Search Results (967)

The increasing urgency to mitigate plastic pollution has accelerated the shift from linear manufacturing toward circular systems. This review synthesizes current advances in mechanical, chemical, biological, and upcycling pathways, emphasizing how artificial intelligence (AI) is reshaping decision-making, performance prediction, and system-level optimization. Intelligent sensing technologies—such as FTIR, Raman spectroscopy, hyperspectral imaging, and LIBS—combined with Machine Learning (ML) classifiers have improved material identification, reduced reject rates, and enhanced sorting precision. AI-assisted kinetic modeling, catalyst performance prediction, and enzyme design tools have improved process intensification for pyrolysis, solvolysis, depolymerization, and biocatalysis. Life Cycle Assessment (LCA)-integrated datasets reveal that environmental benefits depend strongly on functional-unit selection, energy decarbonization, and substitution factors rather than mass-based comparisons alone. Case studies across Europe, Latin America, and Asia show that digital traceability, Extended Producer Responsibility (EPR), and full-system costing are pivotal to robust circular outcomes. Upcycling strategies increasingly generate high-value materials and composites, supported by digital twins and surrogate models. Collectively, evidence indicates that AI moves from supportive instrumentation to a structural enabler of transparency, performance assurance, and predictive environmental planning. The convergence of AI-based design, standardized LCA frameworks, and inclusive governance emerges as a necessary foundation for scaling circular plastic systems sustainably. Full article

Catalytic pyrolysis (CP) of biomass is a promising method for producing sustainable hydrogen because lignocellulosic biomass is widely available, renewable, and approximately carbon-neutral. CP of biomass is influenced by complex, interdependent process parameters, making optimization challenging and time-consuming using traditional methods. This study investigated a two-stage machine learning (ML) framework fortified with Bayesian optimization to enhance hydrogen production from CP. The ML models were used to classify and predict hydrogen yield using a dataset of 306 points with 14 input features. The classification stage identified conditions favorable for good hydrogen yield, while the regression model (second stage) quantitatively predicted hydrogen yield. The random forest classifier and regressor demonstrated superior capabilities, achieving R² scores of 1.0 and 0.8, respectively. The model demonstrated strong agreement with experimental data and effectively captured the key factors driving hydrogen production. Shapley Additive exPlanation (SHAP) identified temperature and catalyst properties (nickel loading) as the most influential parameters. The inverse analysis framework validated the model’s ability to determine optimal conditions for predicting targeted hydrogen yields by comparing it to experimental data reported in the literature. This AI-driven approach provides a scalable and data-efficient tool for optimizing processes in sustainable hydrogen production. Full article

Understanding the influence of tree species and their intrinsic traits on biochar yield and carbon retention is essential for optimizing the conversion of biomass to biochar in carbon-negative systems. While it is well-established that pyrolysis temperature and broad feedstock categories significantly affect biochar properties, the extent of species-level variation within woody biomass under standardized pyrolysis conditions remains insufficiently quantified. Here, we synthesized biochar from seven common subtropical tree species at 600 °C under oxygen-limited smoldering conditions and quantified three key indices: biochar yield (Y), carbon recovery efficiency (ηC), and carbon enrichment factor (EC). We further examined the relationships of these indices with feedstock characteristics (initial carbon content, wood density) and functional group identity (conifer vs. broadleaf). Analysis of variance revealed significant interspecific differences in ηC but weaker effects on Y, indicating that species identity primarily governs carbon retention rather than total mass yield. Broadleaf species (Liquidambar formosana, Castanea mollissima) exhibited consistently higher ηC and EC than conifers (Pinus massoniana, P. elliottii), reflecting higher lignin content and wood density that favor aromatic char formation. Principal component and cluster analyses clearly separated coniferous and broadleaf taxa, accounting for over 80% of total variance in carbon-related traits. Regression models showed that feedstock carbon content, biochar carbon content, and wood density together explained 15.5% of the variance in ηC, with feedstock carbon content exerting a significant negative effect, whereas wood density correlated positively with carbon retention. These findings demonstrate that tree species and their functional traits jointly determine carbon fixation efficiency during smoldering. High initial carbon content alone does not guarantee enhanced carbon recovery; instead, wood density and lignin-derived structural stability dominate retention outcomes. Our results underscore the need for trait-based feedstock selection to improve biochar quality and carbon sequestration potential, and provide a mechanistic framework linking species identity, functional traits, and carbon stabilization in biochar production. Full article

