In order to understand the pyrolysis mechanism of β-O-4 type lignin dimers, a pyrolysis model is proposed which considers the effects of functional groups (hydroxyl, hydroxymethyl and methoxyl) on the alkyl side chain and aromatic ring. Furthermore, five specific β-O-4 type lignin dimer model compounds are selected to investigate their integrated pyrolysis mechanism by density functional theory (DFT) methods, to further understand and verify the proposed pyrolysis model. The results indicate that a total of 11 pyrolysis mechanisms, including both concerted mechanisms and homolytic mechanisms, might occur for the initial pyrolysis of the β-O-4 type lignin dimers. Concerted mechanisms are predominant as compared with homolytic mechanisms throughout unimolecular decomposition pathways. The competitiveness of the eleven pyrolysis mechanisms are revealed via different model compounds, and the proposed pyrolysis model is ranked in full consideration of functional groups effects. The proposed pyrolysis model can provide a theoretical basis to predict the reaction pathways and products during the pyrolysis process of β-O-4 type lignin dimers.

Coal is a major contributor to the global emission of nitrogen oxides (NO_x). The NO_x formation during coal utilization typically derives from the thermal decomposition of N-containing compounds (e.g., pyrrolic groups). NH₃ and HCN are common precursors of NO_x from the decomposition of N-containing compounds. The existence of H₂O has significant influences on the pyrrole decomposition and NO_x formation. In this study, the effects of H₂O on pyrrole pyrolysis to form NO_x precursors HCN and NH₃ are investigated using the density functional theory (DFT) method. The calculation results indicate that the presence of H₂O can lead to the formation of both NH₃ and HCN during pyrrole pyrolysis, while only HCN is formed in the absence of H₂O. The initial interaction between pyrrole and H₂O determines the N products. NH₃ will be formed when H₂O attacks the C₂ position of pyrrole with its hydroxyl group. On the contrary, HCN will be generated instead of NH₃ when H₂O attacks the C₃ position of pyrrole with its hydroxyl group. In addition, the DFT calculations clearly indicate that the formation of NH₃ will be promoted by H₂O, whereas the formation of HCN is inhibited.

The energy use analysis of coal-fired power plant units is of significance for energy conservation and consumption reduction. One of the most serious problems attributed to Chinese coal-fired power plants is coal waste. Several units in one plant may experience a practical rated output situation at the same time, which may increase the coal consumption of the power plant. Here, we propose a new hybrid methodology for plant-level load optimization to minimize coal consumption for coal-fired power plants. The proposed methodology includes two parts. One part determines the reference value of the controllable operating parameters of net coal consumption under typical load conditions, based on an improved K-means algorithm and the Hadoop platform. The other part utilizes a support vector machine to determine the sensitivity coefficients of various operating parameters for the net coal consumption under different load conditions. Additionally, the fuzzy rough set attribute reduction method was employed to obtain the minimalist properties reduction method parameters to reduce the complexity of the dataset. This work is based on continuously-measured information system data from a 600 MW coal-fired power plant in China. The results show that the proposed strategy achieves high energy conservation performance. Taking the 600 MW load optimization value as an example, the optimized power supply coal consumption is 307.95 g/(kW·h) compared to the actual operating value of 313.45 g/(kW·h). It is important for coal-fired power plants to reduce their coal consumption.