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Role of biosorbent ficus religiosa leaf powder (frlp) in chemical speciation of chromium: a green method for separation

100%

Goyal P. , Srivastava S.

tom Vol. 34, no. 4

35-45

The present study explores the unexploitcd sorption property of the unmodified Ficus religiosa leaf powder (FRLP) for decontamination and a possible method of separation of environmentally important two oxidation states of chromium (Cr(lII) and Cr(VI)) from aqueous media. Sorption studies using standard practices were carried out in batch experiments as functions of biomass dosage, metal concentration, contact time, particle size and pH. Sorption studies result into the standardization of optimum conditions for the removal of Cr(III) 82.47% and Cr(VI) 88.23% as follows: biomass dosage (4.0 g), initial metal concentration in the aqueous system (Cr(III) 25 mg-dm-1, Cr(VI) 50 mg-dm'), particle size (105 urn) at pH (Cr(lll) - 6.5 and Cr(VI) -2.5). The adsorption data were fitted in Freundlich and Langmuir isotherms. Studies of academic interest like kinetics studies revealed that adsorption equilibrium in each case followed first order equation. Morphological changes observed in the scanning electron micrograph of native and exhausted biomass indicate the existence of biosorption phenomenon. Fourier transform infrared spectrometry of exhausted leaf biomass highlights amino acid - Cr interactions responsible for sorption phenomenon. Regeneration of exhausted biomass was attempted for several cycles for its effective reusability.

Applications of improved SVM framework in modeling in mechanics

80%

Pagalthivarthi K. V. , Mittal A. , Goyal P. , Gupta G. R.

tom Vol. 14, No. 3

509-523

The problem of empirical data modeling is pertinent to several mechanics domains. Empirical data modeling involves a process of induction to build up a model of the system from which responses of the system can be deduced for unobserved data. Machine learning tools can model underlying non-linear function given training data without imposing prior restriction on the type of function. In this paper, we show how Support Vector Machines (SVM) can be employed to solve design problems involving optimizations over parametric space and parameter prediction problems that are recurrent in engineering domain. The problem considered is diffuser design where the optimal value of pressure recovery parameter can be obtained very efficiently by SVM based algorithm even in a large search space. In addition, locating the position of points on a string vibrating in a damped medium serves as an appropriate prediction problem. A grid-searching algorithm is proposed for automatically choosing the best parameters of SVM, thus resulting in a generic framework. The results obtained by SVM are shown to be theoretically sound and a comparison with other approaches such as spline interpolation and Neural Networks shows the superiority of our framework.