Browsing by Author "Sharma, S.K."

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Development of machine learning model for the prediction of selectivity to light olefins from catalytic cracking of hydrocarbons
(Elsevier Ltd, 2025) Mafat, I.H.; Sharma, S.K.; Surya, D.V.; Sankar Rao, C.S.; Maity, U.; Barupal, A.; Jasra, R.
Light olefins are the primary building block for the production of petrochemicals and polymers. Light olefins are largely produced from steam/catalytic cracking of naphtha or ethane/propane. Selectivity to light olefins is significantly dependent on the reaction conditions. In this article, several machine learning models are developed and tested to predict the selectivity of ethylene and propylene using seven input features. For this study, a total of eight ML models consisting of adaptive boost, extreme gradient boost, categorical boost, light gradient boost, decision tree with bagging, random forest, k-nearest neighbour, and artificial neural models are developed. The extreme gradient boost model gave the highest prediction accuracy for the ethylene selectivity, while the light gradient boost gave the highest R2 for the propylene selectivity. The SHAP analysis showed the input parameter's importance ranking for ethylene predictions as temperature > number of carbon atoms > Si/Al ratio > acidity > weight hourly space velocity > effect of diluent > number of hydrogen atoms. The importance ranking of input parameters for propylene selectivity was observed as weight hourly space velocity > acidity > temperature > Si/Al ratio > effect of diluent > number of carbon atoms > number of hydrogen atoms. © 2024 Elsevier Ltd
Exploring machine learning applications in chemical production through valorization of biomass, plastics, and petroleum resources: A comprehensive review
(Elsevier B.V., 2024) Mafat, I.H.; Surya, D.V.; Sharma, S.K.; Sankar Rao, C.
Machine learning (ML) is a subtype of artificial intelligence that uses a computer's ability to learn from a given set of accessible data. ML is becoming prominent in almost every business, including the domain of chemical engineering, where there have been numerous researches and investigations. This article provides a detailed overview of the use of ML in the production and characterization study of biomass, polymers, and petroleum products. Categories of ML, including classification, regression, and clustering, are also investigated to get a deeper understanding of ML. From this review, it can be concluded that ML has aided in numerous domains, such as the prediction of biomass energy, the stability of crude oil based on NMR spectroscopy, the calculation of gasoline's octane number, the estimation of fuel oil's kinematic viscosity, the classification of waste plastics, and the estimation of drilling efficiency in petroleum reservoirs, among others. Apart from this, ML has also been playing a significant role in the microwave-assisted pyrolysis of biomass, polymers, and petroleum resources. ML substantially influences chemical engineering and is especially useful for enhancing system efficiency and monitoring processes that are difficult to understand manually. Although several obstacles are associated with ML, such as black box behavior, the need for a large amount of data, and the difficulty of understanding the predictions, deploying the model in the future is uncomplicated once the learning program has been trained. © 2024 Elsevier B.V.
Impedance spectroscopy study of zinc oxide incorporated iron borate glass-ceramic
(Elsevier B.V., 2021) Ramteke, R.; Kumari, K.; Bhattacharya, S.; Sharma, S.K.; Rahman, M.R.
Here, the effects of zinc oxide (ZnO) on impedance and dielectric properties of the ZnO incorporated iron borate (Fe3BO6) glass-ceramics were studied using impedance spectroscopy in a wide range of frequency (1 Hz – 1 MHz) and temperature (25 °C–250 °C). With ZnO addition, the ?? and tan? values were reduced significantly, the strength of the relaxation process also decreased, along with a decrease in conductivity. Activation energies associated with modulus and conductivity plots suggest that similar type of charge carriers was responsible for the relaxation and conduction processes. The analysis of both complex impedance and conductivity show the negative temperature coefficient of resistance (NTCR) behavior of the samples. The thermistor constant B-values of 5ZnO and 10ZnO were found to be 7223 and 7088 respectively. The study of the NTCR properties suggests a potential candidate for thermistor applications. © 2021 Korean Physical Society
Probing the luminescence behavior of Dy3+/Eu3+ co-doped gadolinium molybdate phosphors under the impact of swift heavy ions
(Springer, 2024) Dutta, S.; Som, S.; Meena, M.L.; Sharma, S.K.
This study delves into the impact of lithium (Li3+) and silver (Ag7+) ion irradiation on the structure and luminescence of Dy3+ doped and Dy3+/Eu3+ co-doped Gd2MoO6 phosphors, synthesized via the hydrothermal method. To explore the influence of ions with varied mass and energy, 30 MeV Li3+ and 100 MeV Ag7+ ions were employed at different fluences. We elucidate the induced effects based on defect formation and the role of these ions’ linear energy transfer (LET) within the irradiated material. SRIM software estimates the depth profile of the ions. Irradiation of the Gd2MoO6 phosphors with Li3+ and Ag7+ ions resulted in the formation of disordered lattices or tracks, modifying their structural, optical, and luminescence properties, which were analyzed by various techniques, including X-ray diffraction, scanning electron microscopy, diffuse reflectance, and photoluminescence. Thermoluminescence (TL) tests and calculations of trapping parameters were conducted to evaluate the dosimetric potential. The findings reveal a more pronounced effect of silver ions compared to lithium ions on the structural and luminescence behavior of doped and codoped Gd2MoO6 phosphors due to their higher atomic weight. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.