Digital Chemical Engineering最新文献

{"title":"CFD-based optimization of dynamic cyclones with variable vortex length using GMDH artificial neural network","authors":"Hamed Safikhani ,&nbsp;Somayeh Davoodabadi Farahani ,&nbsp;Lakhbir Singh Brar ,&nbsp;Faroogh Esmaeili","doi":"10.1016/j.dche.2025.100241","DOIUrl":"10.1016/j.dche.2025.100241","url":null,"abstract":"<div><div>Dynamic cyclone separators with adjustable vortex length are widely used in industrial applications such as particle collection and air pollution control. However, optimizing their performance remains a challenge due to complex fluid–particle interactions. This research introduces a three-step multi-objective optimization framework for dynamic cyclones with adjustable vortex length. Initially, computational fluid dynamics (CFD) simulations are utilized to examine airflow behavior in different cyclone designs. The Reynolds-averaged Navier-Stokes (RANS) equations, combined with the Reynolds stress turbulence model, are employed to model turbulence. The Eulerian-Lagrangian approach is used to track particle motion, while the Discrete Random Walk technique simulates velocity variations. In the second phase, data obtained from the numerical simulations is used to construct objective function models, focusing on minimizing pressure drop and maximizing collection efficiency. These models are developed using artificial neural networks based on the Group Method of Data Handling (GMDH). The final step involves optimizing the cyclone designs through the non-dominated sorting genetic algorithm (NSGA). The Pareto front is generated and analyzed, offering valuable insights into cyclone design improvements. The findings highlight that an optimized design for dynamic cyclones with variable vortex length can only be achieved through a systematic multi-objective optimization approach.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100241"},"PeriodicalIF":3.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143904429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RESPONSE- a resilient framework to manage cyber-attacks on cyber-physical process systems","authors":"Luyang Liu,&nbsp;Zaman Sajid,&nbsp;Costas Kravaris,&nbsp;Faisal Khan","doi":"10.1016/j.dche.2025.100238","DOIUrl":"10.1016/j.dche.2025.100238","url":null,"abstract":"<div><div>This paper presents a novel framework – <u>Res</u>ilient <u>P</u>rocess c<u>ON</u>trol <u>S</u>yst<u>E</u>m (RESPONSE) - to address the critical challenge of cyberattacks on cyber-physical process systems (CPS). The RESPONSE emphasizes adaptability to existing systems, operational stability independent of detection mechanism reliability, and enhanced system continuity and recovery during and after cyber incidents. RESPONSE is built on the National Institute of Standards and Technology (NIST) cybersecurity recommendation by leveraging redundant control architecture, secure detection mechanism, and integral error manipulation to maintain safe operations under attack conditions. It has transformative potential for enhancing CPS security, reliability, and economic performance. A comparative analysis and case studies are presented to demonstrate the framework’s ability to mitigate cyber threats, minimize downtime, and ensure rapid recovery.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100238"},"PeriodicalIF":3.0,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143898993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning in modeling, analysis and control of electrochemical reactors: A tutorial review","authors":"Wenlong Wang ,&nbsp;Zhe Wu ,&nbsp;Dominic Peters ,&nbsp;Berkay Citmaci ,&nbsp;Carlos G. Morales-Guio ,&nbsp;Panagiotis D. Christofides","doi":"10.1016/j.dche.2025.100237","DOIUrl":"10.1016/j.dche.2025.100237","url":null,"abstract":"<div><div>Electrochemical reactors play a critical role in various industrial sectors, including energy storage, chemical production, and environmental engineering. The complexity of these systems – arising from coupled electrochemical reactions with mass, heat and charge transport phenomena – poses significant challenges in modeling, analysis, and control. Machine learning (ML) has emerged as a promising tool for addressing these challenges by providing data-driven solutions to complex process modeling, optimization, and advanced control. This tutorial review explores the state-of-the-art applications of ML in electrochemical reactor systems, including ML-based modeling techniques and ML-based advanced control strategies, followed by the discussions of practical challenges and their solutions. An electrochemical carbon dioxide (CO₂) reduction reactor is used as a demonstration example to show the effectiveness of various modeling and control methods. In addition, an integrated data infrastructure platform is presented for the digitalization and control of the electrochemical CO₂ reduction reactor. By identifying current gaps and future opportunities, this article provides a roadmap for leveraging ML tools to improve the analysis and operation of electrochemical reactors.