The characteristics and water/oil sorption effectiveness ofkapok fibre, sugarcane bagasse and rice husks have been compared. The three biomass types were subjected to field emission scanning electron microscopy-energy dispersive X-ray spectroscopy and surface tension analyses for liquid-air and oil-water systems were conducted. Both kapok fibre and sugarcane bagasse exhibit excellent oil sorption capabilities for diesel, crude, new engine and used engine oils as their oil sorption capacities all exceed 10 g/g. The synthetic sorbent exhibits oil sorption capacities comparable with sugarcane bagasse, while rice husks exhibit the lowest oil sorption capacities among all the sorbents. Kapok fibre shows overwhelmingly high oil-to-water sorption (O/W) ratios ranging from 19.35 to 201.53 while sugarcane bagasse, rice husks and synthetic sorbent have significantly lower O/W ratios (0.76-2.69). This suggests that kapok fibre is a highly effective oil sorbent even in well-mixed oil-water media. An oil sorbent suitability matrix is proposed to aid stakeholders in evaluating customized oil removal usage of the natural sorbents.
A Neural Network Internal Model Control (NN-IMC) strategy is investigated, by establishing inverse and forward model based neural network (NN). Further for developing the model has been selected suitable adaptive filter. Two types of NN-based inverse model (i.e. with and without disturbance input) were accurately simulated. The results indicated that the neural networks are capable to establish forward and inverse model rapidly from the couple of input-output open loop data of single distillation column binary system with a good root mean square error (RMSE). The simulation results revealed that NN-IMC with appropriate learning rate -momentum is capable to pursue the set-point changes and to reject the disturbance changes without steady state error or oscillations. NN-IMC with inverse model which contains disturbance input (modified NN-IMC) offer better performance than without it (conventional NN-IMC).
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