Pub Date : 2024-11-20DOI: 10.1021/acs.iecr.4c03881
Danlong Yang, Yingzhe Liu, Yuling Shi, Qianqian Pan, Yangeng Lan, Jianhong Xu, Tao Wang
Silica aerogel microspheres are known for their unique features which are better than those of the bulk silica aerogel, including excellent biocompatibility and flowability, and have a broader application prospect for biomedicine, adsorption purification, catalytic reactions, energy storage, and sensing. However, the production of high-quality silica aerogel microspheres still poses significant challenges, such as low sphericity, difficult size adjustment, uneven morphology, and a prolonged drying process, especially ambient pressure drying for several hours. Herein, we report a novel process for efficiently synthesizing high-quality silica aerogel microspheres through the use of microfluidic and ambient pressure drying techniques. In the novel process, the colloidal sol microdroplets were prepared in stepped T-microchannels to achieve size adjustment with a narrow diameter distribution and good sphericity. A new drying process under ambient pressure at high temperature was proposed, which realized the rapid drying under ambient pressure for 10 min while the linear shrinkage of the microspheres was less than 5%. Highly spherical and uniform silica aerogel microspheres with diameters adjustable from 50–300 μm were successfully fabricated. The prepared silica aerogel microspheres exhibited high mesoporosity along with ultralow density, high specific surface area, and high hydrophobicity. In addition, the factors that significantly influence the final morphology of the silica aerogel microspheres have been thoroughly researched. This innovative process offers a new approach for the efficient synthesis of high-quality silica aerogel microspheres.
众所周知，二氧化硅气凝胶微球具有优于块状二氧化硅气凝胶的独特性能，包括良好的生物相容性和流动性，在生物医药、吸附净化、催化反应、储能和传感等方面具有更广阔的应用前景。然而，高质量二氧化硅气凝胶微球的生产仍然面临着巨大的挑战，如球形度低、尺寸调整困难、形态不均匀、干燥过程漫长，尤其是需要数小时的常压干燥。在此，我们报告了一种利用微流体和常压干燥技术高效合成高质量二氧化硅气凝胶微球的新工艺。在新工艺中，胶体溶胶微滴是在阶梯式 T 型微通道中制备的，以实现粒度调整，使其具有窄直径分布和良好的球形度。提出了一种新的高温常压干燥工艺，实现了常压下 10 分钟的快速干燥，同时微球的线性收缩率小于 5%。成功制备了直径在 50-300 μm 之间可调的高球形均匀二氧化硅气凝胶微球。所制备的二氧化硅气凝胶微球具有高中疏度、超低密度、高比表面积和高疏水性。此外，还对影响二氧化硅气凝胶微球最终形态的重要因素进行了深入研究。这种创新工艺为高效合成高质量二氧化硅气凝胶微球提供了一种新方法。
{"title":"Efficient Fabrication of Well-Shaped and Monodisperse Silica Aerogel Microspheres by Microfluidics and Rapid Ambient Pressure Drying","authors":"Danlong Yang, Yingzhe Liu, Yuling Shi, Qianqian Pan, Yangeng Lan, Jianhong Xu, Tao Wang","doi":"10.1021/acs.iecr.4c03881","DOIUrl":"https://doi.org/10.1021/acs.iecr.4c03881","url":null,"abstract":"Silica aerogel microspheres are known for their unique features which are better than those of the bulk silica aerogel, including excellent biocompatibility and flowability, and have a broader application prospect for biomedicine, adsorption purification, catalytic reactions, energy storage, and sensing. However, the production of high-quality silica aerogel microspheres still poses significant challenges, such as low sphericity, difficult size adjustment, uneven morphology, and a prolonged drying process, especially ambient pressure drying for several hours. Herein, we report a novel process for efficiently synthesizing high-quality silica aerogel microspheres through the use of microfluidic and ambient pressure drying techniques. In the novel process, the colloidal sol microdroplets were prepared in stepped T-microchannels to achieve size adjustment with a narrow diameter distribution and good sphericity. A new drying process under ambient pressure at high temperature was proposed, which realized the rapid drying under ambient pressure for 10 min while the linear shrinkage of the microspheres was less than 5%. Highly spherical and uniform silica aerogel microspheres with diameters adjustable from 50–300 μm were successfully fabricated. The prepared silica aerogel microspheres exhibited high mesoporosity along with ultralow density, high specific surface area, and high hydrophobicity. In addition, the factors that significantly influence the final morphology of the silica aerogel microspheres have been thoroughly researched. This innovative process offers a new approach for the efficient synthesis of high-quality silica aerogel microspheres.","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"143 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672872","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}
