Recent advances of thermochemical conversion processes for biorefinery

Automated machine learning-aided prediction and interpretation of gaseous by-products from the hydrothermal liquefaction of biomass

Hydrothermal liquefaction (HTL) is a thermochemical conversion technology that produces bio-oil from wet biomass without drying. However, by-product gases will inevitably be produced, and their formation is unclear. Therefore, an automated machine learning (AutoML) approach, automatically training without human intervention, was used to aid in predicting gaseous production and interpreting the formation mechanisms of four gases (CO2, CH4, CO, and H2). Specifically, four accurate optimal single-target models based on AutoML were developed with elemental compositions and HTL conditions as inputs for four gases. Herein, the gradient boosting machine (GBM) performed excellently with train R2 ≥ 0.99 and test R2 ≥ 0.80. Then, the screened GBM algorithm-based ML multi-target models (maximum average test R2 = 0.89 and RMSE = 0.39) were built to predict four gases simultaneously. Results indicated that biomass carbon, solid content, pressure, and biomass hydrogen were the top four factors for gas production from HTL of biomass. This study proposed an AutoML-aided prediction and interpretation framework, which could provide new insight for rapid prediction and revelation of gaseous compositions from the HTL process.

Synergetic effects during co-pyrolysis of waste textiles and Ca/Fe-rich industrial sewage sludge: Reaction kinetics and product distribution

Co-pyrolysis is a promising technology for industrial organic waste to utilize their unique resource and energy properties for efficient conversion into valuable products. This study was the first time to characterize the co-pyrolysis of waste textiles with Ca-rich industrial sludge and Fe-rich industrial sludge on a laboratory-scale fixed bed. The properties, mechanisms, gas, oil and carbon production were investigated as a function of temperature and mixing type. Co-pyrolysis increased the total weight loss from 50.05 % to 69.81 % for Ca-rich industrial sludge mixed with 50 % waste textiles and from 49.13 % to 70.01 % for Fe-rich industrial sludge mixed with 50 % waste textiles. The activation energy of co-pyrolysis was approximately 50 % lower compared to the pyrolysis of waste textiles alone. The optimal reaction model for the different reaction stages for all samples was three diffusion (D3). Co-pyrolysis resulted in lower CO and CO2 emission temperatures of about 25-110 °C and produced more short-chain organic compounds (C < 10). Co-pyrolysis produced more aldehydes and ketones organics. Moreover, co-pyrolysis char exhibited an elevated level of fatty alkyl side chains and bridge branching, as well as higher degrees of aromatization and stability. This study offers valuable insights into the potential application of pyrolysis for the management of Ca/Fe-rich industrial sludge and waste textiles, thereby serving as a basis for future utilization endeavors.

Sewage sludge pyrolysis 'kills two birds with one stone': Biochar synergies with persulfate for pollutants removal and energy recovery

The disposal and resource utilization of sewage sludge (SS) have always been significant challenges for environmental protection. This study employed straightforward pyrolysis to prepare iron-containing sludge biochar (SBC) used as a catalyst and to recover bio-oil used as fuel energy. The results indicated that SBC-700 could effectively activate persulfate (PS) to remove 97.2% of 2,4-dichlorophenol (2,4-DCP) within 60 min. Benefiting from the appropriate iron content, oxygen-containing functional groups and defective structures provide abundant active sites. Meanwhile, SBC-700 exhibits good stability and reusability in cyclic tests and can be easily recovered by magnetic separation. The role of non-radicals is emphasized in the SBC-700/PS system, and in particular, single linear oxygen (1O2) is proposed to be the dominant reactive oxygen. The bio-oil, a byproduct of pyrolysis, exhibits a higher heating value (HHV) of about 30 MJ/kg, with H/C and O/C ratios comparable to those of biodiesel. The energy recovery rate of the SS pyrolysis system was calculated at 80.5% with a lower input cost. In conclusion, this investigation offers a low-energy consumption and sustainable strategy for the resource utilization of SS while simultaneously degrading contaminants.

Progress and challenges in valorisation of biomass waste from ornamental trees pruning through pyrolysis processes. Prospects in the bioenergy sector

Nowadays, the scarcity of energy resources is promoting the search for alternative energy sources, boosting interest in the use of forest lignocellulosic residue in the energy sector. In this study, the focus is on the energy recovery from two lignocellulosic residues originated during the pruning of ornamental trees (Horse Chestnut, CI, and False Acacia, FA). Both conventional and flash pyrolysis techniques were applied. The experimental pyrolysis variables were obtained from the study of the thermal behaviour of the pruning residues in thermogravimetric analysis. It was carried out under 5 heating rates and kinetic parameters were estimated using Flynn-Wall-Ozawa method. Results denoted higher maximum mass loss rate values for the same release temperature regions under FA experiments. Also, FA samples had lower final residues for the processes. However, activation energy values were so close for both species. FA was also linked to the faster reactions according frequency factor outcomes. Conventional pyrolysis of pruning residues was carried out in a horizontal oven of original design at a heating rate of 25 °C/min, at 750 °C and 60 min of permanence at that temperature; flash pyrolysis was tested in that oven at 750 and 850 °C. In these pyrolysis processes, three fractions were obtained: bio-char, bio-oil and gas. The physicochemical attributes of the bio-chars suggested their potential utility as biofuels (28.4-29.8 MJ/kg), adsorbent precursors or soil additives. Conventional pyrolysis bio-oils had a dominant monoaromatic hydrocarbons nature, with phenols being the most abundant (≥60%), while flash bio-oils contain mainly polycyclic aromatic hydrocarbons. Conventional pyrolysis gases contained up to 60 vol% of CO2; flash pyrolysis gases had high combustible gas content (CO, CH4, H2) and a low CO2 content (<25 vol%). As a result, their calorific value (18.06 MJ/kg) exhibited a threefold increase compared to the gas produced through conventional pyrolysis (6.04 MJ/kg).

