Microencapsulation of 1-methylimidazole using solid epoxy resin: study on microcapsule residence time and properties of the system

In this study for the first time 1-methylimidazole was microencapsulated successfully by solid epoxy resin using solvent evaporation method. Fourier transform infrared spectroscopy, scanning electron microscopy, thermal gravimetric and differential scanning calorimetry were used for characterization of microcapsule and epoxy resin/microcapsule systems. The results revealed that although the solid epoxy shell was in contact with imidazole curing agent for a long time, it still remained as a thermoplastic. The chain length of the solid epoxy resin was grown in anionic polymerization process which led to an increase in the melting temperature from 64 to 78 °C. On the other hand, all epoxy functional groups of the shell did not participate in the curing reaction of resin with core curing agent of the microcapsule. The results showed that by increasing the residence time of the microcapsules, the number of epoxy groups of liquid epoxy decreased slightly but the rate of complex viscosity increase or the rate of curing reaction was increased and the cured epoxy system exhibited a single-phase morphology. On the other hand, in the presence of microcapsules the curing reaction of epoxy resin was successfully carried out and the curing temperature and the onset of viscosity increase or gel time at 120 °C were not more or less affected by sufficiently long contact time of epoxy and microcapsule.     

Computational Fluid Dynamics Study of a Styrene Polymerization Reactor

The computational fluid dynamics (CFD) approach was adopted to simulate benzoyl peroxide (BPO)‐initiated styrene polymerization in a laboratory‐scale continuous stirred‐tank reactor (CSTR). The CFD results revealed the effects of non‐homogeneity and the short‐circuiting of the unreacted styrene and initiator on the reactor performance. The study also investigated the effects of the impeller speed and the residence time on the conversion and the flow behavior of the system. The CFD simulation showed that intense mixing remained confined to a small region near the impeller. With increasing impeller speed, it was found that the perfectly mixed region near the impeller expanded, thus reducing non‐homogeneity. Different contours were generated and exhibited the effect of the mixing parameters on the propagation rate and styrene conversion. The monomer and initiator conversions predicted with the CFD model were compared to those obtained with a CSTR model. The CFD model accounts for the non‐ideality behavior of the polymerization reactor, and hence conversion predictions are more realistic.     

Large deformation and mechanics of flexible isotropic membrane ballooning in three dimensions by differential quadrature method

This paper presents a computationally efficient and accurate new methodology in the differential quadrature analysis of structural mechanics for flexible membranes ballooning in three dimensions under a negative air pressure differential. The differential quadrature method is employed to discretize the resulting equations in the axial direction as well as for the solution procedure. The weighting coefficients employed are not exclusive, and any accurate and efficient method such as the generalized differential quadrature method may be used to produce the methods weighting coefficients. A second-order paraboloid of revolution is assumed to describe the ballooning shape. This study asserts the accuracy, convergency, and efficiency of the methodology by solving some typical stability, straining analysis membrane problems, and comparing the results with those of the exact solutions and/or those of physical tests.     

Tomography images to analyze the deformation of the cavern in the continuous‐flow mixing of non‐Newtonian fluids

Tomography, an efficient nonintrusive technique, was employed to visualize the flow in continuous‐flow mixing and to measure the cavern volume (V_c) in batch mixing. This study has demonstrated an efficient method for flow visualization in the continuous‐flow mixing of opaque fluids using two‐dimensional (2‐D) and 3‐D tomograms. The main objective of this study was to explore the effects of four inlet‐outlet configurations, fluid rheology (0.5–1.5% xanthan gum concentration), high‐velocity jet (0.317–1.660 m s^?1), and feed flow rate (5.3 × 10^?5?2.36 × 10^?4 m³ s^?1) on the deformation of the cavern. Dynamic tests were also performed to estimate the fully mixed volume (V_{fully mixed}) for the RT, A310, and 3AM impellers in a continuous‐flow mixing system, and it was found that V_{fully mixed} was greater than V_c. Incorporating the findings of this study into the design criteria will minimize the extent of nonideal flows in the continuous‐flow mixing of complex fluids and eventually improve the quality of end‐products. © 2013 American Institute of Chemical Engineers AIChE J, 60: 315–331, 2014     

Vector optimization of laser solid freeform fabrication system using a hierarchical mutable smart bee-fuzzy inference system and hybrid NSGA-II/self-organizing map

The purpose of current investigation is to develop a robust intelligent framework to achieve efficient and reliable operating process parameters for laser solid freeform fabrication (LSFF) process as a recent and ongoing topic of investigation. Firstly, based on mutable smart bee algorithm (MSBA) and fuzzy inference system (FIS) two models are developed to identify the clad hight (deposited layer thickness) and the melt pool depth as functions of scanning speed, laser power and mass powder. Using the obtained model, the well-known multiobjective evolutionary algorithm called non-dominated sorting genetic algorithm (NSGA-II) is used for multi-criterion optimization of LSFF process. According to the available reported information and also the author’s experiments, it is observed that the obtained Pareto front is not justifiable since it fails to cover the entire Pareto hyper-volume due to the lack of intensified exploration. To tackle this deficiency, authors execute a post optimization process through utilizing a competitive unsupervised machine learning approach known as self-organizing map (SOM) with cubic spatial topology. Achieved results indicate that this grid based network is capable of enhancing the intensification of Pareto solutions since its synaptic weights successfully imitate the characteristics of non-dominated solutions (optimal values of mass powder, laser power and scanning speed). For extracting the corresponding objective functions of these non-dominated synaptic weights, MSBA–FIS is used again to map the operating parameters to objective functions space. After the termination of abovementioned procedures, a valuable archive, containing a set of non-dominated solutions, is obtained which lets the authors to make a deliberate engineering trade-off. Simulation experiments reveal that the proposed intelligent framework is highly capable to cope with complex engineering systems. Besides, it is observed that MSBA is more efficient in evolving the structure of hierarchical fuzzy inference system in comparison with classic hierarchical GA-FIS model. This rises from the simple structure of MSBA that turns it into a fast and robust algorithm for handling constraint distributed systems (i.e. hierarchical FIS in current investigation). The obtained results also indicate that the introduced intelligent framework is applicable for optimal design of complex engineering systems where there exists no analytical formulation that describes the phenomenon as well as information of optimal operating parameters.     

