Publications |

2024

FOCAPD 2024
Fast, Accurate, and Robust Fault Detection and Diagnosis of Industrial Processes

Alireza Miraliakbar and Zheyu Jiang

Systems and Control Transactions, Jul 2024

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Modern industrial processes are continuously monitored by a large number of sensors. Despite having access to large volumes of historical and online sensor data, industrial practitioners still face challenges in the era of Industry 4.0 in effectively utilizing them to perform online process monitoring and fast fault detection and diagnosis. To target these challenges, in this work, we present a novel framework named “FARM” for Fast, Accurate, and Robust online process Monitoring. FARM is a holistic monitoring framework that integrates (a) advanced multivariate statistical process control (SPC) for fast anomaly detection of nonparametric, heterogeneous data streams, and (b) modified support vector machine (SVM) for accurate and robust fault classification. Unlike existing general-purpose process monitoring frameworks, FARM’s unique hierarchical architecture decomposes process monitoring into two fault detection and diagnosis, each of which is conducted by targeted algorithms. Here, we test and validate the performance of our FARM monitoring framework on Tennessee Eastman Process (TEP) benchmark dataset. We show that SPC achieves faster fault detection speed at a lower false alarm rate compared to state-of-the-art benchmark fault detection methods. In terms of fault classification diagnosis, we show that our modified SVM algorithm successfully classifies 17 out of 20 of the fault scenarios present in the TEP dataset. Compared with the results of standard SVM trained directly on the original dataset, our modified SVM improves the fault classification accuracy significantly.
@article{alireza:2024:focapd, author = {Miraliakbar, Alireza and Jiang, Zheyu}, title = {Fast, Accurate, and Robust Fault Detection and Diagnosis of Industrial Processes}, doi = {https://doi.org/10.69997/sct.108451}, journal = {Systems and Control Transactions}, volume = {3}, year = {2024}, month = jul, pages = {322-329}, }
Processes
MOLA: Enhancing Industrial Process Monitoring Using a Multi-Block Orthogonal Long Short-Term Memory Autoencoder

Fangyuan Ma, Cheng Ji, Jingde Wang, and 3 more authors

Processes, Dec 2024

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In this work, we introduce MOLA, a multi-block orthogonal long short-term memory autoencoder paradigm, to conduct accurate, reliable fault detection of industrial processes. To achieve this, MOLA effectively extracts dynamic orthogonal features by introducing an orthogonality-based loss function to constrain the latent space output. This helps eliminate the redundancy in the features identified, thereby improving the overall monitoring performance. On top of this, a multi-block monitoring structure is proposed, which categorizes the process variables into multiple blocks by leveraging expert process knowledge about their associations with the overall process. Each block is associated with its specific orthogonal long short-term memory autoencoder model, whose extracted dynamic orthogonal features are monitored by distance-based Hotelling’s T2 statistics and quantile-based cumulative sum (CUSUM) designed for multivariate data streams that are nonparametric and heterogeneous. Compared to having a single model accounting for all process variables, such a multi-block structure significantly improves overall process monitoring performance, especially for large-scale industrial processes. Finally, we propose an adaptive weight-based Bayesian fusion (W-BF) framework to aggregate all block-wise monitoring statistics into a global statistic that we monitor for faults. Fault detection speed and accuracy are improved by assigning and adjusting weights to blocks based on the sequential order in which alarms are raised. We demonstrate the efficiency and effectiveness of our MOLA framework by applying it to the Tennessee Eastman process and comparing the performance with various benchmark methods.
@article{fangyuan:2024:mola, author = {Ma, Fangyuan and Ji, Cheng and Wang, Jingde and Sun, Wei and Tang, Xun and Jiang, Zheyu}, title = {MOLA: Enhancing Industrial Process Monitoring Using a Multi-Block Orthogonal Long Short-Term Memory Autoencoder}, doi = {https://doi.org/10.3390/pr12122824}, journal = {Processes}, volume = {12}, month = dec, year = {2024}, number = {12}, pages = {2824}, }

