Digital Chemical Engineering最新文献

{"title":"Machine learning for asphaltene polarizability: Evaluating molecular descriptors","authors":"Arun K. Sharma,&nbsp;Owen McMillan,&nbsp;Selsela Arsala,&nbsp;Supreet Gandhok,&nbsp;Rylend Young","doi":"10.1016/j.dche.2025.100244","DOIUrl":"10.1016/j.dche.2025.100244","url":null,"abstract":"<div><div>Asphaltenes are complex polycyclic organic molecules in crude oil that readily aggregate and precipitate under varying thermodynamic conditions. Their structural heterogeneity influences key physicochemical properties, including solubility, stability, and reactivity. Molecular polarizability, a crucial property governing intermolecular interactions and electronic behavior, remains challenging to predict due to this structural diversity. This study employs machine learning models to predict isotropic polarizability using two sets of molecular descriptors: WHIM and GETAWAY. A dataset of 255 asphaltene structures was analyzed using stratified sampling, generating 10 independent training (80 %) and testing (20 %) splits. The Wolfram Language’s Predict function evaluated multiple machine learning algorithms—including Random Forest, Decision Tree, Gradient Boosted Trees, Nearest Neighbors, Linear Regression, Gaussian Process, and Neural Network—through an automated model selection process, serving as an AutoML framework. Linear regression was the best-performing model in 9 out of 10 splits for GETAWAY descriptors. GETAWAY-based models achieved an average mean absolute deviation of 0.0920 ± 0.0030 and standard deviation of 0.113 ± 0.004, significantly outperforming WHIM-based models (MAD = 0.173 ± 0.007, STD = 0.224 ± 0.008) with paired <em>t</em>-tests confirming statistical significance (<em>p</em> &lt; 0.001). While R² values were reported, their interpretability was limited by heterogeneity and narrow property ranges in some test sets. These findings demonstrate the effectiveness of AutoML-guided approaches for predicting molecular properties and identify GETAWAY descriptors as a robust, efficient basis for polarizability prediction. Accurate prediction of polarizability is essential for modeling intermolecular forces and improving force field design in petroleum and materials chemistry, issues that are central to industrial and chemical applications.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100244"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144184928","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":"Industrial multi-machine data aggregation, AI-ready data preparation, and machine learning for virtual metrology in semiconductor wafer and slider production","authors":"Feiyang Ou ,&nbsp;Julius Suherman ,&nbsp;Chao Zhang ,&nbsp;Henrik Wang ,&nbsp;Sthitie Bom ,&nbsp;James F. Davis ,&nbsp;Panagiotis D. Christofides","doi":"10.1016/j.dche.2025.100242","DOIUrl":"10.1016/j.dche.2025.100242","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Smart Manufacturing (SM), which is short for “Smart (Predictive, Preventive, Proactive) zero incident, zero emissions Manufacturing,” describes manufacturing’s digital transformation in which factories, supply chains and ecosystems are integrated, interoperable, and interconnected. Smart Manufacturing is rooted in AI, Machine Learned (ML), and Data Synchronized (DS) modeling to tap into invaluable operating data. By making data actionable at larger scales, SM opens new ways to increase productivity, precision, and process performance. Smart Manufacturing applied to front-end wafer manufacturing in the semiconductor industry offers significant opportunity to increase production throughput and ensure precision by increasing staff and operational productivity. Front-end wafer manufacturing involves multi tool operations for complex material processing that requires a high degree of precision and extensive product qualification. There is a high degree of commonality with semiconductor manufacturing tools, for example etching, that are well instrumented. Companies are already collecting large amounts of operational data from these tools that can be aggregated and leveraged for virtual metrology and other control, diagnostic, and management solutions. AI/ML/DS modeling involves monitoring the state of an operation in real-time to continuously learn and improve on human centered, automated, and autonomous actions. This operational data are embedded in invaluable machine, process, product, and material behaviors as interaction complexities, linearities/non-linearities, and dimensional effects. Because of machine commonalities, data can be selected to draw out operational value across machines. Today’s data science offers considerable capability for qualifying, assessing alignment and contribution, aggregating, and engineering data for more robust modeling. We refer to this as a Data-first strategy to process, engineer and model with AI-Ready data. In this paper, we address AI-Ready data for a virtual metrology solution focused on etching measurement PASS/FAIL classification and milling depth prediction regression tasks using operational data from production machine tools. If the quality of the product can be predicted, the productivity of the metrology process can be increased, which in turn increases the productivity of the overall operation. In a previous paper, we considered how to aggregate data from different etch tools in the same processes at different factories within Seagate Technology and proposed a method for data aggregation and demonstrated its value (Ou et al., 2024). The present paper considers how to process and engineer datasets from two different etch tool processes: wafer and slider production. The data processing approaches when used systematically with appropriate ML algorithms demonstrate the potential for reducing metrological interventions in semiconductor manufacturing. Advanced machine learning techniques are used to t","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100242"},"PeriodicalIF":3.0,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147463","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":"Automation and control of an experimental protonic membrane steam methane reforming system","authors":"Dominic Peters ,&nbsp;Xiaodong Cui ,&nbsp;Yifei Wang ,&nbsp;Christopher G. Donahue ,&nbsp;Jake Stanley ,&nbsp;Carlos G. Morales-Guio ,&nbsp;Panagiotis D. Christofides","doi":"10.1016/j.dche.2025.100240","DOIUrl":"10.1016/j.dche.2025.100240","url":null,"abstract":"<div><div>Nickel dispersion on doped barium-zirconate ceramics is a state-of-the-art material formulation used to fabricate proton conducting membranes that can reform methane at lower operational temperatures (600 to 800 °<span><math><mi>C</mi></math></span>). Although steady-state operational data have been reported for these ion-conducting ceramic reformers, transient datasets are uncommon and not readily available. Moreover, the automation of protonic membrane reformers is a major technical challenge for the commercialization of modular thermo-electrochemical hydrogen generators with highly nonlinear process dynamics. Here, a multi-input multi-output feedback control scheme has been designed from a relative gain array analysis of three process variables for an experimental 500 W (thermal and electrochemical power consumption) protonic membrane reforming system. Specifically, the proposed control architecture automatically calculates hydrogen separation rate setpoints while safely and effectively reaching hydrogen production rate setpoints and desired steam-to-carbon ratios. The control architecture also drives the system to 99.6% methane conversion at a current density of 0.564 ± 0.0125 A<span><math><mi>⋅</mi></math></span>cm⁻² at 788 °<span><math><mi>C</mi></math></span>. Internal temperature fluctuations are mostly constrained to <span><math><mo>±</mo></math></span> 6.00 °<span><math><mi>C</mi></math></span> <span><math><mi>⋅</mi></math></span>min⁻¹, which improves catalyst longevity when operating at hydrogen recovery rates exceeding 50%. Chief among these findings is an experimental demonstration of a control scenario that alters the hydrogen production rate setpoint every 150 min without sacrificing system-wide controllability. Integrator windup scenarios and counterproductive control actions are also avoided through rational controller design and proper controller tuning exercises. Industrial-scale applications of protonic membrane reformers may therefore be automated to control up to three process variables and have up to three additional control degrees of freedom for process intensification and optimization, making for well-governed, autonomous hydrogen generation units.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100240"},"PeriodicalIF":3.0,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084476","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":"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}