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In this study, a cost‐effective and stable heterogeneous acidic carbocatalyst (CZnLS950) derived from Na‐lignosulfonate (LS), a side product of the paper industry, was employed to produce hydrocarbon fuels through the pyrolysis of waste cooking oil (WCO) and crude natural‐oil extracted from sunflower seeds, aligning with the principles of the circular economy. To enhance its practicality in industrial settings, the catalyst was synthesized in pellet form, enabling easy separation from the biofuel produced during the reaction. CZnLS950 exhibited remarkable catalytic efficiency in the pyrolysis of WCO, resulting in a 71 wt. % liquid biofuel yield under mild conditions. This performance is attributed to the unique synthesis procedure of acidic carbocatalyst, which utilizes LS and nano ZnO (20 nm) to create a hierarchical pore structure with acidic properties (1.1 mmol of NH3 g−1). Stability and reusability of the carbocatalyst were evaluated, and the results showed excellent stability with small catalytic deactivation (~5 wt. %) after the fourth use. Attempts at distinct catalytic mechanisms for WCO and sunflower seeds crude natural‐oil pyrolysis were provided to understand the processes involved in obtaining the two different biofuels produced. Overall, this study sets the stage for exploring Lignosulfonate‐based materials to achieve renewable biofuel from recycling streams. [ABSTRACT FROM AUTHOR]

The paper and paper products sector is a crucial component of the Turkish economy, characterized by significant interactions with various other sectors. Türkiye imports substantial amounts of paper, playing a vital role in the growth and sustainability of this sector. Accurate import forecasting is essential for strategic planning and resource management. This study aims to forecast the imports of the Turkish paper sector for the period from April 2023 to March 2024 using two artificial neural network (ANN) models: Multilayer Perceptron (MLP) and Radial Basis Function (RBF). The dataset, obtained from the Turkish Statistical Institute (TurkStat), covers 219 months of data from 2005 to 2023. The dependent variable is Türkiye’s monthly import value of paper and paper products, while the independent variables include the monthly average US Dollar exchange rate, monthly imports of Türkiye, the Manufacturing Industry Production Index, the Paper Production Index, and the monthly exports of paper and paper products from Türkiye. The MLP model forecasts that the monthly imports of paper and paper products will range between 270 to 300 million USD, while the RBF model predicts values between 268 and 321 million USD. These findings underscore the efficacy of ANNs in providing accurate and reliable forecasts. This study addresses a gap in the literature by applying ANN methods to forecast imports in the paper and paper products sector, presenting a novel approach that can assist companies in making better-informed decisions regarding inventory management, production planning, and marketing strategies. By leveraging the advanced computational power and pattern recognition capabilities of ANNs, the study aims to enhance the strategic planning processes in the paper and paper products industry. The traditional methods often used in trade data analysis and forecasting are limited in capturing the complex and non-linear relationships present in economic data. This study's application of ANNs offers a significant advancement by utilizing models that can better handle such complexities. The accuracy of the MLP and RBF models highlights their potential as valuable tools for economic forecasting, providing insights that are crucial for optimizing supply chain operations and improving market responsiveness. The results indicate that companies can achieve better operational performance and increased customer satisfaction by effectively forecasting future import requirements. The originality of this study lies in its methodological approach, utilizing ANN models to forecast import values in a sector where traditional methods have been predominant. This innovative approach not only contributes to the existing body of knowledge but also offers practical applications for businesses within the sector. The detailed analysis of the data, combined with the robust modeling techniques employed, provides a comprehensive framework for understanding the dynamics of paper imports and making strategic decisions based on accurate predictions. In conclusion, the study demonstrates the significant success of artificial neural networks in predicting import values for the Turkish paper and paper products sector. The findings provide valuable information that can aid companies in strategic planning, enhancing their ability to manage inventory, plan production, and develop effective market strategies. The research contributes to the literature by filling a gap with its innovative approach, offering new perspectives and practical applications for improving decision-making processes in the industry. [ABSTRACT FROM AUTHOR]