2023

ESCAPE33
Online monitoring and robust, reliable fault detection of chemical process systems

Zheyu Jiang

Computer Aided Chemical Engineering, Jun 2023

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Nowadays, large amounts of data are continuously collected by sensors and monitored in chemical plants. Despite having access to large volumes of historical and online sensor data, industrial practitioners still face several challenges in effectively utilizing them to perform process monitoring and fault detection, because: 1) fault scenarios in chemical processes are naturally complex and cannot be exhaustively enumerated or predicted, 2) sensor measurements continuously produce massive arrays of high-dimensional big data streams that are often nonparametric and heterogeneous, and 3) the strict environmental, health, and safety requirements established in the facilities demand uncompromisingly high reliability and accuracy of any process monitoring and fault detection tool. To address these challenges, in this paper, we introduce a robust and reliable chemical process monitoring framework based on statistical process control (SPC) that can monitor nonparametric and heterogeneous high-dimensional data streams and detect process anomalies as early as possible while maintaining a pre-specified in-control average run length. Through an illustrative case study of the classical Tennessee Eastman Process, we demonstrate the effectiveness of this novel chemical process monitoring framework.
@article{jiang:2023:escape, title = {Online monitoring and robust, reliable fault detection of chemical process systems}, journal = {Computer Aided Chemical Engineering}, volume = {52}, pages = {1621-1626}, year = {2023}, month = jun, doi = {http://dx.doi.org/10.1016/B978-0-443-15274-0.50258-4}, author = {Jiang, Zheyu}, keywords = {Process monitoring, fault detection, statistical process control, CUSUM, big data streams}, }
ESCAPE33
A data-driven modeling approach for water flow dynamics in soil

Zeyuan Song and Zheyu Jiang

Computer Aided Chemical Engineering, Jun 2023

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Modeling and predicting soil moisture is essential for precision agriculture, smart irrigation, drought prevention, etc. Estimating root zone soil moisture from surface or near-surface soil moisture data is typically achieved by solving a hydrological model that describes water movement through soils. Advanced agro-hydrological models today use the Richards equation, a highly nonlinear, degenerate elliptic-parabolic partial differential equation that captures irrigation, precipitation, evapotranspiration, runoff, and drainage. State-of-the-art Richards equation solvers employ either a finite difference, finite element, or finite volume discretization framework in space. In this paper, we introduce a novel computational framework to solve generic n-dimensional Richards equation by introducing global random walk and deep neural network to a modified finite volume method (FVM). Furthermore, for n-dimensional Richards equation, we introduce multipoint flux approximation to the FVM framework. Through these innovations, our novel computational framework effectively utilizes the underlying physics behind the Richards equation, which enhances the speed and accuracy of the solution process. Through an illustrative case study, we demonstrate the efficiency and effectiveness of our computational framework and show that it correctly characterizes the physical relationships among soil moisture content, pressure head, and flux.
@article{song:2023:escape, title = {A data-driven modeling approach for water flow dynamics in soil}, journal = {Computer Aided Chemical Engineering}, volume = {52}, pages = {819-824}, year = {2023}, doi = {http://dx.doi.org/10.1016/B978-0-443-15274-0.50131-1}, author = {Song, Zeyuan and Jiang, Zheyu}, month = jun, keywords = {Soil moisture, Richards equation, finite volume method, neural network, random walk}, }
ESCAPE33
Stochastic optimization of agrochemical supply chains with risk management