Composition of Lignocellulose Hydrolysate in Different Biorefinery Strategies: Nutrients and Inhibitors

The hydrolysis and biotransformation of lignocellulose, i.e., biorefinery, can provide human beings with biofuels, bio-based chemicals, and materials, and is an important technology to solve the fossil energy crisis and promote global sustainable development. Biorefinery involves steps such as pretreatment, saccharification, and fermentation, and researchers have developed a variety of biorefinery strategies to optimize the process and reduce process costs in recent years. Lignocellulosic hydrolysates are platforms that connect the saccharification process and downstream fermentation. The hydrolysate composition is closely related to biomass raw materials, the pretreatment process, and the choice of biorefining strategies, and provides not only nutrients but also possible inhibitors for downstream fermentation. In this review, we summarized the effects of each stage of lignocellulosic biorefinery on nutrients and possible inhibitors, analyzed the huge differences in nutrient retention and inhibitor generation among various biorefinery strategies, and emphasized that all steps in lignocellulose biorefinery need to be considered comprehensively to achieve maximum nutrient retention and optimal control of inhibitors at low cost, to provide a reference for the development of biomass energy and chemicals.

Lignocellulosic biomass fertilizers: Production, characterization, and agri-applications

The growing focus on sustainable agriculture and optimal resource utilization has spurred investigations into lignocellulosic biomass as a potential source for producing environmentally friendly fertilizers. This paper reviews recent advancements in the production and application of innovative fertilizers derived from lignocellulose. It highlights potential in enhancing agricultural productivity and reducing environmental impacts such as carbon footprint and water pollution. The paper outlines various methods for conversion, highlighting the unique advantages of chemical, enzymatic, and microbiological processes, for converting lignocellulosic biomass into nutrient-rich fertilizers. The study compares the efficacy of lignocellulosic fertilizers to traditional fertilizers in promoting crop growth, enhancing soil health, and reducing nutrient losses. The results demonstrate the potential of lignocellulosic biomass-derived fertilizers in promoting resource efficiency and sustainable agriculture. While this research significantly contributes to the existing body of knowledge, further studies on long-term impacts and scalability are recommended for the development of innovative and sustainable agricultural practices.

Domestic wastewater sludge valorization: Multi-criteria evaluation of anaerobic digestion vs. hydrothermal liquefaction

The emerging hydrothermal liquefaction (HTL) process is evaluated against the classical anaerobic digestion (AD) processes for stabilizing wastewater sludges and recovering their energy- and nutrient-contents. Although HTL affords faster stabilization, better process stability, and liquid fuel and sterile fertilizer recovery, it suffers from higher energy demand and lower technology readiness level. For a rational comparison of these pathways, a multi-criteria evaluation is conducted considering 21 technical, environmental, economic, and social criteria. Criteria values for the HTL-pathway were derived from laboratory tests while those for the AD-pathway were compiled from literature. Of the 16 process alternatives evaluated, the AD-pathway including nitrogen-recovery by air-stripping and phosphorus recovery by the MEPHREC® process ranked first followed by the HTL-pathway. This multi-criteria study suggests that the HTL-pathway could be engineered as a superior alternative for sludge stabilization and resource recovery if phosphorus recovery and its technology readiness level could be improved.

Artificial intelligence methods for modeling gasification of waste biomass: a review

Gasification is a highly promising thermochemical process that shows considerable potential for the efficient conversion of waste biomass into syngas. The assessment of the feasibility and comparative advantages of different biomass and waste gasification schemes is contingent upon a multifaceted combination of interrelated criteria. Conventional analytical approaches employed to facilitate decision-making rely on a multitude of inadequately defined parameters. Consequently, substantial efforts have been directed toward enhancing the efficiency and productivity of thermochemical conversion processes. In recent times, artificial intelligence (AI)-based models and algorithms have gained prominence, serving as indispensable tools for expediting these processes and formulating strategies to address the growing demand for energy. Notably, machine learning (ML) and deep learning (DL) have emerged as cutting-edge AI models, demonstrating exceptional effectiveness and profound relevance in the realm of thermochemical conversion systems. This study provides an overview of the machine learning (ML) and deep learning (DL) approaches utilized during gasification and evaluates their benefits and drawbacks. Many industries and applications related to energy conversion systems use AI algorithms. Predicting the output of conversion systems and subjects linked to optimization are two of this science's critical applications. This review sheds light on the burgeoning utility of AI, particularly ML and DL, which have garnered significant attention due to their applications in productivity prediction, process optimization, real-time process monitoring, and control. Furthermore, the integration of hybrid models has become commonplace, primarily owing to their demonstrated success in modeling and optimization tasks. Importantly, the adoption of these algorithms significantly enhances the model's capability to tackle intricate challenges, as DL methodologies have evolved to offer heightened accuracy and reduced susceptibility to errors. Within the scope of this study, an exhaustive exploration of ML and DL techniques and their applications has been conducted, uncovering existing research knowledge gaps. Based on a comprehensive critical analysis, this review offers recommendations for future research directions, accentuating the pivotal findings and conclusions derived from the study.