Leveraging micro- and nanoengineering, functional surfaces revolutionize interactions between materials and their environment, leading to a new era of advanced materials. Functional surfaces are capable of providing a wide range of applications, i.e., antifogging, anti-icing, and antiwetting. These surfaces exhibit remarkable adaptability, improving the performance of microfluidic devices, sensors, and MEMS. Superhydrophobic and superhydrophilic surfaces represent the pinnacle of water repellence and attraction, crucial for enhancing applications like dew water harvesting and condensation-related applications, i.e., heat exchangers. To achieve surfaces with such remarkable properties, several delicate processes have been developed, and today’s request is to improve their durability, repeatability, and reusability. In this work, we present a fabrication process for superhydrophilic and superhydrophobic surfaces based on oxygen plasma micro- and nanotexturing, followed by a thin coating deposition of poly(ethylene glycol) (PEG) for superhydrophilicity and plasma deposition of C4F8 for superhydrophobicity. It is demonstrated that the surfaces of both wetting extremes exhibit remarkable stability in their wetting properties, maintaining stable water static contact angles (WSCAs) of 161° (for the 9 min plasma micronanotextured superhydrophobic surface) or 0° (for the 9 min plasma micronanotextured and PEG-coated superhydrophilic surface) for more than 4 months of storage in ambient conditions. Superhydrophilic surfaces, which are more prone to wetting property deterioration, are additionally tested using water immersion tests for 14 days, and it is shown that the use of the PEG coating on plasma micronanotextured surfaces enhances the superhydrophilic property stability (WSCA: 25° compared to 63° for the uncoated plasma-textured surface). Finally, the surfaces are probed by dew water harvesting experiments in which no significant performance deterioration is observed and water collection rate (WCR) reduction during aging (after storage) is 20% in the case of the superhydrophobic and less than 5% for the superhydrophilic PEG-coated surface. More vulnerable to wetting, superhydrophilic surfaces are also tested in terms of reusability (i.e., after multiple uses of the same surfaces), and it is found that the WCR decrease is less than 17% (for the 6 min plasma micronanotextured and PEG-coated surfaces).
{"title":"Durable Surfaces of Both Wettability Extremes with Stable Dew Harvesting Performance During Liquid–Vapor-Phase Transitions","authors":"Dimitrios Nioras, Evangelos Gogolides, Kosmas Ellinas","doi":"10.1021/acs.iecr.4c02374","DOIUrl":"https://doi.org/10.1021/acs.iecr.4c02374","url":null,"abstract":"Leveraging micro- and nanoengineering, functional surfaces revolutionize interactions between materials and their environment, leading to a new era of advanced materials. Functional surfaces are capable of providing a wide range of applications, i.e., antifogging, anti-icing, and antiwetting. These surfaces exhibit remarkable adaptability, improving the performance of microfluidic devices, sensors, and MEMS. Superhydrophobic and superhydrophilic surfaces represent the pinnacle of water repellence and attraction, crucial for enhancing applications like dew water harvesting and condensation-related applications, i.e., heat exchangers. To achieve surfaces with such remarkable properties, several delicate processes have been developed, and today’s request is to improve their durability, repeatability, and reusability. In this work, we present a fabrication process for superhydrophilic and superhydrophobic surfaces based on oxygen plasma micro- and nanotexturing, followed by a thin coating deposition of poly(ethylene glycol) (PEG) for superhydrophilicity and plasma deposition of C<sub>4</sub>F<sub>8</sub> for superhydrophobicity. It is demonstrated that the surfaces of both wetting extremes exhibit remarkable stability in their wetting properties, maintaining stable water static contact angles (WSCAs) of 161° (for the 9 min plasma micronanotextured superhydrophobic surface) or 0° (for the 9 min plasma micronanotextured and PEG-coated superhydrophilic surface) for more than 4 months of storage in ambient conditions. Superhydrophilic surfaces, which are more prone to wetting property deterioration, are additionally tested using water immersion tests for 14 days, and it is shown that the use of the PEG coating on plasma micronanotextured surfaces enhances the superhydrophilic property stability (WSCA: 25° compared to 63° for the uncoated plasma-textured surface). Finally, the surfaces are probed by dew water harvesting experiments in which no significant performance deterioration is observed and water collection rate (WCR) reduction during aging (after storage) is 20% in the case of the superhydrophobic and less than 5% for the superhydrophilic PEG-coated surface. More vulnerable to wetting, superhydrophilic surfaces are also tested in terms of reusability (i.e., after multiple uses of the same surfaces), and it is found that the WCR decrease is less than 17% (for the 6 min plasma micronanotextured and PEG-coated surfaces).","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"18 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142673128","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}
