Effects of hydration parameters on chemical properties of biocrudes based on machine learning and experiments

Lignocellulose-Derived Energy Materials and Chemicals: A Review on Synthesis Pathways and Machine Learning Applications

Lignocellulose biomass, Earth's most abundant renewable resource, is crucial for sustainable production of high-value chemicals and bioengineered materials, especially for energy storage. Efficient pretreatment is vital to boost lignocellulose conversion to bioenergy and biomaterials, cut costs, and broaden its energy-sector applications. Machine learning (ML) has become a key tool in this field, optimizing pretreatment processes, improving decision-making, and driving innovation in lignocellulose valorization for energy storage. This review explores main pretreatment strategies - physical, chemical, physicochemical, biological, and integrated methods - evaluating their pros and cons for energy storage. It also stresses ML's role in refining these processes, supported by case studies showing its effectiveness. The review examines challenges and opportunities of integrating ML into lignocellulose pretreatment for energy storage, underlining pretreatment's importance in unlocking lignocellulose's full potential. By blending process knowledge with advanced computational techniques, this work aims to spur progress toward a sustainable, circular bioeconomy, particularly in energy storage solutions.

Cellulose de-polymerization is selective for bioethanol refinery and multi-functional biochar assembly using brittle stalk of corn mutant

As lignocellulose recalcitrance principally restricts for a cost-effective conversion into biofuels and bioproducts, this study re-selected the brittle stalk of corn mutant by MuDR-transposon insertion, and detected much reduced cellulose polymerization and crystallinity. Using recyclable CaO chemical for biomass pretreatment, we determined a consistently enhanced enzymatic saccharification of pretreated corn brittle stalk for higher-yield bioethanol conversion. Furthermore, the enzyme-undigestible lignocellulose was treated with two-step thermal-chemical processes via FeCl2 catalysis and KOH activation to generate the biochar with significantly raised adsorption capacities with two industry dyes (methylene blue and Congo red). However, the desirable biochar was attained from one-step KOH treatment with the entire brittle stalk, which was characterized as the highly-porous nanocarbon that is of the largest specific surface area at 1697.34 m2/g and 2-fold higher dyes adsorption. Notably, this nanocarbon enabled to eliminate the most toxic compounds released from CaO pretreatment and enzymatic hydrolysis, and also showed much improved electrochemical performance with specific capacitance at 205 F/g. Hence, this work has raised a mechanism model to interpret how the recalcitrance-reduced lignocellulose is convertible for high-yield bioethanol and multiple-function biochar with high performance.

Advances in machine learning for high value-added applications of lignocellulosic biomass

Lignocellulose can be converted into biofuel or functional materials to achieve high value-added utilization. Biomass utilization process is complex and multi-dimensional. This paper focuses on the biomass conversion reaction conditions, the preparation of biomass-based functional materials, the combination of biomass conversion and traditional wet chemistry, molecular simulation and process simulation. This paper analyzes the mechanism, advantages and disadvantages of important machine learning (ML) methods. The application examples of ML in different aspects of high value utilization of lignocellulose are summarized in detail. The challenges and future prospects of ML in this field are analyzed.

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.

Artificial intelligence technologies in bioprocess: Opportunities and challenges

Bioprocess control and optimization are crucial for tapping the metabolic potential of microorganisms, and which have made great progress in the past decades. Combination of the current control and optimization technologies with the latest computer-based strategies will be a worth expecting way to improve bioprocess further. Recently, artificial intelligence (AI) emerged as a data-driven technique independent of the complex interactions used in mathematical models and has been gradually applied in bioprocess. In this review, firstly, AI-guided modeling approaches of bioprocess are discussed, which are widely applied to optimize critical process parameters (CPPs). Then, AI-assisted rapid detection and monitoring technologies employed in bioprocess are summarized. Next, control strategies according to the above two technologies in bioprocess are analyzed. Lastly, current research gaps and future perspectives on AI-guided optimization and control technologies are discussed. This review provides theoretical guidance for developing AI-guided bioprocess optimization and control technologies.