Algae from Cr-containing infiltrate bioremediation for valorised chemical production - Seasonal availability, composition, and screening studies on hydrothermal conversion

Integrating bioremediation of toxic wastewater with value-added production is increasing interest, but - due to some essential problems - it is hardly applied in industrial practice. The aim of the study was an annual observation of the taxonomic and biochemical composition of various Cr-resistant algal communities grown in the existing Cr-containing infiltrate treatment system, selection of the most suitable algal biomass for infiltrates bioremediation and chromium-loaded algae conversion under mild subcritical conditions. Considering continuous availability and relatively constant chemical composition, Cladophora sp. was selected for utilisation in the chromium bioremediation system, simultaneously as a waste biomass source suitable for hydrothermal conversion. Screening studies conducted in a continuous pilot plant confirmed the possibility of selective extraction of saccharides and their separation from the metals remaining in the solid residual. The negligible concentration of metals in the obtained sugar-rich aqueous phase is essential for its further use in biotechnological processes.

Carbon dioxide-assisted thermochemical conversion of magnetically harvested harmful algae into syngas and metal biochar

With rising of harmful algae blooming and toxin exposure, practical utilization of harmful algae has been developed. This work aimed to magnetically harvest Microcystis aeruginosa (MA) using iron oxides and investigate the feasibility of algae/iron oxides mixture as feedstock in pyrolytic platform to produce syngas and metal biochar. Carbon dioxide (CO2) was used as a feeding gas to enhance the production efficiency of syngas and also functioned pH controller for better MA harvesting and toxin removal. CO2 support brought multiple benefits: magnetite (Fe3O4) and maghemite (γ-Fe2O3) recovered MA in a relatively short period of time (∼1 min), the recovered biomass generated 34-fold increased carbon monoxide, and metal biochar adsorbed higher amount of toxin from MA (2.8-fold). Pyrolytic utilization of harmful algae supported by CO2 and iron oxides could be one of promising techniques for evolution of metal biochar to remove toxin, while efficiently recover biomass and enhance syngas production.

A review on modelling of thermochemical processing of biomass for biofuels and prospects of artificial intelligence-enhanced approaches

Biofuels from lignocellulosic biomass converted via thermochemical technologies can be renewable and sustainable, which makes them promising as alternatives to conventional fossil fuels. Prior to building industrial-scale thermochemical conversion plants, computational models are used to simulate process flows and conditions, conduct feasibility studies, and analyse process and business risk. This paper aims to provide an overview of the current state of the art in modelling thermochemical conversion of lignocellulosic biomass. Emphasis is given to the recent advances in artificial intelligence (AI)-based modelling that plays an increasingly important role in enhancing the performance of the models. This review shows that AI-based models offer prominent accuracy compared to thermodynamic equilibrium modelling implemented in some models. It is also evident that gasification and pyrolysis models are more matured than thermal liquefaction for lignocelluloses. Additionally, the knowledge gained and future directions in the applications of simulation and AI in process modelling are explored.

Application of q-rung orthopair fuzzy based SWARA-COPRAS model for municipal waste treatment technology selection

Despite several methods available for the treatment of solid wastes, the management of municipal solid waste is still a crucial and complex process. The available methods for waste treatment range from advanced to conventional techniques. The identification of a proper method for municipal solid waste management involves several techno-eco and environmental considerations. To solve the real-world problems of municipal waste management, the research proposed an integrated q-rung orthopair fuzzy number-based stepwise weight assessment ratio analysis-complex proportional assessment (SWARA-COPRAS) mathematical model to rank the waste treatment techniques. The research aimed to develop a systematic approach for a suitable selection of waste treatment methods. Ten (10) different alternatives for waste treatments were ranked against seven (07) different techno-eco and environmental criteria. The ambiguity in the decision was handled by the q-rung orthopair fuzzy numbers. The proposed integrated model has identified upcycling and recycling of waste having priority values of 100% and 99.9%, respectively, as the suitable practices for the successful management of generated solid wastes, whereas landfilling has obtained a minimum priority value of 66.782% and, therefore, is least preferable for waste management. The ranking of the alternatives followed the sequence as upcycling > recycling > pyrolysis > hydrolysis > biotechnological > core plasma pyrolysis > incineration > composting > gasification > landfilling. The comparison between the rankings of the proposed model with other techniques has revealed that the values of Spearman's rank correlation coefficient are in the range of 0.8545 to 0.9272; thereby, the robustness of the proposed model is verified. Sensitivity analysis for the criteria weight has showed that the ranking results are influenced significantly by the change in criteria weights and suggested that an accurate estimation of the criteria weight is decisive in determining the overall ranking of the alternative. The study has provided a framework for decision-making in the technology selection for solid waste management.

Characteristics of deactivation and thermal regeneration of Nb-doped V2O5-WO3/TiO2 catalyst for NH3-SCR reaction

This study investigated the effect of Nb doping into V₂O₅-WO₃/TiO₂ (VWT) catalyst for removing NO_xvia the SCR (selective catalytic reduction) by NH₃. The experimental results exhibited that Nb can improve the reactivity of the VWT catalyst at low temperatures. The addition of Nb also enhanced the tolerance to SO₂ and H₂O. The de-NO_x efficiency of the V₂O₅-WO₃-Nb₂O₅/TiO₂ (VWNbT) catalyst was increased up to 12% over that of the VWT catalyst at 240 °C when the catalyst was poisoned for 24 h. The prepared catalysts were characterized by FT-IR, XRD, XPS, and N₂ physisorption, elemental analysis. The results showed that the ammonium bisulfate (ABS) was less formed in the VWNbT than in the VWT. Moreover, evolved gas analysis was performed to examine the thermal decomposition behavior of the poisoned catalyst, and confirmed that the ABS deposited on the catalyst was sufficiently decomposed between about 300 and 400 °C. In particular, to most effectively recover the characteristics and activity of the catalysts, thermal treatment at a temperature of 400 °C is suitable.