The Effect of Operating Conditions and Design Parameters on the Dynamic Behaviour of Agitated Pulp Stock Chests

Agitated pulp chests provide attenuation of high‐frequency disturbances in pulp properties ahead of many pulping and papermaking unit operations. The effect of process and design variables on the extent of non‐ideal flows was examined in a laboratory‐scale pulp chest. Dynamic tests were made using the frequency‐modulated random binary input of a brine solution with the pulp feed to determine the magnitude of non‐ideal flows (channeling, recirculation and stagnant zones). Results showed that the degree of upset attenuation is a function of impeller momentum flux and fibre mass concentration and that existing design criteria underpredict the power needed to produce a fully mixed system.     

Chaos oscillator differential search combined with Pontryagin’s minimum principle for simultaneous power management and component sizing of PHEVs

Over the past decade, plug-in hybrid electric vehicles (PHEVs) have found a good reputation in the automotive industry due to the fact that they neatly satisfy the existing tight environmental regulations and fuel economy requirements. Recently, there has been more interest in the design optimization of the PHEV powertrains to improve their operational characteristics to the maximum possible extent. The PHEV powertrains are complicated systems and include different controllers and components which should operate corporately to guarantee the acceptable performance of the vehicle. The reported investigations indicate that improving the performance of PHEVs is a very arduous task because both control strategies and component sizes should be optimized in tandem; however, in most of the previous studies, the focus has been on improving one of the above-mentioned aspects, which does not result in the most efficient design. The main goal of the current study is to take advantage of a bi-level optimization framework which combines the optimizations of both powertrain component sizes and power management controller for a specific PHEV, namely 2012 Toyota plug-in Prius. The bi-level optimizer comprises a chaos-enhanced differential evolutionary algorithm, which is in charge of the component sizing, and a classical optimal control approach based on the Pontryagin’s minimum principle, which optimizes the vehicle power management strategy. A high-fidelity model of the vehicle is developed in the Autonomie software. This high-fidelity model is used to identify the parameters of a reduced model representing the vehicle dynamics by means of the homotopy analysis method, and the resulting model is then employed for the optimization procedure. The results of the numerical experiments indicate that by considering both component sizing and control strategy optimization, a very powerful tool is developed which can significantly improve the total fuel cost (F _C ), acceleration time (T _acc ), and battery state of charge (SOC) trajectory of the vehicle.     

A novel unsupervised classification approach for network anomaly detection by k-Means clustering and ID3 decision tree learning methods

This paper presents a novel host-based combinatorial method based on k-Means clustering and ID3 decision tree learning algorithms for unsupervised classification of anomalous and normal activities in computer network ARP traffic. The k-Means clustering method is first applied to the normal training instances to partition it into k clusters using Euclidean distance similarity. An ID3 decision tree is constructed on each cluster. Anomaly scores from the k-Means clustering algorithm and decisions of the ID3 decision trees are extracted. A special algorithm is used to combine results of the two algorithms and obtain final anomaly score values. The threshold rule is applied for making the decision on the test instance normality. Experiments are performed on captured network ARP traffic. Some anomaly criteria has been defined and applied to the captured ARP traffic to generate normal training instances. Performance of the proposed approach is evaluated using five defined measures and empirically compared with the performance of individual k-Means clustering and ID3 decision tree classification algorithms and the other proposed approaches based on Markovian chains and stochastic learning automata. Experimental results show that the proposed approach has specificity and positive predictive value of as high as 96 and 98%, respectively.     

A Receding Horizon Sliding Controller for Automotive Engine Coldstart: Design and Hardware‐in‐the‐Loop Testing With an Echo State Network High‐Fidelity Model

The aim of the current study is to probe the potential of receding horizon sliding control (RHSC) technique for reducing the coldstart hydrocarbon (HC) emissions of automotive spark‐ignited (SI) engines. The RHSC approach incorporates the potentials of sliding control (SC) and nonlinear model predictive control (NMPC) to employ the future information of the considered engine to keep the system's trajectories close to a stable manifold. To calculate the control commands, the authors adopt an efficient optimization technique, known as the multivariate quadratic fit sectioning algorithm (MQFSA), and also, define three different objective functions, based on l₁, l₂, and l_∞ norms. To demonstrate the efficacy of RHSC controller, its performance is compared with two other well‐known controllers extracted from the literature, namely NMPC and Pontryagin's minimum principle (PMP)‐based controllers. Through numerical simulations for three distinctive operating conditions, it is demonstrated that the RHSC controller is very effective for reducing the total tailpipe HC emissions over the coldstart period of the considered engine system. Moreover, by conducting a hardware‐in‐the‐loop (HIL) test using an echo state network high‐fidelity model, it is indicated that the computational speed of calculating control commands is fast enough to enable RHSC to be used for real‐time implementations in practice.