Building of calorific value model of straw biomass based on industrial analysis indexes

Abstract: To build a prediction model of gross calorific value and net calorific value, this article discusses the influence of industrial analysis indexes of straw biomass on the calorific value and the feasibility of predicting calorific value. 172 straw samples has been collected, including 31 rape straws, 36 wheat straws, 86 rice straws, and 19 maize straws. Moisture, volatile matter, ash, fixed carbon, gross calorific value, and net calorific value were measured by standard methods. The statistics of measured values showed that the ranges for the above six indexes were 2.72%-8.04%、63.79%-76.25%、3.57%-16.97%、11.94%-17.03%、14.88-17.58 kJ/g, and 13.37-16.13 kJ/g respectively, and the means were 5.61%、69.53%、10.28%、14.58%、16.20 kJ/g、and 14.74 kJ/g respectively. The ash of rice straw is higher than that of rape straw、wheat straw and maize straw，and the calorific value was lower. According to a simple correlation analysis, we found that volatiles and fixed carbon have a very significant positive correlation to calorific value. Accordingly, a very significant negative correlation was achieved for ash with calorific value. Simultaneously, there is a high degree of correlation between volatile matter or ash and caloric value, but it a lower degree of correlation to fixed carbon; there is an important collinearity between the moisture、volatiles、ash, and fixed carbon, the influence of which must be reduced or eliminated. Among different approaches to establishing and comparing prediction models, the results indicated that principal component regression is the best method, because it (a) effectively eliminated the impact of collinearity between the industrial analysis indexes, (b) preserved the integrity of the information about industrial analysis indexes, and (c) attained the greatest accuracy of the final prediction model.. Using principal component regression to establish a prediction model of gross calorific value and net calorific value, the determination coefficient of the prediction model of gross calorific value was 0.91, the predicted standard deviation was 0.20 kJ/g, and the relative standard deviation was 1.25%. The determination coefficient of the prediction model of net calorific value was 0.91, the predicted standard deviation was 0.20 kJ/g, and the relative standard deviation was 1.33%. In the 20 samples used for the external validation, the predicted standard deviation of the gross calorific value was 0.18 kJ/g, and the relative standard deviation was 1.09%; the predicted standard deviation of the net calorific value was 0.19 kJ/g, and the relative standard deviation was 1.29%. The prediction result is obtained ideally. We concluded that a calorific value model of straw biomass based on industrial analysis indexes predicts the gross and net calorific values accurately, and that industrial analysis indexes of straw biomass can help in predicting the calorific value of straw biomass. Consequently, this study can provide a reference method for use in biomass straw energy utilization.