Synthesis,characterization, and thermal aging behavior of HCl‐doped polyaniline/TRGO nanocomposites

In this article polyaniline (PANI) nanocomposites containing thermally reduced graphene oxide (TRGO) were synthesized and characterized before and after thermal aging. The nanocomposites were prepared through in situ oxidative polymerization of aniline in the presence of TRGO nanoplatelets. FTIR and Raman spectroscopies, XRD, FESEM, and electrical conductivity measurements were used to characterize synthesized materials. PANI/TRGO nanocomposites showed considerably higher electrical conductivity when compared to pure PANI, which was associated with the higher electrical conductivity of TRGO and increased crystallinity of PANI in the presence of TRGO. Pure PANI and PANI/TRGO nanocomposites were thermally aged at 70, 80, 90, and 100 °C. The results showed that the characteristic time of thermal aging process is higher for PANI/TRGO nanocomposites and increases with TRGO loading, which indicates better stability of conductivity during thermal aging process. On the other hand, the characteristic time of thermal aging reduced with aging temperature and a fast decrease was observed from 80 to 90 °C. Improved resistance over thermal aging can be attributed to the barrier effect of TRGO nanoplatelets to the dopant molecules, which retards conductivity degradation in the thermal aging process. Furthermore, TRGO increases PANI crystallinity and it can also prevent crystallinity reduction during thermal aging process. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 44635.     

A predictive controller based on dynamic matrix control for a non-minimum phase robot manipulator

In this paper a hybrid control strategy is presented based on Dynamic Matrix Control (DMC) and feedback linearization methods for designing a predictive controller of five bar linkage manipulator as a MIMO system (two inputs and two outputs). Analyzing the internal dynamic of robot shows the open loop system is unstable and non-minimum phase, so in order to apply the predictive controller, special modifications are needed. These modifications on non-minimum phase behavior are performed using feedback linearization procedure based on state space realization. The design objective is to track a desirable set point as well as time varying trajectories as a command references with globally asymptotical stabilization. The proposed controller is applied to nonlinear fully coupled model of the typical five bar linkage manipulator with non-minimum phase behavior. Simulation results show that the proposed controller has good efficiency. The step responses of system with and without feedback linearization process illustrated that the mentioned modification for stabilizing is performed properly. After applying the proposed predictive controller, the joint angle of robot tracks the reference input while another input acts as the disturbance and vice versa.     

Viability of Lactobacillus acidophilus in rice bran‐enriched stirred yoghurt and the physicochemical and sensory characteristics of product during refrigerated storage

This study was aimed at investigating the fortification of probiotic yoghurt with rice bran to increase nutritional properties of the product. The different levels of rice bran (0.3%, 0.6%, 0.9% and 1.2%) were incorporated into milk. The yoghurt samples were produced after pasteurisation, addition of starter culture and 1% Lactobacillus acidophilus suspension (6 × 10⁸ CFU mL^?1) and incubation. During sample storage in refrigerator, the viability of L. acidophilus, viscosity and physicochemical and sensory properties of product were investigated. Rice bran significantly increased the viability of L. acidophilus (P < 0.05). In addition, all probiotic yoghurts incorporating rice bran indicated higher viscosity and acidity and lower pH and syneresis compared to plain yoghurts. Furthermore, increments in rice bran incorporation levels resulted in a reduction in consumers' sample preferences. In general, the addition of rice bran at a suitable level could increase L. acidophilus viability and improve quality attributes of yoghurt.     

Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization

This paper presents a novel modified interactive honey bee mating optimization (IHBMO) base fuzzy stochastic long-term approach for determining optimum location and size of distributed energy resources (DERs). The Monte Carlo simulation method is used to model the uncertainties associated with long-term load forecasting. A proper combination of several objectives is considered in the objective function. Reduction of loss and power purchased from the electricity market, loss reduction in peak load level and reduction in voltage deviation are considered simultaneously as the objective functions. First, these objectives are fuzzified and designed to be comparable with each other. Then, they are introduced into an IHBMO algorithm in order to obtain the solution which maximizes the value of integrated objective function. The output power of DERs is scheduled for each load level. An enhanced economic model is also proposed to justify investment on DER. An IEEE 30-bus radial distribution test system is used to illustrate the effectiveness of the proposed method.     