Boosting hydrogen production via deoxygenation-sorption-enhanced biomass gasification

Gasification is one of the most promising approaches to accomplishing efficient utilization of biomass, nevertheless, it shows severe problems of low efficiency and syngas quality, which deserves further improvements. In this regard, deoxygenation-sorption-enhanced biomass gasification is proposed and experimentally explored using deoxidizer-decarbonizer materials (xCaO-Fe) for intensified hydrogen production. The materials follow the deoxygenated looping of Fe⁰-3e^-↔Fe³⁺ as an electron donor and the decarbonized looping of CaO + CO₂ ↔ CaCO₃ as a CO₂ sorbent. Specifically, the H₂ yield and CO₂ concentration reach 7.9 mmol·g^-1 biomass and 10.5 vol%, which increases by 311% and decreases by 75%, respectively, compared with conventional gasification, confirming the promotion effect of deoxygenation-sorption enhancement. Fe embedded within the CaO phase is successfully constructed with the formation of functionalized interface structure, affirming the strong interaction between CaO and Fe. This study brings in a new concept for biomass utilization via synergistic deoxygenation and decarbonization, which will substantially boost high-quality renewable hydrogen production.

Effects of Ni/Al2O3 catalyst treatment condition on thermocatalytic conversion of spent disposable wipes

Municipal solid waste (MSW) management is an essential municipal service. Proper waste treatment is an important part of the waste management. Thermocatalytic waste upcycling has recently gained great interest and attention as a method to extract value from waste, which potentially substitutes traditional waste treatment methods. This study aims at demonstrating the potential for thermocatalytic waste upcycling using spent disposable wipes as an MSW surrogate. Two different Ni/Al2O3 catalysts were prepared, treated under two different atmospheres (N2 and CO2). The catalyst treated in N2 (Ni/Al2O3-N2) exhibited a higher surface metallic Ni site than the catalyst treated in CO2 (Ni/Al2O3-CO2). The use of the Ni/Al2O3-N2 increased the yield of gas pyrolysate and decreased the yield of byproduct (e.g., wax), compared with no catalyst and the Ni/Al2O3-CO2. In particular, the Ni/Al2O3-N2 catalyst affected the generation of gaseous hydrogen (H2) by increasing the H2 yield by up to 102% in comparison with the other thermocatalytic systems. The highest H2 yield obtained with the Ni/Al2O3-N2 was attributed to the most surface metallic Ni sites. However, the Ni/Al2O3-N2 catalyst led to char having a lower higher heating value than the other catalysts due to its lowest carbon content. The results indicated that the reduction treatment environment for Ni/Al2O3 catalyst influences thermocatalytic conversion product yields of spent disposable wipes, including enhanced H2 production.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at 10.1007/s11814-023-1461-8.

A review of biowaste remediation and valorization for environmental sustainability: Artificial intelligence approach

Biowaste remediation and valorization for environmental sustainability focuses on prevention rather than cleanup of waste generation by applying the fundamental recovery concept through biowaste-to-bioenergy conversion systems - an appropriate approach in a circular bioeconomy. Biomass waste (biowaste) is discarded organic materials made of biomass (e.g., agriculture waste and algal residue). Biowaste is widely studied as one of the potential feedstocks in the biowaste valorization process due to its being abundantly available. In terms of practical implementations, feedstock variability from biowaste, conversion costs and supply chain stability prevent the widespread usage of bioenergy products. Biowaste remediation and valorization have used artificial intelligence (AI), a newly developed idea, to overcome these difficulties. This report analyzed 118 works that applied various AI algorithms to biowaste remediation and valorization-related research published between 2007 and 2022. Four common AI types are utilized in biowaste remediation and valorization: neural networks, Bayesian networks, decision tree, and multivariate regression. The neural network is the most frequent AI for prediction models, the Bayesian network is utilized for probabilistic graphical models, and the decision tree is trusted for providing tools to assist decision-making. Meanwhile, multivariate regression is employed to identify the relationship between experimental variables. AI is a remarkably effective tool in predicting data, which is reportedly better than the conventional approach owing to its characteristics of time-saving and high accuracy. The challenge and future work in biowaste remediation and valorization are briefly discussed to maximize the model's performance.

