Digital Chemical Engineering最新文献

{"title":"Enhancing cybersecurity of nonlinear processes via a two-layer control architecture","authors":"Arthur Khodaverdian ,&nbsp;Dhruv Gohil ,&nbsp;Panagiotis D. Christofides","doi":"10.1016/j.dche.2025.100233","DOIUrl":"10.1016/j.dche.2025.100233","url":null,"abstract":"<div><div>This work proposes a novel two-layer multi-key control architecture to enhance the resilience of nonlinear chemical processes to cyberattacks. The architecture consists of an upper-layer nonlinear controller and a lower-layer of encrypted linear controllers. The nonlinear controllers process unencrypted sensor data to determine optimal control actions, which are then used to estimate the closed-loop state trajectory using a first-principle model of the plant. This trajectory is sampled and mapped to a valid subset before encryption, which can lead to minor inaccuracies. The resulting encrypted state-space data samples are used as set-points for the lower-layer controllers, which can be implemented using encrypted signals, allowing for obfuscation of the computation and transmission of the applied control inputs, thereby enhancing cybersecurity. This study further improves security by taking advantage of the Single-Input-Single-Output nature of some linear control methods to allocate a unique encryption key to each linear controller and its respective sensor data. Two nonlinear chemical process applications, including a benchmark chemical reactor example and one application modeled through the use of Aspen Dynamics, are used to demonstrate the application of the proposed two-layer architecture.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100233"},"PeriodicalIF":3.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Green hydrogen extraction from natural gas transmission grids using hybrid membrane and PSA processes optimized via bayesian techniques","authors":"Homa Hamedi,&nbsp;Torsten Brinkmann","doi":"10.1016/j.dche.2025.100234","DOIUrl":"10.1016/j.dche.2025.100234","url":null,"abstract":"<div><div>Green hydrogen (H₂) is a leading enabler for the decarbonization of hard-to-abate industries where electrification is either uneconomical or infeasible. Establishing an adequate and cost-effective infrastructure for hydrogen distribution remains one of the primary barriers to its widespread adoption. A promising short-term solution to this challenge involves H₂ storage and co-transportation via existing gas grids. For H₂ extraction from distribution gas grids, standalone pressure swing adsorption systems are considered the most viable option, whereas a hybrid process is suggested in the literature for transmission gas networks. This article presents a comprehensive techno-economic model for the proposed hybrid process, developed using an integrated platform based on Aspen Adsorption and Aspen Custom Modeler. The system consists of a single-stage hollow fiber Matrimid membrane module, followed by a 4-bed adsorption process operating in 8 sequential steps to meet H₂ market purity requirements with an acceptable recovery rate. Since the performances of these two separation modules, as an integrated system, significantly influence each other, the study identifies a unique opportunity to minimize separation costs through process optimization. To reduce computational time, a cyclic steady-state approach was employed to simulate the PSA process. Bayesian optimization, facilitated by the integration of Python with Aspen Adsorption, was used to efficiently identify the optimal solution with a minimal number of objective function evaluations. The levelized cost of H₂ separation (99.0 % purity at 10 bar) from natural gas containing 10 % H₂ at pressures of 35 bar and 60 bar is estimated to be 2.7310 and, $2.5116/kg-H₂, respectively. These estimates correspond to a scenario with 10 identical trains, each handling a feed flowrate of 200 kmol/hr. Increasing the number of trains keeps the cost contribution of PSA constant; however, the total cost decreases as the compression fixed cost is distributed across more trains.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100234"},"PeriodicalIF":3.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A tutorial review of policy iteration methods in reinforcement learning for nonlinear optimal control","authors":"Yujia Wang ,&nbsp;Xinji Zhu ,&nbsp;Zhe Wu","doi":"10.1016/j.dche.2025.100231","DOIUrl":"10.1016/j.dche.2025.100231","url":null,"abstract":"<div><div>Reinforcement learning (RL) has been a powerful framework for designing optimal controllers for nonlinear systems. This tutorial review provides a comprehensive exploration of RL techniques, with a particular focus on policy iteration methods for the development of optimal controllers. We discuss key theoretical aspects, including closed-loop stability and convergence analysis of learning algorithms. Additionally, the review addresses practical challenges encountered in real-world applications, such as the development of accurate process models, incorporating safety guarantees during learning, leveraging physics-informed machine learning and transfer learning techniques to overcome learning difficulties, managing model uncertainties, and enabling scalability through distributed RL. To demonstrate the effectiveness of these approaches, a simulation example of a chemical reactor is presented, with open-source code made available on GitHub. The review concludes with a discussion of open research questions and future directions in RL-based control of nonlinear systems.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100231"},"PeriodicalIF":3.