Applied Science Private University

About: Applied Science Private University is a education organization based out in Amman, Jordan. It is known for research contribution in the topics: Population & Catalysis. The organization has 4124 authors who have published 5299 publications receiving 116167 citations.

A knowledge-based expert system to assess power plant project cost overrun risks

Abstract: Preventing cost overruns of such infrastructure projects as power plants is a global project management problem. The existing risk assessment methods/models have limitations to address the complicated nature of these projects, incorporate the probabilistic causal relationships of the risks and probabilistic data for risk assessment, by taking into account the domain experts’ judgments, subjectivity, and uncertainty involved in their judgments in the decision making process. A knowledge-based expert system is presented to address this issue, using a fuzzy canonical model (FCM) that integrates the fuzzy group decision-making approach (FGDMA) and the Canonical model (i.e. a modified Bayesian belief network model). The FCM overcomes: (a) the subjectivity and uncertainty involved in domain experts’ judgment, (b) significantly reduces the time and effort needed for the domain experts in eliciting conditional probabilities of the risks involved in complex risk networks, and (c) reduces the model development tasks, which also reduces the computational load on the model. This approach advances the applications of fuzzy-Bayesian models for cost overrun risks assessment in a complex and uncertain project environment by addressing the major constraints associated with such models. A case study demonstrates and tests the application of the model for cost overrun risk assessment in the construction and commissioning phase of a power plant project, confirming its ability to pinpoint the most critical risks involved in this case, the complexity of the lifting and rigging heavy equipment, inadequate work inspection and testing plan, inadequate site/soil investigation, unavailability of the resources in the local market, and the contractor's poor planning and scheduling.

Saturation of Stimulated Backscattered Radiation in Laser Plasmas

Abstract: Measurements of the back reflection from laser-produced plasmas have been carried out for laser intensities of up to 1.5\ifmmode\times\else\texttimes\fi{}${10}^{16}$ W/${\mathrm{cm}}^{2}$ with pulse durations of 120 psec using spherical low-$Z$ targets. The reflectivity of the plasma increases with incident intensity but saturates at a value of 10%. The intensity-dependent reflectivity is attributed to stimulated Brillouin scattering.

Ceria Promoted Cu-Ni/SiO2 Catalyst for Selective Hydrodeoxygenation of Vanillin

Abstract: A ceria (CeO2) promoted Cu-Ni bimetallic catalyst supported on SiO2 (Cu-Ni/CeO2-SiO2) was prepared and evaluated for catalytic hydrodeoxygenation (HDO) of vanillin. Silica supported monometallic Cu and Ni catalysts and bimetallic Cu-Ni catalyst (Cu/SiO2, Ni/SiO2, and Cu-Ni/SiO2), without a ceria promoter, were also synthesized and tested for the same application. The highest conversion of vanillin was achieved with the Cu-Ni/CeO2-SiO2 catalyst. Vanillyl alcohol was the sole product in the initial 2 h, followed by the formation of 2-methoxy-4-methylphenol, which was observed. Characterization of the synthesized catalysts revealed the presence of overlapping crystalline phases of CuO, NiO, and CeO2 on the Cu-Ni/CeO2-SiO2 surface. We extended our study to find out the results of using CeO2 as the support of the Cu-Ni bimetallic catalyst (Cu-Ni/CeO2). Partial incorporation of Cu and Ni cations into the ceria lattice took place, leading to the decrease of specific surface area and a concomitant compromise in the conversion. In the case of the Cu-Ni/CeO2-SiO2 catalyst, the higher conversion was accredited to the facile formation of Cu+ active centers by the synergistic interaction between Ce+4/Ce+3 and Cu+2/Cu+ redox couples and the incorporation of oxygen vacancies on the catalyst surface.