Optimization of proteases pretreatment on natural dyeing of wool using response surface methodology

Natural dyes and enzymes have attracted a lot of attention due to their non-hazardous nature. In this research, wool fabrics were pretreated with the commercial protease at different concentrations over various times. The dyeing process was then carried out on the treated fabrics with the commercial madder and cochineal. Also, the central composite design analysis was used to design the relation between dye exhaustion and some properties of dyed wool including tensile strength, alkaline solubility, water drop absorption, and weight loss based on Design of Expert software. The response surface methodology was also applied to find the optimum conditions for the wool fabric pretreatment. The scanning electron microscopy was employed to indicate the influences of proteases on the fabric surface. The optimized proteases pretreatment on the wool surfaces has considerably improved the absorption of madder and cochineal and minimized the damage to appropriate physical properties. The adsorption kinetic of madder and cochineal on enzymatic wool fiber was fitted with a pseudo-second-order model. The rate of dyeing at different temperatures, as well as the values of standard affinity, entropy, and enthalpy, was calculated. The activation energy of dyeing with madder and cochineal at different temperatures are 23.79 and 30.96 kJ/mol, respectively, considering that these values are in the typical activation energy for physisorption. It was also found that wash, light, wet and dry rub fastness properties of the samples dyed along with protease have not changed significantly. This can be easily scaled up in the industry as a simple method using the commercial materials.     

Modeling the mean grain size of synthesized nanopowders produced by mechanical alloying

Gene expression programming (GEP) optimization tool has been utilized to predict the mean grain size of nanopowders synthesized by mechanical alloying. 86 data were collected from the literature, randomly divided into 65 and 21 sets and then, respectively, were trained and tested by 11 different GEP models. The differences between the models were in their linking functions (addition and multiplication) and sub expression trees (3, 4, 5, 6, 7 and 8). The method of calculation of the mean grain size, milling time, annealing temperature, produced phases after mechanical alloying, vial speed and ball to powder ratio were considered as input variables to predict mean grain size of nanopowders as output. The obtained results from training and testing of the different models showed that some of them are capable to predict mean grain size of the synthesized nanopowders in the considered range. However, the best results were obtained by using 7 sub expression trees addition as linking function. R² value of the trained and tested suggested model showed this situation.     

Analysis of Thermal Nonequilibrium Inverse Heat Transfer in a Porous Channel

This article is concerned with the one-dimensional transient inverse heat conduction between two parallel plates filled with a porous medium under a nonthermal equilibrium condition between the solid and the fluid phases. The estimation of transient heat flux using the conjugate gradient method (CGM) along with the differential adjoint equations has been carried out under nonthermal equilibrium conditions between two phases. Derivation of the adjoint differential equations in the case of nonthermal equilibrium and calculation of gradient function from coupled adjoint equations are presented in detail here. The transient wall heat flux imposed on the porous boundary is estimated using the aforementioned method, and results show that sensor locations and existing error in the measured data have important effects on the calculated heat flux. Nonetheless, accurate heat flux estimation is quite achievable.     

Computer-aided Prediction of the ZrO2 Nanoparticles’ Effects on Tensile Strength and Percentage of Water Absorption of Concrete Specimens

In the present paper,two models based on artificial neural networks and genetic programming for predicting split tensile strength and percentage of water absorption of concretes containing ZrO2 nanoparticles have been developed at different ages of curing.For building these models,training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted.The data used in the multilayer feed forward neural networks models and input variables of genetic programming models were arranged in a format of eight input parameters that cover the cement content,nanoparticle content,aggregate type,water content,the amount of superplasticizer,the type of curing medium,age of curing and number of testing try.According to these input parameters,in the neural networks and genetic programming models,the split tensile strength and percentage of water absorption values of concretes containing ZrO2 nanoparticles were predicted.The training and testing results in the neural network and genetic programming models have shown that two models have strong potential for predicting the split tensile strength and percentage of water absorption values of concretes containing ZrO2 nanoparticles.It has been found that neural network(NN) and gene expression programming(GEP) models will be valid within the ranges of variables.In neural networks model,as the training and testing ended when minimum error norm of network gained,the best results were obtained and in genetic programming model,when 4 genes were selected to construct the model,the best results were acquired.Although neural network have predicted better results,genetic programming is able to predict reasonable values with a simpler method rather than neural network.     

Computational modeling of strong and weak discontinuities using the extended finite element method

In this article, the extended finite element method is employed to solve problems, including weak and strong discontinuities. To this end, a level set framework is used to represent the discontinuities location, and the Heaviside and Branch function are included in the standard finite element method. The case of two arbitrary curved cracks is solved numerically and stress intensity factor (SIF) values at the crack tips are calculated based on the evaluation of the crack tip opening displacement. Afterwards, J-integral methodology is adopted to evaluate the SIFs for isotropic and anisotropic bi-material interface crack problems. Numerical results are verified with those presented in the literature.     

Utilization of Machine Learning Methods in Modeling Specific Heat Capacity of Nanofluids

Nanofluids are extensively applied in various heat transfer mediums for improving their heat transfer characteristics and hence their performance. Specific heat capacity of nanofluids, as one of the thermophysical properties, performs principal role in heat transfer of thermal mediums utilizing nanofluids. In this regard, different studies have been carried out to investigate the influential factors on nanofluids specific heat. Moreover, several regression models based on correlations or artificial intelligence have been developed for forecasting this property of nanofluids. In the current review paper, influential parameters on the specific heat capacity of nanofluids are introduced. Afterwards, the proposed models for their forecasting and modeling are proposed. According to the reviewed works, concentration and properties of solid structures in addition to temperature affect specific heat capacity to large extent and must be considered as inputs for the models. Moreover, by using other effective factors, the accuracy and comprehensive of the models can be modified. Finally, some suggestions are offered for the upcoming works in the relevant topics.