Scientia Iranica
Volume:15 Issue: 4, 2008

TiCl3(OTf) catalyzed eciently the protection of aromatic aldehydes with acetic anhydride in the absence of solvent at room temperature with excellent yields. By this method, aliphatic aldehydes were also converted to their corresponding acylals, in the presence of a mixture of acetic anhydride and acetic acid at high yields.

A method based on cold vapor atomic absorption spectrometry was used for the determination of trace mercury (Hg+2) and methylmercury (MeHg+) in aqueous samples. The mercury in the sample was concentrated in a column packed with bis (2-mercaptobenzothiazole), immobilized on microcrystalline naphthalene. The method was optimized for di erent parameters a ecting the pre-concentration process, in order to obtain better sensitivity. The recovery test showed that the method is quite reliable for use in the determination of trace amounts of mercury in aqueous samples. The method was applied to the speciation of mercury in river water and in the euents of wastewater treatment plants.

D-glucosamine, as a representative polyfunctional compound, is a bioactive amino sugar. In this study, the gas phase thermochemical properties of D-glucosamine, including its Metal Ion Anity (MIA), metal binding sites, Anion Anity (AA), acidity and proton anity, have been explored, using the Density Functional Theory (DFT) and a 6-311++Gbasis set. The summary of the MIA and AA results (in kcal/mol􀀀1) are: Li+= 67.6, Na+= 51.1, K+= 37.3, Mg2+= 207.9, Ca2+= 150.4, Zn2+= 251.2, Cl􀀀= 27.4, CN􀀀= 28.0. The acidity values calculated at di erent sites, including four -OH groups and one -NH2 group, range from 344.0 to 373.0 kcal/mol􀀀1. These results, surprisingly, indicate how drastically the presence of multiple -OH and -NH2 groups may vary the thermochemical properties of a polyfunctional bioorganic compound, such as D-glucosamine.

The compound, alkaline lead acetate, Pb(CH3COO)2: PbO:H2O (ALA), was purchased from the Fluka Company. The X-ray di raction lm and SEM electron microgaph of this compound were obtained and reported in this paper for the rst time. The thermal behavior of alkaline lead acetate (ALA) was studied using thermogravimetery (TGA) and Di erential Scanning Calorimetery (DSC) techniques under an O2 gas atmosphere from 25 to 600C. Four distinct energy changes associated with weight changes were observed, all of them being exothermic in the DSC results. The amount of
H for each peak is reported.

Alcohols and trimethylsilyl and tetrahydropyranyl ethers are eciently oxidized to their corresponding carbonyl compounds, using Fe(NO3)3.9H2O, in the presence of KBr, under mild and completely heterogeneous reaction conditions, with good to high yields.

A new chromate-selective electrode, based on the N, N'' butylen bis (saliciliden iminato) copper (II) complex as the membrane carrier, was developed. The electrode exhibited a good Nernstian slope of 􀀀28:8  0:5 mV/decade and a linear range of 3:0  10􀀀6 􀀀 1:0  10􀀀1 mol.L􀀀1 for chromate. The limit of detection was 3:0  10􀀀6 mol.L􀀀1. It had a fast response time of 5-10 sec and could be used for more than three months. The selective coecients were determined by the Fixed Interference Method (FIM). A chromate-selective electrode could be used in the pH range of 6.5-10.5. It was employed as an indicator electrode for the direct determination of chromate in real samples.

Al(HSO4)3 as an acidic salt, i.e. a mild, convenient and ecient reagent, was applied for the conversion of compounds containing a hydroxyl group to primary carbamates, at room temperature, with high yield and purity and without any epimerization, under solvent-free conditions

In order to determine the yield distribution of thermal cracking of gasoil, a pilot plant was designed and setup. A systematic experimental design was utilized based on CCD (central composite design). The feed ow rate, steam ratio and temperature were considered as factors to design the experiments. Some statistical models were tuned based on the results of conducted experiments. The models were evaluated with lack-of- t and R-squared tests. The results of the analysis proved that the models tted well with the experimental results of the considered products. Based on the experiments and statistical models the pilot plant was optimized in order to maximize the net pro t. At the optimum point coil outlet temperature, the feed ow rate and steam ratio were, respectively, equal to 843.8C, 6.02 g/min and 0.46. Regarding these conditions, the yield of ethylene, propylene and butadiene and rate of coke formation would be 23.82 wt%, 12.13 wt%, 2.42 wt% and 14:44  10􀀀7 g/cm2.sec, respectively.