Biochar, a carbon-rich material produced through the pyrolysis of wood residues and agricultural byproducts, has carbon storage capacity and potential as a low-carbon construction material. This study predicts the compressive strength of cementitious composites in which cement is partially replaced with biochar using machine learning models. A total of 716 data samples were analyzed, including 480 experimental measurements and 236 literature-derived values. Input variables included the water-to-cement ratio (W/C), biochar content, cement, sand, aggregate, silica fume, blast furnace slag, superplasticizer, and curing conditions. Predictive performance was evaluated using Multiple Linear Regression (MLR), Elastic Net Regression (ENR), Support Vector Regression (SVR), and Gradient Boosting Machine (GBM), with GBM showing the highest accuracy. Further optimization was conducted using XGBoost, Light Gradient-Boosting Machine (LightGBM), CatBoost, and NGBoost with GridSearchCV and Optuna. LightGBM achieved the best predictive performance (mean absolute error (MAE) = 3.3258, root mean squared error (RMSE) = 4.6673, mean absolute percentage error (MAPE) = 11.19%, and R² = 0.8271). SHAP analysis identified the W/C and cement content as dominant predictors, with fresh water curing and blast furnace slag also exerting strong influence. These results support the potential of biochar as a partial cement replacement in low-carbon construction material. Full article

This research employed “Response Surface Methodology (RSM)” to assess the effectiveness of electrocoagulation (EC) in treating olive mill wastewater (OMW) before applying adsorption with olive stone biochar (OS) as a sustainable adsorbent. Several parameters, including reaction time, current density (CD), inter-electrode distance, and the number of electrodes, were optimized. Analysis using Minitab 22.2 resulted in robust regression models with high coefficients of determination (R²). The optimal parameters were CD of 12.41 mA/cm², a time of 45.61 min, an inter-electrode spacing of 1 cm, and a maximum of 6 electrodes, resulting in an energy consumption (ENC) of 9.85 kWh/m³. Significant pollutant percentage removals were achieved: 72.32% for total Kjeldahl nitrogen (TKN), 80.74% for turbidity, 57.44% for total phenol (TPh), 56.9% for soluble chemical oxygen demand (COD_soluble), and 56.6% for total chemical oxygen demand (COD_total). After the EC, the adsorption of pollutants was conducted using OS biochar that was generated through the pyrolysis of OS at a temperature of 500 °C. FTIR analysis of the biochar revealed key absorption bands that indicated the presence of inorganic compounds, aromatic C=C, and phenolic groups O-H. The integrated EC and adsorption (ECA) process demonstrated markedly higher efficiencies, with TPh removal reaching 61.41%, turbidity reduction at 81.92%, TKN reduction at 77.78%, COD_soluble reduction at 70.31%, COD_total reduction at 65.1%, and project cost of $2.88/m³. The ECA process presents a promising treatment approach for OMW. Full article

This manuscript reports the preparation, surface characterization, and modeling of chars and activated carbons obtained from avocado biomass for hydrogen storage. Activated carbons were prepared from avocado biomass via the following stages: (a) pyrolysis of avocado biomass, (b) impregnation of the avocado-based char using an aqueous lithium solution, and (c) thermal activation of lithium-loaded avocado char. The synthesis conditions of char and activated carbon samples were tailored to maximize their hydrogen adsorption properties at 77 K, where the impact of both pyrolysis and activation conditions was assessed. The hydrogen storage mechanism was discussed based on computational chemistry calculations and multilayer adsorption simulation. The modelling focuses on the analysis of the saturation of activated carbon active sites via the adsorption of multiple hydrogen molecules. The results showed that the activated carbon samples displayed adsorption capacities higher than their char counterparts by 71–91% because of the proposed activation protocol. The best activated carbon obtained from avocado residues showed a maximum hydrogen adsorption capacity of 142 cm³/g, and its storage performance can compete with other carbonaceous adsorbents reported in the literature. The hydrogen adsorption mechanism implied the formation of 2–4 layers on activated carbon surface, where physical interactions via oxygenated functionalities played a relevant role in the binding of hydrogen dimers and trimers. The results of this study contribute to the application of low-cost activated carbons from residual biomass as a storage medium in the green hydrogen supply chain. Full article

