AIChE Journal最新文献

{"title":"Optimizing supramolecular interactions within metal–organic frameworks for ultra‐high purity propylene purification","authors":"Tong Li, Lu Zhang, Yong Wang, Xiaoxia Jia, Hui Chen, Yongjian Li, Qi Shi, Lin‐Bing Sun, Jinping Li, Banglin Chen, Libo Li","doi":"10.1002/aic.18646","DOIUrl":"https://doi.org/10.1002/aic.18646","url":null,"abstract":"Purifying ultra‐high purity propylene (&gt;99.995%) with an energy‐efficient adsorptive separation method is a promising yet challenging technology that remains unfulfilled. Instead of solely considering the effect of adsorbents on guest molecules, we propose a synergistic adsorption mechanism for the deep removal of propane and propyne, utilizing supramolecular interactions in both “host‐guest” and “guest‐guest” systems. Through modulation of the pore environment, Ni‐DMOF‐DM exhibits exceptionally high adsorption capacities for propane and propyne (171 and 197 cm<jats:sup>3</jats:sup>/g at ambient temperature and pressure, respectively), and unprecedented propane/propylene separation selectivity (2.74). Theoretical calculations confirm the geometric interactions of C‐H···π bonds and C‐H···O hydrogen bonds resulting from host‐guest interactions, alongside C‐H···H guest‐guest interactions within the confined pore space. Breakthrough experiments demonstrated that ultra‐high purity propylene (propane &lt; 0.005% and propyne &lt; 1.0 ppm) can be directly collected from ternary mixtures on Ni‐DMOF‐DM, achieving a productivity of up to 152.14 L/kg.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"197 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637128","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":"Liquid holdup of gas–liquid two-phase flow in micro-packed beds reactors","authors":"Keyi Chen, Yangcheng Lu","doi":"10.1002/aic.18636","DOIUrl":"https://doi.org/10.1002/aic.18636","url":null,"abstract":"Liquid holdup is a crucial factor in the study of hydrodynamic behaviors in the micro-packed bed reactor (μPBR). In this work, the values of liquid holdup are studied with the weighing method with good accuracy. The packed bed is a tube made of stainless steel with a length of 20 cm and an inner diameter of 4 mm, packed with 177–250 μm or 350–500 μm microbeads. The gas and liquid flow rates vary from 5 to 20 mL/min and 0.25 to 2 mL/min, respectively. A new hypothesis of the flow regions is proposed based on the experimental results. Furthermore, a new set of empirical correlation is built with great agreement, particularly for viscous liquids, whose viscosity ranges from 0.99 to 5.98 mPa·s, showing an atypical tendency.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"13 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142601979","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":"Reverse design of molecule-process-process networks: A case study from HEN-ORC system","authors":"Xiaodong Hong, Xuan Dong, Zuwei Liao, Jingyuan Sun, Jingdai Wang, Yongrong Yang","doi":"10.1002/aic.18643","DOIUrl":"https://doi.org/10.1002/aic.18643","url":null,"abstract":"The integrated design of the heat exchanger network (HEN) and organic Rankine cycle (ORC) system with new working fluids is a complex optimization problem. It involves navigating a vast design space across working fluid molecules, ORC processes, and networks. In this article, a new two-stage reverse strategy is developed. The optimal HEN-ORC configurations and operating conditions, and the thermodynamic properties of the hypothetical working fluid are identified by an equation of state (EOS) free HEN-ORC model in the first stage. With two developed group contribution-artificial neural network thermodynamic property prediction models, working fluid molecules are screened out in the second stage from a database containing more than 430,000 hydrofluoroolefins (HFOs). The presented method is employed in two cases, where new working fluids are found. The total annual cost of Case 1 is 12%–22% lower than the literature, and the power output of Case 2 is 5%–8% higher than the literature.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"43 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142599985","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":"Vapor–liquid phase equilibrium prediction for mixtures of binary systems using graph neural networks","authors":"Jinke Sun, Jianfei Xue, Guangyu Yang, Jingde Li, Wei Zhang","doi":"10.1002/aic.18637","DOIUrl":"https://doi.org/10.1002/aic.18637","url":null,"abstract":"Vapor–liquid phase equilibrium (VLE) plays a crucial role in chemical process design, process equipment control, and experimental process simulation. However, experimental acquisition of VLE data is a challenging and complex task. As an alternative to experimentation, VLE data prediction offers great convenience and utility. In this article, an artificial intelligence network is proposed to predict the temperature and the vapor phase composition of binary mixtures. We constructed a graph neural network (GNN) and designed an uncertainty-aware learning and inference mechanism (UALF) in the prediction process. The model was tested on both a self-constructed dataset and a publicly available dataset. The results demonstrate that the proposed method effectively reveals the phase equilibrium properties of the target data. This work presents a novel approach for predicting vapor–liquid phase equilibrium in binary systems and proposes innovative ideas for investigating phase equilibrium mechanisms and principles.