Resource recovery from discarded COVID-19 PPE kit through catalytic fast pyrolysis

During the COVID-19 pandemic, the world saw an exponential surge in the production of Personal Protective Equipment (PPE) kits, which eventually got discarded in the biomedical waste stream. In this study, thirteen different polymer samples from the PPE kit were collected and characterized using Fourier transform infrared spectrometer, thermogravimetric analysis, and analytical pyrolysis-gas chromatograph/mass spectrometry. The characterization data showed that about 94 % by mass of components were made of only three polymers, viz. polypropylene (PP, 75.6 wt %), polyethylene terephthalate (PET, 12.5 wt %), and polycarbonate (PC, 6 wt %). The analytical pyrolysis of the PPE coverall suit (PP) yielded mainly alkenes containing 2,4-dimethyl-1-heptene as the major compound with 17 wt % yield at 600 °C. The pyrolysates from face shield (PET) were rich in benzoic acid (5.8 wt %) and acetophenone (4.8 wt %), while those from safety goggles (PC) were rich in phenol (17.6 wt %) and p-cresol (12.4 wt %) at 600 °C. HZSM-5 and HY zeolites were used for the catalytic upgradation of pyrolysates especially from PP, PET and PC. The temperature and feed-to-catalyst ratio were optimized by performing catalytic fast pyrolysis experiments at 500 °C, 600 °C and 700 °C with different feed-to-catalyst ratios 1:2, 1:4, and 1:6 (w/w). The yield of aromatic hydrocarbons, viz., BTEX (benzene, toluene, ethylbenzene, xylenes) and naphthalene, was maximum (∼25.7 wt %) from PP coverall when HY catalyst was used at 600 °C and 1:6 (w/w) loading. In the case of PET face shield, the total yield of BTEX, naphthalene and biphenyl was maximum (27.9 wt %) at 600 °C and 1:4 (w/w) of HZSM-5, while in the case of PC goggles, it was maximum (18.6 wt %) at 700 °C and 1:4 (w/w) of HY. This study shows that the entire PPE kit can be valorized via catalytic fast pyrolysis to generate petrochemical products and platform molecules like monoaromatic hydrocarbons at high selectivities.

Algal-derived biochar as an efficient adsorbent for removal of Cr (VI) in textile industry wastewater: Non-linear isotherm, kinetics and ANN studies

Textile industries release effluent that contains the vast majority of heavy metals in which Cr (VI) is a toxic carcinogenic element that causes an environmental problem. The aim of the work is to synthesize algae-derived biochar derived from algae using slow pyrolysis at an operating temperature of 500 °C, a heating rate of 10 °C/min and a residence time of 60 min and to use it as an adsorbent to remove Cr (VI). The batch experiment was carried out using different concentrations of Cr (VI) (1, 10, 25, 50, 100, 125, 150 and 200 ppm) at different intervals of time (2.5, 5, 10, 15, 30, 60, 120 and 240 min). The maximum removal percentage of Cr (VI) is 97.88% for the metal concentration of 1 ppm exhibiting non-linear adsorption isotherm (Langmuir, Freundlich, Dubinin-Radushkevich, and Temkin models) and kinetic models (pseudo-first order, pseudo-second order, nth order, and intra-particle diffusion) were analyzed using a solver add-in of Microsoft Excel. According to the results, the Langmuir isotherm model (R² = 0.999) and pseudo-nth order models are suitable to describe monolayer adsorption and the process kinetics, respectively. The maximum adsorption capacity of algal biochar to adsorb is 186.94 mg/g. For the prediction of the optimal removal efficacy, an artificial neural network of the MLP-2-7-1 model was used. The results obtained are useful for future work using algal biochar as an adsorbent of Cr (VI) from textile wastewater to achieve sustainable development goals.

Upcycling textile waste using pyrolysis process

Rapidly changing fashion trends have generated tremendous amounts of textile waste globally. Textile waste is composed of a variety of substances (natural, synthetic, organic, and inorganic fibers). The inhomogeneity and complex nature of textile waste makes recycling economically challenging. Pyrolysis is a thermochemical process that transforms waste feedstocks of an inhomogeneous and complex nature into value added products (i.e., waste upcycling). This article provides a systematic review of the currently available and investigated pyrolysis processes to upcycle textile waste (e.g., material and energy recovery). The challenges in the pyrolysis process of textile waste are discussed, and relevant future research needs are recommended. Despite these challenges, pyrolysis will be an effective end-of-life option for textile waste if continuous research and development activities are conducted.

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 and statistical analysis for biomass torrefaction: A review

Torrefaction is a remarkable technology in biomass-to-energy. However, biomass has several disadvantages, including hydrophilic properties, higher moisture, lower heating value, and heterogeneous properties. Many conventional approaches, such as kinetic analysis, process modeling, and computational fluid dynamics, have been used to explain torrefaction performance and characteristics. However, they may be insufficient in actual applications because of providing only some specific solutions. Machine learning (ML) and statistical approaches are powerful tools for analyzing and predicting torrefaction outcomes and even optimizing the thermal process for its utilization. This state-of-the-art review aims to present ML-assisted torrefaction. Artificial neural networks, multivariate adaptive regression splines, decision tree, support vector machine, and other methods in the literature are discussed. Statistical approaches (SAs) for torrefaction, including Taguchi, response surface methodology, and analysis of variance, are also reviewed. Overall, this review has provided valuable insights into torrefaction optimization, which is conducive to biomass upgrading for achieving net zero.

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.

Predicting the high heating value and nitrogen content of torrefied biomass using a support vector machine optimized by a sparrow search algorithm

A support vector machine (SVM) model with RBF kernel function combined with sparrow search algorithm (SSA) optimization was developed to predict the HHV and nitrogen content (No) values of torrefied biomass based on the feedstock properties and torrefaction conditions. Results showed that SSA optimization significantly improved the prediction performance of the SVM model for both HHV and No. A coefficient of determination (R ²) larger than 0.91 was achieved when the SSA-SVM model was implemented, and the values of RMSE were also fairly acceptable. The agreement between experimental data and SSA-SVM predicted values demonstrated the high predictive precision of the model. This study provides a reference for the utilization of torrefied biomass in solid fuels and the design of torrefaction facilities.