Keeping the levels of soil major nutrients (total nitrogen, TN; available phosphorous, AP; and available potassium, AK) in optimum condition is important to achieve the goals of precision agriculture systems. To address the issues of slow speed and low accuracy in soil nutrient detection, this study developed a prediction model for soil major nutrients content based on an improved Extreme Learning Machine (ELM) algorithm. This model utilizes a soil major nutrients detection system integrating pyrolysis and artificial olfaction. First, the Bootstrap Aggregating (Bagging) ensemble strategy was introduced during the model integration phase to effectively reduce prediction variance through multi-submodel fusion. Second, Generative Adversarial Networks (GAN) were employed for sample augmentation, enhancing the diversity and representativeness of the dataset. Subsequently, a multi-scale convolutional and Efficient Lightweight Attention Network (ELA-Net) was embedded in the feature mapping layer to strengthen the representation capability of soil gas features. Finally, adaptive hyperparameter tuning was achieved using the Adaptive Chaotic Bald Eagle Optimization Algorithm (ACBOA) to enhance the model’s generalization capability. Results demonstrate that this model achieves varying degrees of performance improvement in predicting total nitrogen (R² = 0.894), available phosphorus (R² = 0.728), and available potassium (R² = 0.706). Overall prediction accuracy surpasses traditional models by 8–12%, with significant reductions in both RMSE and MAE. These results demonstrate that the method can rapidly, accurately, and non-destructively estimate key soil nutrients, providing theoretical guidance and practical support for field fertilization, soil fertility assessment, and on-site decision-making in precision agriculture. Full article

This study assesses four hydrogen production pathways (electrolysis, ammonia cracking, steam biomethane reforming, and methane pyrolysis) for an inland refinery under European Renewable Energy Directive III (RED III) goals. Using multicriteria decision analysis (MCDA), economic, environmental, technological, and implementation factors were evaluated. The results show that biomethane reforming offers the lowest cost, while electrolysis provides the best environmental and technological performance. Sensitivity analysis highlights electricity price as the key factor. The MCDA model proved to be effective for systematic comparison and informed strategic decision making. However, RED III regulatory requirements may favor ammonia or electrolysis for renewable fuel of non-biological origin production, emphasizing the need for long-term strategic planning to maintain competitiveness. Full article

Converting agricultural solid waste into porous biochar for HCHO adsorption is considered as a “two birds with one stone” strategy, which can achieve the environmental goal of “treating waste with waste”. Unfortunately, the HCHO adsorption performance of pristine biochar is generally unsatisfactory, which is derived from its poor surface activity and insufficient number of pores. In this study, a series of nitrogen-doped porous biochars with adjustable N-containing groups and porosity were synthesized by one-step pyrolysis of melamine and waste jujube pit in different mass ratios (NBC-x, x represented the mass ratio of melamine to waste jujube pit, x = 4–12) for HCHO adsorption. The HCHO adsorption tests indicated that the insertion of nitrogen-containing species improved the adsorption capacity of pristine biochar (BC). However, after the insertion of excessive nitrogen-containing species, the porosity of the samples significantly decreased due to the blockage of pores, which could be disadvantageous for HCHO adsorption. DFT calculation results showed that N doping (especially pyrrolic-N) significantly increased the maxima of absolute ESP values of the carbonaceous models and consequently enhanced the affinity between polar HCHO and carbonaceous models (varied from −20.65 kJ/mol to −33.26 kJ/mol). Thus, the NBC-8 possessing both substantial nitrogen content (19.81 wt. %) and developed porosity (specific surface area of 223 m²/g) exhibited the highest HCHO uptake of 6.30 mg/g. This was approximately 6.4 times larger than that of BC. This work not only deepens the understanding of the HCHO adsorption mechanism at molecular scale, but also concurrently offers a facile and eco-friendly route of N-doped porous biochar preparation, an efficient technology with high-value utilization of waste biomass resources, and a sustainable method of pollution remediation. Full article

