Industrial & Engineering Chemistry Research最新文献

{"title":"Numerical Study on N2 Removal from the N2/He Mixture by Supersonic Separation Technology","authors":"Baosheng Chen,&nbsp;, ,&nbsp;Yupei Zeng,&nbsp;, ,&nbsp;Nan Peng*,&nbsp;, and ,&nbsp;Ercang Luo*,&nbsp;","doi":"10.1021/acs.iecr.5c02209","DOIUrl":"10.1021/acs.iecr.5c02209","url":null,"abstract":"<p >The application of supersonic separation technology in extracting helium from the N₂/He mixture is the primary innovation of this work, holding noteworthy promise. To begin with, a numerical model is constructed, incorporating governing equations for gas–liquid phase and the condensation model. The study reveals that nitrogen nonequilibrium condensation can be accomplished using the Laval nozzle, demonstrating the potential for N₂/He separation in the supersonic separator. Enhancing nitrogen nonequilibrium condensation within the Laval nozzle can be achieved by lowering the inlet temperature and raising the inlet pressure. Results show that nitrogen experiences intensive homogeneous nucleation over a short duration in the divergent section, leading to an ongoing rise in the nitrogen liquid mass fraction from 0% at the inlet to 10.5% at the outlet. This research verifies the viability of employing the Laval nozzle for nitrogen removal, carrying significant strategic implications for advancing nitrogen removal technologies in the recovery of helium from natural gas.</p>","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"64 41","pages":"20014–20029"},"PeriodicalIF":3.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145235271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural Ordinary Differential Equation and Supervised Gated Recurrent Units Embedded with Historical Variables for Petrochemical Process Prediction","authors":"Jian Long,&nbsp;, ,&nbsp;Jiawei Zhu,&nbsp;, ,&nbsp;Ning Wang,&nbsp;, ,&nbsp;Kai Luo,&nbsp;, ,&nbsp;Yejie Zhao,&nbsp;, and ,&nbsp;Yunmeng Zhao*,&nbsp;","doi":"10.1021/acs.iecr.5c02157","DOIUrl":"10.1021/acs.iecr.5c02157","url":null,"abstract":"<p >Gated recurrent units (GRU) effectively handle dynamic nonlinear data in petrochemical process. However, GRUs mainly focus on temporal dependencies of input variables while neglecting supervisory variables in historical data. Concurrently, conventional discrete-layer neural networks struggle to capture continuous-time system dynamics. These combined limitations impair long-term prediction accuracy. To overcome the limitations of existing time series modeling approaches in capturing complex dynamic behaviors, this study proposes a novel fusion framework that integrates a gated recurrent architecture with neural ordinary differential equations (Neural ODEs). Specifically, we introduce a supervised history-gated recurrent unit (SHGRU), which extends the standard GRU by incorporating historical supervisory variables, thereby enhancing the model’s capacity to capture time-varying hidden dynamics associated with process quality. Building on this foundation, a deep architecture SHGRU deep network (SHGRU-DN) is constructed by stacking multiple layers of SHGRU units, enabling hierarchical feature extraction guided by historical supervision. To further model the continuous-time evolution of system states, we embed an improved Neural ODE into the SHGRU-DN, resulting in a novel dynamic modeling framework termed SHGRU with dynamic Neural ODE (SHGRU-DODE). Extensive experiments on industrial datasets demonstrate the superior predictive accuracy of the proposed model compared to GRU, and the results are close to the true value with an RMSE of 0.0151 when predicting gasoline yield during fluid catalytic cracking. Additional evaluations on the tennessee eastman process and the debutanizer column further validate the model’s superiority.</p>","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"64 41","pages":"20070–20088"},"PeriodicalIF":3.