Tunis University

About: Tunis University is a education organization based out in Tunis, Tunisia. It is known for research contribution in the topics: Population & Thin film. The organization has 11745 authors who have published 15400 publications receiving 154900 citations. The organization is also known as: University of Tunis & UT.

Advances in Methanol Production and Utilization, with Particular Emphasis toward Hydrogen Generation via Membrane Reactor Technology.

Abstract: Methanol is currently considered one of the most useful chemical products and is a promising building block for obtaining more complex chemical compounds, such as acetic acid, methyl tertiary butyl ether, dimethyl ether, methylamine, etc. Methanol is the simplest alcohol, appearing as a colorless liquid and with a distinctive smell, and can be produced by converting CO2 and H2, with the further benefit of significantly reducing CO2 emissions in the atmosphere. Indeed, methanol synthesis currently represents the second largest source of hydrogen consumption after ammonia production. Furthermore, a wide range of literature is focused on methanol utilization as a convenient energy carrier for hydrogen production via steam and autothermal reforming, partial oxidation, methanol decomposition, or methanol–water electrolysis reactions. Last but not least, methanol supply for direct methanol fuel cells is a well-established technology for power production. The aim of this work is to propose an overview on the commonly used feedstocks (natural gas, CO2, or char/biomass) and methanol production processes (from BASF—Badische Anilin und Soda Fabrik, to ICI—Imperial Chemical Industries process), as well as on membrane reactor technology utilization for generating high grade hydrogen from the catalytic conversion of methanol, reviewing the most updated state of the art in this field.

DBSCAN-GM: An improved clustering method based on Gaussian Means and DBSCAN techniques

Abstract: Clustering is one of the most useful methods of intelligent engineering domain, in which a set of similar objects are categorized into clusters. Almost all of the well-known clustering algorithms require input parameters which are hard to determine but have a significant influence on the clustering result. Furthermore, the majority is not robust enough towards noisy data. This paper presents an efficient and effective clustering technique, named DBSCAN-GM that combines Gaussian-Means and DBSCAN algorithms. The idea of DBSCAN-GM is to cover the limitations of DBSCAN, by exploring the benefits of Gaussian-Means: it runs Gaussian-Means to generate small clusters with determined cluster centers, in purpose to estimate the values of DBSAN's parameters. The results of our method show that it is efficient even for large data sets especially data with large dimension and capable to handle noises, contrary to partitioning algorithms such as K-Means or Gaussian-Means. Additionally, DBSCAN-GM does not necessitate any priori information, in contrast to the density clustering DBSCAN obliging two input parameters which are hard to guess, namely Eps (the radius that bounds the neighborhood region of an object) and MinP ts (the minimum number of objects that must exist in the objects neighborhood region). Simulative experiments are carried out on a variety of datasets, which highlight the DBSCAN-GM's effectiveness and cluster validity to check the good quality of clustering results.