Fuel oil from Plastic waste, 06/2019, 07/2021, Conducted research on plastic waste composition and optimized the pyrolysis process, improving fuel oil yield and quality at heater temperature at 350-4000C. Demonstrated commitment to sustainability by reducing plastic waste and minimizing harmful emissions, also created wax candles with left over wax after completion of heating of plastic. The incondensable fumes are projected out through a pipe and connected to Bunsen burner. Conducted GCMS (Gas chromatography and mass spectroscopy) analysis from the lab and got the results in peak with restive to retention time where our obtained oil having closer properties of diesel. Successfully produced high-quality fuel oil, which can be used in industrial feedstocks and automobiles., https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3710529 Valuable Products from ANANAS COMOSUS (Pineapple) waste, 01/2021, 07/2021, It is a literature review, which is to explore methods for extracting valuable products from pineapple waste, contributing to waste reduction and economic sustainability. Provided 7 different uses from waste of pineapple. Published a journal on this review on “International Journal of Science Engineering and Technology (IJSET)”., https://www.ijset.in/wp-content/uploads/IJSET_V9_issue3_305.pdf Bioplastics from OPUNTIA FICUS INDICA (Cactus), 01/2021, 07/2021, Evaluated the viability of cactus plants as a renewable source for bioplastics, analyzing composition, growth patterns, and sustainability. Developed methods to extract biopolymers from cactus plants, optimizing for maximum yield and minimal energy consumption. Formulated and tested bioplastic blends, ensuring compliance with industry standards, and demonstrated a sustainable alternative to traditional plastics. Machine learning assisted selection of adsorption – based carbon dioxide capture materials, 08/2023, 12/2023, Created a novel machine learning-assisted method for identifying the optimum adsorption materials with high capture capacity and selectivity in carbon capture processes based on their textural properties and operating conditions. Tested with new ML algorithms like Artificial Neural Networks (ANN), K-Nearest Neighbours for better adsorbent classification.