In recent years, the search for economic and environmentally friendly alternatives has become a global necessity to achieve sustainability and preserve raw materials. From this concept, natural bitumen (NB) derived from sulphur springs is now one of the most promising alternative energy resources for many applications, especially in asphalt pavement construction. Its low price and abundance characterise NB since sulphur springs produce thousands of tonnes of NB annually and are used in very limited fields. Two main objectives were adopted for this work. The first objective is to examine the virgin NB properties from five sulphur springs and compare them with petroleum asphalt. The second objective is to enhance NB properties by appl

Structure type and disorder have become important questions in catalyst design, with the most active catalysts often noted to be “disordered” or “amorphous” in nature. To quantify the effects of disorder and structure type systematically, a test set of manganese(III,IV) oxides was developed and their reactivity as oxidants and catalysts tested against three substrates: methylene blue, hydrogen peroxide, and water. We find that disorder destabilizes the materialsthermodynamically, making them stronger chemical oxidantsbut not necessarily better catalysts. For the disproportionation of H2O2 and the oxidative decomposition of methylene blue, MnOx-mediated direct oxidation competes with catalytically mediated oxidation, making the most

Catalytic removal of the S-content from thiophene is a central step in efforts aiming to reduce the environmental burdens of transportation fuels. In this contribution, we investigate the hydrodesulfurization (HDS) mechanisms of thiophene (C4H4S) over γ-Mo2N catalyst by means of density functional theory (DFT) calculations. The thiophene molecule preferentially adsorbs in a flat mode over 3-fold fcc nitrogen hollow sites. The HDS mechanism may potentially proceed either unimolecularly (direct desulfurization) or via H-assisted reactions (hydrogenation). Due to a sizable activation barrier required for the first Csingle bondS bond scission of 54.6 kcal/mol, we predict that the direct desulfurization to contribute rather very insignificant

Physical adsorption by nitrogen gas was studied on seven commercial platinum reforming catalysts (RG-402, RG-412, RG-432, RG-451, RG 422,RG-482, PS-10), four prepared platinum catalysts (0.1%Pt/alumina, 0.2 %Pt/alumina, 0.45 %Pt/alumina and 0.55% Pt/alumina), and -alumina support. Physical adsorption was carried out by using Accelerated Surface Area and Porosimetry (ASAP 2400 device) at 77 K . The results indicate that the surface area in genaral decreases with increasing platinum percentage, high platinum loaded (0.45% and 0.55%) it was found that the percent increasing in surface area was lower than those obtained for low platinum loaded catalysts , and at very higher platinum loading 0.6 %Pt , some reduction in surface area was

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de

This paper introduces a non-conventional approach with multi-dimensional random sampling to solve a cocaine abuse model with statistical probability. The mean Latin hypercube finite difference (MLHFD) method is proposed for the first time via hybrid integration of the classical numerical finite difference (FD) formula with Latin hypercube sampling (LHS) technique to create a random distribution for the model parameters which are dependent on time t . The LHS technique gives advantage to MLHFD method to produce fast variation of the parameters’ values via number of multidimensional simulations (100, 1000 and 5000). The generated Latin hypercube sample which is random or non-deterministic in nature is further integrated with the FD method t

Data <span>transmission in orthogonal frequency division multiplexing (OFDM) system needs source and channel coding, the transmitted data suffers from the bad effect of large peak to average power ratio (PAPR). Source code and channel codes can be joined using different joined codes. Variable length error correcting code (VLEC) is one of these joined codes. VLEC is used in mat lab simulation for image transmission in OFDM system, different VLEC code length is used and compared to find that the PAPR decreased with increasing the code length. Several techniques are used and compared for PAPR reduction. The PAPR of OFDM signal is measured for image coding with VLEC and compared with image coded by Huffman source coding and Bose-