A comprehensive review on the advances of bioproducts from biomass towards meeting net zero carbon emissions (NZCE)

This review investigates the development of bioproducts from biomass and their contribution towards net zero carbon emissions. The promising future of biomasses conversion techniques to produce bioproducts was reviewed. The advances in anaerobic digestion as a biochemical conversion technique have been critically studied and contribute towards carbon emissions mitigation. Different applications of microalgae biomass towards carbon neutrality were comprehensively discussed, and several research findings have been tabulated in this review. The carbon footprints of wastewater treatment plants were studied, and bioenergy utilisation from sludge production was shown to mitigate carbon footprints. The carbon-sinking capability of microalgae has also been outlined. Furthermore, integrated conversion processes have shown to enhance bioproducts generation yield and quality. The anaerobic digestion/pyrolysis integrated process was promising, and potential substrates have been suggested for future research. Lastly, challenges and future perspectives of bioproducts were outlined for a contribution towards meeting carbon neutrality.

Valorization of biomass through gasification for green hydrogen generation: A comprehensive review

Green and sustainable hydrogen from biomass gasification processes is one of the promising ways to alternate fossil fuels-based hydrogen production. First off, an overview of green hydrogen generation from biomass gasification processes is presented and the corresponding possible gasification reactions and the effect of respective experimental criteria are explained in detail. In addition, a comprehensive explanation of the catalytic effect on tar reduction and hydrogen generation via catalytic gasification is presented regarding the functional mechanisms of various types of catalysts. Furthermore, the commercialization aspects, the associated technical challenges and barriers, and the prospects of a biomass gasification process for green hydrogen generation are discussed. Finally, this comprehensive review provides the related advancements, challenges, and great insight of biomass gasification for the green hydrogen generation to realize a sustainable hydrogen society via biomass valorization.

Comparative study of machine learning methods integrated with genetic algorithm and particle swarm optimization for bio-char yield prediction

In this study, Machine learning (ML) models integrated with genetic algorithm (GA) and particle swarm optimization (PSO) have been developed to predict, evaluate, and analyze biochar yield using biomass properties and process operating conditions. Comparative study of different ML algorithms integrated with GA and PSO were performed to improve the ML models architecture and parameters selection. The results proposed that Ensembled Learning Tree (ELT-PSO) model outperformed all other models and is favored for biochar yield prediction (R² = 0.99, RMSE = 2.33). The partial dependence plots (PDPs) analysis shows the potential effects of each influencing parameter impact on the biochar yield and as well as shows that how these factors will interact during the pyrolysis process. A user-friendly software was developed based on the ELT-PSO model to avoid extensive and expensive experimentations without requiring considerable ML understanding. Difference recorded by GUI was less than 2% with experimental yield.

Biochar derived carbonaceous material for various environmental applications: Systematic review

Biochar is the solid material produced from the carbonization of organic feedstock biomass. This material has several unique characteristics such as greater carbon content, good electrical conductivity, high stability and large surface area, which can be applied in several research areas such as generation of power and wastewater treatment. In connection with this, recently, the investigations on biochar significantly focus on the removal of toxic heavy metals since the biochar material is easily available and environmentally friendly. According to an environmental analytical device, biochar-derived carbonaceous material has been additionally applied to the synthesis of an effective, sensitive, and low-cost electrochemical sensor. Biochar with an assessment of electrochemical properties has engaged with different redox reactions in water. In this survey, electrochemical ways of behaving of biochar in light of the electrochemical structures were analytically compiled as well as the impact from biomass sources and manufacturing process including carbonization strategies, pre-treatment/changed techniques. This review emphasizes the various synthesis methods of biochar form organic feedstock, properties and different modulations of biochar for the bioremediation of heavy metals. This review study emphasizes the utilization of biochar as sensing platform and supercapacitor for electrode fabrication in electrochemical biosensor to enhance the remediation of toxic contaminants from water streams and by switching the less ecological traditional materials. Brief information on the techniques employed for packaging biochar as carbon electrode is summarized. Scope in the aspect of environmental concern of biochar, future challenges and prospects are proposed in detail.

Influence of Pyrolysis Temperature on Biochar Produced from Lignin–Rich Biorefinery Residue

The biorefinery concept is growing rapidly for bio-based production of fuels and products, and steam explosion is by far the most applied pre-treatment technology allowing the delignification of lignocellulosic biomass. Within the bioethanol production process, pyrolysis of lignin-rich residue (LRR), for producing char to be used in a wide variety of applications, presents a viable way to recover materials and energy, helping to improve the sustainability of the whole production chain. In the present study, it is shown that yields, elemental composition and porosity characteristics of LLR-char are significantly different from those of char produced from alkali lignin. Both products yields and char composition were more similar to the typical values of woody and herbaceous biomasses. The chemical characterization of the chars’ organic matrices as well as the content of the main inorganic species suggest the opportunity to perform pyrolysis at low temperatures for producing high yields of chars suitable to be used as carbon sink or soil fertilizers. The BET values of the chars obtained at final temperatures in the range 500–700 °C seem to be promising for char-application processes involving surface phenomena (e.g., adsorption, catalyst support), thus encouraging further analyses of char-surface chemistry.

