Global minimization of multi-funnel functions using particle swarm optimization

In this paper, we consider the problem of finding the global minimum of multi-funnel-shaped functions with many local minima, which is a well-known and interesting problem in computational biology. First, the particle swarm optimization algorithms are briefly reviewed. Then, we have applied a variant of it with linear decreasing inertia weight to solve the underlying global optimization problem. Our computational experiments on several known test problems show the efficiency of the particle swarm optimization algorithm in comparison with global convex quadratic underestimator algorithms that are widely used in the literature.     

Mullite ceramic membranes for industrial oily wastewater treatment: experimental and neural network modeling

In this paper, results of an experimental and modeling of separation of oil from industrial oily wastewaters (desalter unit effluent of Seraje, Ghom gas wells, Iran) with mullite ceramic membranes are presented. Mullite microfiltration symmetric membranes were synthesized from kaolin clay and alpha-alumina powder. The results show that the mullite ceramic membrane has a high total organic carbon and chemical oxygen demand rejection (94 and 89%, respectively), a low fouling resistance (30%) and a high final permeation flux (75 L/m2 h). Also, an artificial neural network, a predictive tool for tracking the inputs and outputs of a non-linear problem, is used to model the permeation flux decline during microfiltration of oily wastewater. The aim was to predict the permeation flux as a function of feed temperature, trans-membrane pressure, cross-flow velocity, oil concentration and filtration time, using a feed-forward neural network. Finally the structure of hidden layers and nodes in each layer with minimum error were reported leading to a 4-15 structure which demonstrated good agreement with the experimental measurements with an average error of less than 2%.     

Biostabilised icosahedral gold nanoparticles: synthesis,cyclic voltammetric studies and catalytic activity towards 4-nitrophenol reduction

A green and cost-effective biosynthetic approach for the preparation of icosahedral gold nanoparticles (AuNPs) using an aqueous leaf extract of Polygonum minus as reducing and stabilising factor is described. The reduction of Au³⁺ ions to elemental Au rapidly occurred and is completed within 20 minutes at room temperature. The size of the nanoparticles is highly sensitive to the AuCl₄^?/leaf extract concentration ratio and pH. Transmission electron microscopy and X-ray diffraction data indicated that the nanoparticles were in a crystalline shape (face-centred cubic), mostly icosahedral and nearly monodispersed with an average size of 23 nm. Cyclic voltammetric studies suggested that flavonoids, such as quercetin and myricetin present in the leaf extract had a tendency to donate electrons to Au³⁺ ions and the formation of elemental Au was possible due to the transfer of electrons from these flavonoids to Au³⁺ ions. Infrared absorption of the AuNPs and the leaf extract revealed that the oxidised (quinone) form of quercetin and myricetin were presumably the main stabilising agents in the formation of stable nanoparticles. The present biosynthesis of AuNPs was simple, rapid, cost-effective and environmentally friendly. The newly prepared biostabilised icosahedral AuNPs show good catalytic activity in the reduction of 4-nitrophenol (4-NP) to 4-aminophenol (4-AP).     

An efficient algorithm for solving a new mathematical model for a quay crane scheduling problem in container ports

This paper presents a novel, mixed-integer programming (MIP) model for the quay crane (QC) scheduling and assignment problem, namely QCSAP, in a container port (terminal). Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult. This paper, thus, proposes a genetic algorithm (GA) to solve the above-mentioned QCSAP for the real-world situations. Further, the efficiency of the proposed GA is compared against the LINGO software package in terms of computational times for small-sized problems. Our computational results suggest that the proposed GA is able to solve the QCSAP, especially for large sizes.     

Fe3O4@SiO2-SnCl4: An Efficient Catalyst for the Synthesis of Xanthene Derivatives under Ultrasonic Irradiation

Grafting of SnCl₄ on the Fe₃O₄@SiO₂ nanoparticles afforded Fe₃O₄@SiO₂-SnCl₄ as a novel inorganic heterogenous catalyst, which was characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), vibrating sample magnetometry (VSM), Field Emission Scanning Electron Microscopy (FE-SEM), and transmission electron microscopy (TEM). In this research, we report a convenient and efficient direct protocol for the preparation of xanthene derivatives via condensation of β-naphthol, dimedone, or mixture of β-naphthol and dimedone with various aromatic aldehydes in the presence of the catalytic amount of Fe₃O₄@SiO₂-SnCl₄ under ultrasonic irradiation. This procedure has a lot of advantages such as: very easy reaction conditions, absence of any tedious workup, or purification, and much milder method. The corresponding products have been obtained in excellent yields, high purity, and short reaction times.     

Prediction of the Surface Tension of Hydrocarbons

Prediction of the surface tension of hydrocarbons is required in many chemical engineering calculations. In this work a robust artificial neural network code has been used in MATLAB software (The MathWorks, Natick, MA) to predict the surface tension prediction of 61 hydrocarbons. Experimental data is divided into two parts (70% for training and 30% for testing). Optimal configuration of network is obtained with minimization of prediction error on testing data. The accuracy of our proposed model is compared to four well-known empirical equations. Results showed that artificial neural network was more accurate than these empirical equations. Average relative deviation of our artificial neural network model is 0.93 while average relative deviation of the Brock-Bird, Pitzer, Zuo-Stenby, and Sastri-Rao equations are 6.30, 6.48, 5.73, and 6.33, respectively.     

Prediction of permeation flux decline during MF of oily wastewater using genetic programming

Genetic programming is an orderly method for getting computers to regularly solve a problem. The genetic programming creates a computer program from an obtained data and solves the problem. In this work, treatment of oily wastewaters with synthesized mullite ceramic microfiltration membranes was studied and a new approach for modeling of the membrane flux is presented. The model used input parameters for operating conditions (flux and filtration time) and feed oily wastewater quality (oil concentration, temperature, trans-membrane pressure and cross-flow velocity). The genetic programming utilized here delivers a mathematical function for the membrane flux as a function of the independent variables stated above. Parameters for controlling and termination criterion for a run are provided by the user. Result is provided as a tree of functions and terminals. The results thus obtained from the genetic programming model demonstrated good representation of the experimental data with an average error of less than 5%.     

Microwave distillation followed by headspace single drop microextraction coupled to gas chromatography-mass spectrometry (GC–MS) for fast analysis of volatile components of Echinophora platyloba DC

To avoid the traditional and time consuming hydrodistillation, the analyses of volatile components in aerial parts of Echinophora platyloba DC was carried out by a simple microwave distillation followed by headspace single drop microextraction (MD–HS–SDME) coupled to gas chromatography–mass spectrometry (GC–MS). The headspace volatile compounds were collected after irradiation using a single drop of n-heptadecan. The extraction conditions were optimised using the relative peak areas as index. The chemical composition of the MD–HS–SDME extracts was confirmed according to their retention indexes and mass spectra. Fifty-three components were extracted and identified by using the MD–HS–SDME method. E-β-ocimene (53.81%), R-d-decalactone (12.75%), α-pinene (6.43%), n-heptanol (6.27%), β-phellanderne (2.70%) and linalool (1.89%) were the major constituents.