Machine learning prediction of cellulose-rich materials from biomass pretreatment with ionic liquid solvents

The adsorption routes of 4IR technologies for effective desulphurization using cellulose nanocrystals: Current trends, challenges, and future perspectives

The combustion of liquid fuels as energy sources for transportation and power generation has necessitated governments worldwide to direct petroleum refineries to produce sulphur-free fuels for environmental sustainability. This review highlights the novel application of artificial intelligence for optimizing and predicting adsorptive desulphurization operating parameters and green isolation conditions of nanocellulose crystals from lignocellulosic biomass waste. The shortcomings of the traditional modelling and optimization techniques are stated, and artificial intelligence's role in overcoming them is broadly discussed. Also, the relationship between nanotechnology and artificial intelligence and the future perspectives of fourth industrial revolution (4IR) technologies for optimization and modelling of the adsorptive desulphurization process are elaborately discussed. The current study surveys different adsorbents used in adsorptive desulphurization and how biomass-based nanocellulose crystals (green adsorbents) are suitable alternatives for achieving cleaner fuels and environmental sustainability. Likewise, the present study reports the challenges and potential solutions to fully implementing 4IR technologies for effective desulphurization of liquid fuels in petroleum refineries. Hence, this study provides insightful information to benefit a broad audience in waste valorization for sustainability, environmental protection, and clean energy generation.

Enzymatic pretreatment for cellulose nanofiber production: Understanding morphological changes and predicting reducing sugar concentration

Enzymatic pretreatment plays a crucial role in producing cellulose nanofibers (CNFs) before fibrillation. While previous studies have explored how treatment severity affects CNF characteristics, there remains a lack of suitable parameters to monitor real-time enzymatic processes and fully comprehend the link between enzymatic action, fibrillation, and CNF properties. This study focuses on evaluating the impact of enzyme charge (using a monocomponent endoglucanase) and treatment time on cellulose fiber morphology and reducing sugar generation. For the first time, a random forest (RF) model is developed to predict reducing sugar concentration based on easily measurable process conditions (e.g., stirrer power consumption) and fiber/suspension characteristics like fines content and apparent viscosity. Polarized light optical microscopy was found to be a suitable technique to evaluate the morphological changes that fibers experience during enzymatic pretreatment. The research also revealed that endoglucanases initially induce surface fibrillation, releasing fine fibers into the suspension, followed by fiber swelling and shortening. Furthermore, the effect of enzymatic pretreatment on resulting CNF characteristics was studied at two fibrillation intensities, indicating that a high enzyme charge and short treatment times (e.g., 90 min) are sufficient to produce CNFs with a nanofibrillation yield of 19-23 % and a cationic demand ranging from 220 to 275 μeq/g. This work introduces a well-modeled enzymatic pretreatment process, unlocking its potential and reducing uncertainties for future upscaling endeavors.

Oxygen-Containing Quaternary Phosphonium Salts (oxy-QPSs): Synthesis, Properties, and Cellulose Dissolution

In the present study, the synthesis of oxygen-containing quaternary phosphonium salts (oxy-QPSs) was described. Within this work, structure-property relationships of oxy-QPSs were estimated by systematic analysis of physical-chemical properties. The influence of the oxygen-containing substituent was examined by comparing the properties of oxy-QPSs in homology series as well as with phosphonium analog-included alkyl side chains. The crystal structure analysis showed that the oxygen introduction influences the conformation of the side chain of the oxy-QPS. It was found that oxy-QPSs, using an aprotic co-solvent, dimethylsulfoxide (DMSO), can dissolve microcrystalline cellulose. The cellulose dissolution in oxy-QPSs appeared to be dependent on the functional group in the cation and anion nature. For the selected conditions, dissolution of up to 5 wt% of cellulose was observed. The antimicrobial activity of oxy-QPSs under study was expected to be low. The biocompatibility of oxy-QPSs with fermentative microbes was tested on non-pathogenic Saccharomyces cerevisiae, Lactobacillus plantarum, and Bacillus subtilis. This reliably allows one to safely address the combined biomass destruction and enzyme hydrolysis processes in one pot.

