A new mechanistic model to predict gas–liquid interface shape of gas–liquid flow through pipes with low liquid loading

Devising a new mechanistic method to predict gas–liquid interface shape in horizontal pipes is concerned in this article. An experiment was conducted to find the pressure gradients of air–water flow through a 1‐in. pipe diameter. Comparing results of model with some experimental data available in the literature demonstrates that the model provides quite better predictions than existed models do. This model also predicts flow regime transition from stratified to annular flow better than Apparent Rough Surface and Modified Apparent Rough Surface models for both 1‐ and 2‐in. pipe diameters. The model also leads to reliable predictions of wetted wall fraction experimental data. Although one parameter of new model was evaluated based on air–water flow pressure loss experimental data for 1 in. pipe, it was considerably successful to predict pressure drop, liquid holdup, stratified‐annular transition and wetted wall fraction for other gas–liquid systems and pipe diameters. © 2014 American Institute of Chemical Engineers AIChE J, 61: 1043–1053, 2015     

An improved mechanism for capacity payment based on system dynamics modeling for investment planning in competitive electricity environment

Many countries have experienced restructuring in their electric utilities. This restructuring has presented the power industries with new challenges, the most important of which is long-term investment planning under uncertain conditions. This paper presents an improved mechanism for capacity payment. The mechanism has been investigated based on system dynamic modeling. In our proposed mechanism, generators will recover a part of their investment through capacity payment. While the payment for any plant remains constant during the operation period, it depends on the investment needed to build it. The main factors affecting long-term planning have been considered in our model. The approach can be used to investigate the effects of fixed as well as variable capacity payment in market investment. We used the probability density function of load as a new concept to calculate average market price. Delays in unit constructions, estimation of demand, and market capacity growth during construction periods have been included in the proposed algorithm as parameters, which affect the regulator's decision for changing capacity payment. The model can be used by regulators to investigate strategies that may affect the fluctuations in the market.     

Modeling the oxidative coupling of methane using artificial neural network and optimizing of its operational conditions using genetic algorithm

The effect of some operating conditions such as temperature, gas hourly space velocity (GHSV), CH₄/O₂ ratio and diluents gas (mol% N₂) on ethylene production by oxidative coupling of methane (OCM) in a fixed bed reactor at atmospheric pressure was studied over Mn/Na₂WO₄/SiO₂ catalyst. Based on the properties of neural networks, an artificial neural network was used for model developing from experimental data. To prevent network complexity and effective data input to the network, principal component analysis method was used and the number of output parameters was reduced from 4 to 2. A feed-forward back-propagation network was used for simulating the relations between process operating conditions and those aspects of catalytic performance including conversion of methane, C₂ products selectivity, C2 yielding and C₂H₄/C₂H₆ ratio. Levenberg-Marquardt method is presented to train the network. For the first output, an optimum network with 4-9-1 topology and for the second output, an optimum network with 4-6-1 topology was prepared. After simulating the process as well as using ANNs, the operating conditions were optimized and a genetic algorithm based on maximum yield of C₂ was used. The average error in comparing the experimental and simulated values for methane conversion, C₂ products selectivity, yield of C₂ and C₂H₄/C₂H₆ ratio, was estimated as 2.73%, 10.66%, 5.48% and 10.28%, respectively.     

Active nano-CuPt3 electrocatalyst supported on graphene for enhancing reactions at the cathode in all-vanadium redox flow batteries

Graphene-supported monometallic (Pt) and bimetallic (CuPt₃) cubic nanocatalysts have been investigated as new positive electrode materials for improving the VO₂⁺/VO²⁺ redox process occurring in the vanadium redox flow batteries (VRB). High-resolution transmission electron microscopy (HRTEM) and scanning electron microscopy (SEM) have been employed to characterize the electrodes. The presence of the CuPt₃ nanocubes on graphene conferred higher electrocatalytic activity due to the much higher electroactive area compared to that obtained with the Pt nanoparticles. The electrochemical surface area of the nano-(CuPt₃)-decorated graphene electrode was 105% higher compared to non-decorated graphene, being then a promising alternative for improving the VRB.     