Multi-wall carbon nanotubes supported molybdenyl acetylacetonate: Efficient and highly reusable catalysts for epoxidation of alkenes with tert-butyl hydroperoxide

Efficient epoxidation of olefins catalyzed by MoO₂(acac)₂ supported on amines functionalized MWCNTs is reported. The MWCNTs bearing carboxylic acid groups were modified with 2-aminophenol and 2-aminothiophenol. These amine–MWCNTs act as bidentate ligand for attachment of Mo catalyst. These catalysts were characterized by elemental analysis, scanning electron microscopy, FT-IR and diffuse reflectance UV–Vis spectroscopic methods. The prepared catalysts were used for efficient epoxidation of different alkenes such as cyclic and linear ones with tert-butyl hydroperoxide in refluxing 1,2-dichloroethane. These heterogeneous catalysts can be reused several times without significant loss of their catalytic activity.     

Development of a simulation-based optimisation for controlling operation allocation and material handling equipment selection in FMS

Flexible manufacturing system (FMS) is described as a set of computerised numerical controlled machines, input–output buffers interconnected by automated material handling devices. This paper develops a bi-objective operation allocation and material handling equipment selection problem in FMS with the aim of minimising the machine operation, material handling and machine setup costs and maximising the machine utilisation. The proposed model is solved by a modified chaotic ant swarm simulation based optimisation (CAS²O) while applying pre-selection and discrete recombination operators is surveyed a capable method to simulate different experiments of FMS problems. A test problem is selected from the literature to evaluate the performance of the proposed approach. The results validate the effectiveness of the proposed method to solve the FMS scheduling problem.     

Non-dominated ranked genetic algorithm for a multi-objective mixed-model assembly line sequencing problem

The increasing market demand for product variety forces manufacturers to design mixed-model assembly lines (MMAL) on which a variety of product models similar to product characteristics are assembled. This paper presents a method combining the new ranked based roulette wheel selection algorithm with Pareto-based population ranking algorithm, named non-dominated ranking genetic algorithm (NRGA) to a just-in-time (JIT) sequencing problem when two objectives are considered simultaneously. The two objectives are minimisation the number of setups and variation of production rates. This type of problem is NP-hard. Various operators and parameters of the proposed algorithm are reviewed to calibrate the algorithm by means of the Taguchi method. The solutions obtained via NRGA are compared against solutions obtained via total enumeration (TE) scheme in small problems and also against four other search heuristics in small, medium and large problems. Experimental results show that the proposed algorithm is competitive with these other algorithms in terms of quality and diversity of solutions.     

Graph-neural network approach in cellular manufacturing on the basis of a binary system

In the past several years, many studies have been carried out on cellular manufacturing. Group technology is a manufacturing philosophy in which similar parts are identified and grouped together to take advantage of their similarities in manufacturing and design. The main problem in the development of cellular manufacturing is that of cell formation. In this paper, a graph-neural network approach is given for cell formation problems in group technology. Effort has been made to develop an algorithm that is more reliable than conventional methods. A graph-neural network has the advantages of fast computation and the ability to handle large scale industrial problems without the assumption of any parameter and the least exceptional elements in the presence of bottleneck machines and/or bottleneck parts. Two examples from the literature have been solved to demonstrate the advantages of the algorithm.     

A DEA approach for comparing solution efficiency in U-line balancing problem using goal programming

Line balancing problem plays an important role in the decision making process to increase efficiency and productivity. Recently, U-shaped layout in many production lines has replaced the traditional straight line layout using just-in-time concept. Here, we propose a model, using multi-objective decision making approach to the U-shaped line balancing problem, to offer enhanced decision maker flexibility, by allowing for conflicting goals. The assembly line operation efficiency is the most significant aim in our study, and this efficiency relates to management of resources and the solution of line balancing problem. First, the U-shaped line balancing problem is solved considering the model's goals. Then, the index function of assembly line balancing is determined and the efficiencies of the optimal solution outputs are evaluated using data envelopment analysis (DEA). Finally, the discrimination weakness and distribution of illogical weight in simple DEA models are resolved using a mixed method.