Exploring the Influence of Methylene Diphenyl Diisocyanate as a Modifier for Ethylene Vinyl Acetate/Thermoplastic Polyurethane Blends

Ethylene vinyl acetate (EVA)/thermoplastic polyurethane (TPU) blend at various blend ratios has been modified via reactive processing with 4,4′-methylene diphenyl diisocyanate (MDI). Modification of the blends with even small amount of MDI shows significant improvement in physico-mechanical properties for EVA/TPU 50/50 and 30/70 blends, and it is also supported by the superior melt rheological behavior and dramatic improvement in oil resistance property. After the treatment of electron beam (dose range: 50–150?kGy), radiation crosslinked EVA/TPU (30:70) blend reveals further improvement in various properties. This particular material can find potential application as cable sheathing component.     

Melt Rheological Behavior and Creep Response of EVA/TPU Blends: Exploring the Effect of Electron Beam Irradiation and Peroxide Cross-linking

The present study focuses on the variation of the melt rheological characteristics and the creep behavior of both electron beam-cross-linked and peroxide-cured ethylene vinyl acetate/thermoplastic polyurethane blends. The variation of complex viscosity, complex modulus, storage modulus, and loss modulus was evaluated over a wide range of frequency and strain amplitude using rubber process analyzer and the effect of radiation dose and peroxide concentration was investigated in detail. The creep study using dynamic mechanical analyzer shows that the creep behavior of the blends significantly improves after cross-linking and the creep compliance gradually decreases with the increasing radiation dose and peroxide content. An attempt was also made to pursue a comparative rheological and creep study among the peroxide-cured, electron beam-cross-linked and the coagent-treated dynamically vulcanized samples.     

High‐performance electromagnetic interference shielding material based on an effective mixing protocol

A facile and economic method is developed for the fabrication of new lightweight materials with high electromagnetic interference (EMI) shielding performance, good mechanical properties and low electrical percolation threshold through melt mixing. Electrical properties, DC conductivity, EMI shielding performance and mechanical properties of poly(trimethylene terephthalate) (PTT)/multiwalled carbon nanotube (MWCNT) nanocomposites with varying filler loading of MWCNTs were investigated. High‐resolution transmission electron microscopy was used to determine the distribution of MWCNTs in the PTT matrix. The newly developed nanocomposites show excellent dielectric and EMI shielding properties. Theoretical electrical percolation threshold was achieved at 0.21 wt% loading of MWCNTs, due to the high aspect ratio and the three‐dimensional network formation of MWCNTs. Experimental DC conductivity values were compared with those of theoretical models such as the Voet, Bueche and Scarisbrick models, which showed good agreement. The PTT/3% MWCNT composite showed an EMI shielding value of ～38 dB (99.99% attenuation) with a sample thickness of 2 mm. Power balance was used to determine the actual contribution of reflection, absorption and transmission loss to the total EMI shielding value. The nanocomposites showed good tensile and impact properties and the composite with 2% MWCNTs exhibited an improvement in tensile strength of as much as 96%. © 2018 Society of Chemical Industry     

江苏德邦化工集团有限公司，江苏连云港 222023

1 简介 印度第一个纯碱厂始建于1932年,位于印度西部古吉拉特邦的Dhrangadhra,装置能力为50t/d。 作为基础无机化工产品,因为整个印度无天然碱矿,全部产品--包括轻灰和重灰只能以合成法生产。     

Effective parameter estimation within a multi-dimensional population balance model framework

This study considers optimization problems with multi-dimensional population balance models embedded. The objective function is formulated as a least-squares problem, minimizing the difference between target data and simulated model output and the goal is to find model parameter values that best fit the data. Results show that derivative-free methods, such as the Nelder–Mead simplex method, fail to converge to an optimal solution. A similar result was obtained with gradient-based methods such as BFGS, quasi-Newton, Newton, Gauss–Newton, Levenberg–Marquardt and SQP, and with a stochastic genetic algorithm. It was hypothesized that three main issues could contribute to these convergence failures: (1) gradients were calculated based on finite differences, and as a result of improper step size determination, the numerical error could be prohibitive resulting in inaccurate derivative information, (2) the parameters may not be identifiable and (3) numerical instability could occur during the course of optimization. To circumvent these issues, this work addresses the calculation of derivative information based on automatic differentiation and sensitivity analysis to ensure increased accuracy. Issues such as parameter identifiability are also dealt with by analyzing an accurate Fisher information matrix. Given the computational burden in calculating accurate Jacobians and Hessians, compounded by the potential nonsmoothness introduced into the objective function as a result of granule nucleation, other optimization strategies may be warranted and this work addresses those accordingly. Overall, by systematically assessing the problem formulation and mechanisms, the results show that substantial improvements in convergence can be achieved by utilizing appropriate optimization techniques, thus leading to more successful and optimal parameter estimation.     

