Coal blending optimization under uncertainty

Abstract Coal blending is one of several options available for reducing sulfur emissions from coal-fired power plants. However, decisions about coal blending must deal with uncertainty and variability in coal properties, and with the effect of off-design coal characteristics on power plant performance and cost. To deal with these issues, a multi-objective chance-constrained optimization model is developed for an illustrative coal blending problem. Sulfur content, ash content and heating value are treated as normally distributed random variables. The objectives of the model include minimizing the: 1) expected (mean) costs of coal bending; 2) standard deviation of coal blending costs; 3) expected sulfur emissions; and 4) standard deviation in sulfur emissions. The cost objective function includes coal purchasing cost, ash disposal cost, sulfur removal cost, and fuel switching costs. Chance constraints include several risk measures, such as the probability of exceeding the sulfur emission standard. Several results are presented to illustrate the model.

[1]  J. Evans,et al.  Experience with blending, handling, and combustion of low cost western coals , 1991 .

[2]  Edward S. Rubin,et al.  Modeling and assessment of advanced processes for integrated environmental control of coal-fired power plants. Final report , 1986 .

[3]  Edward S. Rubin,et al.  Development of the integrated environmental control model , 1993 .

[4]  C. Revelle,et al.  The Linear Decision Rule in Reservoir Management and Design: 1, Development of the Stochastic Model , 1969 .

[5]  Simon D. Handelsman,et al.  Mineral resource management by personal computer , 1987 .

[6]  Tsunemi Watanabe,et al.  Stochastic programming models for air quality management , 1993, Comput. Oper. Res..

[7]  Edward A. McBean,et al.  Chance-constrained/stochastic linear programming model for acid rain abatement—I. Complete colinearity and noncolinearity , 1985 .

[8]  Frederick S. Hillier,et al.  Introduction of Operations Research , 1967 .

[9]  I. Grossmann,et al.  A combined penalty function and outer-approximation method for MINLP optimization : applications to distillation column design , 1989 .

[10]  A. Charnes,et al.  Deterministic Equivalents for Optimizing and Satisficing under Chance Constraints , 1963 .

[11]  R. C. Rittenhouse.  Action builds on 1990 Clean Air Act compliance , 1992 .

[12]  J. Makansi.  Clean air act amendments : the engineering response , 1991 .

[13]  Ignacio E. Grossmann,et al.  A modelling and decomposition strategy for the MINLP optimization of process flowsheets , 1989 .

[14]  R. Smock.  Utilities struggle with acid rain control compliance decisions , 1991 .

[15]  M. Gershon.  Computerized approaches to coal blending , 1988 .

[16]  Edward S. Rubin,et al.  Modeling of integrated environmental control systems for coal-fired power plants , 1988 .

[17]  Alan Gleit,et al.  Time Series Analysis of Coal Data from Preparation Plants , 1982 .

[18]  J. Makansi.  Coal analyzers provide critical link for plant control functions , 1989 .

[19]  E. L. Gillenwater,et al.  Linear Programming to Improve Raw Coal Blending: A New Twist to an Old Tool , 1987 .