Global optimization of multicomponent distillation configurations: 1. Need for a reliable global optimization algorithm

Nonazeotropic multicomponent mixtures are often separated into products by distillation configurations containing multiple distillation columns. One method of calculating the minimum vapor duty of a configuration is to sequentially calculate the minimum vapor duty of each mixture as it is split into two streams within a given column starting from the feed column. The other method simultaneously manipulates all the splits to yield the overall minimum vapor duty of the entire configuration. Of these two methods, the sequential minimization is attractive as it can be analytically solved. However, through extensive computations, we find that the sequential minimization method is not a valid substitute for the simultaneous minimization method. As the number of components in the feed increases, the fraction of the basic configurations for which sequential method yields a reasonable estimate decreases rapidly, thereby emphasizing the need for a more robust and reliable global optimization algorithm. © 2012 American Institute of Chemical Engineers AIChE J, 59: 971–981, 2013     

Green fluorescent organic nanoparticles by self-assembly induced enhanced emission of a naphthalene diimide bolaamphiphile

Naphthalene diimide (NDI) bolaamphiphilic molecules (1) self-assemble in water to form organic nanoparticles, which exhibit self-assembly induced preassociated excimer formation and hence an enhanced green fluorescence.     

Hierarchical description of deformation in block copolymer TPEs

We report on the deformation behavior of commercially relevant lamellar and cylindrical tri-block copolymers poly (styrene-b-ethylene-co-butylene-b-styrene) (SEBS) with two different compositions. The structural changes that occur at various length scales have been studied using a simultaneous small- and wide-angle X-ray scattering (SAXS/WAXS) during uni-axial tensile deformation of the polygrain samples. SAXS provides information about changes that occur upon deforming the glassy cylindrical or lamellar PS domains. WAXS, on the other hand, is sensitive to the crystallographic structure of the rubbery mid-block. Deformation calorimetry has been used to determine the energetics involved. The combined results from the various techniques indicate that the deformation takes place in three stages. First, at small strains, dilation occurs in the rubbery phase. At intermediate strains, the hard lamellar or cylindrical domains undergo micro-buckling, which is associated with a downturn in the stress–strain curve. Finally, we interpret that at higher strains, the bent lamellar/cylindrical domains rotate in the stretching direction resulting in a significant shear on the rubbery mid-block. This in turn leads to strain-induced crystallization in these materials. Although we could not prove it by WAXS, deformation calorimetry (which is more sensitive than the WAXS) was utilized to show its presence.     

Development and implementation of a generic pasture growth model (CLASS PGM)

This paper describes the development, testing and implementation of a generic pasture growth model (CLASS PGM) which can be used to simulate growth of composite pasture types of multiple species that may be summer or winter active, perennial or annual. The model includes carbon assimilation through photosynthesis and respiration followed by tissue growth, turnover and senescence. Environmental conditions as well as soil water, nutrient and salinity status influences pasture growth and tissue dynamics. The model allows the user to simulate a range of grazing management strategies. Concepts and theoretical basis of the pasture growth model is based upon the detailed technical report on pasture and crop growth modules (Johnson, 2003). For water balance computations, PGM is internally linked to the Richards’ equation - based hydrology tool, Unsaturated Moisture Movement Model U3M-1D (Vaze et al., 2004b) PGM is supported by a windows based user friendly graphical users interface (GUI). The model can be downloaded free from the Catchment Modelling Toolkit website supported by the eWater Cooperative Research Centre (http://www.toolkit.net.au/class). This paper gives an overview of the model structure, model inputs and outputs and the soils related inbuilt database. Results from model validation using long term observed data for soil moisture, pasture herbage mass and grazing for a grazing experiment at Wagga Wagga, New South Wales, Australia are discussed. When compared with herbage mass and soil moisture data from the experiment, PGM was found to adequately simulate the patterns and amplitudes of pasture growth and soil moisture recorded in the experiment.     

Fuzzy filtering for robust bioconcentration factor modelling

This study introduces a fuzzy filtering based technique for rendering robustness to the modelling methods. We consider a case study dealing with the development of a model for predicting the bioconcentration factor (BCF) of chemicals. The conventional neural/fuzzy BCF models, due to the involved uncertainties, may have a poor generalization performance (i.e. poor prediction performance for new chemicals). Our approach to improve the generalization performance of neural/fuzzy BCF models consists of (1) exploiting a fuzzy filter to filter out the uncertainties from the modelling problem, (2) utilizing the information about uncertainties, being provided by the fuzzy filter, for the identification of robust BCF models with an increased generalization performance. The approach has been illustrated with a data set of 511 chemicals (Dimitrov, S., Dimitrova, N., Parkerton, T., Comber, M., Bonnell, M., Mekenyan, O., 2005. Base-line model for identifying the bioaccumulation potential of chemicals. SAR and QSAR in Environmental Research 16 (6), 531–554) taking different types of neural/fuzzy modelling techniques.     