Unlocking the potential of transesterification catalysts for biodiesel production through machine learning approach

The growing demand for fossil fuels has motivated the search for a renewable energy source, and biodiesel has emerged as a promising and environmentally friendly alternative. In this study, machine learning techniques were employed to predict the biodiesel yield from transesterification processes using three different catalysts: homogeneous, heterogeneous, and enzyme. Extreme gradient boosting algorithms showed the highest accuracy in predictions, with a coefficient of determination accuracy of nearly 0.98, as determined through a 10-fold cross-validation of the input data. The results indicated that linoleic acid, behenic acid, and reaction time were the most crucial factors affecting biodiesel yield predictions for homogeneous, heterogeneous, and enzyme catalysts, respectively. This research provides insights into the individual and combined effects of key factors on transesterification catalysts, contributing to a deeper understanding of the system.

Machine Learning Approach for Determining and Optimizing Influential Factors of Biogas Production from Lignocellulosic Biomass

Machine learning (ML) was used to predict specific methane yields (SMY) with a dataset of 14 features from lignocellulosic biomass (LB) characteristics and operating conditions of completely mixed reactors under continuous feeding mode. The random forest (RF) model was best suited for predicting SMY with a coefficient of determination (R²) of 0.85 and root mean square error (RMSE) of 0.06. Biomass compositions greatly influenced SMYs from LB, and cellulose prevailed over lignin and biomass ratio as the most important feature. Impact of LB to manure ratio was assessed to optimize biogas production with the RF model. Under typical organic loading rates (OLR), optimum LB to manure ratio of 1:1 was identified. Experimental results confirmed influential factors revealed by the RF model and provided the highest SMY of 79.2% of the predicted value. Successful applications of ML for anaerobic digestion modelling and optimization specifically for LB were revealed in this work.

Exposing and understanding synergistic effects in co-pyrolysis of biomass and plastic waste via machine learning

During co-pyrolysis of biomass with plastic waste, bio-oil yields (BOY) could be either induced or reduced significantly via synergistic effects (SE). However, investigating/ interpreting the SE and BOY in multidimensional domains is complicated and limited. This work applied XGBoost machine-learning and Shapley additive explanation (SHAP) to develop interpretable/ explainable models for predicting BOY and SE from co-pyrolysis of biomass and plastic waste using 26 input features. Imbalanced training datasets were improved by synthetic minority over-sampling technique. The prediction accuracy of XGBoost models was nearly 0.90 R² for BOY while greater than 0.85 R² for SE. By SHAP, individual impact and interaction of input features on the XGBoost models can be achieved. Although reaction temperature and biomass-to-plastic ratio were the top two important features, overall contributions of feedstock characteristics were more than 60 % in the system of co-pyrolysis. The finding provides a better understanding of co-pyrolysis and a way of further improvements.

Machine learning and circular bioeconomy: Building new resource efficiency from diverse waste streams

Biorefinery systems are playing pivotal roles in the technological support of resource efficiency for circular bioeconomy. Meanwhile, artificial intelligence presents great potential in handling scientific tasks of high-dimensional complexity. This review article scrutinizes the status of machine learning (ML) applications in four critical biorefinery systems (i.e. composting, fermentation, anaerobic digestion, and thermochemical conversions) as well as their advancements against traditional modeling techniques of mechanistic approach. The contents cover their algorithm selections, modeling challenges, and prospective improvements. Perspectives are sketched to further inform collective efforts on crucial aspects. The multidisciplinary interchange of modeling knowledge will enable a more progressive digital transformation of sustainability efforts in supporting sustainable development goals.

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.

Bioresource Upgrade for Sustainable Energy, Environment, and Biomedicine

We conceptualize bioresource upgrade for sustainable energy, environment, and biomedicine with a focus on circular economy, sustainability, and carbon neutrality using high availability and low utilization biomass (HALUB). We acme energy-efficient technologies for sustainable energy and material recovery and applications. The technologies of thermochemical conversion (TC), biochemical conversion (BC), electrochemical conversion (EC), and photochemical conversion (PTC) are summarized for HALUB. Microalgal biomass could contribute to a biofuel HHV of 35.72 MJ Kg-1 and total benefit of 749 $/ton biomass via TC. Specific surface area of biochar reached 3000 m2 g-1 via pyrolytic carbonization of waste bean dregs. Lignocellulosic biomass can be effectively converted into bio-stimulants and biofertilizers via BC with a high conversion efficiency of more than 90%. Besides, lignocellulosic biomass can contribute to a current density of 672 mA m-2 via EC. Bioresource can be 100% selectively synthesized via electrocatalysis through EC and PTC. Machine learning, techno-economic analysis, and life cycle analysis are essential to various upgrading approaches of HALUB. Sustainable biomaterials, sustainable living materials and technologies for biomedical and multifunctional applications like nano-catalysis, microfluidic and micro/nanomotors beyond are also highlighted. New techniques and systems for the complete conversion and utilization of HALUB for new energy and materials are further discussed.