Saba Ghasemi Naraghi and Zheyu Jiang

Computer Aided Chemical Engineering, Jun 2023

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The global agrochemical market is highly consolidated, with large multinational companies accounting for a major share of the market. Thus, even for a single agrochemical product, its supply chain typically involves many possible paths connecting the raw material sources of active ingredients to final customers. In addition to structural complexity, agrochemical supply chains are also subject to seasonality and various unique uncertainties, thereby demanding high system resilience and implementation of risk management strategies in the face of these uncertainties and disruptions. In this study, we formulate and optimize the supply chain of an agrochemical active ingredient by formulating a stochastic mixed-integer nonlinear programming (MINLP) model. This MINLP formulation is scenario-based with demand uncertainty addressed by Value-at- Risk (VaR) and Conditional Value-at-Risk (CVaR). For the first time, we propose to reformulate these nonlinear CVaR constraints using perspective reformulation techniques. We show that these perspective cuts give a tight approximation of the original MINLP model. Through an illustrative case study, we compare the results and performance of the original MINLP and the reformulated MILP.
@article{saba:2023:escape, title = {Stochastic optimization of agrochemical supply chains with risk management}, journal = {Computer Aided Chemical Engineering}, month = jun, volume = {52}, pages = {3335-3340}, year = {2023}, doi = {http://dx.doi.org/10.1016/B978-0-443-15274-0.50532-1}, author = {Ghasemi Naraghi, Saba and Jiang, Zheyu}, keywords = {Agrochemical supply chain, Value-at-Risk, Conditional Value-at-Risk, perspective cuts, mixed-integer nonlinear programming}, }
FOCAPO/CPC 2023
A Data-Driven Random Walk Approach for Solving Water Flow Dynamics in Soil Systems

Zeyuan Song and Zheyu Jiang

In Proceedings of Foundations of Computer-Aided Process Operations and Chemical Process Control Conference, Jan 2023

In production

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Modeling and predicting soil moisture is essential for precision agriculture, smart irrigation, and drought prevention. In this work, we introduce a novel data-driven random walk (DRW) approach to solve n-dimensional Richards equation, a complex partial differential equation that characterizes water flow dynamics in soil. This advanced computational framework integrates multiple features, including finite volume discretization, adaptive L-scheme, multi-layer neural networks, and the concept of random walk to enable fast and accurate numerical solution of the Richards equation. Through an illustrative example, we demonstrate the accuracy and attractiveness of this novel approach. In particular, we show that our DRW approach can implicitly capture the underlying physical relationships among soil moisture content, pressure head, and flux, which will enable more accurate characterization of water flow dynamics.
@inproceedings{song:2023:richards, author = {Song, Zeyuan and Jiang, Zheyu}, title = {A Data-Driven Random Walk Approach for Solving Water Flow Dynamics in Soil Systems}, booktitle = {Proceedings of Foundations of Computer-Aided Process Operations and Chemical Process Control Conference}, year = {2023}, month = jan, address = {San Antonio, TX}, note = {In production}, }
FOCAPO/CPC 2023
A Shortcut Model for Multicomponent Homogeneous Azeotropic Distillation

Zheyu Jiang

In Proceedings of Foundations of Computer-Aided Process Operations and Chemical Process Control Conference, Jan 2023

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Distillation of multicomponent mixtures forming one or more azeotropes is ubiquitous in chemical process industries. The minimum reflux ratio of a distillation column is directly related to its energy consumption and capital cost. Thus, it is a key parameter for distillation systems design, operation, and comparison. However, this problem remains an open challenge to researchers and industrial practitioners due to the highly nonideal nature of azeotropic systems. In this work, we present a simple and easy-to-use shortcut method to analytically calculate the minimum reflux ratio for a broad class of multicomponent homogeneous azeotropic mixture separations. Compared with existing techniques, this method does not involve any rigorous tray-by-tray calculation and is iteration free. Through an illustrative example, we demonstrate the accuracy and effectiveness of this new approach.
@inproceedings{jiang:2023:azeotrope, author = {Jiang, Zheyu}, title = {A Shortcut Model for Multicomponent Homogeneous Azeotropic Distillation}, booktitle = {Proceedings of Foundations of Computer-Aided Process Operations and Chemical Process Control Conference}, year = {2023}, month = jan, address = {San Antonio, TX}, }
IISE 2023
Stochastic Optimization of Global Agrochemical Supply Chains with Risk Management: Modeling and Reformulation

