Inexact chance-constrained nonlinear programming method for coal blending in power plants

Coal blending is a kind of clean coal combustion technology suitable for China, and it can improve economic and combustion performance of power plants. The key of this technology lies in the optimization calculations. However, decisions about coal blending must deal with uncertainty and variability in coal properties, so a hybrid inexact chance-constrained nonlinear programming (ICCNLP) model was developed for coal blending problems under uncertainty. The ICCNLP could directly handle uncertainties presented as both intervals and probability density distributions, and assess the risk of violating various constraints, such as the probability of exceeding the sulfur emission standard, for accomplishing a minimizing system cost. The results indicate that feasible and stable interval solutions will be obtained and some decision alternatives can be generated by adjusting decision variable values within their solution intervals according to applicable conditions.