Modeling of key reaction pathways: Zeolite catalyzed alkylation processes

A six lump kinetic model that considers the key reactions for the zeolite catalyzed alkylation process is presented. The influence of different reactions and rate limiting steps on reactor performance is examined by coupling an appropriate reactor model that accounts for different back-mixing on reactor scale, with a zeolite particle model which accounts for the diffusion inside the zeolite pore, the alkylation reaction, and zeolite deactivation. Model predictions are compared with experimental results and lead to conclusions that hydride transfer and oligomerization reactions are the key kinetic steps affecting the overall performance of zeolite catalyzed alkylation processes. It is suggested that higher alkylate yield and longer zeolite activity are achieved by increasing the intrinsic hydride transfer rate and the ratio of feed isobutane to n-butene (P/O) concentration. For a given P/O feed ratio, achieving close to plug flow for isobutane and high back-mixing for n-butene further enhances local P/O ratio and yield. Furthermore, optimal zeolite catalyst design should consider the egg shell type of Brønsted acid site distribution and a lower silicon to alumina (Si/Al) ratio.     

Tapered element oscillating microbalance (TEOM) studies of isobutane,n‐butane and propane sorption in β‐ and Y‐zeolites

A TEOM is used to elucidate the adsorption/desorption characteristics of alkylation reactants on USY‐ and β‐zeolites. Equilibrium adsorption isotherms were obtained on USY‐ and β‐zeolites using n‐butane, isobutane and propane as proxy reactant molecules (T = 303–398 K, adsorbate partial pressure 0–1.2 bar). Analysis of the transient adsorption/desorption profiles of these molecules from either a bed of the zeolite or pelletized particles of the crystals (with mean size < 1 μm) demonstrate that diffusion in the secondary meso‐/macroporous structure formed in the packing or the pellets controls the overall sorption rates. The experimental adsorption/desorption profiles from the pelletized zeolites were regressed with available mathematical models to obtain effective meso‐/macropore diffusivities for reactant molecules, and nearly perfect fits of the experimental and the modeled profiles. Taking into account the dead volume in the system, a criterion for reliable measurements of either micropore or mesopore diffusivities by the TEOM technique is derived. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

A novel adaptive sampling based methodology for feasible region identification of compute intensive models using artificial neural network

Identification of feasible region of operations in multivariate processes is a problem of interest in several fields. This is particularly challenging when the process model is black-box in nature and/or is computationally expensive, as analytical solutions are not available and the number of possible model evaluations is limited. An efficient methodology is required to identify samples where the model is evaluated for developing a computationally efficient surrogate model. In this work, an artificial neural network based surrogate model is proposed which is integrated with a statistical-based approach (Jack-knifing) to estimate the variance of the surrogate model prediction. This allows implementation of an adaptive sampling approach where new samples are identified close to the feasible region boundary or in regions of high prediction uncertainty. The proposed approach performs better than a previously published kriging based method for different dimensionality case studies.     

FORCED CONVECTION HEAT TRANSFER OF INTERACTING SPHERES

A basic understanding of the highly nonlinear interaction effects of closely spaced spheres on fluid mechanics and heat transfer parameters is important for improving the design of a variety of thermal fluid-particle systems. Fluid flow patterns, isotherms, as well as Nusselt number distributions and extended correlations are presented for steady laminar axisym-metric flow past a linear array of three spheres. A finite-element method is used to solve the complete Navier-Stokes and heat transfer equations for the system parameter ranges 1 Re_d  200 (Reynolds number) 2 d_ij10 ( particle spacings)and 0. 1  prRe_d 2000 (Piclet number). The verified computer simulation results indicate that convection heat transfer parameters of interacting spherical particles deviate significantly from solitary spheres for all Reynolds and Piclet numbers.     

Flexible Hardware Acceleration for Instruction-Grain Lifeguards

Instruction-grain lifeguards monitor executing programs at the granularity of individual instructions to quickly detect bugs and security attacks, but their fine-grain nature incurs high monitoring overheads. This article identifies three common sources of these overheads and proposes three techniques that together constitute a general-purpose hardware acceleration framework for lifeguards.