Machine learning based analysis of reaction phenomena in catalytic lignin depolymerization

Computational Advances in Ionic Liquid Applications for Green Chemistry: A Critical Review of Lignin Processing and Machine Learning Approaches

The valorization and dissolution of lignin using ionic liquids (ILs) is critical for developing sustainable biorefineries and a circular bioeconomy. This review aims to critically assess the current state of computational and machine learning methods for understanding and optimizing IL-based lignin dissolution and valorization processes reported since 2022. The paper examines various computational approaches, from quantum chemistry to machine learning, highlighting their strengths, limitations, and recent advances in predicting and optimizing lignin-IL interactions. Key themes include the challenges in accurately modeling lignin's complex structure, the development of efficient screening methodologies for ionic liquids to enhance lignin dissolution and valorization processes, and the integration of machine learning with quantum calculations. These computational advances will drive progress in IL-based lignin valorization by providing deeper molecular-level insights and facilitating the rapid screening of novel IL-lignin systems.

Electrochemical Valorization of Glycerol via Electrocatalytic Reduction into Biofuels: A Review

Electrochemical conversion of underutilized biomass-based glycerol into high-value-added products provides a green approach for biomass and waste valorization. Plus, this approach offers an alternative to biofuel manufacturing procedure, under mild operating conditions, compared to the traditional thermochemical routes. Nevertheless, glycerol has been widely valorized via electrooxidation, with lower-value products generated at the cathode, ignoring the electroreduction. Here, a review of the efficient glycerol reduction into various products via the electrocatalytic reduction (ECR) process was presented. This review has been built upon the background of glycerol underutilization and theoretical knowledge about the state-of-the-art ECR. The experimental understanding of the processing parameter influences towards electrochemical efficiency, catalytic activity, and product selectivity are comprehensively reviewed, based on the recent glycerol ECR studies. We conclude by outlining present issues and highlighting potential future research avenues for enhanced ECR application.

Biochar production and its environmental applications: Recent developments and machine learning insights

Biochar production through thermochemical processing is a sustainable biomass conversion and waste management approach. However, commercializing biochar faces challenges requiring further research and development to maximize its potential for addressing environmental concerns and promoting sustainable resource management. This comprehensive review presents the state-of-the-art in biochar production, emphasizing quantitative yield and qualitative properties with varying feedstocks. It discusses the technology readiness level and commercialization status of different production strategies, highlighting their environmental and economic impacts. The review focuses on integrating machine learning algorithms for process control and optimization in biochar production, improving efficiency. Additionally, it explores biochar's environmental applications, including soil amendment, carbon sequestration, and wastewater treatment, showcasing recent advancements and case studies. Advances in biochar technologies and their environmental benefits in various sectors are discussed herein.

Prediction of Higher Heating Values in Bio-Oil from Solvothermal Biomass Conversion and Bio-Oil Upgrading Given Discontinuous Experimental Conditions

Both the conversion of lignocellulosic biomass to bio-oil (BO) and the upgrading of BO have been the targets of many studies. Due to the large diversity and discontinuity seen in terms of reaction conditions, catalysts, solvents, and feedstock properties that have been used, a comparison across different publications is difficult. In this study, machine learning modeling is used for the prediction of final higher heating value (HHV) and ΔHHV for the conversion of lignocellulosic feedstocks to BO, and BO upgrading. The models achieved coefficient of determination (R2) scores ranging from 0.77 to 0.86, and the SHapley Additive exPlanations (SHAP) values were used to obtain model explainability, revealing that only a few experimental parameters are largely responsible for the outcome of the experiments. In particular, process temperature and reaction time were overwhelmingly responsible for the majority of the predictions, for both final HHV and ΔHHV. Elemental composition of the starting feedstock or BO dictated the upper possible HHV value obtained after the experiment, which is in line with what is known from previous methodologies for calculating HHV for fuels. Solvent used, initial moisture concentration in BO, and catalyst active phase showed low predicting power, within the context of the data set used. The results of this study highlight experimental conditions and variables that could be candidates for the creation of minimum reporting guidelines for future studies in such a way that machine learning can be fully harnessed.

Machine Learning Model Insights into Base-Catalyzed Hydrothermal Lignin Depolymerization

Lignin, an abundant component of plant matter, can be depolymerized into renewable aromatic chemicals and biofuels but remains underutilized. Homogeneously catalyzed depolymerization in water has gained attention due to its economic feasibility and performance but suffers from inconsistently reported yields of bio-oil and solid residues. In this study, machine learning methods were used to develop predictive models for bio-oil and solid residue yields by using a few reaction variables and were subsequently validated by doing experimental work and comparing the predictions to the results. The models achieved a coefficient of determination (R2) score of 0.83 and 0.76, respectively, for bio-oil yield and solid residue. Variable importance for each model was calculated by two different methodologies and was tied to existing studies to explain the model predictive behavior. Based on the outcome of the study, the creation of concrete guidelines for reporting in lignin depolymerization studies was recommended. Shapley additive explanation value analysis reveals that temperature and reaction time are generally the strongest predictors of experimental outcomes compared to the rest.

Critical Techniques for Overcoming the Diffusion Limitations in Heterogeneously Catalytic Depolymerization of Lignin

Heterogeneously catalyzed depolymerization of lignin to value-added chemicals is increasingly attractive but highly challengeable. Particularly, the diffusion limitation of lignin macromolecule to the solid catalyst surface is a big barrier, which significantly decreases the yield of monomer while increasing char formation. Therefore, for the potential industrial utilization of lignin, new knowledge focused on the size of lignin particles is of great importance to offer guidance for promoting lignin depolymerization and suppressing condensation in the heterogeneously catalytic systems. In this Review, the size of lignin particles and macromolecules are summarized. Previous approaches for improving the mass diffusion including enhancing the solubility of lignin and exploitation of hierarchical and "solubilized" materials are also discussed. Based on these, a constructive perspective is proposed. Thus, this work provides a new insight on the rational design of heterogeneous catalytic techniques for efficient utilization of the aromatic polymer of lignin.

Machine learning for hydrothermal treatment of biomass: A review

Hydrothermal treatment (HTT) (i.e., hydrothermal carbonization, liquefaction, and gasification) is a promising technology for biomass valorization. However, diverse variables, including biomass compositions and hydrothermal processes parameters, have impeded in-depth mechanistic understanding on the reaction and engineering in HTT. Recently, machine learning (ML) has been widely employed to predict and optimize the production of biofuels, chemicals, and materials from HTT by feeding experimental data. This review comprehensively analyzed the application of ML for HTT of biomass and systematically illustrated basic ML procedure and descriptors for inputs and outputs of ML models (e.g., biomass compositions, operation conditions, yield and physicochemical properties of derived products) that could be applied in HTT. Moreover, this review summarized ML-aided HTT prediction of yield, compositions, and physicochemical properties of HTT hydrochar or biochar, bio-oil, syngas, and aqueous phase. Ultimately, future prospects were proposed to enhance predictive performance, mechanistic interpretation, process optimization, data sharing, and model application during ML-aided HTT.

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

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-SO₃H) 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-SO₃H 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.