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"61 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142580231","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":"Development and validation of a controlled heating apparatus for long-term MRI of 3D microfluidic tumor models","authors":"Hassan Alkhadrawi,&nbsp;Kokeb Dese,&nbsp;Dhruvi M. Panchal,&nbsp;Alexander R. Pueschel,&nbsp;Kasey A. Freshwater,&nbsp;Amanda Stewart,&nbsp;Haleigh Henderson,&nbsp;Michael Elkins,&nbsp;Raj T. Dave,&nbsp;Hunter Wilson,&nbsp;John W. Bennewitz,&nbsp;Margaret F. Bennewitz","doi":"10.1002/aic.18638","DOIUrl":"10.1002/aic.18638","url":null,"abstract":"<p>Conventional testing of novel contrast agents for magnetic resonance imaging (MRI) involves cell and animal studies. However, 2D cultures lack dynamic flow and <i>in vivo</i> MRI is limited by regulatory approval of long-term anesthesia use. Microfluidic tumor models (MTMs) offer a cost-effective, reproducible, and high throughput platform for bridging cell and animal models. Yet, MRI of microfluidic devices is challenging, due to small fluid volumes generating low sensitivity. For the first time, an MRI of MTMs was performed at low field strength (1 T) using conventional imaging equipment without microcoils. To enable longitudinal MRI, we developed (1) CHAMP-3 (controlled heating apparatus for microfluidics and portability) which heats MTMs during MRI scans and (2) an MRI-compatible temperature monitoring system. CHAMP-3 maintained chip surface temperature at ~37°C and the media inside at ~35.5°C. Enhanced T₁-weighted MRI contrast was achieved in 3D MTMs with free manganese (Mn²⁺) solutions and Mn²⁺ labeled tumor cells.</p>","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"70 12","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142580722","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":"Nanoscale wettability characterization—Interpreting droplet morphological evolution in nanopores","authors":"Wenzhen Chu, Kaiqiang Zhang","doi":"10.1002/aic.18623","DOIUrl":"https://doi.org/10.1002/aic.18623","url":null,"abstract":"Nanoscale wettability, crucial for various disciplines in science and engineering, challenges traditional theory, particularly the Young's equation. This study proposes and validates a modified format of the Young's equation under nano-confinement and, for the first time, the nano-confined droplet morphological evolution and transition are investigated from thermodynamic theories and molecular dynamics simulation. The morphologies of droplets in nano-silts, identified as double-cap, single-cap, and bridge-shaped, underscore the critical roles of line tension and nano-confinement in characterizing wetting behavior. In hydrophobic nano-slits, droplets transition from the double-cap to the single-cap shape at the critical point of &lt;span data-altimg=\"/cms/asset/8aadb0a1-cdb7-4e0f-a20c-71a72b38c4be/aic18623-math-0001.png\"&gt;&lt;/span&gt;&lt;mjx-container ctxtmenu_counter=\"157\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"&gt;&lt;mjx-math aria-hidden=\"true\" location=\"graphic/aic18623-math-0001.png\"&gt;&lt;mjx-semantics&gt;&lt;mjx-mrow data-semantic-children=\"2,4\" data-semantic-content=\"3\" data-semantic- data-semantic-role=\"division\" data-semantic-speech=\"r 0 divided by upper H\" data-semantic-type=\"infixop\"&gt;&lt;mjx-msub data-semantic-children=\"0,1\" data-semantic- data-semantic-parent=\"5\" data-semantic-role=\"latinletter\" data-semantic-type=\"subscript\"&gt;&lt;mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mi&gt;&lt;mjx-script style=\"vertical-align: -0.15em;\"&gt;&lt;mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"integer\" data-semantic-type=\"number\" size=\"s\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mn&gt;&lt;/mjx-script&gt;&lt;/mjx-msub&gt;&lt;mjx-mo data-semantic- data-semantic-operator=\"infixop,/\" data-semantic-parent=\"5\" data-semantic-role=\"division\" data-semantic-type=\"operator\" rspace=\"1\" space=\"1\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mo&gt;&lt;mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"5\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mi&gt;&lt;/mjx-mrow&gt;&lt;/mjx-semantics&gt;&lt;/mjx-math&gt;&lt;mjx-assistive-mml display=\"inline\" unselectable=\"on\"&gt;&lt;math altimg=\"urn:x-wiley:00011541:media:aic18623:aic18623-math-0001\" display=\"inline\" location=\"graphic/aic18623-math-0001.png\" overflow=\"scroll\" xmlns=\"http://www.w3.org/1998/Math/MathML\"&gt;&lt;semantics&gt;&lt;mrow data-semantic-=\"\" data-semantic-children=\"2,4\" data-semantic-content=\"3\" data-semantic-role=\"division\" data-semantic-speech=\"r 0 divided by upper H\" data-semantic-type=\"infixop\"&gt;&lt;msub data-semantic-=\"\" data-semantic-children=\"0,1\" data-semantic-parent=\"5\" data-semantic-role=\"latinletter\" data-semantic-type=\"subscript\"&gt;&lt;mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-seman","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"87 