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100237"},"PeriodicalIF":3.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances to modeling and solving infinite-dimensional optimization problems in InfiniteOpt.jl","authors":"Evelyn Gondosiswanto,&nbsp;Joshua L. Pulsipher","doi":"10.1016/j.dche.2025.100236","DOIUrl":"10.1016/j.dche.2025.100236","url":null,"abstract":"<div><div>This paper details two extensions for the unifying abstraction behind <span>InfiniteOpt.jl</span>: infinite-dimensional generalized disjunctive programming (InfiniteGDP) and GPU-compatible direct transcription solution techniques with an abstraction called InfiniteSIMD-NLP. <span>InfiniteOpt.jl</span> is a Julia package that provides an efficient framework for formulating and solving a wide range of infinite-dimensional optimization (InfiniteOpt) problems. The InfiniteGDP abstraction builds upon traditional GDP techniques to enable intuitive modeling of discrete events and complex logic over continuous domains (e.g., position, time, and/or uncertainty); this abstraction is implemented in <span>InfiniteDisjunctiveProgramming.jl</span>. Moreover, the InfiniteSIMD-NLP abstraction, implemented in <span>InfiniteExaModels.jl</span>, exploits the recurrent structure of transcribed InfiniteOpt problems to efficiently discretize, differentiate, and solve such problems on high performance CPU and GPU architectures. We use a diverse set of case studies in dynamic, PDE-constrained, and stochastic optimization to demonstrate the relative merits of these abstraction extensions. The results demonstrate the utility of the InfiniteGDP abstraction to model continuous space–time switching constraints and how the InfiniteSIMD-NLP abstraction is able to accelerate the solution of InfiniteOpt problems by one to two orders-of-magnitude relative to existing state-of-the-art approaches.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100236"},"PeriodicalIF":3.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward predicting CO2 loading capacity in monoethanolamine (MEA) aqueous solutions using deep belief network","authors":"Mahdi Abdi-Khanghah ,&nbsp;Fahimeh Hadavimoghaddam ,&nbsp;Saeid Atashrouz ,&nbsp;Elnaz Nasirzadeh ,&nbsp;Meftah Ali Abuswer ,&nbsp;Mehdi Ostadhassan ,&nbsp;Ahmad Mohaddespour ,&nbsp;Abdolhossein Hemmati-Sarapardeh","doi":"10.1016/j.dche.2025.100235","DOIUrl":"10.1016/j.dche.2025.100235","url":null,"abstract":"<div><div>The viability of CO₂ capture projects, particularly through absorption with monoethanolamine (MEA) and other commercial absorbents, strongly depends on the CO₂ loading capacity. Therefore, comprehending the impact of variables on the CO₂ loading capacity of MEA is crucial in designing CO₂ capture units, which can be further optimized through multi-objective optimization. To this end, four machine learning models—Bagging Regression (BR), Categorical Boosting (CatBoost), Deep Belief Network (DBN), and Gaussian Process Regression with Rational Quadratic kernel function (GPR-RQ)—were utilized to predict the CO₂ loading capacity of MEA aqueous solutions. Temperature, partial pressure of CO₂, and MEA concentration were inputted into the intelligent network to calculate the CO₂ loading capacity. The binary values of R² and standard deviation (SD), which were 0.9889 and 0.0628 for Bagging Regression, 0.9932 and 0.06586 for CatBoost, 0.9957 and 0.0588 for GPR-RQ, and 0.9971 and 0.0329 for DBN, confirm that DBN has the highest accuracy in statistical analysis, followed by GPR-RQ, CatBoost, and Bagging Regression. Additionally, graphical methods like scattered plots and relative deviation plots corroborate the superior performance of the DBN model over all other intelligent techniques. By conducting a relevancy factor analysis on DBN outcomes, sensitivity analysis demonstrates that pressure has the most significant influence among the inputs. Furthermore, the Leverage technique affirms that the DBN model has a substantial degree of validity in forecasting the CO₂ loading capacity of MEA. Finally, 3-D image plots were systematically examined to analyze the binary interactive effect of (temperature, CO₂ partial pressure), (temperature, MEA concentration), and (CO₂ partial pressure, MEA concentration) on the carbon absorption efficiency, which is essential to reach the net-zero emission purpose.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100235"},"PeriodicalIF":3.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"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}