Directional methanolysis of kitchen waste for the co-production of methyl levulinate and fatty acid methyl esters: Catalytic strategy and machine learning modeling. (January 2023)

• Record Type: Journal Article
• Authors: Zhang, Luxin; Xing, Xu; Liu, Yuting; Shi, Weiwei; Wang, Mingzhe
• Is Part Of: Bioresource technology. Volume 367(2023)
• Publication Date: 2023-01
• Subjects: Kitchen waste -- Valorization -- Methanolysis -- Methyl levulinate -- FAMEs -- Machine learning; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2022.128274
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: ML and FAMEs were synthesized synchronously from real kitchen waste. C/Si composite and Zr/USY catalyze the formation of esters synergistically. This strategy has applicability to kitchen waste with different constituents. Machine learning models were built for prediction and feature importance analysis. Abstract: To add value to ordinary kitchen waste, heterogeneous acid-base catalytic methanolysis was conducted to produce high-value liquid biofuels, methyl levulinate (ML) and fatty acid methyl esters (FAMEs). Yields of 53.3 % ML and 98.5 % FAME were achieved by methanolysis of kitchen waste under the co-catalysis of carbon–silica composite (C/Si-SO3 H) and zirconium modified ultrastable Y zeolite (Zr/USY). These target products can be easily recovered from the methanolic phase and can be purified at the end of the reaction. The collaborative combination of C/Si-SO3 H and Zr/USY exhibited higher activity than their commercial counterpart. This strategy can be applied to differently composed kitchen waste and kitchen waste with different water content. Product yields were predicted using an artificial neural network method, and the relative importance of the influencing factors was investigated by the random forest method. The systematic insight gained from this work supports the value-added utilization of kitchen waste.