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on the Switching Model Predictive Control Algorithm in Batch Polymerization Process","authors":"Jong Nam Kim ,&nbsp;Chun Bae Ma ,&nbsp;Hyok Jo ,&nbsp;Un Chol Han ,&nbsp;Hyon-Tae Pak ,&nbsp;Son Il Hong ,&nbsp;Ri Myong Kim","doi":"10.1016/j.dche.2025.100232","DOIUrl":"10.1016/j.dche.2025.100232","url":null,"abstract":"<div><div>In the batch polymerization process, temperature control is generally a challenging task. In this paper, a new switching model predictive control algorithm that can be effectively used for the temperature control of batch polymerization process is developed and its effectiveness is verified by introducing it to industrial batch polyvinyl chloride polymerization process. Firstly, a general analysis of the polymerization process is conducted, and based on this, the reaction starting point is determined. Secondly, a switching model identification method considering the reaction starting point and the reaction heat generated after the reaction starts is proposed. Finally, a switching model predictive control algorithm that determines the optimal manipulated value based on the on-line updated step response model is constructed, and a cascade control system using this algorithm is introduced to the temperature control of batch polyvinyl chloride suspension polymerization process. The results show that the proposed control system can significantly improve temperature control performance (overshoot: 0.2%, root mean square error: 0.3) compared to before introduction (overshoot: 1.1%, root mean square error: 1.2ྟC) .</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100232"},"PeriodicalIF":3.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time process safety and systems decision-making toward safe and smart chemical manufacturing","authors":"Austin Braniff ,&nbsp;Sahithi Srijana Akundi ,&nbsp;Yuanxing Liu ,&nbsp;Beatriz Dantas ,&nbsp;Shayan S. Niknezhad ,&nbsp;Faisal Khan ,&nbsp;Efstratios N. Pistikopoulos ,&nbsp;Yuhe Tian","doi":"10.1016/j.dche.2025.100227","DOIUrl":"10.1016/j.dche.2025.100227","url":null,"abstract":"<div><div>The ongoing digital transformation has created new opportunities for chemical manufacturing with increasing plant interconnectivity and data accessibility. This paper reviews state-of-the-art research developments which offer the potential for real-time process safety and systems decision-making in the digital era. An overview is first presented on online process safety management approaches, including dynamic risk analysis and fault diagnosis/prognosis. Advanced operability and control methods are then discussed to achieve safely optimal operations under uncertainty (e.g., flexibility analysis, safety-aware control, fault-tolerant control). We highlight the connections between systems-based operation and process safety management to achieve operational excellence while proactively reducing potential safety losses. We also review the developments and showcases of digital twins paving the way to actual cyber–physical integration. Outstanding challenges and opportunities are identified such as safe data-driven control, integrated operability, safety and control, cyber–physical demonstration, etc. Toward this direction, we present our ongoing developments of the REal-Time Risk-based Optimization (RETRO) framework for safe and smart process operations.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100227"},"PeriodicalIF":3.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surrogate-based flowsheet model maintenance for Digital Twins","authors":"Balázs Palotai ,&nbsp;Gábor Kis ,&nbsp;János Abonyi ,&nbsp;Ágnes Bárkányi","doi":"10.1016/j.dche.2025.100228","DOIUrl":"10.1016/j.dche.2025.100228","url":null,"abstract":"<div><div>Digital Twins (DTs) are transforming industrial processes by providing virtual models that mirror physical systems, enabling real-time monitoring and optimization. A major challenge in DTs in process industry, is maintaining the accuracy of flowsheet simulation models due to changes like equipment degradation and operational shifts. This study proposes a novel surrogate-based approach for the automated calibration of these models, which reduces reliance on manual adjustments and adapts to changes in the physical system. This study leverages surrogate models and particle swarm optimization to incorporate modeling considerations and measurement uncertainties, thereby automating model calibration and reducing manual interventions. In a refinery case study, our approach reduced calibration time for the sour water stripper Hysys model by 80% while maintaining the desired accuracy. These results highlight the method’s potential to enhance flowsheet model accuracy in digital twin systems and to support more robust and adaptable DT applications.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100228"},"PeriodicalIF":3.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of forward and forward–backward Bayesian filters","authors":"Daniel Martins Silva,&nbsp;Argimiro Resende Secchi","doi":"10.1016/j.dche.2025.100224","DOIUrl":"10.1016/j.dche.2025.100224","url":null,"abstract":"<div><div>This paper investigates a forward–backward filtering approach comprised of forward filters and backward smoothers assimilating estimations of a moving horizon estimation. Those evaluations were carried out for extended, unscented, and cubature combinations of the Kalman filters, besides a particle filter, an ensemble Kalman filter, and a moving horizon estimation. Three simulation scenarios were defined for two nonlinear case studies with different complexity to evaluate the estimation accuracy and computational time under different uncertainty conditions. The backward smoothing was found to degenerate for longer horizons; however, it improved the estimation accuracy with smaller horizons in most simulation scenarios in comparison to the respective filters alone. In addition, the method successfully reduced steady-state estimation bias under model mismatch with a small increase in computational time. The performance of the forward–backward filtering was found to be sensitive to active constraint; however, this drawback does not outweigh the meaningful performance improvements found in this study.