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The natural surface water, especially available through rivers, is the main source of healthy water for the living beings throughout the world from ancient days as it consists of all essential minerals. With the advent of industrialization, gradually even the most prominent rivers have been polluted in all parts of the world. Although there are lots of technologies, nanofiltration (NF) has been chosen to transform river water into healthy water due to its unique advantages of retaining optimum TDS (with essential minerals required for human body), consuming of lower energy, and no usage of any chemicals. The prominent parameters of surface water and macro/microminerals of treated water have been analyzed. It is shown that NF is better in producing healthy water with high flux by consuming low energy.

The objective of the present study is to understand the combustion behavior and to estimate kinetic parameters for combustion and oxy-combustion of calcined pet coke (CPC) employing thermogravimetric analysis (TGA), which is crucial for subsequent design and modeling of the combustion systems. In order to estimate the kinetics, the onset reaction temperature (ORT) is estimated using TGA for both the systems, and all subsequent experiments are conducted at temperatures higher than the ORT. The kinetic parameters viz., activation energy (E a ) and pre-exponential factor (A), are estimated using the shrinking particle model (SPM) and weight fraction model (WFM). While SPM assumes uniform particle size and first-order intrinsic kinetics, WFM is used to estimate even order of reaction besides E a and A. Prediction from SPM fits better to the data obtained from TGA, albeit with WFM estimating the order of the reaction as 0.6 in this case. The present study will be useful in employing the predicted kinetic data to design an industrial scale pet coke combustor. Artificial neural network (ANN) modeling is applied to isothermal TGA data to predict the TG curves of combustion and oxy-combustion of CPC. The ANN model predicted the TG curve with a high degree of accuracy, i.e., with a coefficient of determination in the order of 0.99999. The agreement between the experimental and predicted data substantiates the accuracy of the ANN model.

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