Shipboard CO2 capture (SCC) processes face significant challenges, including high costs and the need for extra heating energy to capture 90% of the CO2. Therefore, this study proposes advanced designs and an integration framework using correlation analysis and machine learning-based optimization to achieve the energy- and cost-effective SCC process. Specifically, we develop CO2 capture and ship engine simulators, which are validated and then applied to develop conventional and four advanced designs for the SCC process. Next, a first deep neural network (DNN) model is developed as a surrogate model to precisely predict the performance of the conventional design at low computation cost, serving as the basis for formulating two optimization problems. The optimization results reveal that capturing 90% of CO2 by using the conventional design requires an additional 1.369 MW of heating energy, costing 108.583 $/tCO2. Then, the four advanced designs are analyzed to exhibit their potential for reducing the CO2 capture cost and heating energy, with correlation methods identifying SCC using lean vapor compression (LVC-SCC) design as the most feasible design. Finally, a second DNN-based surrogate model is developed for the LVC-SCC design before being used to formulate the third optimization problem. The optimization results confirm that the LVC-SCC design leverages available heating energy sources to capture 90% of CO2 (approximately 8.89 tCO2/h) at 53.54 $/tCO2, emitting only 0.46 ppm monoethanolamine. Moreover, compared to the conventional design, the LVC-SCC design significantly reduces the cost, heating energy, and cooling energy by approximately 49.8%, 15%, and 12%, respectively. The proposed designs, the machine learning-based optimization approach, and the resulting findings provide valuable solutions for driving the international shipping industry toward achieving net-zero greenhouse gas emissions by 2050.
{"title":"Advanced Designs and Optimization for Efficiently Enhancing Shipboard CO2 Capture","authors":"Dat-Nguyen Vo, Xuewen Zhang, Kuniadi Wandy Huang, Xunyuan Yin","doi":"10.1021/acs.iecr.4c02817","DOIUrl":"https://doi.org/10.1021/acs.iecr.4c02817","url":null,"abstract":"Shipboard CO<sub>2</sub> capture (SCC) processes face significant challenges, including high costs and the need for extra heating energy to capture 90% of the CO<sub>2</sub>. Therefore, this study proposes advanced designs and an integration framework using correlation analysis and machine learning-based optimization to achieve the energy- and cost-effective SCC process. Specifically, we develop CO<sub>2</sub> capture and ship engine simulators, which are validated and then applied to develop conventional and four advanced designs for the SCC process. Next, a first deep neural network (DNN) model is developed as a surrogate model to precisely predict the performance of the conventional design at low computation cost, serving as the basis for formulating two optimization problems. The optimization results reveal that capturing 90% of CO<sub>2</sub> by using the conventional design requires an additional 1.369 MW of heating energy, costing 108.583 $/tCO<sub>2</sub>. Then, the four advanced designs are analyzed to exhibit their potential for reducing the CO<sub>2</sub> capture cost and heating energy, with correlation methods identifying SCC using lean vapor compression (LVC-SCC) design as the most feasible design. Finally, a second DNN-based surrogate model is developed for the LVC-SCC design before being used to formulate the third optimization problem. The optimization results confirm that the LVC-SCC design leverages available heating energy sources to capture 90% of CO<sub>2</sub> (approximately 8.89 tCO<sub>2</sub>/h) at 53.54 $/tCO<sub>2</sub>, emitting only 0.46 ppm monoethanolamine. Moreover, compared to the conventional design, the LVC-SCC design significantly reduces the cost, heating energy, and cooling energy by approximately 49.8%, 15%, and 12%, respectively. The proposed designs, the machine learning-based optimization approach, and the resulting findings provide valuable solutions for driving the international shipping industry toward achieving net-zero greenhouse gas emissions by 2050.","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"53 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142673066","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}
Pub Date : 2024-11-20DOI: 10.1021/acs.iecr.4c03040
Omar Péter Hamadi, Tamás Varga
Catalytic pyrolysis presents a promising avenue for mitigating plastic waste accumulation by converting it into valuable products. In this study, we investigate the application of computational methods integrating molecular similarities and the Kovats retention index to enhance the accuracy of qualitative analysis in catalytic pyrolysis processes. Utilizing gas-chromatography data and high-level measurement results, molecular compositions of pyrolysis products are determined and the consistency of molecular composition across various experimental conditions is evaluated. Despite encountering challenges such as algorithm failures due to high computational costs, our analysis reveals significant insights into the molecular composition of pyrolysis products. Through the utilization of molecular similarity methods, the potential to refine the estimation of molecular compositions is also demonstrated, particularly in scenarios in which retention index database accuracy is uncertain. Our findings underscore the importance of further refining computational methods and formulating additional constraints based on high-level measurements to enhance the accuracy of molecular composition estimates.