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145235270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative Adversarial Network-Enhanced Heterogeneous Ensemble Learning for Interpretable Prediction of CO2-to-Methanol Catalyst Performance","authors":"Qingchun Yang,&nbsp;, ,&nbsp;Dongwen Rong,&nbsp;, ,&nbsp;Zhao Wang,&nbsp;, ,&nbsp;Qiwen Guo,&nbsp;, ,&nbsp;Jingsong Guan,&nbsp;, ,&nbsp;Yichun Dong,&nbsp;, and ,&nbsp;Huairong Zhou*,&nbsp;","doi":"10.1021/acs.iecr.5c02507","DOIUrl":"10.1021/acs.iecr.5c02507","url":null,"abstract":"<p >Accurate prediction of catalyst performance in CO₂-to-methanol (CTM) conversion remains challenging due to the scarcity of experimental data and the complexity of feature interactions. To address these issues, this study proposes an interpretable gene-adversarial network-enhanced heterogeneous ensemble modeling (GAN-HEM) framework. It integrates four synergistic key functional modules: data augmentation, feature interaction analysis, ensemble modeling, and interpretable analysis. Comparing the variational autoencoder-based data augmentation method demonstrates that the GAN has significant advantages in maintaining the global consistency and local geometric features of the data manifold structure. After the multivariate evaluation of feature association within the hybrid data set, it was found that retaining all the identified CTM catalyst features is beneficial for enhancing the predictive accuracy and generalization ability of the prediction model. Therefore, this data set is further applied to develop various homogeneous and heterogeneous ensemble learning models of the CTM process. The hyperparameters of these models are automatically optimized using the Bayesian algorithm-based Optuna approach. Results indicated that the optimized heterogeneous ensemble learning architecture has the highest prediction accuracy (<i>R</i>² = 0.9314 and RMSE = 0.2636), significantly outperforming homogeneous models through its capacity to capture complex nonlinear feature interactions. The Shapley additive explanation-based interpretability analysis identifies reaction temperature as the dominant feature (&gt;39% contribution). Partial dependence plots reveal competitive selectivity: higher temperature favors CO (thermodynamic constraints), while increased pressure and heating rate enhance methanol selectivity (kinetic promotion). This framework accelerates CTM catalyst discovery and optimization, providing high-fidelity prediction and actionable design insights.</p>","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"64 41","pages":"19797–19816"},"PeriodicalIF":3.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145235274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temperature-Driven Strategy for Engineering of Al-Doped ZnO Mesocrystals’ Hierarchical Architecture","authors":"Guohui Yang*,&nbsp;, ,&nbsp;Marcel Kévin Jiokeng Dongmo,&nbsp;, ,&nbsp;Leon Salomon,&nbsp;, ,&nbsp;Simon Buchheiser,&nbsp;, ,&nbsp;Thomas Meurer,&nbsp;, and ,&nbsp;Hermann Nirschl,&nbsp;","doi":"10.1021/acs.iecr.5c02055","DOIUrl":"10.1021/acs.iecr.5c02055","url":null,"abstract":"<p >Nanomaterial functionality is influenced by particle size, morphology, and composition. This study introduces a novel temperature-controlled sol–gel method to tailor hierarchical nanostructures, utilizing Al-doped ZnO (AZO) nanoparticles as a case study. AZOs’ electrical and optical properties are critical for photovoltaics applications, where controlling crystal properties to optimize conductivity and transparency is key. AZO nanoparticles were synthesized from zinc acetylacetonate hydrate, aluminum isopropoxide, and benzylamine in a closed batch reactor. By adjusting temperature profiles, controlling synthesis temperature and ramp-up duration under an exponential (PT1) heating scheme, the method enabled the formation of a three-level hierarchical architecture, in which primary AZO nanocrystals assembled into mesocrystals that subsequently aggregated into larger structures. Characterization via electron microscopy, X-ray scattering, and dynamic light scattering reveals that minor temperature variations (105 to 125 °C) affect particle morphologies, exhibiting tunable size, ranging from 18 to 41 nm, with higher temperatures promoting aggregation despite existing structure shrinkage. This highlights the strong dependency of nucleation and structural formation on temperature profiles, offering a versatile method for tuning nanomaterials for advanced applications.