Research On Evaluation Method Of Slagging Tendency For Microalgae And Terrestrial Biomass Ash Based On Multiple Regression And Wavelet Basis Function

With the increase of national demand for energy,biomass fuel with the advantages of environmental protection can expand the use of renewable energy.Direct combustion of biomass fuel is a simple and effective utilization method.But it is adversely affected by ash deposition on boiler operation,efficiency,slagging,corrosion,and safety during combustion.Therefore,it is of great significance to study the problem of ash slagging in biomass combustion.At present,there is little information about the comparison of combustion characteristics between microalgae and terrestrial biomass.It is still a new topic to study the slagging characteristics of microalgae during combustion.Now the traditional methods for predicting biomass slagging are limited and have contingency.Thus,it is necessary to explore the evaluation method of slagging tendency in biomass combustion.In this paper,intelligent methods based on multiple regression and neural network algorithm were proposed to predict the slagging tendency of biomass fuel.The slagging tendency of microalgae combustion was studied by means of scanning electron microscope,X-ray fluorescence,ash melting temperature,single index,and ternary phase diagram;the effect of washing treatment on the reduction of microalgae slagging was explored.The applicability of existing slagging indices and ternary phase diagram in microalgae and 33terrestrial biomass were evaluated and compared.Based on 90 kinds of biomass ash obtained under different combustion conditions,a comprehensive estimating index for slagging(R_slag)was established based on multiple regression method.An improved ant colony-wavelet basis function compound model was developed for slagging tendency estimation of biomass combustion.The results showed that the P₂O₅content of Chlorella was higher than that of other terrestrial biomass.Both water washing and acid washing could effectively removed potassium and sulfur from Chlorella.The higher phosphorus content may be the reason for the higher slagging tendency of microalgae.Traditional methods for predicting biomass slagging need mutual support,and the uncertainty is high.The results of Spearman correlation analysis showed that each element had a certain coupling relationship to slagging.The prediction results of slagging tendency of R_slagbased on multiple regression model were highly matched with the actual slagging results（Regression coefficient R²=0.9229）.Comparing the prediction of biomass slagging with six single neural network models,it was found that the Wavenn neural network（WNN）had the least error fluctuation in the prediction model of biomass slagging.The improved ant colony-wavelet basis function composite model introduced adaptive mutation and inertia weight to improve the global and local optimization of ant colony algorithm.The predicted R²（0.9373）by wavelet basis function after optimization was 24.25%higher than that of WNN model predicted.Compared with the traditional theoretical analysis method,the new index and model developed in this study take into account the combustion conditions of boiler equipment,ash content,calorific value and all important slagging elements,and the accuracy of estimation is higher.This study can help designers to quickly verify the tendency of biomass slagging,and provide a cheap,accurate,and simple method to estimation biomass slagging.