Comparing the Effects of Microwave and Conventional Heating Methods on the Evaporation Rate and Quality Attributes of Pomegranate (<Emphasis Type="Italic">Punica granatum</Emphasis> L.) Juice Concentrate

Pomegranate juice was concentrated by conventional heating and microwave heating at different operational pressures (12, 38.5, and 100 kPa), and their effects on evaporation rate and quality attributes of concentrated juice were investigated. The final juice concentration of 40° Brix was achieved in 140, 127, and 109 min at 100, 38.5, and 12 kPa, respectively, by using conventional heating. Applying microwave energy decreased required times to 118, 95, and 75 min. The changes in color, anthocyanin content, and antioxidant capacity during concentration processes were investigated. L*, a*, and b* parameters were measured to estimate the intensity of color loss. All Hunter color parameters decreased with time. Results showed that the degradation of color, anthocyanins, and antioxidant activity were more important in conventional heating compared to microwave heating method. Degradation rates increases by increasing process pressure. A first-order kinetics model was applied to modeling changes in total solid content, anthocyanin content, and antioxidant capacity.     

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.     

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.     

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.     

Radiosynthesis and biodistribution of [^18F]-tetracosactide using a semi-automated [^18F]SFB production module

In order to prepare a specific melanocortin type 2 receptor (MC2R) ligand, b1-24-corticotrophin was pre-pared in one-step reaction with [18F] SFB and b-1-24-corticotrophin pharmaceutical solution (1 mg/mL, pH=6.5). [18F]SFB was prepared in a semi-automated module in two steps with an overall radiochemical yield of 47% to EOB (not-decay corrected) in 90 min. The 18F-labeled intermediates and 18F-labeled peptide was checked by RTLC and HPLC. The results show that the radiochemical purity is >95% and the yield to EOB (not-decay corrected) is 29% for final 18F-labeled peptide at optimized conditions. Preliminary in vivo studies in normal mice were performed to deter-mine biodistribution of the 18F-labeled peptide for 150 min. The results show that the major tracer uptake is consistent with the natural distribution of MC2R receptors in mammals. Testes/blood and testes/muscle ratios for 18F-labeled peptide at 150 min were 184 and 1.56, respectively, and adipocyte/blood and adipocyte/muscle ratios at 120 min were 221 and 142, respectively. The data support the specific receptor binding of the radiolabeled peptide as reported for MC2R receptor accumulation in adipocytes and testes and demonstrates the retention of biological activity of the pep-tide. This tracer can be used in detection of MC2R distribution in malignancies and sex organ diseases.     

Solving the stochastic support vector regression with probabilistic constraints by a high-performance neural network model

This paper offers a recurrent neural network to support vector machine (SVM) learning in stochastic support vector regression with probabilistic constraints. The SVM is first converted into an equivalent quadratic programming (QP) formulation in linear and nonlinear cases. An artificial neural network for SVM learning is then proposed. The presented neural network framework guarantees obtaining the optimal solution of the SVM problem. The existence and convergence of the trajectories of the network are studied. The Lyapunov stability for the considered neural network is also shown. The efficiency of the proposed method is shown by three illustrative examples.

     

Enhanced oil recovery from carbonate reservoirs by spontaneous imbibition of low salinity water

An experimental study was performed to investigate the impact of low salinity water on wettability alteration in carbonate core samples from southern Iranian reservoirs by spontaneous imbibition. In this paper, the effect of temperature, salinity, permeability and connate water were investigated by comparing the produced hydrocarbon curves. Contact angle measurements were taken to confirm the alteration of surface wettability of porous media. Oil recovery was enhanced by increasing the dilution ratio of sea water, and there existed an optimum dilution ratio at which the highest oil recovery was achieved. In addition, temperature had a very significant impact on oil recovery from carbonate rocks. Furthermore, oil recovery from a spontaneous imbibition process was directly proportional to the permeability of the core samples. The presence of connate water saturation inside the porous media facilitated oil production significantly. Also, the oil recovery from porous media was highly dependent on ion repulsion/attraction activity of the rock surface which directly impacts on the wettability conditions. Finally, the highest ion attraction percentage was measured for sodium while there was no significant change in pH for all experiments.     

Computer simulation of the optical properties of high-temperature cermet solar selective coatings

A computer simulation is developed to calculate the solar absorptance and thermal emittance of various configurations of cermet solar selective coatings. Special attention has been paid to those material combinations, which are commonly used in high-temperature solar thermal applications. Moreover, other material combinations such as two-, three- and four-cermet-layer structures as solar selective coatings have been theoretically analyzed by computer simulation using three distinct physical models of Ping Sheng, Maxwell–Garnett and Bruggeman. The novel case of two-cermet-layer structure with different cermet components has also been investigated. The results were optimized by allowing the program to manipulate the metal volume fraction and thickness of each layer and the results compared to choose the best possible configuration. The calculated results are within the range of 0.91–0.97 for solar absorptance and 0.02–0.07 for thermal emittance at room temperature.     

Seismic soil–structure interaction analysis of wind turbines in frequency domain

In this paper, the seismic behavior of wind turbines sitting on a finite flexible soil layer is investigated in three‐dimensional space. A numerical algorithm formulated in frequency domain is proposed in order to simulate the dynamic soil–structure interaction (SSI). The wind turbine is discretized using finite element method (FEM) while, the underlying soil is represented by complex dynamic stiffness functions based on cone models. A parametric study consisting of 24 ground motions and three soil profiles is carried out, and different response quantities of the wind tower model are calculated and presented in the paper. The free‐field ground motions are estimated based on an equivalent linear approach using SHAKE2000 computer software. Transfer functions for total acceleration of the wind tower are obtained under the considered soil profiles and the modal frequencies of the coupled wind turbine–soil foundation are estimated. It is shown that the response quantities such as displacement, rotation, acceleration, base shear and moment are significantly affected by SSI, although the effect of SSI on the fundamental frequencies of the wind tower is insignificant. The moment and shear force distribution along the height of the tower is highly influenced as the soil stiffness decreases. The change in seismic demand distribution along the tower height because of SSI is not addressed by simplified design approached and should be carefully considered in seismic design of wind towers. Copyright © 2016 John Wiley & Sons, Ltd.