Evaluation of the moment-rotation curve of I beam-to-CFT column connection with endplate,using mechanical modeling

Through this study, a method is presented to estimate moment-rotation (M-) curve of I beam-to-the concrete filled tube (CFT) connections. This method is based on the components method in which components are analyzed and modeled as different mechanical models. These components are considered as springs with specific mechanical properties of stiffness, strength, and rigid bars. Their force-displacement relationship is assumed bi-linear and 3-linear. The connection behavior is assumed through assembling the stiffness of different components. In this study, connection of I beam-to-CFT column with bolted endplate connection under monotonic loading, which has been tested in the laboratory, is evaluated. It must be mentioned that those bolts which penetrated into the column depth have been used in order to create continuity between flanges of the column. The suggested method has been compared with the experimental test and the advanced finite element model in terms of stiffness, strength, and rotational capacity; moreover, its operation was satisfactory.     

Double-command fuzzy control of a nonlinear CSTR

In this research, double-command control of a nonlinear chemical system is addressed. The system is a stirred tank reactor; two flows of liquid with different concentrations enter the system through two valves and another flow exits the tank with a concentration between the two input concentrations. Fuzzy logic was employed to design a model-free double-command controller for this system in the simulation environment. In order to avoid output chattering and frequent change of control command (leading to frequent closing-opening of control valves, in practice) a damper rule is added to the fuzzy control system. A feedforward (steady state) control law is also derived from the nonlinear mathematical model of the system to be added to feedback (fuzzy) controller generating transient control command. The hybrid control system leads to a very smooth change of control input, which suits real applications. The proposed control system offers much lower error integral, control command change and processing time in comparison with neuro-predictive controllers.     

A novel online affine model identification of multivariable processes using adaptive neuro-fuzzy networks

This paper presents a novel systematic identification methodology for online affine modeling of multivariable processes using adaptive neuro-fuzzy networks. The proposed approach introduces an integrated procedure to simultaneously estimate a number of adaptive neuro-fuzzy networks with simple and compact dynamic structures to realize a multivariable affine model identification in real-time. A new fuzzy rule significance concept, based on a generic time-weighted rule activation record (WRAR), together with a measure of time-weighted root mean square (WRMS) error are incorporated to maintain efficient structural and parametric mechanisms for proper adaptation of the resulting neuro-fuzzy networks. An extended Kalman filter (EKF) algorithm is developed to adaptively adjust the neuro-fuzzy free parameters corresponding to the nearest created fuzzy rules. Extensive simulation test studies will be conducted to explore the capabilities of the proposed identification approach to adaptively develop online multivariable affine dynamic models for a highly nonlinear and time-varying continues stirred tank reactor (CSTR) and a highly nonlinear binary distillation column as two challenging benchmark problems.     

Band structure effect on the electron current oscillation in ultra-scaled GaSb Schottky MOSFET: tight-binding approach

This paper explores band structure effect on the quantum transport of a low-dimensional GaSb Schottky MOSFET (SBFET) for the implementation of III–V transistor with a low series resistance. Precise treatment of the full band structure is employed applying sp ³ d ⁵ s ^? tight-binding (TB) formalism. A remarkable distinction between the thickness dependent effective masses extracted from the TB and the bulk values imply that the quantum confinement modifies the device performance. Strong transverse confinement leads to the effective Schottky barrier height increment. Owing to the adequate enhanced Schottky barriers at low drain voltages, a double barrier gate modulated potential well is formed along the channel. The double barrier profile creates a longitudinal quantum confinement and induces drain current oscillation at low temperatures. Significant factors that may affect the current oscillation are thoroughly investigated. Current oscillation is gradually smoothed out as the gate length shrinks down in ultra scaled structure. The results in this paper are paving a way to clarify the feasibility of this device in nanoscale regime.     