Chemical looping gasification (CLG) based on interconnected fluidized beds is a viable technology to produce a syngas stream for chemical and fuel production. In this work, microalgae are studied for use in the CLG process; more specifically, the intrinsic kinetics of char gasification have been analyzed, as it is important for the fuel conversion and design of reactor systems. Char produced from fast pyrolysis was used in a thermogravimetric analyzer (TGA) for intrinsic kinetics analysis, and measures were made to eliminate the interparticle and external particle gas diffusion. The effect of typical operational variables, such as temperature, concentration of gasification agents (H₂O and CO₂), and concentration of gasification products (H₂ and CO), were investigated. The TGA data is used to derive a suitable gasification model that can best fit the experimental data. The fitting with experiments then generates values of the model’s kinetics parameters. Based on the model and the kinetics values, the activation energies in the gasification with steam and CO₂ were calculated to be 43.3 and 91.6 kJ/mol, respectively. The model has a good capability in the prediction of the gasification profile with H₂O and CO₂ under a complex reacting atmosphere. Full article

In fluidized bed conversion processes such as pyrolysis and combustion, defluidization mainly arises from particle agglomeration, which originates from the surface coating of primary bed materials (e.g., sand) by partially liquefied feedstock components, e.g., plastics or biomass. For reliable operation, the probability of occurrence of defluidization must be quantifiable. However, existing models are either computationally expensive or difficult to transfer across feedstocks with different rheological behaviors. Furthermore, such transferability challenges are particularly pronounced in technically relevant systems involving liquefied components, such as molten polymers and ash-derived silicate melts. In this study, we propose two new coating approaches: (i) a simplified full coating model, where a fraction of bed particles is directly assumed to be fully covered upon feed introduction, and (ii) a partial coating model, where only local surface regions of particles are coated. The proposed models are implemented within a Monte Carlo framework and validated against experimental data reported in the literature for polyethylene and polypropylene pyrolysis as well as for wheat straw combustion. Across all cases, the model predictions capture the experimentally observed defluidization behavior reported in reference studies (e.g., with coefficients of determination of $R^2=0.912$ for the polymer series and $R^2=0.917$ for the wheat straw series). Beyond model validation, several model-based analyses and discussions are further conducted based on the characteristics of the proposed framework. Overall, the developed methodology provides a generalized basis for analyzing coating-driven defluidization across polymers and biomass, with potential extensions to co-pyrolysis, co-gasification, and other thermochemical conversion processes. Full article

This study examined the thermal degradation of pulverized Musa sapientum (banana) peel waste through thermogravimetric measurements and thermokinetic modelling. For the first time, it also incorporated backpropagation deep learning to model pyrolysis traces, enabling the prediction and optimization of the process. Physicochemical characterization confirmed the material’s lignocellulosic composition. TGA was performed between 30 and 950 °C at heating rates of 5, 10, 20, and 40 °C min⁻¹, identifying a primary devolatilization range of 190 to 660 °C. The application of a backpropagation machine learning technique to the processed TGA data enabled the estimation of arbitrary constants that accurately captured the characteristic behaviour of the experimental data ($R^2~0.99$). This modelling and simulation approach achieved a significant reduction in training loss—decreasing from 35.9 to 0.07—over 47,688 epochs and 1.4 computational hours. Sensitivity analysis identified degradation temperature as the primary parameter influencing the thermochemical conversion of BP biomass. Furthermore, analyzing deconvoluted DTG traces via Criado master plots revealed that the 3D diffusion model (Jander [D3]) is the most suitable reaction model for the hemicellulose, cellulose, and lignin components, followed by the R2 and R3 geometrical contraction models. The estimated overall activation energy values obtained through the Starink (STK) and Friedman (FR) model-free isoconversional kinetic methods were 82.8 ± 3.3 kJ.mol⁻¹ and 97.6 ± 3.9 kJ.mol⁻¹, respectively. The thermodynamic parameters estimated for the pyrolysis of BP indicate that the formation of activated complexes is endothermic, endergonic, and characterized by reduced disorder, thereby establishing BP as a potential candidate material for bioenergy generation. Full article

