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The present work aims to study the evaluation of the microstructure, tensile properties, hardness, corrosion behaviour of the various grades of rebars. The microstructures of all rebar samples comprise an outer tempered martensite ring with an inner core of ferrite-pearlite in between a narrow bainitic transition zone. Maximum hardness is achieved at the periphery which gradually decreases towards the centre. Chinese grade has a similarity with the Fe 600 rebar in terms of strength and % elongation, whereas Fe 500D and Fe 500 have lower strength but higher ductility. The EDS analyses of corrosion products obtained after immersion test in 5% NaCl solution for a period of 7 days apparently indicate the occurrences of iron oxy-hydroxides and iron oxides. X-ray diffraction and FTIR studies of the corrosion products formed on all thermomechanically treated (TMT) rebar surfaces after the aforesaid immersion test primarily indicate the presence of Lepidocrocite (γ-FeOOH), Goethite (α-FeOOH), Magnetite (Fe 3 O 4 ) and Maghemite (γ-Fe 2 O 3 ). Fe 500 (12 mm) and Chinese rebars have lower corrosion resistance as compared to the other rebars in 5% NaCl solution. Nevertheless, all the TMT rebars are corrosion resistant and can be satisfactorily used for construction purposes.

This paper proposes a hybrid process modeling and optimization formalism integrating artificial neural networks (ANNs) and genetic algorithms (GAs). The resultant ANN-GA strategy has the advantage that it allows process modeling and optimization exclusively on the basis of process input-output data. In the hybrid strategy, first an ANN-based process model is developed from the input-output process data. Next, the input space of the model representing process input variables is optimized using GAs, with a view to simultaneously maximize multiple process output variables. The GAs are stochastic optimization methods possessing certain unique advantages over the commonly used gradient-based deterministic algorithms. The efficacy of the hybrid formalism has been evaluated for modeling and optimizing the zeolite (TS-1)-catalyzed benzene hydroxylation to phenol reaction whereby several sets of optimized operating conditions have been obtained. A few optimized solutions have also been subjected to the experimental verification, and the results obtained thereby matched the GA-maximized values of the three reaction output variables with a good accuracy.

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Proper treatment of heavy metal ions present in wastewaters is a major concern. With extensive usage in various industries, Cr(VI) contamination has become threatening for the environment. Biosorption is a favorable technique for heavy metals removal. In the present study, dried cyanobacterial consortium of Dinophysis caudata and Dinophysis acuminata were used to assess its biosorption capability. The surface texture and morphology of the biosorbent were obtained through scanning electron microscopy. The presence of different chemical bonds, namely hydroxyl, C-H and C-N, was confirmed through FTIR study. Pseudo-second-order Mckay-Ho model was found to perform best to fit the kinetic data. Temkin adsorption isotherm model fit best to the equilibrium data. Response surface methodology (RSM) was employed to optimize Cr(VI) abatement. Effect of initial concentration (IC) of metal ion, temperature, pH variation and amount of adsorbent (AD) were studied during batch study. Maximum Cr(VI) abatement after 5 min contact time was 80.77% for an IC of Cr(VI) of 25 mg/L, at pH 11 and 45 °C with the AD of 2.5 g/L. The optimum removal conditions as shown by RSM study were IC of Cr(VI): 15 mg/L, AD: 1 g/L, pH: 11, and the removal was predicted as 81.72%. Artificial neural network-based model was further developed based on experimental points which indicated that the model can predict abatement of Cr(VI) for various operating conditions with reasonably high accuracy.

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This article presents two hybrid robust process optimization approaches integrating artificial neural networks (ANN) and stochastic optimization formalisms -genetic algorithms (GAS) and simultaneous perturbation stochastic approximation (SPSA

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