CHAMOMILE (Matricaria recutita) EXTRACT AS A CORROSION INHIBITOR FOR MILD STEEL IN HYDROCHLORIC ACID SOLUTION

The corrosion inhibition of mild steel in hydrochloric acid solution by chamomile (Matricaria recutita) extract (CE) was investigated through electrochemical (polarization, EIS) and surface analysis (optical microscopy/AFM/SEM) techniques. The effects of inhibitor concentration, temperature, and pH were evaluated. Thermodynamic parameters were calculated and adsorption studies were carried out. Finally, the surface morphology was investigated. The electrochemical studies showed that CE acts as a mixed-type corrosion inhibitor with predominantly anodic behavior. CE was adsorbed physically on the metal surface and obeyed the Langmuir adsorption isotherm. It impeded the corrosion processes by changing the activation energy. In the presence of CE, the metal surface was more uniform than the surface in the absence of inhibitor. Maximum inhibition efficiency (IE) was 93.28%, which was obtained at 22°C in 7.2 g/L of inhibitor in 1 M HCl solution.     

Modification to adaptive model reduction for regulation of distributed parameter systems with fast transients

We focus on output feedback control of distributed processes whose infinite dimensional representation in appropriate Hilbert subspaces can be decomposed to finite dimensional slow and infinite dimensional fast subsystems. The controller synthesis issue is addressed using a refined adaptive proper orthogonal decomposition (APOD) approach to recursively construct accurate low dimensional reduced order models (ROMs) based on which we subsequently construct and couple almost globally valid dynamic observers with robust controllers. The novelty lies in modifying the data ensemble revision approach within APOD to enlarge the ROM region of attraction. The proposed control approach is successfully used to regulate the Kuramoto‐Sivashinsky equation at a desired steady state profile in the absence and presence of uncertainty when the unforced process exhibits nonlinear behavior with fast transients. The original and the modified APOD approaches are compared in different conditions and the advantages of the modified approach are presented. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4595–4611, 2013     

STUDIES OF IRANIAN HEAVY OILS PERTINENT TO RESERVOIR CONDITIONS FOR THEIR AUTO-IGNITION TO INITIATE FIRE FLOODING

In this work, the potential for the auto-ignition of Iranian heavy oil during in situ combustion (ISC) process conditions was studied. Kinetic studies were carried out using thermal analysis techniques. Effects of oxygen partial pressure, reservoir pressure, and clay on the auto-ignition condition were investigated. Based on the experimental results obtained, a kinetic equation was derived for each of the different oil samples in the presence of different sands. The effect of partial pressure of oxygen in the injected air showed that at atmospheric pressure, low temperature combustion (LTC) was initiated at 275°C. Also, enriching the injected air by oxygen lowers the initial LTC temperature by up to 50°C. ARC experiments were undertaken to extend the studies to reservoir pressure conditions (1300 psi). It was found that activation energy in the LTC region was lowered as a consequence. As a result, initiation of LTC commenced at 115°C when air was injected. The effect of clay as a catalyst was also studied, and it was found that the activation energy decreases considerably when clay is present in the system. Experiments in a high-pressure combustion tube showed that LTC was initiated in the temperature range 120°–150°C, which is in line with the results obtained in the ARC. Fire flooding was sustained during the combustion tube test.     

Processing of Ultrafine-Grained Twinning-Induced Plasticity Steel Using Martensite Treatment

For the first time, martensite treatment was used to fabricate an ultrafine-grained (UFG) twinning-induced plasticity (TWIP) steel. The effects of cold rolling with 70 pct reduction at the liquid nitrogen temperature and subsequently annealing at 973 K (700 °C) for 5 to 20 minutes on the microstructure and mechanical properties of Fe-22Mn-0.4C-1.5Al-1Si TWIP steel were investigated. The results showed that a fully recrystallized UFG TWIP steel with a mean grain size of about 400 to 600 nm can be produced by the designed martensite treatment. The UFG TWIP steel exhibited high yield and tensile strengths and relatively high ductility.     

Experimental and Numerical Study of Turbulent Fluid Flow and Heat Transfer of Al2O3/Water Nanofluid in a Spiral-Coil Tube

Enhancement of heat transfer by nanofluids is reported by a large number of researchers. In this study, numerical and experimental investigation of heat transfer and flow characteristics of Al₂O₃/water nanofluid flowing in a spiral-coil tube is performed for various flow conditions. The spiral-coil tube is immersed horizontally in a hot water bath maintained at 60°C. Experiments are conducted in a turbulent flow regime using distilled water and nanofluid with 0.5%, 1%, and 1.5% particle volume concentrations. Also, a computational fluid dynamics methodology is used to simulate heat transfer and flow characteristics corresponding to the experimental measurements and for further flow conditions. Simulation results are compared with the experimental measurements, and 85% agreement between the results is observed. The results showed that convective heat transfer coefficient of nanofluid is enhanced up to 61% compared with that of the base fluid. Based on the experimental measurements, a new correlation is developed to predict convection heat transfer from nanofluids in spiral-coil tubes.     