Secured data offloading using reinforcement learning and Markov decision process in mobile edge computing

Mobile Internet services are developing rapidly for several applications based on computational ability such as augmented/virtual reality, vehicular networks, etc. The mobile terminals are enabled using mobile edge computing (MEC) for offloading the task at the edge of the cellular networks, but offloading is still a challenging issue due to the dynamism, and uncertainty of upcoming IoT requests and wireless channel state. Moreover, securing the offloading data enhanced the challenges of computational complexities and required a secure and efficient offloading technique. To tackle the mentioned issues, a reinforcement learning-based Markov decision process offloading model is proposed that optimized energy efficiency, and mobile users' time by considering the constrained computation of IoT devices, moreover guarantees efficient resource sharing among multiple users. An advanced encryption standard is employed in this work to fulfil the requirements of data security. The simulation outputs reveal that the proposed approach surpasses the existing baseline models for offloading overhead and service cost QoS parameters ensuring secure data offloading.     

A numerical study of singularly perturbed generalized Burgers-Huxley equation using three-step Taylor-Galerkin method

In the present work, a numerical study has been carried out for the singularly perturbed generalized Burgers-Huxley equation using a three-step Taylor-Galerkin finite element method. A Burgers-Huxley equation represents the traveling wave phenomena. In singular perturbed problems, a very small positive parameter, ?, called the singular perturbation parameter is multiplied with the highest order derivative term. As this parameter tends towards zero, the problem exhibits boundary layers. The traditional methods fail to capture the boundary layers when ? becomes very small. In this paper a three-step Taylor-Galerkin finite element method is used to capture the boundary layers. The method is third-order accurate and has inbuilt upwinding. Stability analysis has been carried out and the numerical results show that the method is efficient in capturing the boundary layers.     

Measurement of Ambient Ammonia over the National Capital Region of Delhi,India

Mixing ratio of ambient ammonia (NH₃) was measured at various locations of the National Capital Region (NCR) of Delhi, India using a NH₃-analyzer during January 2010 to June 2012 in campaign mode. The present study has been carried out on campaign based measurement of mixing ratios of NH₃ and NO _x for short period of time over the NCR of Delhi represent the indicative values over the region. The average mixing ratio of ambient NH₃ was 20.9 ± 1.6 ppb during the period. The maximum average mixing ratio of ambient NH₃ (28.8 ± 3.0 ppb) was recorded in an industrial area surrounded by intensive vehicular traffic followed by an agricultural farm (27.5 ± 2.1 ppb), whereas the minimum (6.4 ± 1.2 ppb) was recorded in the semi-urban area. The diurnal trend of NH₃ depended on the ambient temperature at most of the sites and was affected by wind direction. Ambient NH₃ was correlated with the NO _x mixing ratio suggesting that the vehicular emission may be one of the sources of ambient NH₃ in the NCR of Delhi. However, long-term measurements of ambient NH₃ and their precursors will lead to seasonal variation of source apportionment over the NCR, Delhi, India.     

Autonomic cloud resource provisioning and scheduling using meta-heuristic algorithm

We investigate that resource provisioning and scheduling is a prominent problem due to heterogeneity as well as dispersion of cloud resources. Cloud service providers are building more and more datacenters due to demand of high computational power which is a serious threat to environment in terms of energy requirement. To overcome these issues, we need an efficient meta-heuristic technique that allocates applications among the virtual machines fairly and optimizes the quality of services (QoS) parameters to meet the end user objectives. Binary particle swarm optimization (BPSO) is used to solve real-world discrete optimization problems but simple BPSO does not provide optimal solution due to improper behavior of transfer function. To overcome this problem, we have modified transfer function of binary PSO that provides exploration and exploitation capability in better way and optimize various QoS parameters such as makespan time, energy consumption, and execution cost. The computational results demonstrate that modified transfer function-based BPSO algorithm is more efficient and outperform in comparison with other baseline algorithm over various synthetic datasets.