Research On Biomass Briquetting Device And Optimum Technologic Parameters Of Densification

It not only can relief the crisis of energy sourses, but also can reduce the severe pollution produced by fossil energy to take advantage of biomass energy. However, it limits the storage, transport and application of biomass energy due to its low density. It can not only resolve the proposed problem, but also can improve the burning stability and efficiency of biomass energy by densifying the biomass raw material.However, the use of biomass densification unit is face d with problems, such as severe abrasion, high consumption and briquettes with poor quality. The structual parameters of biomass units and the processing parameters of densification are researched and optimized. The research can extend the life of densification units, reduce the energy consumption and improve the quality of briquettes. The researches have been done as follows.Firstly, the mechanical constructure, the hydraulic system and the heating system of the densification unit have been designed. The stress anlysis of the biomass in the mold has been done. To analysize the stress of the mold, the densification process of the biomass has been simulated based on the ABAQUS softwares.The shape and the structure parameters of the mold has been optimized with the purpose of relieving the abrasion of the mold and improving the combustion efficiency.Secondly, the biomass densification experiments have been designed. The pressure, temperature, moisture of raw material, loading velocity and the swell time can be adjusted in the experiment. The orthogonal experiment has been doned of the densification process of the saw dust and straw. The influence of processing parameters on the densification quality has been analysised. The important degree of various factors of densification has been comput ed through variance analysis.Finally, the numerical relationship between the processing parameters and the quality of the densification has been analysized. And then the relationship has been mathematically modeled with the way of least square fitting and the way of least squares support vector machine. The accuracy and generalization performance of the both algorithms. The relationship of the densification parameters and the global optimization criterion has been computed with model obtained before. The processing parameters of biomass densification ha ve been optimized using genetic optimized algorithm.