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Gasification is the cleanest and most efficient process of utilizing fossil fuels for energy production. Gasification efficiency and product gas quality depend mainly on Equivalence Ratio (ER), Steam-to-Fuel Ratio (SFR), and Gasification Temperature (GT). This study aims at developing an Artificial Neural Network (ANN) model for the prediction of optimum parameters and resulting syngas composition. Very few studies have been reported on ANN modeling with a limited scope of predicting the heating value and composition of syngas. However, none of the previous works are reported on the prediction of optimum operating conditions based on fuel properties so far. With the recent surge in the variety of feedstocks, it is increasingly difficult to determine optimum operating conditions and corresponding syngas composition in a conventional manner. In this scenario, ANNs would be handy in faster and accurate prediction of operating conditions. In the present work, a multi-layer feed forward back propagation network has been proposed for modeling gasification process. The first stage of study involves identification of suitable network architecture and its parameters such that the performance measures are achieved. In the second stage, the network is trained with experimental data available in the literature. The accuracy of the network depends on the amount of data. Since available experimental data is limited, training data is generated using Minitab software. The input parameters used in the model are feedstock composition from the ultimate analysis, and output parameters are operating conditions ER, SFR, GT, and syngas composition (amount of H2, CO, CO2, and CH4). A 5-5-1 network is used for estimating optimum parameters and an 8-5-1 network is used for estimating syngas components. The predicted data is in good agreement with experimental results with a correlation coefficient of above 0.91 and mean square error less than 0.001.

Carbonate substituted nanohydroxyapatite (CHA) was synthesized and utilized for the removal of reactive red and reactive blue dye from aqueous solution, as it mimics the composition of conventional adsorbent animal bone charcoal. Also ionic substitution seems to alter the surface nature of the apatite structure. Physicochemical nature of adsorbent was characterized by XRD, FT-IR and SEM analysis. Adsorption as a function of contact time, adsorbent dosage and pH were studied by batch mode adsorption technique. Kinetic studies were performed to correlate the experimental kinetic data with theoretical models in order to understand the adsorption mechanism and the reaction rate.

Biocompatible luminescent materials have received much attention for the development of novel bioprobes. In the present work, we have synthesized the flower-like hydroxyapatite (HA) nanostructure from eggshell biowaste via a simple and rapid microwave conversion process. The synthesized product is identified as Mg containing B-type carbonated HA. It showed an intense blue emission between 360 nm to 550 nm with maximum around 430 nm under UV light excitation (λex= 344 nm). This blue emission might result from the carbonate related impurities present in the structure of HA and it can be a potential luminescent material for the development biocompatible probes.

Synthesis of Zn or CO32− substituted nano‐hydroxyapatite (HA) and its physico‐chemical properties have been well documented. However, the effects of the simultaneous substitution of Zn and CO32− in nano‐HA have not been reported. In the present study, Zn and CO32− substitutions in nano HA independently and concurrently have been done by wet precipitation method and characterized by XRD and FT‐IR for its phase purity and chemical homogeneity. Further modulations of the bioactivity and thermal stability of HA due to the substitutions have been studied.