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.

Evaluation of Polymers as Direct Thickeners for CO2 Enhanced Oil Recovery

Abstract: In this paper, two commercial polymers, poly(vinyl ethyl ether) (PVEE) and poly(1-decene) (P-1-D), are tested to thicken CO2 for CO2 enhanced oil recovery (EOR). First, a series of laboratory tests are conducted to measure the cloud-point pressures of either polymer at different polymer solubilities in supercritical CO2 and the equilibrium interfacial tensions (IFTs) of a light crude oil-pure or polymer-thickened CO2 system under different reservoir conditions. Second, a capillary viscometer is used to measure the viscosities of polymer-thickened CO2 at different test pressures. Third, a total of six high-pressure CO2 coreflood tests are performed to examine the effects of polymer-thickened CO2 on the total CO2 EOR. It is found that at the same and low polymer solubility in pure CO2, the measured cloud-point pressure of PVEE is much lower than that of P-1-D. The measured equilibrium IFT for polymer-thickened CO2 at a high pressure is much lower than that for pure CO2. The PVEE- or P-1-D-thickened CO2 visc...

Neural network models for software development effort estimation: a comparative study

Abstract: Software development effort estimation (SDEE) is one of the main tasks in software project management. It is crucial for a project manager to efficiently predict the effort or cost of a software project in a bidding process, since overestimation will lead to bidding loss and underestimation will cause the company to lose money. Several SDEE models exist; machine learning models, especially neural network models, are among the most prominent in the field. In this study, four different neural network models--multilayer perceptron, general regression neural network, radial basis function neural network, and cascade correlation neural network--are compared with each other based on: (1) predictive accuracy centred on the mean absolute error criterion, (2) whether such a model tends to overestimate or underestimate, and (3) how each model classifies the importance of its inputs. Industrial datasets from the International Software Benchmarking Standards Group (ISBSG) are used to train and validate the four models. The main ISBSG dataset was filtered and then divided into five datasets based on the productivity value of each project. Results show that the four models tend to overestimate in 80 % of the datasets, and the significance of the model inputs varies based on the selected model. Furthermore, the cascade correlation neural network outperforms the other three models in the majority of the datasets constructed on the mean absolute residual criterion.