A Meta-Analysis of Catalytic Hydrogenation of Levulinic Acid to γ-Valerolactone
Ambereen Aziz Niaze
Indian Institute of Technology Delhi, Department of Chemical Engineering, 110016 Hauz Khas, New Delhi, India
Search for more papers by this authorDeepa Dhaker
Indian Institute of Technology Delhi, Department of Chemical Engineering, 110016 Hauz Khas, New Delhi, India
Search for more papers by this authorCorresponding Author
Dr. Sreedevi Upadhyayula
Indian Institute of Technology Delhi, Department of Chemical Engineering, 110016 Hauz Khas, New Delhi, India
Correspondence: Dr. Sreedevi Upadhyayula ([email protected]), Department of Chemical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India.Search for more papers by this authorAmbereen Aziz Niaze
Indian Institute of Technology Delhi, Department of Chemical Engineering, 110016 Hauz Khas, New Delhi, India
Search for more papers by this authorDeepa Dhaker
Indian Institute of Technology Delhi, Department of Chemical Engineering, 110016 Hauz Khas, New Delhi, India
Search for more papers by this authorCorresponding Author
Dr. Sreedevi Upadhyayula
Indian Institute of Technology Delhi, Department of Chemical Engineering, 110016 Hauz Khas, New Delhi, India
Correspondence: Dr. Sreedevi Upadhyayula ([email protected]), Department of Chemical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India.Search for more papers by this authorAbstract
Selective hydrogenation of biomass-derived levulinic acid (LA) to γ-valerolactone (GVL) is an important reaction in biomass upgradation to produce valuable fuels and chemicals. Increasing demand for renewable energy sources has stipulated growth in catalytic research and has generated a massive amount of experimental data over the years. Inside this numerous data, fresh insights into property-performance associations can be found. However, the incomplete existence and unclear structure of these data records have hampered systematic information extraction thus far. This research proposes a meta-analysis approach for identifying associations between the physico-chemical properties of a catalyst and its reaction performance. The approach combines data from literature with insights from textbooks and statistical methods. A hypothesis is formulated based on a researcher's intuition and statistical significance is checked against the data. Repetitive hypothesis refining gives robust, clear, and analyzable chemical models. The results can be used to guide future research and catalyst production. The method for catalytic hydrogenation of LA to GVL is shown and validated here.
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