Silica-modified magnetite Fe3O4 nanoparticles grafted with sulfamic acid functional groups: an efficient heterogeneous catalyst for the synthesis of 3,4-dihydropyrimidin-2(1H)-one and tetrahydrobenzo[b]pyran derivatives

Silica-modified magnetite-polyoxometalates functionalized with sulfamic acid groups as hybrid nanoparticles were prepared by sulfonation of diamine-functionalized propyl group grafted on the magnetic silica-coated Fe₃O₄ nanoparticles. This heterogeneous nanocatalyst was explored to present high catalytic performance for the synthesis of 3,4-dihydropyrimidinones and tetrahydrobenzo[b]pyrans under mild reaction conditions. The properties of this nanocatalyst were characterized by FT- infrared, energy-dispersive X-ray spectrum, scanning electron microscope, X-ray diffraction, X-ray fluorescence and elemental analysis. Easy separation of the nanocatalyst by using an external magnet, recyclability, non-toxicity, versatility and high stability of the catalyst combined with low reaction times and excellent yields make the present protocol very useful and attractive for the synthesis of the titled products.     

Modular neural-SVM scheme for speech emotion recognition using ANOVA feature selection method

The speech signal consists of linguistic information and also paralinguistic one such as emotion. The modern automatic speech recognition systems have achieved high performance in neutral style speech recognition, but they cannot maintain their high recognition rate for spontaneous speech. So, emotion recognition is an important step toward emotional speech recognition. The accuracy of an emotion recognition system is dependent on different factors such as the type and number of emotional states and selected features, and also the type of classifier. In this paper, a modular neural-support vector machine (SVM) classifier is proposed, and its performance in emotion recognition is compared to Gaussian mixture model, multi-layer perceptron neural network, and C5.0-based classifiers. The most efficient features are also selected by using the analysis of variations method. It is noted that the proposed modular scheme is achieved through a comparative study of different features and characteristics of an individual emotional state with the aim of improving the recognition performance. Empirical results show that even by discarding 22% of features, the average emotion recognition accuracy can be improved by 2.2%. Also, the proposed modular neural-SVM classifier improves the recognition accuracy at least by 8% as compared to the simulated monolithic classifiers.     

Mechanistic study of electrochemical oxidation of o-dihydroxybenzenes in the presence of 4-hydroxy-1-methyl-2(1H)-quinolone: Application to the electrochemical synthesis

Electrochemical oxidation of o-dihydroxybenzenes (1a and 1b) has been studied in the presence of 4-hydroxy-1-methyl-2(1H)-quinolone (3) as a nucleophile in aqueous solution using cyclic voltammetry and controlled-potential coulometry. The results indicate that the o-quinones derived from o-dihydroxybenzenes (1a and 1b) participate in 1,4-(michael) addition reactions with 3 to form the corresponding new o-dihydroxybenzene derivatives (6a and 6b). We propose a mechanism for the electrode process. The efficient electrochemical synthesis of 6a and 6b has been successfully performed at carbon rod electrodes in an undivided cell in good yield and purity. The products have been characterized after purification by IR, ¹H NMR, ¹³C NMR and MS.     

A new approach for rotation-invariant and noise-resistant texture analysis and classification

The analysis and classification of images, such as texture images, is one of the substantial and important fields in image processing. Due to destructive effects of image rotation and noise, the stability and efficiency of texture analysis and classification methods are an important research area. In this paper, a new method for texture analysis and classification has been proposed which is based on a particular combination of wavelet, ridgelet and Fourier transforms as well as support vector machine. The proposed method has been evaluated for 13 texture datasets produced by three original datasets containing 25 and 111 original textures from Brodatz database and 24 original textures from OUTEX database. These datasets comprise 415584 and 93600 rotated noise-free and noisy texture images for Brodatz database and also 49920 noisy and 4320 noise-free texture images for OUTEX database, respectively. Simulation results demonstrate the capability, efficiency and also stability of the proposed method especially for real-time rotation-invariant and noise-resistant texture analysis and classification.