Synthesis of sustainable chemicals from waste tea powder and Polystyrene via Microwave-Assisted in-situ catalytic Co-Pyrolysis: Analysis of pyrolysis using experimental and modeling approaches

In the current study, catalytic co-pyrolysis was performed on waste tea powder (WTP) and polystyrene (PS) wastes to convert them into value-added products using KOH catalyst. The feed mixture influenced the heating rates (17-75 °C/min) and product formation. PS promoted the formation of oil and WTP enhanced the char formation. The maximum oil yield (80 wt%) was obtained at 15 g:5 g, and the maximum char yield (44 wt%) was achieved at 5 g:25 g (PS:WTP). The pyrolysis index (PI) increased with the increase in feedstock quantity. High PI was noticed at 25 g:5 g, and low PI was at 5 g:5 g (PS:WTP). Low energy consumption and low pyrolysis time enhanced the PI value. Significant interactions were noticed during co-pyrolysis. The obtained bio-oil was analyzed using GC-MS and a plausible reaction mechanism is presented. Catalyst and co-pyrolysis synergy promoted the formation of aliphatic and aromatic hydrocarbons by reducing the oxygenated products.

Production of biochar from crop residues and its application for biofuel production processes - An overview

A sustainable carbon-neutral society is imperative for future generations, and biochars and biofuels are inevitable choice to achieve this goal. Crop residues (CR) such as sugarcane bagasse, corn stover, and rice husk are promising sustainable resources as a feedstock for biochars and biofuels. Extensive research has been conducted on CR-based biochar production not only in environmental remediation areas but also in application for biofuel production. Here, the distribution and resource potential of major crop residues are presented. The production of CR-biochar and its applications in biofuel production processes, focusing on the latest research are discussed. Finally, the challenges and areas of opportunity for future research in terms of CR supply, CR-biochar production, and CR-biochar utilization for biofuel production are proposed. Compared with other literature reviews, this study can serve as a guide for the establishment of sustainable, economical, commercial CR-based biorefineries.

MgO-modified activated biochar for biojet fuels from pyrolysis of sawdust on a simple tandem micro-pyrolyzer

The aim of this work was to study on MgO-modified KOH activated biochar (AC) catalysts, in the pyrolysis of sawdust for the direct production of bio-jet fuels using a tandem micro-pyrolyzer. AC catalysts with various MgO contents (5 to 20 wt%) were synthesized using an impregnation method. The mesopores generated (4 to 18 nm) in the carbon has a great potential in the conversion of oxygenated to jet fuel. The importance of basic nature in the catalysts is demonstrated with the maximum bio-jet fuel yield of 29 % at 10 % MgO. Further, the temperature of 600 °C and a catalyst/sawdust ratio of 10 are identified as the optimal conditions. The nanosize of MgO and the synergism of acid and base sites seemed to enhance deoxygenation, via decarboxylation and decarbonylation, and oligomerization, which are required for jet fuel formation in high amounts from sawdust pyrolysis.

Strategies to enhance enzymatic hydrolysis of lignocellulosic biomass for biorefinery applications: A review

Global interest in lignocellulosic biorefineries has increased in the recent past due to technological advancements in sustainable and cost-effective production of numerous commodity and speciality chemicals and fuels from renewable lignocellulosic biomass (LCB). As a result, the market value of biorefinery products has also increased over the time, with an estimated worth of USD 867.7 billion by 2025. However, biorefinery operations, especially enzymatic hydrolysis, suffer from many challenges that limits the cost-effectiveness of conversion of LCB. Therefore, it is essential to understand and address these challenges in future biorefineries. The paper focuses on recent trends and challenges in enzymatic hydrolysis of LCB during lignocellulosic biorefinery operation for greener synthesis of energy, fuels, chemicals and other high-value products. Insights into the gaps in knowledge and technological challenges have also been addressed together with focus on future research needs and perspectives of enzymatic hydrolysis of LCB for biorefinery applications.

Understanding of synergy in non-isothermal microwave-assisted in-situ catalytic co-pyrolysis of rice husk and polystyrene waste mixtures

Rice husk (RH) and polystyrene (PS) wastes were converted into value-added products using microwave-assisted catalytic co-pyrolysis. The graphite susceptor (10 g) along with KOH catalyst (5 g) was mixed with the feedstock to understand the products and energy consumption. RH promoted the char yield (20-34 wt%) and gaseous yields (16-25 wt%) whereas PS enhanced the oil yield (23-70 wt%). Co-pyrolysis synergy induced an increase in gaseous yields (14-53 wt%) due to excessive cracking. The specific microwave energy consumption dramatically decreased in co-pyrolysis (5-22 kJ/g) compared to pyrolysis (56-102 kJ/g). The pyrolysis index increased (17-445) with the increase in feedstock quantity (5-50 g). The obtained oil was composed of monoaromatics (74%) and polyaromatics (18%). The char was rich in carbon content (79.5 wt%) and the gases were composed of CO (24%), H₂ (12%), and CH₄ (22%).

Antibiotic bioremediation by new generation biochar: Recent updates

The evolving multidrug resistance in microbes with increasing antibiotic pollution is becoming a severe global crisis. Recent developments on antibiotic remediations by biochar are promising. Advancements in biochar engineering enhanced biochar remediation efficiency to another level through developing new interactions and bonding abilities with antibiotic pollutants. Especially chemical/metal-composite modification significantly increased catalysis of biochar. The review's main focus is to underline biochar efficiency for the abatement of emerging antibiotic pollutants. Moreover, to relate feedstock, production conditions, and engineering techniques with biochar properties. Also, modification strategies are reviewed to obtain biochar or their composites before examining improved remediation potential ranging from 20 to 552 mg g^-1 for various antibiotics. Biochar offers different interactions depending on the surface functionalities e.g., π-π stacking, electrostatic, H-bonding, etc. Biochar and related composites have also been reviewed for remarkable properties e.g., photocatalysis, adsorption, and oxidation processes. Furthermore, future research directions and opportunities for biochar research are discussed.