Saba Ghasemi Naraghi and Zheyu Jiang

In Proceedings of 2023 IISE Annual Conference and Expo 2023, May 2023

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The global agrochemical market is highly consolidated, with large multinational companies accounting for a major share of the market. Thus, even for a single agrochemical product, its global supply chain typically involves numerous paths connecting the raw material sources to the final customers. Besides structural complexity, agrochemical supply chains are also subject to seasonality and other unique uncertainties, thereby posing a need for risk management tools and strategies. In this study, we model and optimize an agrochemcial supply chain by developing and solving a stochastic mixed-integer quadratic constrained program (MIQCP). We model and control the demand uncertainty in this scenario-based MIQCP using variance. We also apply perspective reformulation techniques to convert the MIQCP to a mixed-integer linear program (MILP). Computational experiment results from an illustrative example show that, successively introducing perspective cuts to the reformulated MILP not only leads to a tight approximation of the original MIQCP model, but is also more computationally efficient than directly solving the MIQCP.
@inproceedings{saba:2023:iise, author = {Ghasemi Naraghi, Saba and Jiang, Zheyu}, title = {Stochastic Optimization of Global Agrochemical Supply Chains with Risk Management: Modeling and Reformulation}, booktitle = {Proceedings of 2023 IISE Annual Conference and Expo 2023}, year = {2023}, month = may, address = {New Orleans, LA}, eprint = {2401.12348}, archiveprefix = {arXiv}, doi = {https://doi.org/10.48550/arXiv.2401.12348}, }
IISE 2023
The Effect of Different Optimization Strategies to Physics-Constrained Deep Learning for Soil Moisture Estimation

Jianxin Xie, Bing Yao, and Zheyu Jiang

In Proceedings of 2023 IISE Annual Conference and Expo 2023, May 2023

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Soil moisture is a key hydrological parameter that has significant importance to human society and the environment. Accurate modeling and monitoring of soil moisture in crop fields, especially in the root zone (top 100 cm of soil), is essential for improving agricultural production and crop yield with the help of precision irrigation and farming tools. Realizing the full sensor data potential depends greatly on advanced analytical and predictive domain-aware models. In this work, we propose a physics-constrained deep learning (P-DL) framework to integrate physics-based principles on water transport and water sensing signals for effective reconstruction of the soil moisture dynamics. We adopt three different optimizers, namely Adam, RMSprop, and GD, to minimize the loss function of P-DL during the training process. In the illustrative case study, we demonstrate the empirical convergence of Adam optimizers outperforms the other optimization methods in both mini-batch and full-batch training.
@inproceedings{xie:2023:iise, author = {Xie, Jianxin and Yao, Bing and Jiang, Zheyu}, title = {The Effect of Different Optimization Strategies to Physics-Constrained Deep Learning for Soil Moisture Estimation}, booktitle = {Proceedings of 2023 IISE Annual Conference and Expo 2023}, year = {2023}, month = may, address = {New Orleans, LA}, pages = {121-126}, }

2022

AIChE
Minimum Reflux Calculation for Multicomponent Distillation in Multi-Feed, Multi-Product Columns: Mathematical Model

Zheyu Jiang, Mohit Tawarmalani, and Rakesh Agrawal

AIChE Journal, May 2022

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Multi-feed, multi-product distillation columns are ubiquitous in multicomponent distillation systems. The minimum reflux ratio of a distillation column is directly related to its energy consumption and capital cost. Thus, it is a key parameter for distillation systems design, operation, and comparison. In this series, we present the first accurate shortcut based algorithmic method to determine the minimum reflux condition for any general multi-feed, multi-product (MFMP) distillation column separating any ideal multicomponent mixture. The classic McCabe-Thiele or Underwood method is a special case of this general approach. Compared with existing techniques, this method does not involve any rigorous tray-by-tray calculation, nor does it require guessing of key components. In this first part of the series, we present the mathematical model for a general MFMP column, derive constraints for feasible separation and minimum reflux condition, discuss their geometric interpretations, and present an illustrative example to demonstrate the effectiveness of our approach.
@article{jiang:2022:mfmp1, author = {Jiang, Zheyu and Tawarmalani, Mohit and Agrawal, Rakesh}, title = {Minimum Reflux Calculation for Multicomponent Distillation in Multi-Feed, Multi-Product Columns: {M}athematical Model}, journal = {AIChE Journal}, year = {2022}, volume = {68}, number = {12}, pages = {e17929}, doi = {https://doi.org/10.1002/aic.17929}, eprint = {https://aiche.onlinelibrary.wiley.com/doi/pdf/10.1002/aic.17929}, }

