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Research and Innovation:
Publications

BITS places a premium on innovation by transforming ideas into a sustained commitment to research and knowledge creation. Furthermore, we emphasise contribution through novel and useful patentable inventions.

Know more about the publications we hold across various departments

BITS emphasises innovation by translating ideas into a continuous commitment to research and knowledge creation. In addition, we also emphasise contribution through the novel and useful patentable inventions. 

13247

PILANI

7322

HYDERABAD

4638

GOA

1685

DUBAI

Carbonaceous catalysts (biochar and activated carbon) from agricultural residues and their application in production of biodiesel: A review, Chem. Eng. Res. Des., vol. 203, pp. 759–788, Mar. 2024


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Carbon Dioxide Adsorption over Activated Biocarbons Derived from Lemon Peel, Molecules, vol. 29, Sep. 2024


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Tin and lanthanum modified Ni/CeO2 catalyst systems for lowtemperature steam reforming of ethanol, Int. J. Hydrogen Energy, vol. 50, pp. 239–260, Jan. 2024


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Peer Reviewed Journal Publications


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Books Content


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Khurshid, A., & Pani, A. K. (2024). An integrated approach combining randomized kernel PCA, Gaussian mixture modeling and ICA for fault detection in non-linear processes. Measurement Science and Technology, 35(7), 076208.


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Khurshid, A., & Pani, A. K. (2023). Machine learning approaches for data-driven process monitoring of biological wastewater treatment plant: A review of research works on benchmark simulation model no. 1 (bsm1). Environmental Monitoring and Assessment, 195(8), 916.


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Vijayan, S. V., Mohanta, H. K., Rout, B. K., & Pani, A. K. (2023). Adaptive soft sensor design using a regression neural network and bias update strategy for non-linear industrial processes. Measurement Science and Technology, 34(8), 085012.


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Palla, G. L. P., & Pani, A. K. (2023). Independent component analysis application for fault detection in process industries: Literature review and an application case study for fault detection in multiphase flow systems. Measurement, 209, 112504.


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Pani, A. K. (2022). Non-linear process monitoring using kernel principal component analysis: A review of the basic and modified techniques with industrial applications. Brazilian Journal of Chemical Engineering, 39(2), 327-344.


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