Prediction of induction time for natural gas components during gas hydrate formation in the presence of kinetic hydrate inhibitors in a flow mini‐loop apparatus

The objective of this work is the prediction of induction time (t_i) for simple gas hydrate formation in the presence or absence of kinetic hydrate inhibitors at various conditions based on the Kashchiev and Firoozabadi model in a flow mini‐loop apparatus. For this purpose, the t_i model is developed for simple gas hydrate formation in batch system for natural gas components during hydrate formation in a flow mini‐loop apparatus. A laboratory flow mini‐loop apparatus is designed and built up to measure the t_i for simple gas hydrate formation when a hydrate former (such as C₁, C₃, CO₂ and i‐C₄) is contacted with water in the absence or presence of dissolved inhibitor, such as poly vinylpyrrolidone, PVCap and L ‐tyrosine. In each experiment, a water blend saturated with pure gas is circulated up to a required pressure. Pressure is maintained at a constant value during experimental runs by means of the required gas make‐up. The average absolute deviation (AAD) of the predicted t_i values from the corresponding experimental data are found to be about 11% and 9.4% for gas hydrate formation t_i in the presence or absence of kinetic hydrate inhibitors, respectively. © 2012 Canadian Society for Chemical Engineering     

Synthesis of kappa-carrageenan-g-poly(acrylamide)/sepiolite nanocomposite hydrogels and adsorption of cationic dye

In this study, nanocomposite hydrogels from grafting of acrylamide onto kappa-carrageenan biopolymer were prepared in the presence of sepiolite clay. Methylenebisacrylamide and ammonium persulfate were used as cross-linker and initiator, respectively. The sepiolite nanoclay was introduced into hydrogel matrix without any chemical treatment. The structure of nanocomposites was investigated by FTIR, SEM, TEM, and TGA techniques. The TEM image showed that sepiolite exists as individual needle’s shape. The swelling of hydrogels were studied in distilled water, salt solutions, and various pHs. The obtained nanocomposites were evaluated to remove of cationic crystal violet (CV) dye from water. The kinetic and isotherm of adsorption of dye onto nanocomposites were studied and analyzed according to kinetic and isotherm models. The results showed that the pseudo-second-order adsorption kinetic was predominated for the adsorption of CV onto nanocomposites. The experimental equilibrated adsorption capacity of nanocomposites was analyzed using Freundlich and Langmuir isotherm models. The results corroborated that the experimental data fit the Langmuir isotherm the best. By varying the pH of initial dye solution, while the clay-free hydrogel showed relatively pH-independent adsorption behavior, the nanocomposites depicted pH-dependent adsorption.     

Synthesis of novel polyimides containing side‐chain azo‐2‐naphthol moieties

An amine‐ester derivative of isoeugenol was prepared in three steps. This amine‐ester was converted to diazonium salt and subsequently was reacted with 2‐naphthol and a novel isoeugenol ester‐azo derivative as a new monomer was obtained in quantitative yield. This monomer was characterized by high‐field ¹H‐NMR, IR, and elemental analysis and then was used for the preparation of model compound and polymerization reactions. 4‐Phenyl‐1,2,4‐triazoline‐3,5‐dione was allowed to react with this new monomer. The reaction was very fast and gave only one double adduct by Diels–Alder and ene pathways in excellent yield. The polymerization reactions of novel monomer with bistriazolinediones [bis(p‐3,5‐dioxo‐1,2,4‐triazolin‐4‐ylphenyl)methane and 1,6‐bis(3,5‐dioxo‐1,2,4‐triazolin‐4‐yl)hexane] were carried out in N,N‐dimethylacetamide at room temperature. The reactions were exothermic, fast, and gave novel heterocyclic polyimides by repetitive Diels–Alder‐ene polyaddition reactions. Some structural characterization and physical properties of these novel heterocyclic polyimides are reported. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 89: 1942–1951, 2003     

CuI Immobilized on Tricationic Ionic Liquid Anchored on Functionalized Magnetic Hydrotalcite (Fe3O4/HT-TIL-CuI) as a Novel,Magnetic and Efficient Nanocatalyst for Ullmann-Type C–N Coupling Reaction