</p>","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"64 41","pages":"19908–19923"},"PeriodicalIF":3.9,"publicationDate":"2025-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.5c02055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145226619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Coke Distribution on the Diffusion of Alkanes in the MFI Zeolite","authors":"Jiamin Yuan, Yuzhou Fan, Xiaomin Tang, Jiujiang Wang, Honghai Liu, Hongjuan Zhao, Zhiqiang Liu, Anmin Zheng","doi":"10.1021/acs.iecr.5c02173","DOIUrl":"https://doi.org/10.1021/acs.iecr.5c02173","url":null,"abstract":"The diffusion behavior of molecules within the intricate channels of zeolite catalysts is a key factor of catalytic performance and product selectivity. In this study, we systematically investigated the influence of coke distribution within the zigzag and straight channels of the MFI zeolite on the diffusion behavior of alkanes. Our findings reveal that short-chain alkanes (e.g., CH₄) demonstrate dynamic interconversion between zigzag and straight channels of the MFI zeolite in coke-free systems. When one set of channels is obstructed by coke, they preferentially diffuse through the alternative set of channels. However, long-chain alkanes (e.g. <i>n</i>–C₁₂H₂₆) primarily diffuse through straight channels in the coke-free MFI zeolite, with minimal migration to zigzag channels. Therefore, this unidirectional transport behavior becomes severely compromised when coke deposits accumulate in the straight channels, where molecules are effectively trapped between the coke layers. To overcome these diffusion limitations, effective approaches are proposed: (1) elevating the temperature to activate the zigzag channel and (2) introducing mesopores to mitigate coke deposition and diffusion limitations. This study provides valuable insights into the diffusion pathway preferences of molecules within coke-containing zeolite systems and offers a practical reference for optimizing and selecting zeolite catalytic reaction conditions.","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"13 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145216075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning-Based Kinetic Modeling of the CO2 Methanation Reaction over an Industrial Catalyst","authors":"Hugo Pétremand,&nbsp;, ,&nbsp;Julia Witte,&nbsp;, ,&nbsp;Oliver Kröcher,&nbsp;, and ,&nbsp;Emanuele Moioli*,&nbsp;","doi":"10.1021/acs.iecr.5c02947","DOIUrl":"10.1021/acs.iecr.5c02947","url":null,"abstract":"<p >In industrial practice, it is common to use commercially available catalysts to facilitate chemical reactions. Unfortunately, due to the confidential composition and complex properties of the catalysts, a precise understanding of the reaction mechanism and intermediates is often difficult to obtain. This complicates the development of robust kinetic models, delaying an effective reactor design. This raises the question of whether machine learning (ML)-based regressions can reliably describe kinetics without requiring detailed reaction mechanisms. In this work, this research question was addressed by performing kinetic experiments on a commercial Ni/ZrO₂-based CO₂ methanation catalyst (Himetz by Kanadevia Corporation) in an isothermal, fixed bed reactor. A 216-point data set was thus obtained at various temperatures (220–300 °C), partial pressures (0.8–3 bar) and gas hourly space velocities (50,000–700,000 h^–1) combinations. The data set was employed to perform several ML-based regressions, and to compare them in terms of adequacy of the fit, according to root mean squared error (RMSE). The two best performing ML-based models, Gaussian Process Regression and 1-layer neural network were selected and assessed against two standard kinetic modeling approaches: power law and Langmuir–Hinshelwood-Hougen-Watson (LHHW). The ML-based models outperformed the power law according to RMSE and were comparable to the LHHW model to describe the kinetic regime of the catalyst. When implemented in a reactor model, the ML-based regressions also accurately predicted methane yield with results comparable to the state-of-the-art LHHW model and even outperformed the LHHW model by addressing nonideal behavior in a nonisothermal reactor. This showed that ML-based kinetics are promising in situations where little mechanistic information is available, generating models that can be employed for reactor design purposes.