H2-selective mixed matrix membranes modeling using ANFIS,PSO-ANFIS,GA-ANFIS

The novel contribution of the current study is to employ adaptive neuro-fuzzy inference system (ANFIS) for evaluation of H₂-selective mixed matrix membranes (MMMs) performance in various operational conditions. Initially, MMMs were prepared by incorporating zeolite 4A nanoparticles into polydimethylsiloxane (PDMS) and applied in gas permeation measurement. The gas permeability of CH₄, CO₂, C₃H₈ and H₂ was used for ANFIS modeling. In this manner, the H₂/gas selectivity as the output of the model was modeled to the variations of feed pressure, nanofiller contents and the kind of gas, which were defined as input (design) variables. The proposed method is based on the improvement of ANFIS with genetic algorithm (GA) and particle swarm optimization (PSO). The PSO and GA were applied to improve the ANFIS performance. To determine the efficiency of PSO-ANFIS, GA-ANFIS and ANFIS models, a statistical analysis was performed. The results revealed that the PSO-ANFIS model yields better prediction in comparison to two other methods so that root mean square error (RMSE) and coefficient of determination (R²) were obtained as 0.0135 and 0.9938, respectively. The RMSE and R² values for GA-ANFIS were 0.0320 and 0.9653, respectively, and for ANFIS model were 0.0256 and 0.9787, respectively.     

Kinetic Model for Invertase‐Induced Sucrose Hydrolysis: Initial Time Lag

The kinetics of sucrose hydrolysis by invertase was studied in order to find a comprehensive model for the reaction pathway and mechanism. First, three common models of Michaelis‐Menten (MM), substrate inhibition (S²), and substrate clusters' inhibition (S³(I)) were investigated. The third model was found to better predict the initial sucrose concentration. Then, the S³(I) model was modified to cover the remaining pathway (S³(II)). Finally, a new comprehensive model (S³(III)) was evaluated, which in addition to what is considered in the two previously mentioned models (S³(I) and S³(II)) also involved the initial time lag. The model predictions showed an excellent agreement with the experimental data. The mean absolute error for the MM model is significantly reduced for the S³(III) model.     

Energy production trend in Iran and its effect on sustainable development

The purpose of this study is to review the energy production trend from different energy resources in recent decades and its effect on sustainable energy production as one of the basic axis of sustainable development in Iran.Ninety nine percent of energy production in Iran comes from oil & gas and only 1% from renewable energy resources. Since Iran has very rich fossil energy resources, little attention has been paid to explore alternative ways of energy production. Majority of country's income is from oil & gas which put extra pressure on its natural resources. Continuing with the existing trend may lead to a path away from the goals of sustainable development, set for the country. Therefore, the sustainability study should be of interest to decision-makers.     

Modelling of multi-period multi-product production planning considering production routes

The multi-period multi-product (MPMP) production planning problems, generally, deal with matching production levels of individual products with fluctuated demands over planning horizon. The conventional MPMP optimisation models suffer from insufficient utilisation of available capacity of machines. This fallacy is due to inappropriate formulation of machine capacity and material handling constraints. In this study, a novel mathematical model is proposed to simultaneously optimise production quantities and provide information about managerial decisions such as subcontracting, carrying inventory/backordering, and also hiring/layoff personnel. The problem is then formulated as a mixed integer linear programming (MILP) model by applying appropriate linearisation of non-linear components. The objective is to minimise production costs comprising of production, storage, shortage, subcontracting costs and costs associated with hiring/dismissing labourers. Superiority of the proposed model over existing ones, has been initially evaluated by solving the case presented by Byrne and Bakir [Byrne, M.D. and Bakir, M.A., 1999. Production planning using a hybrid simulation-analytical approach. International Journal of Production Economics, 59 (1), 305–311], and then evaluated by comparing the results obtained from solving both the proposed and the conventional MPMP production planning models using a 100-randomly-generated-test-problem.     

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters. Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time. Besides, the selection of appropriate initial seeds can reduce the cluster’s inconsistency. In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm. For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds. Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm. The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively. Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed.     

Experimental and Numerical Investigation of a Novel Spiral Micromixer with Sinusoidal Channel Walls

A novel spiral micromixer with sinusoidal channel walls was designed to enhance the mixing index in the low to intermediate Reynolds number range (1 < Re < 100). To analyze the fluid flow, a set of numerical simulations were performed using the finite-difference method. The microchip was fabricated from polydimethylsiloxane, employing the soft-lithography technique. The degree of mixing was increased by 99.11 % when using the proposed micromixer, compared to 59.44 % for a simple spiral micromixer. The introduced microchannel drastically reduced the mixing length, increasing the mixing index of a 0.5-loop spiral-sinusoidal microchannel compared to that of the simple spiral microchannel with 1.5 loops. The mixing index of the 3-loop mixer was higher than that of the microchannel with 1.5 loops, and its pressure drop was increased.