A systematic study was performed to understand interactions among biomass loading during ionic liquid (IL) pretreatment, biomass type and biomass structures. White poplar and eucalyptus samples were pretreated using 1-ethyl-3-methylimidazolium acetate (EmimOAc) at 110°C for 3h at biomass loadings of 5, 10, 15, 20 and 25wt%. All of the samples were chemically characterized and tested for enzymatic hydrolysis. Physical structures including biomass crystallinity and porosity were measured by X-ray diffraction (XRD) and small angle neutron scattering (SANS), respectively. SANS detected pores of radii ranging from ∼25 to 625Å, enabling assessment of contributions of pores with different sizes to increased porosity after pretreatment. Contrasting dependences of sugar conversion on white poplar and eucalyptus as a function of biomass loading were observed and cellulose crystalline structure was found to play an important role.
In this paper, we propose a method dealing with the problem of image restoration based on the conception of Back propagation Neural Network Algorithm. Conventional Back Propagation Algorithm has its inherited drawbacks, i.e. slow convergence rate, long training time, hard to achieve global minima etc. Recently, several methods introduced the dynamic learning rate and the dynamic momentum coefficient. Our new method applied in this paper improves the effect of learning coefficient η by using a new way to modify the value dynamically. The experimental results show that this helps improving the efficiency overall both in visual effect and quality analysis.
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