</p>","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"64 41","pages":"19864–19875"},"PeriodicalIF":3.9,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.5c02947","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145216042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy Improvement of Adsorptive Natural Gas Dehydration Unit by Layering the Bed","authors":"Saeed Saadat,&nbsp;, ,&nbsp;Joeri F. M. Denayer,&nbsp;, and ,&nbsp;Mohsen Gholami*,&nbsp;","doi":"10.1021/acs.iecr.5c00651","DOIUrl":"10.1021/acs.iecr.5c00651","url":null,"abstract":"<p >This work investigates the energy efficiency improvements achievable by a layered adsorption bed combining 3A zeolite and silica gel for natural gas dehydration via temperature swing adsorption (TSA). Simulation results indicate that extending the silica-gel bed relative to the zeolite layer enhances working capacity and cycle time. For an industrial-scale adsorption bed (3.5 m length, 5.5 m diameter) processing natural gas at a flow rate of 23929 kmol/h with an inlet water content of 1852 ppm, the dehydration cycle time was 760 min using a single 3A zeolite bed. By adopting a layered configuration with silica-gel and 3A zeolite layers at optimized lengths (3.7 and 1.8 m, respectively), the cycle time was extended to 1530 min. This adjustment reduced the number of annual cycles from 632 to 314. It decreased the energy consumption from 19.57 MJ/kg (of water removed) in the single-layer bed to 4.51 MJ/kg in the layered configuration. Additionally, the lower regeneration temperature required for silica gel (150 °C compared to 225 °C for 3A zeolite) contributes significantly to the energy savings. These findings emphasize the potential of layered adsorption beds in optimizing energy consumption and operational efficiency in industrial natural gas dehydration processes.</p>","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"64 41","pages":"19953–19959"},"PeriodicalIF":3.9,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145209818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on the Synthesis of Ni–Al2O3 Catalysts via Topological Transformation and Their Structure–Activity Relationship","authors":"Di Hu,&nbsp;, ,&nbsp;Yue Liu,&nbsp;, ,&nbsp;Lin-Di Xue*,&nbsp;, ,&nbsp;Ying-Jiao Li,&nbsp;, ,&nbsp;Lei Du,&nbsp;, ,&nbsp;Hai-Kui Zou,&nbsp;, ,&nbsp;Bao-Chang Sun*,&nbsp;, and ,&nbsp;Jian-Feng Chen,&nbsp;","doi":"10.1021/acs.iecr.5c02621","DOIUrl":"10.1021/acs.iecr.5c02621","url":null,"abstract":"<p >Nitrohydrogenation is a common process in industry. Here, a highly efficient Ni–Al₂O₃ catalyst derived from Ni–Al layered double hydroxide synthesized in a rotating packed bed (RPB) was first applied in the hydrogenation of 3,4-dichloronitrobenzene (3,4-DCNB) to 3,4-dichloroaniline (3,4-DCA). Leveraging the enhanced mass transfer and micromixing performances by applying RPB, the phenomenon of particle agglomeration and uneven size distribution occurring during catalyst synthesis has been effectively mitigated. The effects of Ni mass fraction, reduction temperature, and time on the physical properties and catalytic performance of the catalyst were investigated. Results showed that the Ni–Al₂O₃ catalysts prepared under optimal conditions (Ni mass fraction of 78 wt %, reduction temperature of 500 °C, reduction time of 4 h) have small Ni particle sizes and high dispersion. When applied to 3,4-DCNB hydrogenation under optimized conditions (65 °C, 1.5 MPa of H₂, 60 min, catalyst loading 12.55 mg/g), both conversion and 3,4-DCA selectivity approached 100%. Furthermore, the kinetic study on the 3,4-DCNB hydrogenation revealed that the reaction has an activation energy of 39.91 kJ/mol and a pre-exponential factor of 2.54 × 10⁴ min^–1. This finding presents an innovative approach for preparing a highly efficient Ni-based catalyst for the hydrogenation of 3,4-DCNB to 3,4-DCA.