Oxy-fuel combustion of coal and biomass blends

The ignition temperature, burnout and NO emissions of blends of a semi-anthracite and a high-volatile bituminous coal with 10 and 20 wt.% of olive waste were studied under oxy-fuel combustion conditions in an entrained flow reactor (EFR). The results obtained under several oxy-fuel atmospheres (21%O2–79%CO2, 30%O2–70%CO2 and 35%O2–65%CO2) were compared with those attained in air. The results indicated that replacing N2 by CO2 in the combustion atmosphere with 21% of O2 caused an increase in the temperature of ignition and a decrease in the burnout value. When the O2 concentration was increased to 30 and 35%, the temperature of ignition was lower and the burnout value was higher than in air conditions. A significant reduction in ignition temperature and a slight increase in the burnout value was observed after the addition of biomass, this trend becoming more noticeable as the biomass concentration was increased. The emissions of NO during oxy-fuel combustion were lower than under air-firing. However, they remained similar under all the oxy-fuel atmospheres with increasing O2 concentrations. Emissions of NO were significantly reduced by the addition of biomass to the bituminous coal, although this effect was less noticeable in the case of the semi-anthracite.This work was carried out with financial support from the Spanish MICINN (Project PS-120000-2005-2) co-financed by the European Regional Development Fund. M.V.G. and L.A. acknowledge funding from the CSIC JAE-Doc and CSIC JAE-Pre programs, respectively, co-financed by the European Social Fund. J.R. acknowledges funding from the Government of the Principado de Asturias (Severo Ochoa program).Peer reviewe

Optimal Power Generation under Uncertainty via Stochastic Programming

: A power generation system comprising thermal and pumpedstorage hydro plants is considered. Two kinds of models for the cost-optimal generation of electric power under uncertain load are introduced: (i) a dynamic model for the short-term operation and (ii) a power production planning model. In both cases, the presence of stochastic data in the optimization model leads to multi-stage and two-stage stochastic programs, respectively. Both stochastic programming problems involve a large number of mixedinteger (stochastic) decisions, but their constraints are loosely coupled across operating power units. This is used to design Lagrangian relaxation methods for both models, which lead to a decomposition into stochastic single unit subproblems. For the dynamic model a Lagrangian decomposition based algorithm is described in more detail. Special emphasis is put on a discussion of the duality gap, the efficient solution of the multi-stage single unit subproblems and on solving the dual problem..