02787nas a2200265   4500008004100000022001400041245012200055210006900177260010600246300001400352490000700366520179500373653002202168653001802190653003002208653001102238653002002249653001502269653002202284100002502306700002702331700001602358700002102374856012602395       2008                        eng d  a0009-250900aIdentification and characterization of flow structures in chemical process equipment using multiresolution techniques0 aIdentification and characterization of flow structures in chemic  aTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLANDbPERGAMON-ELSEVIER SCIENCE LTDcNOV  a5330-53460 v633 a<p>Planar information of velocity from 2D particle image velocimetry (PIV) and large eddy simulation (LES) data have been studied using multiresolution wavelet transform (WT) formalisms, i.e., discrete and continuous WT. Identification of dominant energy containing structures with their characterization in terms of fractal spectra have been carried out for industrially important equipment exhibiting turbulent behavior. These include annular centrifugal contactor, jet loop reactor, ultrasound reactor, channel flow, stirred tank and bubble column reactor. The characterization of their dynamics based on denoising the data and studying the local energy along the WT scales show sensitive variation and this helps in identifying the size and shape of structures. A dependency is seen between mixing time and the higher order moments of length scale distribution, viz., skewness and kurtosis and a generalized correlation has been built up for important types of equipment and associated flow parameters. The correlation is not only based on the knowledge of reactor geometry and operating conditions but also on the flow structures via their statistical parameters. Wavelet transform modulus maxima (WTMM) methodology has been used to study the evolution of structures and their interaction in a reduced dimensionality by evaluating the fractal spectra. Classification studies have been carried out using principal component analysis (PCA) of the fractal spectra. The results obtained show clear classes for the six types of equipments and delineate regimes to obtain benchmark patterns of flow hydrodynamics based on PCA co-ordinates. This methodology offers a generalized way for the optimal design and operation of different types of reactors. (c) 2008 Published by Elsevier Ltd .</p>10aChemical reactors10ahydrodynamics10aLength scale distribution10amixing10aModel reduction10aTurbulence10aWavelet transform1 aDeshpande, Sagar, S.1 aJoshi, Jyeshtharaj, B.1 aKumar, Ravi1 aKulkarni, B., D.  uhttps://library.ncl.res.in/content/identification-and-characterization-flow-structures-chemical-process-equipment-using-002450nas a2200157   4500008004100000245014900041210006900190260000800259300001100267490000800278520181500286100002602101700001602127700002102143856012802164       2016                        eng d00aInverse problem studies of biochemical systems with structure identification of S-systems by embedding training functions in a genetic algorithm0 aInverse problem studies of biochemical systems with structure id  cMAY  a93-1060 v2753 aAn efficient inverse problem approach for parameter estimation, state and structure identification from dynamic data by embedding training functions in a genetic algorithm methodology (ETFGA) is proposed for nonlinear dynamical biosystems using S-system canonical models. Use of multiple shooting and decomposition approach as training functions has been shown for handling of noisy datasets and computational efficiency in studying the inverse problem. The advantages of the methodology are brought out systematically by studying it for three biochemical model systems of interest. By studying a small-scale gene regulatory system described by a S-system model, the first example demonstrates the use of ETFGA for the multifold aims of the inverse problem. The estimation of a large number of parameters with simultaneous state and network identification is shown by training a generalized S-system canonical model with noisy datasets. The results of this study bring out the superior performance of ETFGA on comparison with other metaheuristic approaches. The second example studies the regulation of cAMP oscillations in Dictyostelium cells now assuming limited availability of noisy data. Here, flexibility of the approach to incorporate partial system information in the identification process is shown and its effect on accuracy and predictive ability of the estimated model are studied. The third example studies the phenomenological toy model of the regulation of circadian oscillations in Drosophila that follows rate laws different from S-system power-law. For the limited noisy data, using a priori information about properties of the system, we could estimate an alternate S-system model that showed robust oscillatory behavior with predictive abilities. (C) 2016 Elsevier Inc. All rights reserved.1 aSarode, Ketan, Dinkar1 aKumar, Ravi1 aKulkarni, B., D.  uhttps://library.ncl.res.in/content/inverse-problem-studies-biochemical-systems-structure-identification-s-systems-embedding00621nas a2200157   4500008004100000022001400041245012200055210006900177260000800246300001000254490000700264100002800271700002300299700001600322856012500338       2022                        eng d  a0003-021X00aIn situ direct transesterification process optimization for biodiesel production from aspergillus terreus wet biomass0 aIn situ direct transesterification process optimization for biod  cOCT  a52-530 v991 aBed, Rashmi, Karamchand1 aRavi-Kumar, Ameeta1 aKumar, Ravi  uhttps://library.ncl.res.in/content/situ-direct-transesterification-process-optimization-biodiesel-production-aspergillus