Universiti Teknologi Malaysia

About: Universiti Teknologi Malaysia is a education organization based out in Johor Bahru, Malaysia. It is known for research contribution in the topics: Membrane & Adsorption. The organization has 21644 authors who have published 39500 publications receiving 520635 citations.

A critical evaluation on maximum power point tracking methods for partial shading in PV systems

Abstract: As photovoltaic (PV) system suffers considerable energy loss due to partial shading (PS), various approaches have been proposed to mitigate this problem. Among these, improving the maximum power point tracker (MPPT) algorithm seems to be the most feasible and economical solution. To date, there appears to be an absence of a single review paper that critically evaluates the performance of various types of MPPT algorithms during PS. To fill this gap, fifty prominent works on PS are analyzed from the theoretical and operational point of view. In particular, the paper will closely address the accurate detection for PS occurrence and the efficiency of global peak tracking. For certain selected cases, in-depth analysis is carried out to allow for an improved understanding on the operational intricacies of the algorithm. It is envisaged that this review paper would be a valuable one-stop reference to enable PV professionals to make more informed decisions when designing or choosing new MPPT algorithms for their inverters.

g-C3N4/(Cu/TiO2) nanocomposite for enhanced photoreduction of CO2 to CH3OH and HCOOH under UV/visible light

Abstract: Cu-modified graphitic carbon nitride (g-C3N4) and titanium dioxide (TiO2) nanocomposites for enhanced photocatalytic CO2 reduction with H2O under UV and visible light irradiations have been investigated. The photocatalysts, prepared by pyrolysis and impregnation were characterized by XRD, FE-SEM, TEM, FT-IR, N2 adsorption-desorption, XPS, UV–vis DRS and PL spectroscopy. The Cu-metal loaded over TiO2 and g-C3N4 enhanced CO2 reduction efficiency to CH3OH and HCOOH by fostering carrier charge separation. The Cu-metal in the composite as well as the wt.% ratio of g-C3N4 and TiO2 also influenced the photoactivity and products selectivity. The low band gap, electronic structure and visible light absorption capacity of g-C3N4 facilitated the transfer of photo-generated electrons to Cu/TiO2 in the composite. Moreover, the position of the metal in the composite affected the electrons distribution and hence enhanced the photoactivity. The maximum yield of the products detected under visible light were 2574 and 5069 μmol/g.cat of CH3OH and HCOOH, respectively. The yield of CH3OH under visible light was four fold higher compared to UV-light irradiation. The ratio (30:70) of g-C3N4 and Cu/TiO2 in the composite and the use of visible light improved the efficiency of the photocatalytic system. The stability of the photocatalyst prevailed in continuous CH3OH production under visible light irradiation compared to UV-light in cyclic runs. Possible reaction mechanisms were proposed to understand the movement of electrons and holes and the function of both UV and visible light for CO2 reduction over the g-C3N4/TiO2 (30:70) photocatalyst.

Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm

Abstract: In blasting works, the aim is to provide proper rock fragmentation and to avoid undesirable environmental impacts such as back-break. Therefore, predicting fragmentation and back-break is a significant step in achieving a technically and economically successful outcome. In this paper, considering the robustness of artificial intelligence methods utilized in engineering problems, an artificial neural network (ANN) was applied to predict rock fragmentation and back-break; an artificial bee colony (ABC) algorithm was also utilized to optimize the blasting pattern parameters. In this regard, blasting parameters, including burden, spacing, stemming length, hole length and powder factor, as well as back-break and fragmentation were collected at the Anguran mine in Iran. Root mean square error (RMSE) values equal to 2.76 and 0.53 for rock fragmentation and back-break, respectively, reveal the high reliability of the ANN model. In addition, ABC algorithm results suggest values of 29 cm and 3.25 m for fragmentation and back-break, respectively. For comparison purposes, an empirical model (Kuz-Ram) was performed to predict the mean fragment size in the Anguran mine. A mean fragment size of 33.5 cm shows the ABC algorithm can optimize rock fragmentation with a high degree of accuracy.