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142574644","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":"Preface to the 2024 futures issue of AIChE Journal","authors":"David S. Sholl","doi":"10.1002/aic.18639","DOIUrl":"10.1002/aic.18639","url":null,"abstract":"","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"70 12","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142574667","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":"In situ monitoring of CO2$$ {}_2 $$ sorption on polyethylenimine dynamics through broadband dielectric spectroscopy","authors":"Martin Tress, Soma Ahmadi, Shiwang Cheng","doi":"10.1002/aic.18627","DOIUrl":"https://doi.org/10.1002/aic.18627","url":null,"abstract":"Chemical reactions between carbon dioxide (CO) and amine have been extensively characterized, however, their influence on the dynamics of polyamines remains largely unexplored. In this work, we compare the dynamics of polyethylenimine (PEI) before and after CO absorption through broadband dielectric spectroscopy (BDS). The molecular processes of bulk PEI are very different from those of thin film PEI, highlighting an interesting interface and nano‐confinement effect. Detailed analyses show CO absorption slows down the PEI dynamics, which is consistent with an elevated glass transition temperature of PEI upon CO absorption from differential scanning calorimetry measurements. Further <jats:italic>in situ</jats:italic> kinetic measurements demonstrate nonmonotonic changes in relaxation times or dielectric amplitudes of some relaxation processes during CO sorption or desorption, suggesting an intriguing interplay between CO chemisorption and the dynamics of PEI. These results demonstrate that BDS is a powerful platform to resolve the temporal dynamics changes of polyamines for CO capture.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"33 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142519299","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":"Digital image analysis of gas bypassing and mixing in gas-fluidized bed: Effect of particle shape","authors":"Shreya Chouhan, Ajita Neogi, Hare K. Mohanta, Arvind Kumar Sharma, Navneet Goyal, Priya C. Sande","doi":"10.1002/aic.18633","DOIUrl":"https://doi.org/10.1002/aic.18633","url":null,"abstract":"The study investigates effect of particle shape on gas bypassing and mixing of gas-fluidized Geldart A particles. A shallow fluidized bed (FB), configured at benchscale, was used with digital image analysis (DIA) for the investigation. The extent of scatter of tracer particles throughout the bed was assessed from DIA images of defluidized powder. A novel method employing Jupyter notebook software, was used to directly determine Mixing Index from digital images. Remarkably, platelet-shaped China clay powder displayed the best mixing characteristics (Mixing Index: 0.79) with no significant bypassing. Angular shaped Quartz displayed moderate mixing (Mixing Index: 0.67), but high bypassing (Bypassing Index: 0.75). Contrary to conventional assumptions, spherical-shaped diatomite exhibited poor mixing (Mixing Index: 0.61) with the highest bypassing (Bypassing Index: 0.82). Platelet particles performed well even with fines removal. Most likely, particle shape significantly influenced the number of available particle contact points, tracer migration, and traceronparticle binding.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"110 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142490057","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":"Noise aware parameter estimation in bioprocesses: Using neural network surrogate models with nonuniform data sampling","authors":"Lauren Weir, Nigel Mathias, Brandon Corbett, Prashant Mhaskar","doi":"10.1002/aic.18634","DOIUrl":"https://doi.org/10.1002/aic.18634","url":null,"abstract":"This article demonstrates a parameter estimation technique for bioprocesses that utilizes measurement noise in experimental data to determine credible intervals on parameter estimates, with this information of potential use in prediction, robust control, and optimization. To determine these estimates, the work implements Bayesian inference using nested sampling, presenting an approach to develop neural network- (NN) based surrogate models. To address challenges associated with nonuniform sampling of experimental measurements, an NN structure is proposed. The resultant surrogate model is utilized within a Nested Sampling Algorithm that samples possible parameter values from the parameter space and uses the NN to calculate model output for use in the likelihood function based on the joint probability distribution of the noise of output variables. This method is illustrated against simulated data, then with experimental data from a Sartorius fed-batch bioprocess. Results demonstrate the feasibility of the proposed technique to enable rapid parameter estimation for bioprocesses.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"21 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142486306","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}