The rapid growth and seasonal availability of agricultural materials, such as straws, stalks, husks, shells, and processing wastes, present both a disposal challenge and an opportunity for renewable fuel production. Solar-assisted thermochemical conversion, such as solar-driven pyrolysis, gasification, and hydrothermal routes, provides a pathway to produce bio-oils, syngas, and upgraded chars with substantially reduced fossil energy inputs compared to conventional thermal systems. Recent experimental research and plant-level techno-economic studies suggest that integrating concentrated solar thermal (CSP) collectors, falling particle receivers, or solar microwave hybrid heating with thermochemical reactors can reduce fossil auxiliary energy demand and enhance life-cycle greenhouse gas (GHG) performance. The primary challenges are operational intermittency and the capital costs of solar collectors. Alongside, machine learning (ML) and AI tools (surrogate models, Bayesian optimization, physics-informed neural networks) are accelerating feedstock screening, process control, and multi-objective optimization, significantly reducing experimental burden and improving the predictability of yields and emissions. This review presents recent experimental, modeling, and techno-economic literature to propose a unified classification of feedstocks, solar-integration modes, and AI roles. It reveals urgent research needs for standardized AI-ready datasets, long-term field demonstrations with thermal storage (e.g., integrating PCM), hybrid physics-ML models for interpretability, and region-specific TEA/LCA frameworks, which are most strongly recommended. Data’s reporting metrics and a reproducible dataset template are provided to accelerate translation from laboratory research to farm-level deployment. Full article

The present studies focus on the analysis of the inherent differences between temperature- and pyrolysis-based models and foster a rational and comprehensive understanding of numerical models for lightning strike damage in laminated composites. A systematic methodology combining numerical simulation and pyrolysis kinetics analysis has been developed to examine the inherent differences in damage area and depth, damage threshold, electrical conductivity characteristics, and Joule energy between temperature- and pyrolysis-based models. The results indicate that the pyrolysis-based model demonstrates closer agreement with experimental data in terms of both damage area and damage depth predictions compared to the temperature-based model. The two damage thresholds (500 °C and pyrolysis degree of 0.1) yield equivalent predictions of overall damage, but the temperature-based criterion neglects localized heating rate effects. The pyrolysis-based model exhibits significantly delayed through-thickness conductivity development during initial current conduction compared to the temperature-based model due to the influence of heating rate. This lag results in the pyrolysis-based model predicting larger damage areas and shallower penetration depths. Joule heating analysis further confirms that the pyrolysis-based model exhibits higher overall electrical resistance than the temperature-based model. Through a systematic comparison of temperature- and pyrolysis-based models, this research holds the significance of enhancing the understanding of lightning strike damage mechanisms and advancing the development of high-fidelity numerical models for predicting lightning strike damage in laminated composite. Full article

This study aimed to synthesize a magnetic biochar@ZIF-8 composite derived from almond shell biomass for the adsorption of fluoroquinolones (FQs) from aqueous media. The biochar was prepared under different pyrolysis conditions using a central composite design (CCD) based on temperature and residence time, with biochar yield (%) and ofloxacin adsorption capacity selected as the response variables. Subsequently, the composite was obtained by combining KOH-activated biochar with ZIF-8 and magnetic particles, producing a hierarchically porous material with enhanced surface area and functional groups favorable for adsorption. The physicochemical and morphological properties of the composite were characterized by SEM–EDS, FTIR, BET, TGA, and XRD analyses, confirming the successful incorporation of ZIF-8 and magnetic phases onto the biochar surface. The adsorption performance was systematically evaluated by studying the effects of pH and contact time. The kinetic data fitted well to the pseudo-second-order model, suggesting that chemisorption predominates through π–π stacking, hydrogen bonding, and coordination interactions between FQ molecules and the active sites of the composite. Furthermore, the material exhibited high reusability, maintaining over 84% of its adsorption capacity after four cycles, with efficient magnetic recovery without the need for filtration or centrifugation. Overall, the magnetic biochar@ZIF-8 composite demonstrates a sustainable, cost-effective, and magnetically separable adsorbent for water remediation, transforming almond shell waste into a high-value material within the framework of circular economy principles. Full article