Syngas biomethanation: Current state and future perspectives

In regions highly dependent on fossil fuels imports, biomethane represents a promising biofuel for the transition to a bio-based circular economy. While biomethane is typically produced via anaerobic digestion and upgrading, biomethanation of the synthesis gas (syngas) derived from the gasification of recalcitrant solid waste has emerged as a promising alternative. This work presents a comprehensive and in-depth analysis of the state-of-the-art and most recent advances in the field, compiling the potential of this technology along with the bottlenecks requiring further research. The key design and operational parameters governing syngas production and biomethanation (e.g. organic feedstock, gasifier design, microbiology, bioreactor configuration, etc.) are critically analysed.

Artificial neural networks for the prediction of biochar yield: A comparative study of metaheuristic algorithms

In this study, an integrated framework of artificial neural networks (ANNs) and metaheuristic algorithms have been developed for the prediction of biochar yield using biomass characteristics and pyrolysis process conditions. Comparative analysis of six different metaheuristic algorithms was performed to optimize the ANN architecture and select important features. The results suggested that the ANN model coupled with the Rao-2 algorithm outperformed (R² ∼ 0.93, RMSE ∼ 1.74%) all other models. Furthermore, the detailed information behind the models was acquired, identifying the most influencing factors as follows: pyrolysis temperature (56%), residence time (23%), and heating rate (8%). The partial dependence plot analysis revealed how each influencing factor affected the target variable. Finally, an easy-to-use software tool for predicting biochar yield was built using the ANN-Rao-2 model. This study demonstrates huge potential that machine learning presents in predictive modelling of complex pyrolysis processes, and reduces the time-consuming and expensive experimental work for estimating the biochar yield.

Pyrolysis Mechanism of Wheat Straw Based on ReaxFF Molecular Dynamics Simulations

Biomass has played an increasingly important role in the consumption of energy worldwide because of its renewability and carbon-neutral property. In this work, the pyrolysis mechanism of wheat straw is explored using reactive force field molecular dynamics simulations. A large-scale wheat straw model composed of cellulose, hemicellulose, and lignin is built. After model validation, the temporal evolutions of the main pyrolysis products under different temperatures are analyzed. As the temperature rises, the gas production increases and the tar yield can decrease after peaking. Relatively high temperatures accelerate the generation rates of the main gas and tar species. CO and CO₂ molecules mainly come from the cleavage of CHO₂ radicals, and numerous H₂O molecules are generated on account of dehydration. Moreover, the evolution of six functional groups and pyran and phenyl rings as well as three types of bonds is also presented. It is observed that the phenyl rings reflect improved thermostability. Finally, the pyrolytic kinetics analysis is conducted, and the estimated activation energy of wheat straw pyrolysis is found to be 56.19 kJ/mol. All these observations can help deeply understand the pyrolytic mechanism of wheat straw biomass.

Technical benefits of using methane as a pyrolysis medium for catalytic pyrolysis of Kraft lignin

Catalytic fast pyrolysis of low sulfonated Kraft lignin was performed under different atmospheric environments such as N₂, CH_4, and the gas derived from CH₄ decomposition (CH₄-D). The use of Zn- or Mo-loaded HZSM-5 as catalyst led to a higher pyrolytic oil yield compared to parent HZSM-5 in CH₄ and CH₄-D atmospheres. The yields of benzene, toluene, and xylenes were increased by the synergistic effects from metal loading, higher H/C_eff ratio, higher acidity, and CH₄ activation. The enhanced CH₄ activation via metal loading resulted in higher methylation of alkyl moieties and 33% increase in the total yield of benzene, toluene, and xylenes in comparison to parent HZSM-5. A higher H/C_eff ratio of 6 via CH₄ decomposition led to the formation of a hydro-pyrolysis environment. Moreover, the CH₄-D environment showed H₂/CH₄ ratio of 0.36 in the product gas which warranted the presence of more H₂ under the CH₄-D pyrolysis environment.

Production of Bio-Oils and Biochars from Olive Stones: Application of Biochars to the Esterification of Oleic Acid

Olive stones are a by-product of the olive oil industry. In this work, the valorisation of olive stones through pyrolysis was attempted. Before pyrolysis, half of the samples were impregnated with sulphuric acid. Pyrolysis was carried out in a vertical tubular furnace with a ceramic support. The pyrolysis conditions assayed were: temperature between 400 and 600 °C, heating ramp between 5 and 20 °C∙min−1, and inert gas flow rate between 50 and 300 mL Ar∙min−1. Among them, temperature was the only parameter that influenced the pyrolysis product distribution. The most suitable temperature for obtaining biochar was 400 °C for both non-treated and pre-treated raw material, while for obtaining bio-oil, it was 600 °C for impregnated olive stones and 400 °C for the raw material. The impregnated olives stones led to bio-oils with much higher amounts of high-added-value products such as levoglucosenone and catechol. Finally, the biochars were impregnated with sulphuric acid and assayed as biocatalysts for the esterification of oleic acid with methanol in a stirred tank batch reactor at 60 °C for 30 min. Biochars from non-treated olive stones, which had lower specific surfaces, led to higher esterification yields (up to 96.2%).