{"title":"Computational Insights into Catalytic Pyrolysis: Refining Molecular Composition Estimates Using Kovats Retention Index and Molecular Similarities","authors":"Omar Péter Hamadi, Tamás Varga","doi":"10.1021/acs.iecr.4c03040","DOIUrl":"https://doi.org/10.1021/acs.iecr.4c03040","url":null,"abstract":"Catalytic pyrolysis presents a promising avenue for mitigating plastic waste accumulation by converting it into valuable products. In this study, we investigate the application of computational methods integrating molecular similarities and the Kovats retention index to enhance the accuracy of qualitative analysis in catalytic pyrolysis processes. Utilizing gas-chromatography data and high-level measurement results, molecular compositions of pyrolysis products are determined and the consistency of molecular composition across various experimental conditions is evaluated. Despite encountering challenges such as algorithm failures due to high computational costs, our analysis reveals significant insights into the molecular composition of pyrolysis products. Through the utilization of molecular similarity methods, the potential to refine the estimation of molecular compositions is also demonstrated, particularly in scenarios in which retention index database accuracy is uncertain. Our findings underscore the importance of further refining computational methods and formulating additional constraints based on high-level measurements to enhance the accuracy of molecular composition estimates.","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"10 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672875","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}
Pub Date : 2024-11-19DOI: 10.1021/acs.iecr.4c02577
Nils Pfister, Iana Kraievska, Christian Rohner, Jinhu Dong, Olaf Timpe, Frank Girgsdies, Thomas Lunkenbein, Rohini Khobragade, Jacopo De Bellis, Ferdi Schüth, Annette Trunschke
The catalytic dehydrogenation of propane is an economically interesting process for the production of propylene due to its high selectivity to the olefin and the coupled generation of hydrogen. The catalysts are usually obtained by depositing the active components from solutions onto a support. Here we show that the direct synthesis of alumina-supported platinum catalysts in a ball mill in a single step provides easy access to efficient catalysts that are comparable in performance to materials obtained by more complex synthesis techniques. This was demonstrated by analysis using XRD, N2 adsorption, chemical analysis, FTIR spectroscopy, and electron microscopy and by functional characterization of the catalysts in the dehydrogenation of propane to propylene. Although the ball milling procedure was not optimized, the catalysts exhibit a narrow Pt particle size distribution around 2 nm and are active at comparatively low reaction temperatures, producing in the steady state at 500 °C approximately 300 gpropylene gPt-1 h–1. The selectivity remains very high even at temperatures as high as 550 °C. Sintering of Pt under the harsh reaction conditions is not observed. The scalable method saves energy and avoids waste as no solvents and no thermal or reducing pretreatments are required.