</p>","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"64 41","pages":"19831–19842"},"PeriodicalIF":3.9,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145209819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Industrial Applications of Ionic Liquids in Acetonitrile–Water Azeotrope Separation: Molecular Dynamics and DFT Insights into Azeotrope Breaking Methods","authors":"Yuan Cheng,&nbsp;, ,&nbsp;Jing Wang,&nbsp;, ,&nbsp;Daming Gao*,&nbsp;, ,&nbsp;Hui Zhang,&nbsp;, ,&nbsp;Young Min Kwon,&nbsp;, ,&nbsp;Chouwang Li,&nbsp;, ,&nbsp;Jun Li,&nbsp;, ,&nbsp;Xiaochen Wang,&nbsp;, ,&nbsp;Anqiu Liu,&nbsp;, ,&nbsp;Lingyun Zhang,&nbsp;, and ,&nbsp;Chan Kyung Kim*,&nbsp;","doi":"10.1021/acs.iecr.5c00822","DOIUrl":"10.1021/acs.iecr.5c00822","url":null,"abstract":"<p >Separating acetonitrile–water azeotropic systems is challenging due to unclear molecular-level interaction mechanisms of ionic liquids (ILs). This study investigated ILs ([Emim]Br, [Bmim]Br, [Emim][BF₄], and [Bmim][BF₄]) as entrainers using experimental and theoretical approaches to elucidate the azeotropic point-breaking mechanism. Molecular dynamics (MD) simulations were employed to analyze binary and ternary solutions, while density functional theory (DFT) at B3LYP/6–311 + G(d) was used to refine the complexation energies and Boltzmann-averaged multiple isomers. Vapor–liquid equilibrium (VLE) data (<i>T</i>, <i>x</i>, and <i>y</i>) for the pseudobinary systems of acetonitrile, water, and ILs at 101.33 kPa were measured and correlated using the nonrandom two-liquid (NRTL) model. The results show that ILs exhibit a stronger salting-out effect than solvation, enhancing the relative volatility of acetonitrile and eliminating the azeotropic point. The salting-out efficacy followed the order [Emim]Br &gt; [Bmim]Br &gt; [Emim][BF₄] &gt; [Bmim][BF₄], with [Emim]⁺ and Br^– being the most effective. These findings highlight the ILs’ potential for efficient, sustainable industrial azeotrope separation.</p>","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"64 41","pages":"19995–20013"},"PeriodicalIF":3.9,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145209820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Oxidation of Lignin-Modified Bitumens","authors":"Philip R. Herrington*,&nbsp;, ,&nbsp;Abhirup B. Roy-Chowdhury,&nbsp;, ,&nbsp;Matthew D.W. Sharp,&nbsp;, and ,&nbsp;Lia C. van den Kerkhof,&nbsp;","doi":"10.1021/acs.iecr.5c01391","DOIUrl":"https://doi.org/10.1021/acs.iecr.5c01391","url":null,"abstract":"<p >The effect of added kraft softwood lignin (20 wt %) on the oxidation rates of two different bitumens was studied using a pressure decay method to measure the oxygen absorption into thin films. Bitumen and lignin-modified binders were oxidized for 40 h at 50 °C under oxygen at near atmospheric pressures. Initial experiments using nitrogen showed little difference between the equilibrium saturation concentrations and diffusion coefficients of the original and lignin-modified materials. The absorption of oxygen by the lignin-modified binders (in mol g^–1 of binder) was found to be reduced compared to that of the original bitumen in each case. If the lignin was simply behaving as an inert filler, then a reduction in oxygen absorption of about 20% would be expected. Greater reductions would be expected if the lignin was acting as a bitumen antioxidant. The measured reductions in oxygen absorption were between 7% and 22%, indicating that the lignin was less reactive to oxygen than the bitumens but did not act to reduce the rate of bitumen oxidation. The increase in the shear modulus at 25 °C (and 1.59 Hz) after 40 h of oxidation also showed that the addition of lignin had no marked effect on the oxidation rate.</p>","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"64 41","pages":"20062–20069"},"PeriodicalIF":3.9,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145284077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}