2020

CVTA
A shortcut minimum reflux calculation method for distillation columns separating multicomponent homogeneous azeotropic mixtures

Zheyu Jiang

Le Scientifique, May 2020

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Chemical, pharmaceutical, and agrochemical industries frequently face the challenge of separating multicomponent mixtures exhibiting one or more azeotropes using distillation. The minimum reflux ratio of a distillation column is directly related to its energy consumption and capital cost. Thus, it is a key parameter for distillation systems design, operation, and comparison. Despite its great scientific significance and practical importance, this problem remains an open challenge to researchers and industrial practitioners due to the highly nonideal nature of azeotropic systems. In this work, we present a simple and easy-to-use shortcut method to analytically calculate the minimum reflux ratio for a broad class of multicomponent homogeneous azeotropic mixture separations. Through an illustrative example, we demonstrate the accuracy and effectiveness of our new ideal multicomponent distillation turns out to be a special case of this generalized method. Compared with existing techniques, this method does not involve any rigorous tray-by-tray calculation and is iteration free. Therefore, it can be easily incorporated into a global optimization framework that enables industrial practitioners to, for the first time, quickly synthesize energy-efficient and cost-effective multicomponent azeotropic distillation systems and determine their optimal operating conditions.
@article{jiang:2020:azeotropic, author = {Jiang, Zheyu}, title = {A shortcut minimum reflux calculation method for distillation columns separating multicomponent homogeneous azeotropic mixtures}, journal = {Le Scientifique}, year = {2020}, volume = {2020}, number = {1}, pages = {17-25}, }

2019

ChERD
Process intensification in multicomponent distillation: A review of recent advancements

Zheyu Jiang and Rakesh Agrawal

Chemical Engineering Research and Design, May 2019

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Process intensification (PI) is an emerging concept in chemical engineering that describes the design innovations that lead to significant shrinkage in size and boost in efficiency of a process plant. Distillation, the most commonly used separation technique in the chemical industry, is a crucial component of PI. Here, we systematically discuss the following aspects of PI in non-azeotropic multicomponent distillation: (1) Introducing thermal couplings to eliminate intermediate reboilers and condensers to save energy and capital cost; (2) Improving operability of thermally coupled columns by means of eliminating vapor streams in thermal couplings with only liquid transfers or column section rearrangement; (3) Enabling double and multi-effect distillation of thermally coupled configurations to further reduce heat duty; (4) Performing simultaneous heat and mass integration among thermally coupled columns to reduce the number of columns and heat duty; and (5) Conducting any thermally coupled distillation in n-product streams using 1 to n−2 column shells with operable novel dividing wall columns. We demonstrate these aspects of PI through examples to illustrate how they lead to compact, easy-to-operate, energy efficient and cost effective multicomponent distillation system designs.
@article{jiang:2019:pireview, title = {Process intensification in multicomponent distillation: A review of recent advancements}, journal = {Chemical Engineering Research and Design}, volume = {147}, pages = {122-145}, year = {2019}, issn = {0263-8762}, doi = {https://doi.org/10.1016/j.cherd.2019.04.023}, author = {Jiang, Zheyu and Agrawal, Rakesh}, keywords = {Process intensification, Multicomponent distillation, Separation}, }
CACE
Global optimization of multicomponent distillation configurations: Global minimization of total cost for multicomponent mixture separations