丙烷催化脱氢是生产丙烯的一种经济有效的工艺，因为它对烯烃有很高的选择性，同时还能产生氢气。催化剂通常是通过将溶液中的活性成分沉积到载体上获得的。在这里，我们展示了在球磨机中一步直接合成氧化铝支撑的铂催化剂的方法，这种方法可以轻松获得高效催化剂，其性能可与通过更复杂的合成技术获得的材料相媲美。通过使用 XRD、N2 吸附、化学分析、傅里叶变换红外光谱和电子显微镜进行分析，以及在丙烷脱氢为丙烯的过程中对催化剂进行功能表征，证明了这一点。虽然球磨过程没有得到优化，但催化剂的铂粒径分布较窄，约为 2 纳米，并且在相对较低的反应温度下也很活跃，在 500 °C 的稳定状态下可产生约 300 克丙烯 gPt-1 h-1。即使在高达 550 °C 的温度下，选择性仍然非常高。在苛刻的反应条件下也未发现铂烧结现象。由于不需要溶剂，也不需要热处理或还原预处理，这种可扩展的方法既节约能源，又避免了浪费。
{"title":"A Facile Approach to Alumina-Supported Pt Catalysts for the Dehydrogenation of Propane","authors":"Nils Pfister, Iana Kraievska, Christian Rohner, Jinhu Dong, Olaf Timpe, Frank Girgsdies, Thomas Lunkenbein, Rohini Khobragade, Jacopo De Bellis, Ferdi Schüth, Annette Trunschke","doi":"10.1021/acs.iecr.4c02577","DOIUrl":"https://doi.org/10.1021/acs.iecr.4c02577","url":null,"abstract":"The catalytic dehydrogenation of propane is an economically interesting process for the production of propylene due to its high selectivity to the olefin and the coupled generation of hydrogen. The catalysts are usually obtained by depositing the active components from solutions onto a support. Here we show that the direct synthesis of alumina-supported platinum catalysts in a ball mill in a single step provides easy access to efficient catalysts that are comparable in performance to materials obtained by more complex synthesis techniques. This was demonstrated by analysis using XRD, N<sub>2</sub> adsorption, chemical analysis, FTIR spectroscopy, and electron microscopy and by functional characterization of the catalysts in the dehydrogenation of propane to propylene. Although the ball milling procedure was not optimized, the catalysts exhibit a narrow Pt particle size distribution around 2 nm and are active at comparatively low reaction temperatures, producing in the steady state at 500 °C approximately 300 g<sub>propylene</sub> g<sub>Pt</sub><sup>-1</sup> h<sup>–1</sup>. The selectivity remains very high even at temperatures as high as 550 °C. Sintering of Pt under the harsh reaction conditions is not observed. The scalable method saves energy and avoids waste as no solvents and no thermal or reducing pretreatments are required.","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"56 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670388","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}
Pub Date : 2024-11-19DOI: 10.1021/acs.iecr.4c02956
Eugeniu Strelet, Zhenyu Wang, You Peng, Ivan Castillo, Ricardo Rendall, Marco S. Reis
The collection of data from multiple sources with distinct modalities and varying levels of quality is pervasive in modern industry. Furthermore, associated with each source are often different sampling rates, and some sources may not even have a regular acquisition pattern. These aspects pose significant challenges when developing machine learning (ML) models for predicting target variables, such as product properties, or process key performance indicators (KPIs). Data imputation schemes are a common solution but often require case-by-case analysis to mitigate the risk of introducing unrealistic artifacts, complicating the analysis pipeline and making the process more complex and less scalable. This work introduces a flexible solution for combining redundant sources of information with respect to a target response, considering their associated quality, while accommodating for different sampling rates and information quality. The proposed Regularized Bayesian Fusion (RegBF) approach aims to produce estimates of the target variable with an expected smoothness level, being at the same time compatible with the dominant dynamic mode of the industrial process. The methodology is scalable and flexible, as it can incorporate new data sources, at any time, in the form of either dynamic first-principle models, data-driven ML models, or instrumental information sources (e.g., online or laboratory analytical instruments). The proposed approach is tested in two case studies: one from a Kamyr digester process and the other from a wastewater treatment plant operation.
