Haiyu Liu

Study on fusion characteristics of biomass ash.

The ash fusion characteristics (AFC) of Capsicum stalks ashes, cotton stalks ashes and wheat stalks ashes that all prepared by ashing at 400 degrees C, 600 degrees C and 815 degrees C are consistent after 860 degrees C, 990 degrees C and 840 degrees C, respectively in the ash fusion temperature test and TG. Initial deformation temperature (IDT) increases with decreased K(2)O and went up with increased MgO, CaO, Fe(2)O(3) and Al(2)O(3). Softening temperature (ST), hemispherical temperature (HT) and fluid temperature (FT) do not affected by the concentrations of each element and the ashing temperature obviously. Therefore, the IDT may be as an evaluation index of biomass AFC rather than the ST used as an evaluation index of coal AFC. XRD shows that no matter what the ashing temperature is, the biomass ashes contain same high-temperature molten material. Therefore, evaluation of the biomass AFC should not be simply on the proportion of elements except IDT, but the high-temperature molten material in biomass ash.

Prediction of Calorific Value of Coal Using Real Power Plant Data

With the depletion of coal in the world, coal quality fluctuates and deviates greatly from the designed coal in many large scale coal-fired power plants. This increases the coal consumption while reduces the boiler combustion efficiency and stability. Thus, it is very important to conduct real-time measurement to the quality of the coal for optimizing the operation. The calorific value analysis is a significant part of the coal quality analysis, and regular proximate analysis method can’t meet real-time control requirements. In this chapter, an artificial neural network (ANN) model using real plant data for prediction of net calorific value of coal in a China power plant is reported. A three-layer BP neural network has been adopted. The input parameters selection was optimized with a compromise between smaller number of parameters and higher level of accuracy through sensitivity analysis. The activation function selection was also discussed in details. The results indicate that when the pureline was selected as the activation function for hidden layer and logsig was selected as the activation function for output layer, the prediction is most accurate. The results have shown good potential for predicting the net calorific value of coal using the real time data. This information will enhance the performance of the combustion control system for power utilities.

Decision Making on Most Economical Coal for Coal-Fired Power Plants Under Fluctuating Coal Prices

A long-term (8 years) experimental investigation has been carried out to analyze the effect of coal quality on the economy of power plants and to develop a predictive method for the determination of the most economical coal in a fluctuating coal market. The research results show that the facility maintenance costs, the combustion-supporting oil consumption, and the frequency of tube explosion increase exponentially with the ash content increasing, and the total maintenance costs increase sharply when the quality of the coal declining significantly from the designed coal. Taken into considerations the coal-purchasing costs, facility maintenance costs, emission costs, etc., a comprehensive mathematical model has been developed to predict the most economical coal, which is the coal with an ash content of 28.9% for the power plant investigated. Finally, a rapid calculation method has been proposed to determine the most economical coal in a fluctuating coal price market.

A Improved Fuzzy Analytic Hierarchy Process Application in Prevention High Temperature Corrosion

The mechanism of formation of high temperature corrosion in large scale utility boilers is complex and involves numerous interrelated influencing factors. The fuzzy analytic hierarchy process (FAHP) has been employed in this chapter to make quantitative computations of the factors that influence the formation of the corrosion. When constructing the fuzzy judgment matrix, an affecting degree table of influencing factors was worked out first. Then the score of a pair-wise comparison was computed from a proposed expression r ij = x − y + 0.5. This improved method was applied to the analysis of the high temperature corrosion problem of a 215 MW coal-fired utility boiler, combining with in-site test data. The weights of the influencing factors were determined quantitatively and the main influencing factors were found out. Based on the calculated results, various adjustments to the combustion processes were made and, as a result, high temperature corrosion on the water wall tubes has been found largely reduced on the schedule shutdown of the boiler half year later. Moreover, a real-time high temperature corrosion detection technique can be developed based on the method reported in this chapter.

Development and Application of A Coal Combustion Life Cycle On-line Monitoring System

ABSTRACTIn China, coal quality fluctuates and deviates greatly from the original design in many large-scale coal-fired power plants. To maintain a secure level and combustion efficiency, a “coal combustion life cycle online monitoring system” (CLMS) has been developed and installed on a 215MW boiler in China. Ten temperature detectors have been installed at the heights of three-layer burners along the furnace. To improve the accuracy of measuring unburnt carbon in fly ash, an adjustable microwave test facility was established. An on-line measurement device for unburnt carbon in ash has been newly developed and attached to the convection flue gas pass. To optimize combustion processes, a real-time neural network prediction model for coal calorific value has been developed. By calculating and analyzing the data in computer workstations, safety evaluation of burner ignition, economic analysis and energy loss analysis can be obtained in real time. As for fluctuation in coal quality, application of the system ...