Recent Progress on Tailoring the Biomass-Derived Cellulose Hybrid Composite Photocatalysts

Biomass-derived cellulose hybrid composite materials are promising for application in the field of photocatalysis due to their excellent properties. The excellent properties between biomass-derived cellulose and photocatalyst materials was induced by biocompatibility and high hydrophilicity of the cellulose components. Biomass-derived cellulose exhibited huge amount of electron-rich hydroxyl group which could promote superior interaction with the photocatalyst. Hence, the original sources and types of cellulose, synthesizing methods, and fabrication cellulose composites together with applications are reviewed in this paper. Different types of biomasses such as biochar, activated carbon (AC), cellulose, chitosan, and chitin were discussed. Cellulose is categorized as plant cellulose, bacterial cellulose, algae cellulose, and tunicate cellulose. The extraction and purification steps of cellulose were explained in detail. Next, the common photocatalyst nanomaterials including titanium dioxide (TiO₂), zinc oxide (ZnO), graphitic carbon nitride (g-C₃N₄), and graphene, were introduced based on their distinct structures, advantages, and limitations in water treatment applications. The synthesizing method of TiO₂-based photocatalyst includes hydrothermal synthesis, sol-gel synthesis, and chemical vapor deposition synthesis. Different synthesizing methods contribute toward different TiO₂ forms in terms of structural phases and surface morphology. The fabrication and performance of cellulose composite catalysts give readers a better understanding of the incorporation of cellulose in the development of sustainable and robust photocatalysts. The modifications including metal doping, non-metal doping, and metal-organic frameworks (MOFs) showed improvements on the degradation performance of cellulose composite catalysts. The information and evidence on the fabrication techniques of biomass-derived cellulose hybrid photocatalyst and its recent application in the field of water treatment were reviewed thoroughly in this review paper.

Prediction of carbon dioxide production from green waste composting and identification of critical factors using machine learning algorithms

Controlling carbon dioxide produced from green waste composting is a vital issue in response to carbon neutralization. However, there are few computational methods for accurately predicting carbon dioxide production from green waste composting. Based on the data collected, this study developed novel machine learning methods to predict carbon dioxide production from green waste composting and made a comparison among six methods. After eliminating the extreme outliers from the dataset, the Random Forest algorithm achieved the highest prediction accuracy of 88% in the classification task and showed the top performance in the regression task (root mean square error = 23.3). As the most critical factor, total organic carbon, with the Gini index accounting for about 59%, can provide guidance for reducing carbon emissions from green waste composting. These results show that there is great potential for using machine learning algorithms to predict carbon dioxide output from green waste composting.

Prediction of arabica coffee production using artificial neural network and multiple linear regression techniques

Crop yield and its prediction are crucial in agricultural production planning. This study investigates and predicts arabica coffee yield in order to match the market demand, using artificial neural networks (ANN) and multiple linear regression (MLR). Data of six variables, including areas, productivity zones, rainfalls, relative humidity, and minimum and maximum temperature, were collected for the recent 180 months between 2004 and 2018. The predicted yield of the cherry coffee crop continuously increases each year. From the dataset, it was found that the prediction accuracy of the R² and RMSE from ANN was 0.9524 and 0.0784 tons, respectively. The ANN model showed potential in determining the cherry coffee yields.

An Efficient High-Throughput Screening of High Gentamicin-Producing Mutants Based on Titer Determination Using an Integrated Computer-Aided Vision Technology and Machine Learning

The "design-build-test-learn" (DBTL) cycle has been adopted in rational high-throughput screening to obtain high-yield industrial strains. However, the mismatch between build and test slows the DBTL cycle due to the lack of high-throughput analytical technologies. In this study, a highly efficient, accurate, and noninvasive detection method of gentamicin (GM) was developed, which can provide timely feedback for the high-throughput screening of high-yield strains. First, a self-made tool was established to obtain data sets in 24-well plates based on the color of the cells. Subsequently, the random forest (RF) algorithm was found to have the highest prediction accuracy with an R² value of 0.98430 for the same batch. Finally, a stable genetically high-yield strain (998 U/mL) was successfully screened out from 3005 mutants, which was verified to improve the titer by 72.7% in a 5 L bioreactor. Moreover, the verified new data sets were updated on the model database in order to improve the learning ability of the DBTL cycle.