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100224"},"PeriodicalIF":3.0,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Polynomial Neural Networks for improved AI transparency: An analysis of their inherent explainability (operational rationale) capabilities","authors":"Donovan Chaffart ,&nbsp;Yue Yuan","doi":"10.1016/j.dche.2025.100230","DOIUrl":"10.1016/j.dche.2025.100230","url":null,"abstract":"<div><div>The demand for reliable Artificial Intelligence (AI) models within critical domains such as Chemical Engineering has garnered significant attention towards the use and development of transparent AI methodologies. Nevertheless, the field of AI transparency has received an uneven level of attention, such that crucial aspects like <em>explainability</em> (i.e., the transparency of the AI's operational rationales) have remained understudied. To address this challenge, this study investigates the inherent <em>explainability</em> capabilities of Polynomial Neural Networks (PNNs) for applications within Chemical Engineering. PNNs, which implement higher-order polynomials in lieu of linear expressions within their hidden layer neurons, are inherently nonlinear, and thus do not require an activation function to accurately capture the behavior of a system. Accordingly, these neural networks provide continuous, closed-form algebraic expressions that can be used to ascertain the contributions of individual features in the AI architecture towards the network operational behavior. In order to study this behavior, the PNN method was adopted in this work to capture the relationships of noiseless and noisy data derived according to simple mathematical expressions. The PNN polynomials were then extracted and examined to highlight the insights they provide regarding the system operational rationales. The PNN method was furthermore applied to capture the behavior of a circulating fluidized bed reactor to fully showcase the <em>explainative</em> capability of this method within a Chemical Engineering application. These studies highlight the intrinsic <em>explainability</em> capabilities of PNNs and demonstrated their potential for reliable AI implementations for applications in Chemical Engineering.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100230"},"PeriodicalIF":3.0,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Operability for process flowsheet analysis","authors":"Ulysses Guilherme Ferreira ,&nbsp;Sérgio Mauro da Silva Neiro ,&nbsp;Luís Cláudio Oliveira-Lopes ,&nbsp;Thiago Vaz da Costa ,&nbsp;Heleno Bispo ,&nbsp;Fernando Vines Lima","doi":"10.1016/j.dche.2025.100229","DOIUrl":"10.1016/j.dche.2025.100229","url":null,"abstract":"<div><div>Operability establishes the relationship between available input and achievable output sets through a system's mathematical representation. This work aims to develop a Flowsheet Operability analysis for a chemical process using rigorous models in a process simulator. The analysis focuses on a typical Air Separation Unit (ASU) in UniSim® Design (Honeywell) and integrates the simulator with the open-source Python operability tool (Opyrability) developed at West Virginia University. The performed assessment incrementally adds the output space of the process flowsheet units and examines how one group of units output space affects downstream units. The results underscore the importance of Flowsheet Operability analysis and the inclusion of inter-unit operability spaces for efficiently identifying unfavorable operating conditions that traditional Plantwide Operability analysis might overlook.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100229"},"PeriodicalIF":3.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classifier surrogates to ensure phase stability in optimisation-based design of solvent mixtures","authors":"Tanuj Karia,&nbsp;Gustavo Chaparro,&nbsp;Benoît Chachuat,&nbsp;Claire S. Adjiman","doi":"10.1016/j.dche.2024.100200","DOIUrl":"10.1016/j.dche.2024.100200","url":null,"abstract":"<div><div>The ability to guarantee a single homogeneous liquid phase is a key consideration in computer-aided mixture/blend design (CAM^bD). In this article, we investigate the use of a classifier surrogate of the phase stability condition within a CAM^bD optimisation model for designing solvent mixtures with guaranteed phase stability properties. We show how to develop such classifiers for describing multiple candidate mixtures over a range of compositions and temperatures based on the generation of phase stability data using thermodynamic models such as UNIFAC. We test the approach on two solvent design case studies and illustrate its effectiveness in enabling the <em>in silico</em> design of stable mixtures, simultaneously providing a probability of phase stability as an interpretable metric.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100200"},"PeriodicalIF":3.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}