This paper reports an innovative study that aims to address key issues in the efficient recycling of wastepaper cellulose. The research team utilized the temperature-responsive upper critical solution temperature (UCST) polymer P(NAGA-b-DMA) in combination with the LytA label's affinity for choline analogs. This innovative approach enabled them to successfully develop a novel soluble immobilized enzyme, P(NAGA-b-DMA)-cellulase. This new enzyme has proven highly effective, significantly enhancing the degradation of wastepaper cellulose while demonstrating exceptional stability. Compared with the traditional insoluble immobilized cellulase, the enzyme showed a significant improvement in the pH, temperature stability, recycling ability, and storage stability. A kinetic parameter calculation showed that the enzymatic effectiveness of the soluble immobilized enzyme was much better than that of the traditional insoluble immobilized cellulase. After the immobilization reaction, the Michaelis constant of the immobilized enzyme was only increased by 11.5%. In the actual wastepaper degradation experiment, the immobilized enzyme was effectively used, and it was found that the degradation efficiency of wastepaper cellulose reached 80% of that observed in laboratory conditions. This novel, thermosensitive soluble immobilized cellulase can efficiently catalyze the conversion of wastepaper cellulose into glucose under suitable conditions, so as to further ferment into environmentally friendly biofuel ethanol, which provides a solution to solve the shortage of raw materials and environmental protection problems in the paper products industry. [ABSTRACT FROM AUTHOR]

The demand for high-performance bio-based materials towards achieving more sustainable manufacturing and circular economy models is growing significantly. Kraft lignin (KL) is an abundant and highly functional aromatic/phenolic biopolymer, being the main side product of the pulp and paper industry, as well as of the more recent 2nd generation biorefineries. In this study, KL was incorporated into a glassy epoxy system based on the diglycidyl ether of bisphenol A (DGEBA) and an amine curing agent (Jeffamine D-230), being utilized as partial replacement of the curing agent and the DGEBA prepolymer or as a reactive additive. A D-230 replacement by pristine (unmodified) KL of up to 14 wt.% was achieved while KL–epoxy composites with up to 30 wt.% KL exhibited similar thermo-mechanical properties and substantially enhanced antioxidant properties compared to the neat epoxy polymer. Additionally, the effect of the KL particle size was investigated. Ball-milled kraft lignin (BMKL, 10 μm) and nano-lignin (NLH, 220 nm) were, respectively, obtained after ball milling and ultrasonication and were studied as additives in the same epoxy system. Significantly improved dispersion and thermo-mechanical properties were obtained, mainly with nano-lignin, which exhibited fully transparent lignin–epoxy composites with higher tensile strength, storage modulus and glass transition temperature, even at 30 wt.% loadings. Lastly, KL lignin was glycidylized (GKL) and utilized as a bio-based epoxy prepolymer, achieving up to 38 wt.% replacement of fossil-based DGEBA. The GKL composites exhibited improved thermo-mechanical properties and transparency. All lignins were extensively characterized using NMR, TGA, GPC, and DLS techniques to correlate and justify the epoxy polymer characterization results. [ABSTRACT FROM AUTHOR]

Congestion is a well-known economic phenomenon in which excessive inputs lead to a decrease in the maximum possible outputs. This study analyzes the relationship between congestion and overinvestment using a data envelopment analysis framework to reveal that some congestion can be eliminated by increasing inputs. The proposed approach classifies congestion into two categories, relative and absolute congestion, depending on the possibility of eliminating congestion by increasing inputs. Corresponding methods for identifying and measuring congestion are proposed and employed for empirical analyses examining China's papermaking and paper products industry. We find that most congestion in 2013–2016 is in the relative congestion and could be eliminated by increasing inputs. The primary causes of congestion are identified based on our analysis and corresponding policy suggestions are proposed for eliminating congestion. [ABSTRACT FROM AUTHOR]

Pulp feedstock components (including extractives, lignin and holocellulose) are major products of the pulp and paper industry, and their proportion in production feedstock (e.g., wood) greatly impact pulp yield and pulp production costs. Near-infrared (NIR) spectroscopy offers a potential solution for rapid characterizing physical and chemical properties of woody biomass. However, NIR models are highly specific to the physical form and species of the samples used in the production process. Traditional calibration transfer (CT) methods usually fail to adapt NIR models to significant differences between spectra. Adversarial transfer learning (ATL) strategies, an emerging algorithm in computer imaging, align source and target distributions by introducing adversarial mechanisms. For the first time, deep ATL architecture, coupled with NIR spectroscopy (collected from ASD LabSpecPro spectrometer), was applied to quantitatively predict the pulpwood feedstock component content for adapting models of different physical forms (wood blocks, wood meals) and tree species to each other without the need for constructing a new model. It was discovered that ATL methods, including domain adversarial neural networks (DANN), domain separation networks (DSN), and dynamic adversarial adaptation networks (DAAN) not only remove the differences between wood block and wood meal spectral data sources (i.e., domains), but also make desired predictions for cross-species domain adaptation. The robustness and stability of the transferred models exceed those of traditional CT and transfer learning methods. Our results suggest that ATL models could be effectively adapted to polymorphic, multi-species pulp feedstock data, and can be extended to the detection and inversion of other feedstock properties. [ABSTRACT FROM AUTHOR]