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Tressie Hillier
Graphene has been considered as a promising material for electronics applications in recent years, largely due to its extremely thin body and ultra-high carrier mobility12. Extensive investigations have been carried out on graphene field-effect transistors (G-FETs) which hold promise for radio-frequency applications12,13. However G-FETs currently perform no better than conventional transistors based on silicon or III-V compound semiconductors12. This may be largely attributed to the fact that the graphene device fabrication technology is still immature and falls far behind the conventional semiconductor technology, and this completely offsets the material advantages of graphene. In principle a more predictable electronics application of graphene should focus on a kind of simple device which only requires a very simple fabrication process while takes the full advantages of the remarkable physical properties of graphene. Compared with FET and other electrical devices12,14,15. Hall element is much simpler in device structure and is thus much easier to fabricate. The performance of a Hall element is mainly dependent on the electric properties of the material, most importantly on carrier mobility and concentration1,2,3,4,5,6, rather than on fabrication technology. Since graphene is born with extremely high room temperature carrier mobility and atomically thin body, it is an ideal material for building high-performance Hall elements. In addition, since FET based integrated circuits (ICs) can also be fabricated on graphene12,13, the function of Hall elements can be further enhanced in the future by combining them with those ICs on the same chip. It is thus nature to conclude that graphene could be the ideal material for building high-performance Hall elements and ICs with lower cost and better performance than conventional Hall devices, leading perhaps to the first field of mass electronic applications for graphene.
The complex nature of wastewater treatment has led to search for alternative strategies such as different artificial intelligence (AI) techniques to model the various operational parameters. The present work is aimed at predicting the transmembrane pressure (TMP) as a key operational parameter in the case of anaerobic membrane bioreactor-sequencing batch reactor (AnMBR-SBR) during biohydrogen production using the adaptive neuro-fuzzy inference systems (ANFIS) and artificial neural network (ANN). In both the models, organic loading rates (OLR) ranging from 0.5 to 8.0 g COD/L/d, effluent pH (3.6-6.9), mixed liquor suspended solid (4.6-21.5 g/L) and mixed liquor volatile suspended solid (3.7-15.5 g/L) were used as the input parameters to test TMP as an output parameter. The ANFIS model was trained using the hybrid algorithms for TMP prediction. The higher prediction performance was obtained by using the Gauss membership function with four membership numbers. A back-propagation algorithm was also employed for the feed forward training of ANN model; the best structure was a Levenberg-Marquardt training algorithm with nine neurons in the hidden layer. By employing ANFIS and ANN models, relatively a good prediction of TMP was obtained with the R2 values of 0.93 and 0.88, respectively while the calculated mean square error for TMP in the ANFIS model (7.3 10-3) was lower than that of ANN model (8.02 10-3). The higher R2 and lower MSE values for the ANFIS model exhibited a better TMP prediction performance than the ANN model. Finally, it was observed that in the sensitivity analysis of ANN model, OLR was the most important input parameter on the variation of TMP.