Zheyu Jiang, Tony Joseph Mathew, Haibo Zhang, and 4 more authors

Computers & Chemical Engineering, May 2019

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We introduce a global optimization framework for determining the minimum cost required to distill any ideal or near-ideal multicomponent mixture into its individual constituents using a sequence of columns. This new framework extends the Global Minimization Algorithm (GMA) previously introduced by Nallasivam et al. (2016); and we refer to the new framework as the Global Minimization Algorithm for Cost (GMAC). GMAC guarantees global optimality by formulating a nonlinear program (NLP) for each and every distillation configuration in the search space and solving it using global optimization solvers. The case study presented in this work not only demonstrates the need for developing such an algorithm, but also shows the flexibility and effectiveness of GMAC, which enables process engineers to design and retrofit energy efficient and cost-effective distillation configurations.
@article{jiang:2019:nlpcost, title = {Global optimization of multicomponent distillation configurations: Global minimization of total cost for multicomponent mixture separations}, journal = {Computers & Chemical Engineering}, volume = {126}, pages = {249-262}, year = {2019}, issn = {0098-1354}, doi = {https://doi.org/10.1016/j.compchemeng.2019.04.009}, author = {Jiang, Zheyu and Mathew, Tony Joseph and Zhang, Haibo and Huff, Joshua and Nallasivam, Ulaganathan and Tawarmalani, Mohit and Agrawal, Rakesh}, keywords = {Multicomponent distillation, Distillation configuration, Global optimization, Cost optimization, Process intensification}, }
AIChE
Global minimization of total exergy loss of multicomponent distillation configurations

Zheyu Jiang, Zewei Chen, Joshua Huff, and 3 more authors

AIChE Journal, May 2019

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The operating cost of a multicomponent distillation system comprises two major aspects: the overall heat duty requirement and the temperature levels at which the heat duties are generated and rejected. The second aspect, often measured by the thermodynamic efficiency of the distillation system, can be quantified by its total exergy loss. In this article, we introduce a global optimization framework for determining the minimum total exergy loss required to distill any ideal or near-ideal multicomponent mixture using a sequence of columns. Desired configurations identified by this new framework tend to use milder-temperature reboilers and condensers and are thus attractive for applications such as heat pump assisted distillation. Through a case study of shale gas separations, we demonstrate the effectiveness of this framework and present various useful physical insights for designing energy efficient distillation systems.
@article{jiang:2019:nlpexergy, author = {Jiang, Zheyu and Chen, Zewei and Huff, Joshua and Shenvi, Anirudh A. and Tawarmalani, Mohit and Agrawal, Rakesh}, title = {Global minimization of total exergy loss of multicomponent distillation configurations}, journal = {AIChE Journal}, volume = {65}, number = {11}, pages = {e16737}, keywords = {distillation configuration, exergy analysis, global optimization, multicomponent distillation, thermodynamic efficiency}, doi = {https://doi.org/10.1002/aic.16737}, eprint = {https://aiche.onlinelibrary.wiley.com/doi/pdf/10.1002/aic.16737}, year = {2019}, }

2018

AIChE
Minimum energy of multicomponent distillation systems using minimum additional heat and mass integration sections

Zheyu Jiang, Gautham Madenoor Ramapriya, Mohit Tawarmalani, and 1 more author

AIChE Journal, May 2018

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Heat and mass integration to consolidate distillation columns in a multicomponent distillation configuration can lead to a number of new energy efficient and cost-effective configurations. In this work, a powerful and simple-to-use fact about heat and mass integration is identified. The newly developed heat and mass integrated configurations, which we call as HMP configurations, involve first introducing thermal couplings to all intermediate transfer streams, followed by consolidating columns associated with a lighter pure product reboiler and a heavier pure product condenser. A systematic method of enumerating all HMP configurations is introduced. The energy savings of HMP configurations is compared with the well-known fully thermally coupled (FTC) configurations. HMP configurations can have very similar and sometimes even the same minimum total vapor duty requirement as the FTC configuration is demonstrated, while using far less number of column sections, intermediate transfer streams, and thermal couplings than the FTC configurations.
@article{jiang:2018:hmp, author = {Jiang, Zheyu and Madenoor Ramapriya, Gautham and Tawarmalani, Mohit and Agrawal, Rakesh}, title = {Minimum energy of multicomponent distillation systems using minimum additional heat and mass integration sections}, journal = {AIChE Journal}, volume = {64}, number = {9}, pages = {3410-3418}, keywords = {multicomponent distillation, distillation sequences, distillation configurations, heat and mass integration, process intensification}, doi = {https://doi.org/10.1002/aic.16189}, eprint = {https://aiche.onlinelibrary.wiley.com/doi/pdf/10.1002/aic.16189}, year = {2018}, }
DA 2018
Process Intensification in Multicomponent Distillation