现代工业普遍采用不同模式和不同质量水平的多种来源收集数据。此外，与每个数据源相关的采样率往往不同，有些数据源甚至可能没有固定的采集模式。在开发用于预测目标变量（如产品属性或流程关键性能指标 (KPI)）的机器学习 (ML) 模型时，这些方面会带来巨大挑战。数据估算方案是一种常见的解决方案，但通常需要逐案分析，以降低引入不切实际的人工智能的风险，从而使分析管道复杂化，并使流程变得更加复杂，可扩展性降低。这项工作引入了一种灵活的解决方案，用于结合与目标响应相关的冗余信息源，同时考虑到它们的相关质量，并适应不同的采样率和信息质量。所提出的正则化贝叶斯融合（RegBF）方法旨在产生具有预期平滑度的目标变量估计值，同时与工业流程的主导动态模式相兼容。该方法具有可扩展性和灵活性，因为它可以随时以动态第一原理模型、数据驱动的 ML 模型或工具信息源（如在线或实验室分析仪器）的形式纳入新的数据源。所提出的方法在两个案例研究中进行了测试：一个来自卡米尔消化器工艺，另一个来自污水处理厂运行。
{"title":"Regularized Bayesian Fusion for Multimodal Data Integration in Industrial Processes","authors":"Eugeniu Strelet, Zhenyu Wang, You Peng, Ivan Castillo, Ricardo Rendall, Marco S. Reis","doi":"10.1021/acs.iecr.4c02956","DOIUrl":"https://doi.org/10.1021/acs.iecr.4c02956","url":null,"abstract":"The collection of data from multiple sources with distinct modalities and varying levels of quality is pervasive in modern industry. Furthermore, associated with each source are often different sampling rates, and some sources may not even have a regular acquisition pattern. These aspects pose significant challenges when developing machine learning (ML) models for predicting target variables, such as product properties, or process key performance indicators (KPIs). Data imputation schemes are a common solution but often require case-by-case analysis to mitigate the risk of introducing unrealistic artifacts, complicating the analysis pipeline and making the process more complex and less scalable. This work introduces a flexible solution for combining redundant sources of information with respect to a target response, considering their associated quality, while accommodating for different sampling rates and information quality. The proposed Regularized Bayesian Fusion (RegBF) approach aims to produce estimates of the target variable with an expected smoothness level, being at the same time compatible with the dominant dynamic mode of the industrial process. The methodology is scalable and flexible, as it can incorporate new data sources, at any time, in the form of either dynamic first-principle models, data-driven ML models, or instrumental information sources (e.g., online or laboratory analytical instruments). The proposed approach is tested in two case studies: one from a Kamyr digester process and the other from a wastewater treatment plant operation.","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"128 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670389","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}
High-stability polyimides exhibit tremendous potential for applications in flexible electronics, fibers, and membrane materials. However, screening polyimide structures with superior performance remains a significant challenge. In this study, we combined literature data, machine learning, and molecular dynamics simulations to identify key factors influencing the stability of polyimide structures and screen for high-stability polyimide candidates. Specifically, we utilized interpretable machine learning methods to analyze polyimide systems documented in the literature, aiming to identify crucial substructures that impact polyimide stability. This approach offers valuable insights for the development of high-stability polymers. By integrating diamine and dianhydride structures from both the PubChem database and the literature, we generated a data set containing over 15 million hypothetical polyimides. Using appropriate machine learning models, we conducted high-throughput screening to discover polyimides that simultaneously exhibit high thermal stability and excellent mechanical properties. The selected machine learning models demonstrated strong predictive capability in forecasting four key properties: glass transition temperature (Tg), Young’s modulus (Ym), tensile strength (Ts), and elongation at break (Eg). Based on the predictions from the optimal models and synthetic accessibility scores, we ultimately identified eight polyimide copolymer structures with outstanding stability, with some of their properties validated through all-atom molecular dynamics simulations.
