Predictive Model Based On Wavelet Transform And Artificial Neural Network Methods Of Coal Thermal Conversion

According to the overall consumption structure of energy resources, safe and environmental requirements, clean coal conversion has become an inevitable trend. However, coal conversion is in the high temperature and high pressure. The reaction conditions are very harsh. Moreover, the complexity of coal leads to the result that there are many influence factors in coal conversion process. Those increase the research difficulty, experimental workload as well as cost. Meanwhile, the experimental data often contain noise and spectrum peaks overlap. Therefore, to solve these problems, we use continuous wavelet transform to process experimental data and the improved BP neural network to establish prediction model of coal conversion process. That will provide an important basis and reference for coal conversion process design, optimization, catalyst screening and so on.In this paper, the first established model is prediction model of coal pyrolysis based on the improved BP neural network. We use the continuous wavelet transform to extract informations of the first temperature peak position and peak numbers from the weight loss rate-temperature figures. We select carbon and hydrogen ratio, ash, and volatile as inputs, weight loss, the first temperature peak position and peak numbers as outputs. The results are: in the multi-outputs prediction model, average relative errors of weight loss and the first temperature peak position for test samples are 3.35% and 0.54% respectively. In the single output prediction model, average relative errors are 2.26% and 0.30% respectively. And in the peak numbers prediction model, calculated and measured values are also same. Moreover, compared with the multi-outputs prediction model, single output prediction model has fast training speed, comprehensive study ability, small prediction error, strong generalization ability. Based on the established single output prediction model, it is found that carbon and hydrogen ratio, ash, and volatile all have important effects on the coal pyrolysis process, in which carbon and hydrogen ratio is greatest influence factor. That is consistent with actual theory of coal pyrolysis.The second prediction model is prediction model of coal catalytic gasification based on the improved BP neural network. We select carbon and hydrogen ratio, ash, volatile, type and concentration of catalyst as inputs, gasification efficiency, the initial temperature of gasification and the temperature of maximum gasification rate as outputs. The results are:in the multi-outputs prediction model, average relative errors of gasification efficiency, the initial temperature of gasification and the temperature of maximum gasification rate are 2.68%,3.64% and 1.99% respectively, and, in the single output prediction model, average relative errors are 1.70%,0.58% and 0.55% respectively, which are much smaller than those predicted by regression equation. Moreover, in the single output prediction model, the convergence is fast, prediction results for three output parameters are superior to those of multi-outputs prediction model, prediction precision is high, and generalization ability is strong. Based on the established single output prediction model, it is found that the type of catalyst has a great influence on coal catalytic gasification reaction. Therefore, screening appropriate catalyst is very effectve and important work to improve the efficiency of coal gasification. In this paper, the established prediction models have an important guiding role in coal gasification process and avoid a large number of the repeated experiments.