One of the most important reactions in organic synthesis is Ullmann-type C–N coupling reaction which has been used for preparation of numerous biologically active compounds. In this work, CuI immobilized on tricationic ionic liquid anchored on functionalized magnetic hydrotalcite (Fe₃O₄/HT-TIL-CuI) has been successfully prepared and fully characterized by different techniques, including fourier-transform infrared spectroscopy, vibrating sample magnetometer, thermo gravimetric analysis, transmission electron microscopy, field-emission scanning electron microscopy, energy dispersive X-ray spectroscopy, elemental mapping, zeta potential, X-ray diffraction, temperature programmed desorption of ammonia (NH₃-TPD), temperature-programmed reduction and inductively coupled plasma. The results showed that the as-prepared nanocatalyst possesses plate-like morphology with approximate size of 50 nm and superparamagnetic behavior. Also, total acidity and total hydrogen consumption of the nanocatalyst were measured to be 8.5 and 1.41 mmol g^?1, respectively. This nanocatalyst exhibited favorable performance for C–N coupling reaction among a variety of aryl halides and N(H)-heterocycles (benzimidazoles, pyrazoles and triazoles) in the presence of 2.5 mol% of nanocatalyst without any additives under air atmosphere revealing high yields in all cases. Besides, it is noted that in the present system the desired product can be easily and quickly isolated and nanocatalyst also recovered magnetically from the reaction mixture employing a permanent magnet for at least six consecutive trials without a discernible decrease in catalytic activity which makes the proposed methodology appropriate for industrial. The findings demonstrated the advantages of the present method as no need for neutral atmosphere, appropriate times, recyclability of the catalyst, broad substrate scope, minimization of chemical waste, simple purification of products, easy workup process, and high yields.

     

Modelling qualitative and quantitative parameters of groundwater using a new wavelet conjunction heuristic method: wavelet extreme learning machine versus wavelet neural networks

In recent years, as a result of climate change as well as rainfall reduction in arid and semi‐arid regions, modelling qualitative and quantitative parameters belonging to aquifers has become crucially important. In Iran, as aquifers are treated as the most commonly used drinking water resources, modelling their qualitative and quantitative parameters is enormously important. In this paper, for the first time, values of salinity, total dissolved solids (TDS), groundwater level (GWL) and electrical conductivity (EC) of the Arak Plain, located in Markazi Province, Iran, are simulated by means of four modern artificial intelligence models including extreme learning machine (ELM), wavelet extreme learning machine (WELM), online sequential extreme learning machine (OSELM) and wavelet online sequential extreme learning machine (WOSELM) as well as the MODFLOW software for a 15‐year period monthly. To develop the hybrid artificial intelligence models, the wavelet is employed. First, the effective lags in estimating the qualitative and quantitative parameters of the groundwater are identified using the autocorrelation function (ACF) and the partial autocorrelation function (PACF) analysis. After that, four different models are developed by the selected input combinations and also the ACF and the PACF in the form of different lags for each of ELM, WAELM, OSELM and WOSELM methods. Then, the superior models in simulating the groundwater qualitative and qualitative parameters are detected by conducting a sensitivity analysis. To forecast the electrical conductivity (EC) by the best WOSELM model, the values of the Nash–Sutcliffe efficiency coefficient (NSC), Mean Absolute Error (MAE) and the scatter index (SI) are obtained to be 0.991, 18.005 and 4.28E‐03, respectively. In addition, the most effective lags in estimating these parameters are introduced. Subsequently, the results found by the MODFLOW model are compared with those of the artificial intelligence models and it is concluded that the latter are more accurate. For instance, the scatter index and Nash–Sutcliffe efficiency coefficient values calculated by WOSELM for TDS, respectively, are 5.34E‐03 and 0.991. Finally, an uncertainty analysis is conducted to evaluate the performance of different numerical models. For example, MODFLOW has an underestimated performance in simulating the salinity parameter.     

Multi-objective modelling of day ahead scheduling of MMG-based distribution networks accompanied by DEA considering economics,emissions and power quality

ABSTRACT

This paper presents the state of the art relating to multi-objective modelling for day ahead scheduling of multi micro grid-based distribution networks, using optimal power flow (OPF) accompanied by data envelopment analysis (DEA). In this paper eco-reliability cost function, power quality enhancement and emission reduction are treated as the objective functions and the uncertainties of renewable distributed generations (DGs), load demand and market price are incorporated into the problem. This method is able to find the optimum operation of DGs in grid-connected or isolated MGs, power transaction between each MG and upstream networks/other MGs and hourly reconfiguration instants. For this purpose, firstly OPF is applied to the problem, then the obtained optimal solutions are prioritised by DEA and ranking is done, based on the efficiencies of the optimal solutions. Finally, the provided results validate the practicability of the proposed method and accuracy of the outcomes.