{"title":"Machine Learning-Based High-Throughput Screening for High-Stability Polyimides","authors":"Gaoyang Luo, Feicheng Huan, Yuwei Sun, Feng Shi, Shengwei Deng, Jian-guo Wang","doi":"10.1021/acs.iecr.4c03379","DOIUrl":"https://doi.org/10.1021/acs.iecr.4c03379","url":null,"abstract":"High-stability polyimides exhibit tremendous potential for applications in flexible electronics, fibers, and membrane materials. However, screening polyimide structures with superior performance remains a significant challenge. In this study, we combined literature data, machine learning, and molecular dynamics simulations to identify key factors influencing the stability of polyimide structures and screen for high-stability polyimide candidates. Specifically, we utilized interpretable machine learning methods to analyze polyimide systems documented in the literature, aiming to identify crucial substructures that impact polyimide stability. This approach offers valuable insights for the development of high-stability polymers. By integrating diamine and dianhydride structures from both the PubChem database and the literature, we generated a data set containing over 15 million hypothetical polyimides. Using appropriate machine learning models, we conducted high-throughput screening to discover polyimides that simultaneously exhibit high thermal stability and excellent mechanical properties. The selected machine learning models demonstrated strong predictive capability in forecasting four key properties: glass transition temperature (<i>T</i><sub>g</sub>), Young’s modulus (<i>Y</i><sub>m</sub>), tensile strength (<i>T</i><sub>s</sub>), and elongation at break (<i>E</i><sub>g</sub>). Based on the predictions from the optimal models and synthetic accessibility scores, we ultimately identified eight polyimide copolymer structures with outstanding stability, with some of their properties validated through all-atom molecular dynamics simulations.","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"22 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672877","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}
Pub Date : 2024-11-19DOI: 10.1021/acs.iecr.4c02511
Peiliang Sun, Xiangchen Fang, Yanze Du, Yibao Li, Chong Peng
Core–shell molecular sieves exhibit excellent performance in many catalytic processes due to their adjustable pore distribution and surface structure. This work developed a silicon coating method for molecular sieves and explored the synthesis method of Y/amorphous-silica composite materials. The effects of ultrasound treatment, silicon source addition amount, and template properties on the amorphous silica structure and properties were determined through comparative experiments. The optimal preparation conditions were obtained, achieving adjustable thickness of the molecular sieve shell. Subsequently, based on the optimal preparation conditions of Y/amorphous silica composite materials, a ZSM-5/silicalite-1 composite molecular sieve with core–shell structure was successfully synthesized, and the structural characteristics of the synthesized core–shell molecular sieve were comprehensively analyzed. The amorphous silica core–shell composite materials prepared in this work achieved directional control of shell thickness and pore structure, providing a promising approach for the preparation of core–shell structures.
{"title":"Study on the Preparation and Structural Properties of Core–Shell Hierarchical Pore Molecular Sieve Synthesized by a Silicon Coating Method","authors":"Peiliang Sun, Xiangchen Fang, Yanze Du, Yibao Li, Chong Peng","doi":"10.1021/acs.iecr.4c02511","DOIUrl":"https://doi.org/10.1021/acs.iecr.4c02511","url":null,"abstract":"Core–shell molecular sieves exhibit excellent performance in many catalytic processes due to their adjustable pore distribution and surface structure. This work developed a silicon coating method for molecular sieves and explored the synthesis method of Y/amorphous-silica composite materials. The effects of ultrasound treatment, silicon source addition amount, and template properties on the amorphous silica structure and properties were determined through comparative experiments. The optimal preparation conditions were obtained, achieving adjustable thickness of the molecular sieve shell. Subsequently, based on the optimal preparation conditions of Y/amorphous silica composite materials, a ZSM-5/silicalite-1 composite molecular sieve with core–shell structure was successfully synthesized, and the structural characteristics of the synthesized core–shell molecular sieve were comprehensively analyzed. The amorphous silica core–shell composite materials prepared in this work achieved directional control of shell thickness and pore structure, providing a promising approach for the preparation of core–shell structures.","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"37 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670384","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}
Pub Date : 2024-11-19DOI: 10.1021/acs.iecr.4c02918
Juhyeon Kim, Jiae Ryu, Qiang Yang, Chang Geun Yoo, Joseph Sang-II Kwon
While lignin has garnered significant research interest for its abundance and versatility, its complicated structure poses a challenge to understanding its underlying reaction kinetics and optimizing various lignin characteristics. In this regard, mathematical models, especially the multiscale kinetic Monte Carlo (kMC) method, have been devised to offer a precise analysis of fractionation kinetics and lignin properties. The kMC model effectively handles the simulation of all particles within the system by calculating reaction rates between species and generating a rate-based probability distribution. Then, it selects a reaction to execute based on this distribution. However, due to the vast number of lignin polymers involved in the reactions, the rate calculation step becomes a computational bottleneck, limiting the model’s applicability in real-time control scenarios. To address this, the machine learning (ML) technique is integrated into the existing kMC framework. By training an artificial neural network (ANN) on the kMC data sets, we predict the probability distributions instead of repeatedly calculating them over time. Subsequently, the resulting ANN-accelerated multiscale kMC (AA-M-kMC) model is incorporated into a model predictive controller (MPC), facilitating real-time control of intricate lignin properties. This innovative approach effectively reduces the computational burden of kMC and advances lignin processing methods.