Machine learning aided experimental approach for evaluating the growth kinetics of Candida antarctica for lipase production

A hybrid machine learning (ML) aided experimental approach was proposed in this study to evaluate the growth kinetics of Candida antarctica for lipase production. Different ML models were trained and optimized to predict the growth curves at various substrate concentrations. Results on comparison demonstrate the superior performance of the Gradient boosting regression (GBR) model in growth curves prediction. GBR-based growth kinetics was found to be matching well with the results of the conventional experimental approach while significantly reducing the experimental effort, time, and resources by ∼ 50%. Further, the activity and enzyme kinetics of lipase produced in this study was investigated on hydrolysis of p-nitrophenyl butyrate resulting in a maximum lipase activity of 24.07 U at 44 h. The robustness and significance of developed kinetic models were ensured through detailed statistical analysis. The application of the proposed hybrid approach can be extended to any other microbial process.

Structural Evolution of Graphitic Carbon Derived from Ionic Liquids-Dissolved Cellulose and Its Application as Lithium-Ion Battery Anodes

With an attempt to replace petroleum-derived commercial graphite (CG) with biomass-derived carbon, microcrystalline cellulose (MCC) dissolved in 1-butyl-3-methylimidazolium acetate (BMIMAcO) was facilely carbonized to prepare cellulose-derived carbon under a low-temperature range of 250-1600 °C. TEM and AFM results revealed structural evolution of carbon nanosheets starting from carbon dots. The XRD and Raman results showed that the degree of crystallinity of the MCC-derived carbon was apparently enhanced as the temperature was increased to 93.02% at 1600 °C, while the XPS results revealed that the nitrogen content was greatly reduced with increasing temperature. BMIMAcO not only induced low-temperature graphitization of MCC-derived carbon but also provided nitrogen doping for the carbon. Used as an anode of lithium-ion batteries (LIBs), the carbon synthesized at 750 °C showed the best cyclic stability and reversible capacity (1052.22 mAh g^-1 at 0.5 A g^-1 after 100 cycles and 1017.46 mAh g^-1 at 1 A g^-1 after 1000 cycles) compared to other MCC-derived carbon and CG. In addition, the costs of cellulose-derived carbon are much lower than those of the petroleum-derived graphite, showing environmental and economical merits for LIB anode production.

Machine learning prediction of biocrude yields and higher heating values from hydrothermal liquefaction of wet biomass and wastes

Machine learning (ML) approach was applied for the prediction of biocrude yields (BY) and higher heating values (HHV) from hydrothermal liquefaction (HTL) of wet biomass and wastes using 17 input features from feedstock characteristics (biological and elemental properties) and operating conditions. Several novel ML algorithms were evaluated, based on 10-fold cross-validation, with 3 different sets of input features. An extreme gradient boosting (XGB) model proved to give the best prediction accuracy at nearly 0.9 R² with normal root mean square error (NRMSE) of 0.16 for BY and about 0.87 R² with NRMSE of about 0.04 for HHV. Temperature was found to be the most influential feature on the predictions for both BY and HHV. Meanwhile, feedstock characteristics contributed to the XGB model for more than 55%. Individual effects and interactions of most important features on the predictions were also exposed, leading to better understanding of the HTL system.

The role of machine learning to boost the bioenergy and biofuels conversion

The development and application of bioenergy and biofuels conversion technology can play a significant role for the production of renewable and sustainable energy sources in the future. However, the complexity of bioenergy systems and the limitations of human understanding make it difficult to build models based on experience or theory for accurate predictions. Recent developments in data science and machine learning (ML), can provide new opportunities. Accordingly, this critical review provides a deep insight into the application of ML in the bioenergy context. The latest advances in ML assisted bioenergy technology, including energy utilization of lignocellulosic biomass, microalgae cultivation, biofuels conversion and application, are reviewed in detail. The strengths and limitations of ML in bioenergy systems are comprehensively analysed. Moreover, we highlight the capabilities and potential of advanced ML methods when encountering multifarious tasks in the future prospects to advance a new generation of bioenergy and biofuels conversion technologies.

Challenges and Perspective of Recent Biomass Pretreatment Solvents

The increased demands on renewable and sustainable products require enhancing the current conversion efficiency and expanding the utilization of biomass from a single component (i.e., cellulose) to entire biomass components in the biorefinery concept. Pretreatment solvent plays a critical role in various biorefinery processes. Recent pretreatment solvents such as organic co-solvents, acid hydrotropes, ionic liquids and deep eutectic solvents showed effective biomass fractionation as well as preservation of high-quality cellulose and lignin under mild conditions. Despite these significant enhancements in biomass pretreatment solvent, there are still many challenges, such as feedstock variety, valorization of non-cellulose components, and eco-friendliness of the applied catalyst and solvent. These technical, economic and environmental obstacles should be considered in future biomass pretreatment solvents. In particular, the development of feedstock-agnostic solvent with high fractionation performance for high quality and quantity of all three major components (i.e., cellulose, hemicellulose, and lignin) together would be an ideal direction.

