Control Of Ash Fusibility And Prediction Of Ash Melting Temperature Of Biomass Fuel

Biomass is a carbon-neutral fuel that is environmentally friendly and rich in reserves which has a broad prospect.However,the high content of alkali metal elements and chlorine elements in biomass fuel leads to poor ash fusibility,which is prone to slagging on the heating surface of the boiler and will greatly damage the safety and economy of power generation units.In order to avoid slagging,considering the fuel side,peanut shell was selected as representative fuel mixed with poplar bark and coke powder respectively to make composite briquette fuel,then explore the feasibility of regulating ash fusibility through fuel mixing.And tricalcium phosphate was selected as additive to explore its further improvement effect and mechanism on the ash fusibility of composite briquette fuel.Aiming at the tedious and complicated measurement of ash melting temperature,the prediction model of biomass ash melting temperature was established,and good prediction results were obtained.The main contents of this paper are as follows:(1)Using peanut shell as the main material and mixing it with poplar bark and coke powder respectively to make two types of composite briquette fuels.Conduct ash melting temperature testing,calorific value analysis,thermogravimetric analysis and combustion kinetics analysis on composite briquette fuels,explore the changes in ash fusibility,calorific value,combustion performance and reaction rate of fuel with the increase of the proportion of mixed auxiliary materials,and select appropriate mixing ratios.The results showed that mixing peanut shells with poplar bark would slightly increase the ash melting temperature,slightly reduce the fuel calorific value,the combustion performance and reaction rate basically unchanged.Mixing peanut shell with coke powder can significantly increase the ash melting temperature and fuel calorific value,but it will reduce combustion performance and reaction rate.Therefore,considering the comprehensive fuel performance,the mixing ratio of peanut shell-poplar bark briquette fuel can be 6:4,and the mixing ratio of peanut shell-coke powder briquette fuel can be 6:4 or 7:3.(2)Using methods such as ash melting temperature test,ash macroscopic morphology analysis,SEM analysis and XRD analysis to explore the effect and mechanism of tricalcium phosphate on improving ash fusibility.The results show that tricalcium phosphate can significantly increase the ash melting temperature of composite briquette fuel and extend the temperature interval of four ash melting characteristic temperatures,play a favorable buffering role in the occurrence of ash melting phenomenon.From the macro and micro level observation of ash,it was found that the fuel added with tricalcium phosphate became more loosen in ash after combustion,and the internal agglomeration phenomenon was alleviated.After further exploring the action mechanism of tricalcium phosphate on the fuel ash by XRD analysis,it is known that tricalcium phosphate transforms the low-temperature fusible silicate in the ash into phosphate with higher melting temperature,so that alkali metal elements such as potassium can exist in the form of phosphate with higher melting temperature,avoiding the formation of lowtemperature eutectic formed by SiO2 and alkali metals.(3)Python is used as the programming language to construct four models including empirical formula,support vector machine,BP-neural network and random forest,and the main structural parameters in the models are optimized.The results show that the model with the best fitting effect on the training set data is random forest.The fitting effect of support vector machine and BP neural network is similar but weaker than that of random forest,the empirical formula has the worst fitting effect.The average error rate,average error and coefficient of determination of the random forest in the training set are 4.051%,43.064℃ and 0.910 respectively.The prediction accuracy of the four models for the test set from high to low is random forest,support vector machine,empirical formula,BP-neural network.The average error rate and average error of the random forest model in the test set are 5.052%and 61.271℃respectively.The results show that the average error of the random forest for the prediction of the training set and the test set is about 5%,which can achieve accurate prediction of the ash melting temperature of biomass.It is the most suitable model for the prediction of the ash melting temperature of biomass among the above four models.