{"title":"Real-Time Model Predictive Control of Lignin Properties Using an Accelerated kMC Framework with Artificial Neural Networks","authors":"Juhyeon Kim, Jiae Ryu, Qiang Yang, Chang Geun Yoo, Joseph Sang-II Kwon","doi":"10.1021/acs.iecr.4c02918","DOIUrl":"https://doi.org/10.1021/acs.iecr.4c02918","url":null,"abstract":"While lignin has garnered significant research interest for its abundance and versatility, its complicated structure poses a challenge to understanding its underlying reaction kinetics and optimizing various lignin characteristics. In this regard, mathematical models, especially the multiscale kinetic Monte Carlo (kMC) method, have been devised to offer a precise analysis of fractionation kinetics and lignin properties. The kMC model effectively handles the simulation of all particles within the system by calculating reaction rates between species and generating a rate-based probability distribution. Then, it selects a reaction to execute based on this distribution. However, due to the vast number of lignin polymers involved in the reactions, the rate calculation step becomes a computational bottleneck, limiting the model’s applicability in real-time control scenarios. To address this, the machine learning (ML) technique is integrated into the existing kMC framework. By training an artificial neural network (ANN) on the kMC data sets, we predict the probability distributions instead of repeatedly calculating them over time. Subsequently, the resulting ANN-accelerated multiscale kMC (AA-M-kMC) model is incorporated into a model predictive controller (MPC), facilitating real-time control of intricate lignin properties. This innovative approach effectively reduces the computational burden of kMC and advances lignin processing methods.","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"39 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670386","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}
Given the significant impact of an external electric field on fluidized bed hydrodynamics and the practical importance of rodlike particles, this study examines the behavior of a fluidized bed containing rodlike particles under various external electric fields. Simulations were performed using a coupled computational fluid dynamics-discrete element method, and rodlike particles were generated using a multisphere approach aided by quaternions. The effect of different vertical and horizontal external electric fields on the orientation of particles was investigated. Also, the effect of particle size on their orientation in the presence of constant vertical and horizontal external electric fields was explored in this work. The results showed that increasing the electric field strength and reducing the size of rodlike particles lead to an increment in the tendency of particles to become oriented along the direction of the electric field. Moreover, the effect of the external electric field at various inlet gas velocities on the probability distribution of the porosity in the bed was studied. Finally, the effect of vertical and horizontal electric fields on the bubble diameter was examined. This study offers a deeper understanding of the fluidization of rodlike particles in the presence of an electric field, and its findings can be applied to design and optimize related processes.
{"title":"Effect of External Electric Field on Fluidization of Rodlike Particles Using CFD–DEM","authors":"Saman Kazemi, Hamed Aali, Roxana Saghafian Larijani, Reza Zarghami, Helei Liu, Navid Mostoufi","doi":"10.1021/acs.iecr.4c02474","DOIUrl":"https://doi.org/10.1021/acs.iecr.4c02474","url":null,"abstract":"Given the significant impact of an external electric field on fluidized bed hydrodynamics and the practical importance of rodlike particles, this study examines the behavior of a fluidized bed containing rodlike particles under various external electric fields. Simulations were performed using a coupled computational fluid dynamics-discrete element method, and rodlike particles were generated using a multisphere approach aided by quaternions. The effect of different vertical and horizontal external electric fields on the orientation of particles was investigated. Also, the effect of particle size on their orientation in the presence of constant vertical and horizontal external electric fields was explored in this work. The results showed that increasing the electric field strength and reducing the size of rodlike particles lead to an increment in the tendency of particles to become oriented along the direction of the electric field. Moreover, the effect of the external electric field at various inlet gas velocities on the probability distribution of the porosity in the bed was studied. Finally, the effect of vertical and horizontal electric fields on the bubble diameter was examined. This study offers a deeper understanding of the fluidization of rodlike particles in the presence of an electric field, and its findings can be applied to design and optimize related processes.","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"13 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670382","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}
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