 2.4s. The resulting char and fly ash particles were quickly quenched, collected, and analyzed with a scanning electron microscope (SEM) to determine size and composition. Results indicated that the formation of sodium silicates is favored by higher temperatures and longer residence times. Thermodynamic calculations and the model mixture studies indicated above 1100º C there is little interference in the formation of sodium silicates by sodium sulfates. In the synthetic coal studies, sodium sulfate particles were detected on the surface of the larger sodium silicate fly ash particles formed at lower temperatures. The size and prevalence of the sodium sulfate particles decreases as temperature was increased. Fly ash particle formation was characterized by fragmentation followed by coalescence. Fragmentation was more prevalent at higher temperatures and smaller fly ash particles were formed. Larger particles were formed at lower temperatures, indicating more complete coalescence with some cenosphere formation.
Fly Ash Particle-Size Distribution and Composition: Experimental and Phenomenological Approach
Zygarlicke, C.J.; Ramanathan, M. and Erickson, T.A.
Engineering Foundation Conference on Inorganic Transformations and Ash Deposition During Combustion, Palm Coast, FL, March 1991. Funded by US Department of Energy and ACERC.
Two modeling approaches are being developed which will predict fly ash particle size and composition. Both approaches are phenomenological in that they require detailed coal input data and empirically derived knowledge of inorganic transformation phenomena that occur during coal combustion. The first approach is stochastic in construction and randomly combines initial coal inorganics depending on their distribution in the coal and outputs a predicted fly ash particle size and composition. The second approach is that of an expert system. The predicted fly ash results for Kentucky #9 bituminous coal compared fairly well with experimental fly ash using both modeling approaches.
Erickson, TA
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.
1991
Interaction of Sodium, Sulfur, and Silica During Coal Combustion
Erickson, T.A.; Ludlow, D.K. and Benson, S.A.
Energy & Fuels, 5:539-547, 1991. Funded by Pittsburgh Energy Technology Center.
The interaction of sodium, sulfur, and silica at conditions typical in a pulverized coal furnace was investigated by using both model mixtures and a synthetic coal. The model mixtures consisted of selected inorganic constituents that were well mixed in proportions typically found in low-rank coal. The synthetic coal consisted of a furfuryl alcohol polymer with appropriate amounts of sodium, sulfur, and silica to duplicate the characteristics of low-rank coal. The model mixtures and synthetic coal were burned in a laminar flow (drop-tube) furnace at 900º, 1100º, 1300º, and 1500º C and residence times of 0.1, 0.5, 1.5, and 2.4s. The resulting char and fly ash particles were quickly quenched, collected, and analyzed with a scanning electron microscope (SEM) to determine size and composition. Results indicated that the formation of sodium silicates is favored by higher temperatures and longer residence times. Thermodynamic calculations and the model mixture studies indicated above 1100º C there is little interference in the formation of sodium silicates by sodium sulfates. In the synthetic coal studies, sodium sulfate particles were detected on the surface of the larger sodium silicate fly ash particles formed at lower temperatures. The size and prevalence of the sodium sulfate particles decreases as temperature was increased. Fly ash particle formation was characterized by fragmentation followed by coalescence. Fragmentation was more prevalent at higher temperatures and smaller fly ash particles were formed. Larger particles were formed at lower temperatures, indicating more complete coalescence with some cenosphere formation.
Fly Ash Particle-Size Distribution and Composition: Experimental and Phenomenological Approach
Zygarlicke, C.J.; Ramanathan, M. and Erickson, T.A.
Engineering Foundation Conference on Inorganic Transformations and Ash Deposition During Combustion, Palm Coast, FL, March 1991. Funded by US Department of Energy and ACERC.
Two modeling approaches are being developed which will predict fly ash particle size and composition. Both approaches are phenomenological in that they require detailed coal input data and empirically derived knowledge of inorganic transformation phenomena that occur during coal combustion. The first approach is stochastic in construction and randomly combines initial coal inorganics depending on their distribution in the coal and outputs a predicted fly ash particle size and composition. The second approach is that of an expert system. The predicted fly ash results for Kentucky #9 bituminous coal compared fairly well with experimental fly ash using both modeling approaches.