Waste Reutilization in Polymeric Membrane Fabrication: A New Direction in Membranes for Separation

In parallel to the rapid growth in economic and social activities, there has been an undesirable increase in environmental degradation due to the massively produced and disposed waste. The need to manage waste in a more innovative manner has become an urgent matter. In response to the call for circular economy, some solid wastes can offer plenty of opportunities to be reutilized as raw materials for the fabrication of functional, high-value products. In the context of solid waste-derived polymeric membrane development, this strategy can pave a way to reduce the consumption of conventional feedstock for the production of synthetic polymers and simultaneously to dampen the negative environmental impacts resulting from the improper management of these solid wastes. The review aims to offer a platform for overviewing the potentials of reutilizing solid waste in liquid separation membrane fabrication by covering the important aspects, including waste pretreatment and raw material extraction, membrane fabrication and characterizations, as well as the separation performance evaluation of the resultant membranes. Three major types of waste-derived polymeric raw materials, namely keratin, cellulose, and plastics, are discussed based on the waste origins, limitations in the waste processing, and their conversion into polymeric membranes. With the promising material properties and viability of processing facilities, recycling and reutilization of waste resources for membrane fabrication are deemed to be a promising strategy that can bring about huge benefits in multiple ways, especially to make a step closer to sustainable and green membrane production.

Compositional analysis of bio-oils from hydrothermal liquefaction of tobacco residues using two-dimensional gas chromatography and time-of-flight mass spectrometry

Sustainable energy from biomass is one of the most promising alternative energy sources and is expected to partially replace fossil fuels. Tobacco industries have normally rid their processing residues by landfilling or incineration, affecting the environment negatively. These residues can be used to either extract high-value chemicals or generate bio-energy via hydrothermal liquefaction. The main liquid product or bio-oil consists of highly complicated chemicals. In this work, the bio-oil from hydrothermal liquefaction of tobacco processing residues was generated in a batch reactor at biomass-to-deionized water ratio of 1:3, temperature of 310°C, and 15 min residence time, yielding the maximum liquid products for more than 50% w/w. The liquid products were analyzed, using two-dimensional gas chromatography and time-of-flight mass spectrometry (GC × GC/TOF MS). This technique allowed for a highly efficient detection of numerous compounds. From the results, it was found that hydrothermal liquefaction can cleave biopolymers (cellulose, hemicellulose, and lignin) in tobacco residues successfully. The hydrothermal liquefaction liquid products can be separated into heavy organic, light organic, and aqueous phase fractions. By GC × GC/TOF MS, the biopolymers disintegrated into low molecular weight compounds and classified by their chemical derivatives and functional groups could be detected. The major chemical derivative/functional groups found were cyclic ketones and phenols for heavy organic and light organic, and carboxylic acids and N-containing compounds for the aqueous phase. Additionally, by the major compounds found in this work, simple pathway reactions occurring in the hydrothermal liquefaction reaction were proposed, leading to a better understanding of the hydrothermal liquefaction process for tobacco residues.

Using an SGB Decision Tree Approach to Estimate the Properties of CRM Made by Biomass Pretreated with Ionic Liquids

The use of ionic liquids (ILs) for biomass pretreatment to produce cellulose-rich materials (CRMs) has been well proven. In this research, due to the wide range of applications and ease of using artificial intelligence procedures, on the basis of the algorithm of stochastic gradient boosting (SGB) decision tree, an artificial intelligence approach is proposed to estimate the properties of cellulose-rich materials (CRMs). That being the case, the dataset of the empirical output values was gathered and was randomly broken down into datasets for testing and training. These results show that the best forecasting tool for calculating the properties of CRMs is the developed model. Furthermore, the accuracy of the databank of the biodiesel target values has been examined. In contrast, the influences of model contributed variables on the output have been examined as a new issue. It reveals that the most influencing variable in determining the properties of CRMs is the cellulose enrichment factor. Therefore, this research provides an innovative and accurate tool for predicting the properties of CRMs and sensitivity investigation on effective parameters to help investigators developing the optimized process.