Zheyu Jiang, Gautham Madenoor Ramapriya, Mohit Tawarmalani, and 1 more author

Chemical Engineering Transactions, May 2018

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Process Intensification (PI) is an emerging concept in chemical engineering which describes the design innovations that lead to significant shrinkage in size and dramatic boost in efficiency in a process plant. Distillation, which is one of the most important separation technologies in the chemical industry, is therefore a crucial component in PI. Here, we discuss two aspects of PI in multicomponent distillation: 1) Performing simultaneous heat and mass integration among thermally coupled distillation columns to reduce the number of columns and heat duty requirement; and 2) Conducting any thermally coupled distillation in only a single column shell using a dividing wall column that is fully operable. Through examples, we show that synergistic use of both strategies leads to the design of compact, easy-to-operate, energy efficient and cost effective multicomponent distillation systems.
@article{jiang:2018:piconference, author = {Jiang, Zheyu and Madenoor Ramapriya, Gautham and Tawarmalani, Mohit and Agrawal, Rakesh}, title = {Process Intensification in Multicomponent Distillation}, journal = {Chemical Engineering Transactions}, volume = {69}, pages = {841-846}, doi = {10.3303/CET1869141}, year = {2018}, }

2013

AIChE
Solution-processable exfoliated zeolite nanosheets purified by density gradient centrifugation

Kumar Varoon Agrawal, Berna Topuz, Zheyu Jiang, and 5 more authors

AIChE Journal, May 2013

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Highly crystalline exfoliated MFI-nanosheets can pave the way for large-scale deployment of sub-500-nm zeolite membranes due to their processing and packing advantages. Exfoliated MFI-nanosheets prepared by melt compounding contain a large amount of polymer and unexfoliated particles which are detrimental to the fabrication of ultrathin zeolite membranes. Complete removal of polystyrene from the nanosheet suspension in toluene is demonstrated by centrifugation of the suspension across chlorobenzene as confirmed by thermogravimetric analysis (TGA) data and transmission electron microscopy (TEM) images. Rate-zonal centrifugation in a nonlinear density gradient fractionated exfoliated MFI-nanosheets from unexfoliated particles. The purified nanosheets were highly crystalline as indicated by high-resolution TEM (HRTEM) and electron diffraction (ED). Coating of purified MFI-nanosheets on a smooth α-alumina support, fabricated by filtration of α-alumina suspension, led to a compact, b-oriented, 80-nm-thick film. A mild hydrothermal treatment of the film led to a 200-nm-thick membrane, which demonstrated molecular sieving properties.
@article{kumar:2013:zeolite, author = {Agrawal, Kumar Varoon and Topuz, Berna and Jiang, Zheyu and Nguenkam, Kevin and Elyassi, Bahman and Francis, Lorraine F. and Tsapatsis, Michael and Navarro, Marta}, title = {Solution-processable exfoliated zeolite nanosheets purified by density gradient centrifugation}, journal = {AIChE Journal}, volume = {59}, number = {9}, pages = {3458-3467}, keywords = {zeolite nanosheets, density gradient centrifugation, zeolite membrane, membrane support}, doi = {https://doi.org/10.1002/aic.14099}, eprint = {https://aiche.onlinelibrary.wiley.com/doi/pdf/10.1002/aic.14099}, year = {2013}, }