# Publications

Peer-reviewed publications from CLAMS members

## 2023

- Online monitoring and robust, reliable fault detection of chemical process systems
*Computer Aided Chemical Engineering*Jun 2023Nowadays, 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.

- A data-driven modeling approach for water flow dynamics in soilZeyuan Song, and Zheyu Jiang
*Computer Aided Chemical Engineering*Jun 2023Modeling 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.

- Stochastic optimization of agrochemical supply chains with risk management
*Computer Aided Chemical Engineering*Jun 2023The 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.

- A Data-Driven Random Walk Approach for Solving Water Flow Dynamics in Soil SystemsZeyuan Song, and Zheyu Jiang
*In Proceedings of Foundations of Computer-Aided Process Operations and Chemical Process Control Conference*Jan 2023Modeling 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.

- A Shortcut Model for Multicomponent Homogeneous Azeotropic Distillation
*In Proceedings of Foundations of Computer-Aided Process Operations and Chemical Process Control Conference*Jan 2023Distillation 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.

- Stochastic Optimization of Global Agrochemical Supply Chains with Risk Management: Modeling and Reformulation
*In IISE Annual Conference and Expo 2023 Proceedings*May 2023The 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.

- The Effect of Different Optimization Strategies to Physics-Constrained Deep Learning for Soil Moisture EstimationJianxin Xie, Bing Yao, and Zheyu Jiang
*In IISE Annual Conference and Expo 2023 Proceedings*May 2023Soil 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.

## 2022

- Minimum Reflux Calculation for Multicomponent Distillation in Multi-Feed, Multi-Product Columns: Mathematical ModelZheyu Jiang, Mohit Tawarmalani, and Rakesh Agrawal
*AIChE Journal*May 2022Multi-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.

## 2020

- A shortcut minimum reflux calculation method for distillation columns separating multicomponent homogeneous azeotropic mixtures
*Le Scientifique*May 2020Chemical, 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.

## 2019

- Process intensification in multicomponent distillation: A review of recent advancementsZheyu Jiang, and Rakesh Agrawal
*Chemical Engineering Research and Design*May 2019Process 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.

- Global optimization of multicomponent distillation configurations: Global minimization of total cost for multicomponent mixture separationsZheyu Jiang, Tony Joseph Mathew, Haibo Zhang, and 4 more authors
*Computers & Chemical Engineering*May 2019We 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.

- Global minimization of total exergy loss of multicomponent distillation configurationsZheyu Jiang, Zewei Chen, Joshua Huff, and 3 more authors
*AIChE Journal*May 2019The 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.

## 2018

- Minimum energy of multicomponent distillation systems using minimum additional heat and mass integration sectionsZheyu Jiang, Gautham Madenoor Ramapriya, Mohit Tawarmalani, and 1 more author
*AIChE Journal*May 2018Heat 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.

- Process Intensification in Multicomponent DistillationZheyu Jiang, Gautham Madenoor Ramapriya, Mohit Tawarmalani, and 1 more author
*Chemical Engineering Transactions*May 2018Process 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.

## 2013

- Solution-processable exfoliated zeolite nanosheets purified by density gradient centrifugationKumar Varoon Agrawal, Berna Topuz, Zheyu Jiang, and 5 more authors
*AIChE Journal*May 2013Highly 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.