Data mining seems to be the new buzz word. During the past several years many industries other than the oil and gas industry have realized the potential bene ts of da5ta mining and have established sophisticated operations in order to implement this exciting technology in their respective organizations. Data mining is not new. It has been around for many years. What is new about its current implementation is the incorporation of machine learning techniques. The oil and gas industry has become familiar with machine learning techniques since the early 1990s. Neural networks, genetic optimization and fuzzy logic have been used in numerous applications, from well log interpretations to hydraulic fracturing optimization. Therefore, the new interest in data mining in this industry is not surprising. The industry is at its peak state for bene ting from what data mining has to o er, thanks to an abundance of digital data. A word of caution is in order, which is the main motivation behind writing this paper. As with many other new tools and technologies, the term \Data Mining" can be, and is currently being, misused on several occasions. In this paper, an attempt has been made to answer questions such as; what is Data Mining? How can it be accomplished? What are the essential components of an integrated data mining process and what would be the bene ts of such a process? In addition to answering questions such as those mentioned above, this paper will provide a road map (a set of guidelines) for a successful data mining project. Finally, the paper concludes by applying the presented guidelines to a hydraulic fracturing data set in the DJ basin of the United States Rockies for a data mining study.

Methanol has been of great interest as a substrate for production as a novel feed grade protein in Iran. Up to 160 methylotrophic strains were isolated at the Jahad-e Agriculture Research Engineering Center for Scienti c Research and various batch experiments were carried out at di erent pH, temperature, agitation, aeration rate and methanol concentrations. By these experiments, optimum production conditions were determined for one of the strains using the Taguchi method. The results showed optimum pH around 8 at temperatures of 26 to 30C, aeration equal to 3 vvm and agitation about 800 rpm. The methanol was found to be optimum at concentration equal to 18 g.l􀀀1. In this condition, the biomass yield coecient was 0.52 g/g methanol, biomass dry weight was 20-25 g/l and the crude protein content was about 70- 78%. A deep jet fermentor of 1 m3 capacity, with a working volume of 600 liters, was used for SCP production using isolated strains. In this continuous system, dilution rates ranging from 0.15 to 0.2 lit.h􀀀1 were used to establish optimal condition for biomass production. Under this condition, 30 kg cell dry weight per day was obtained. These experiments showed that the selected methylotrophic strain has good potential for SCP production from methanol as a substrate.

Nowadays, neural networks have a wide range of usage in di erent elds of engineering. In the present work, this method is used to determine a reservoir model. Model identi cation, followed by parameter estimation, is a kind of visual process. Pressure derivative curves showing more features are usually used to determine the reservoir model based on the shape of the curve and no calculation is included. So, it is dicult to convert this kind of visual process to an applicable algorithm for computers. In fact, the model identi cation is a pattern recognition which is best done by an Arti cial Neural Network (ANN). If neural networks were learned successfully, they would be able to categorize di erent shapes into di erent groups, due to their visual characterization. So, their use in such a job would seem to be useful. In this work, it is shown how to train, examine and use neural networks to determine a reservoir model. The input of an ANN is fty points of the normalized pressure derivative type curve. Each ANN is trained, based on a speci c model, and the output of the ANN is the probability of occurrence of a fed curve to the related model.

The drying of solids in a cross ow conveyor belt dryer (continuous operation system), in which particles (thin-layer) move in a wire net conveyor, was theoretically studied. A mathematical modeling that considers the in uence of bed porosity and transient terms in the drying process was developed and the changes in bed porosity and product density with the moisture content of seeds have been considered. Also, to have a more accurate model, some changes have been made to the speci c surface of the seed. The nite volume method was used to solve, numerically, the governing conservation equations. The results of the moisture content of the material (yellow corn kernel), residence time and conveyor length are presented and analyzed in order to modify the model, according to bed porosity, product density and the speci c surface changes of the seed. This model can predict humidity ratio, the temperature of air, moisture content and the temperature of the material throughout the drying process.

In the gas invaded zone of a well fractured carbonate reservoir, gravity drainage mechanism plays the main role in expelling oil from dense, low permeability matrix blocks. Whenever the active mechanism is gravity drainage, rein ltration and capillary continuity phenomena notably in uence the rate and amount of oil recovery from matrix blocks. In the current research, six experiments were performed to study the e ects of a permeable contact area (between two blocks located on top of each other) on rein ltration and capillary continuity. Two of the experiments were performed using single blocks of 30 and 60 cm height, whilst others were stacks of two 30 cm high blocks. Results of the experiments reveal that, by increasing the permeable contact area between blocks, due to capillary continuity and rein ltration, there will be more communication between them, consequently, both ultimate oil recovery and production rate will improve. It is also noticeable that, if the permeable contact area is approximately greater than 20% of the total block surface area, the stack of blocks behaves analogous to a unique block with a height equal to that of the stack. The outcome of the present work, along with data from other studies, such as the e ects of horizontal fracture apertures and fracture dips, can be utilized in order to have a better understanding of block to block e ects. By this, the modeling of the gravity drainage mechanism, while injecting gas into fractured carbonate formations, would be improved.