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In order to overcome the shortage of pure fluid with constant phase change temperature, zeotropic mixtures were employed to construct the organic Rankine cycle (ORC) system for waste heat recovery of exhaust gas of boiler in coal-fired power plant. The temperature glide of zeotropic mixture can reduce the mismatch of temperature in order to make it approach ideal Lorenz cycle. Different types of hydrocarbons (HCs) were selected as basic fluid according to their physical properties, which were of high energy efficiency, low cost and environmental friendliness for organic Rankine cycle. To promote the security level, high-effective fire retardants R13I1/R245fa were added to form binary zeotropic mixtures. Thermodynamic models of ORC with and without internal heat exchanger (IHX) using zeotropic mixture as working fluid were established, by which the power generating capability per unit heat transfer area, exergy efficiency and net power output as functions of proportion of pure fluids were obtained to access the performance of ORC in recovering the waste heat of exhaust gas of boiler. In addition, the factors affecting performance of ORC, including volume flow rate, expansion ratio, and the presence of IHX in system, were investigated to optimize the system design.

Numerous factors can affect the operating performances and the design of the indirect air cooling system of power plant. The present study developes physico-mathematical model to describe the thermo-flow characteristics of air cooling tower for indirect air cooling system. Based on the model, a comprehensive analysis on optimization of air cooling tower is conducted for 600MW indirect air-cooled power generating unit. By using the software VC + +, the indirect air-cooled tower optimization program is developed. With the help of optimization of tower structure, a tower with better structure is used to conduct thermal analysis of the influences of ambient temperature, windward air velocity, and saturated exhaust flow rate on back pressure of turbine. The present study may be of great value on optimization design and safe operation of large-scale indirect air-cooled power plant.

The direct air-cooled power generating units subjected to the environmental natural wind were influenced by the cluster effect of the axial flow fan cluster and the exhaust hot air recirculation simultaneously. The coupling effects would lead to complex variations of fan cooling capacity in different areas of the air-cooled condensers. The divisional regulation experiments were carried out on the axial flow fan cluster of the 2 × 330 MW direct air-cooled power generating units. Based on experimental data, the grey relational analysis approach was applied to obtain the influences of the operating parameters, axial flow fans, as well as environmental conditions on the performances of the objective air-cooled power generating unit quantitatively. The results indicated that the speed and current of axial flow fans in different portions of the fan cluster influenced the back pressure and the coal consumption rate for power supply differently, and the fan speed influenced the performances of the power generating unit more directly than the fan current. The results can be the evidence for taking the divisional regulation of fan cluster against the impact of natural wind on the operating performance of power generating unit.

In addition to the operating parameters, there were numerous factors, including the meteorological and the geographic conditions, as well as the atmospheric environmental conditions, which could affect the performance of the direct air-cooled power generating unit. In the present study, the artificial neural network (ANN) approach was employed to model the back pressure of the steam turbine, one of the most important parameters of the power generating unit. Based on the actual operating data obtained from the on-site experiments of the direct air-cooled power generating unit in north China, the three-layers back propagation ANN model was trained and tested to predict the back pressures of the steam turbine unit under the different operating conditions. The mean relative error (MRE) of the present ANN model was 9.273%, the root mean square error (RMSE) was 1.83 kpa, and the absolute fraction of variance (R2) was 0.9859, which indicated that the predictions agreed well with the actual values. The present ANN model can also reflect the effects of the weather conditions on the back pressure of the unit, such as the rain or the sandstorm and the air humidity. The influence of the environmental natural wind on the unit performance can be described with robustness and reliability by the present ANN model as well.