Predicting the Performance of Particulate Control Equipment

A new probabilistic modeling environment is described which allows the explicit and quantitative representation of the uncertainties inherent in new environmental control processes for SO₂ and NO_x removal. Stochastic analyses provide additional insights into the uncertainties in process performance and cost not possible with conventional deterministic or sensitivity analysis. Applications of the probabilistic modeling framework are illustrated via an analysis of the performance and cost of the fluidized bed copper oxide process, an advanced technology for the control of SO₂ and NO_x emissions from coal-fired power plants. An engineering model of a conceptual commercial-scale system provides the basis for the analysis. The model also captures interactions between the power plant, the SO₂/NO_x removal process, and other components of the emission control system. Results of the analysis address payoffs from process design improvements; the dependence of system cost on process design conditions and the availability of byproduct markets; and the likelihood that the advanced process will yield cost savings relative to conventional technology. The implications of case study results for research planning and comparisons with alternative systems also are briefly discussed.