New sunshine-based models for predicting global solar radiation using PSO (particle swarm optimization) technique

PSO (particle swarm optimization) technique is applied to estimate monthly average daily GSR (global solar radiation) on horizontal surface for different regions of Iran. To achieve this, five new models were developed as well as six models were chosen from the literature. First, for each city, the empirical coefficients for all models were separately determined using PSO technique. The results indicate that new models which are presented in this study have better performance than existing models in the literature for 10 cities from 17 considered cities in this study. It is also shown that the empirical coefficients found for a given latitude can be generalized to estimate solar radiation in cities at similar latitude. Some case studies are presented to demonstrate this generalization with the result showing good agreement with the measurements. More importantly, these case studies further validate the models developed, and demonstrate the general applicability of the models developed. Finally, the obtained results of PSO technique were compared with the obtained results of SRTs (statistical regression techniques) on Angstrom model for all 17 cities. The results showed that obtained empirical coefficients for Angstrom model based on PSO have more accuracy than SRTs for all 17 cities.     

A model of the pacemaking neuron of the respiratory central pattern generator.

We have developed a model of the mammalian respiratory central pattern generator (rCPG) to mimic the salient characteristics of its constituent medullary neurons. This model is designed as a network of Hodgkin-Huxley-type medullary neurons driven by a pacemaking neuron from the pre-B?tzinger complex. Here, we present the results of our model of this pacemaking neuron, based on available voltage clamp data. Implications for the behavior of the rCPG are discussed.     

Forecasting future oil demand in Iran using GSA (Gravitational Search Algorithm)

Growing energy demand of the world, made the major oil and gas exporting countries to have critical role in the energy supply. The geostrategic situation of Iran and its access to the huge hydrocarbon resources placed the country among important areas and resulted in the investment development of oil and gas industry.In this study, a novel approach for oil consumption modeling is presented. Three demand estimation models are developed to forecast oil consumption based on socio-economic indicators using GSA (Gravitational Search Algorithm). In first model (PGIE) oil consumption is estimated based on population, GDP, import and export. In second model (PGML) population, GDP, export minus import, and number of LDVs (light-duty vehicles) are used to forecast oil consumption and in third one (PGMH) population, GDP, export minus import, and number of HDVs (heavy-duty vehicles) are used to estimate oil consumption. Linear and non-linear forms of equations are developed for each model.In order to show the accuracy of the algorithm, a comparison is made with the GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) estimation models which are developed for the same problem. Oil demand in Iran is forecasted up to year 2030.     

Texture and Grain Boundary Network Evolution of Laser Powder Bed Fusion Processed Pure Ni During Post-printing Annealing

Metallurgical and Materials Transactions A - Commercially pure Ni was additively manufactured using laser powder bed fusion. The printed specimens were then subjected to annealing treatments in a...     

Chromium removal from ion-exchange waste brines with calcium polysulfide

Chromium removal from ion-exchange (IX) brines presents a serious challenge to the water industry. Although chromium removal with calcium polysulfide (CaS₅) from drinking waters has been investigated somewhat, its removal from ion-exchange brines has not been evaluated to date. In this study, a Central Composite Design as well as experimental coagulation tests were performed to investigate the influence of pH, CaS₅/Cr(VI) molar ratio, alkalinity, and ionic strength in the removal of chromium from IX brines. The optimal pH range for the process was found to be pH 8-10.3 and brine alkalinity did not affect coagulation. The efficiency of chromium removal improved only slightly when the ionic strength increased from 0.1 M to 1.5 M; no significant difference was observed for an ionic strength change from 1.5 to 2.1 M. For chromium (VI) concentrations typically found in ion-exchange brines, a CaS₅/Cr(VI) molar ratio varying from 0.6 to 1.4 was needed to obtain a final chromium concentration <5 mg/L. Maximum efficiency for total chromium removal was obtained when oxidation reduction potentials were between −0.1 and 0 (V). Solids concentrations (0.2-1.5 g/L) were found to increase proportionally with CaS₅ dosage. The results of this research are directly applicable to the treatment of residual waste brines containing chromium.     

A feasibility study on production and optimization of PVDF/PU polyblend nanofiber layers using expert design analysis

Iranian Polymer Journal - Different polymer blends are electrospun to produce nanofibers with novel combined features. In this study, the feasibility of combining polyurethane (PU) and...     

A new fuzzy c-means method with total variation regularization for segmentation of images with noisy and incomplete data

The objective function of the original (fuzzy) c-mean method is modified by a regularizing functional in the form of total variation (TV) with regard to gradient sparsity, and a regularization parameter is used to balance clustering and smoothing. An alternating direction method of multipliers in conjunction with the fast discrete cosine transform is used to solve the TV-regularized optimization problem. The new algorithm is tested on both synthetic and real data, and is demonstrated to be effective and robust in treating images with noise and missing data (incomplete data).     

ASSESSMENT OF ELECTRICITY DEMAND IN IRAN'S INDUSTRIAL SECTOR USING DIFFERENT INTELLIGENT OPTIMIZATION TECHNIQUES

This study presents application of particle swarm optimization (PSO) and genetic algorithm (GA) methods to estimate electricity demand in Iran's industrial sectors, based on economic indicators. The economic indicators used in this study are number of customers, gross domestic product (GDP), electricity production and price. The models developed in two forms (exponential and linear). Electricity consumption in Iran's industrial sector from 1981 to 2005 is considered as the case of this study. The available data is partly used for finding the optimal, or near optimal, values of the weighting parameters (1981–1999) and partly for testing the models (2000–2005). For the best results of GA, the relative error averages were 1.13% and 1.29% for GA ? DE M _exponential and GA ? DE M _linear and for PSO were 1.03% and 1.69% for PSO ? DE M _exponential and PSO ? DEM_linear. Electricity consumption is forecasted up to year 2030.     

An enhanced AHP–TOPSIS-based load balancing algorithm for switch migration in software-defined networks

The Journal of Supercomputing - Considering a software defined network, distributed controller architecture uses multiple controllers in which each controller manages a part of the network. The...     

A hybrid GA–SQP optimization technique for determination of kinetic parameters of hydrogenation reactions

A hybrid optimization technique, GA–SQP, is developed in which the genetic algorithm (GA) which is a stochastic method is combined with the sequential quadratic programming (SQP) method which is a deterministic method. This method was used to determine the kinetic parameters of the set of highly nonlinear hydrogenation reactions. Catalyst deactivation was also taken into account. The ability of GA and SQP in solving this type of problem was investigated. It was shown that although the SQP is fast, it is not able to solve this problem properly and is very sensitive to the choice of initial point. The GA was able to solve the problem after a large number of generations. It was shown that the new GA–SQP hybrid method is able to determine the final solution considerably faster than the GA while it is not sensitive to the initial point.