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MgO-based sorbents were prepared by impregnation with K2CO3 at different loadings. This study examined the CO2 absorption and regeneration properties of MgO-based sorbents at various pressures. The CO2 capture capacity of the PMI-30 sorbent increased to 204.4 mg CO2/g sorbent with increasing absorption pressure through CO2 absorption by MgO itself and K2CO3 by generating structures, such as MgCO3·3H2O and K2Mg(CO3)2. However, no KHCO3 phase was observed after CO2 absorption at 1, 10, and 20 atm. The CO2 capture capacity of the MgO and PMI-10, 20, 30, and 40 sorbents was the 94.6, 129.9, 156.6, 204.4, and 239.4 mg CO2/g sorbent, respectively. The CO2 capture capacity of MgO in the PMI sorbent was relatively constant regardless of the decreasing MgO content and increasing K2CO3 content. The CO2 absorption ability of MgO was calculated by substracting theoretical CO2 capture capacity of K2CO3 from the total capacity of sorbents. The TPD experiment performed at 1 atm after CO2 absorption at 20 atm showed that the regeneration temperature of the PMI sorbents differed according to the K2CO3 loading. [ABSTRACT FROM AUTHOR]

This study focuses on estimation of NOx emission and selection of input parameters for a coal-fired boiler in a 500 MW power generation plant. Careful selection of input parameters is required not only to improve accuracy of the estimation, but also to reduce the model dimensionality. The initial operating input parameters are determined based on operation heuristics and accumulated operation knowledge; the essential input parameters are selected by sensitivity analysis where the performance of the estimation model is assessed as one or some input parameters are successively eliminated from the computation while all other input parameters are retained. From the sequential input selection process, less than ten input parameters survived out of 36 initial input parameters. Auto-regressive moving average (ARMA) model, artificial neural networks (ANN), partial least-squares (PLS) model, and least-squares support vector machine (LSSVM) algorithm were proposed to express the relationship between the operating input parameters and the content of NOx emission. Historical real-time data obtained from a 500 MW power plant coal-fired boiler were used to test the proposed models. It was found that principal components analysis (PCA) enhances the estimation performance of each model. Among the four proposed estimation models, the LSSVM model coupled with PCA scheme showed the minimum root-mean square error (RMSE) and the best R-square value. [ABSTRACT FROM AUTHOR]