Pub Date : 2025-10-15DOI: 10.1016/j.compchemeng.2025.109456
Collin R. Johnson , Stijn de Vries , Kerstin Wohlgemuth , Sergio Lucia
This paper presents a novel dynamic model for slug flow crystallizers that addresses the challenges of spatial distribution without backmixing or diffusion, potentially enabling advanced model-based control. The developed model can accurately describe the main characteristics of slug flow crystallizers, including slug-to-slug variability but leads to a high computational complexity due to the consideration of partial differential equations and population balance equations. For that reason, the model cannot be directly used for process optimization and control. To solve this challenge, we propose two different approaches, conformalized quantile regression and Bayesian last layer neural networks, to develop surrogate models with uncertainty quantification capabilities. These surrogates output a prediction of the system states together with an uncertainty of these predictions to account for process variability and model uncertainty. We use the uncertainty of the predictions to formulate a robust model predictive control approach, enabling robust real-time advanced control of a slug flow crystallizer.
{"title":"Multi-stage model predictive control for slug flow crystallizers using uncertainty-aware surrogate models","authors":"Collin R. Johnson , Stijn de Vries , Kerstin Wohlgemuth , Sergio Lucia","doi":"10.1016/j.compchemeng.2025.109456","DOIUrl":"10.1016/j.compchemeng.2025.109456","url":null,"abstract":"<div><div>This paper presents a novel dynamic model for slug flow crystallizers that addresses the challenges of spatial distribution without backmixing or diffusion, potentially enabling advanced model-based control. The developed model can accurately describe the main characteristics of slug flow crystallizers, including slug-to-slug variability but leads to a high computational complexity due to the consideration of partial differential equations and population balance equations. For that reason, the model cannot be directly used for process optimization and control. To solve this challenge, we propose two different approaches, conformalized quantile regression and Bayesian last layer neural networks, to develop surrogate models with uncertainty quantification capabilities. These surrogates output a prediction of the system states together with an uncertainty of these predictions to account for process variability and model uncertainty. We use the uncertainty of the predictions to formulate a robust model predictive control approach, enabling robust real-time advanced control of a slug flow crystallizer.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"205 ","pages":"Article 109456"},"PeriodicalIF":3.9,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145322886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The production of monoclonal antibodies in mammalian cells is a highly complex and nonlinear process. The industry standard for controlling this process fails to capture its complex dynamics, leading to batch-to-batch variability. This inherent complexity makes bioprocesses challenging to model purely mechanistically, while the lack of rich experimental datasets and the need for interpretability in control policies further prevent the use of fully data-driven solutions. We propose a hybrid methodology for optimising the nutrient feeding strategy that leverages Reinforcement Learning (RL) with mechanistic models of cellular metabolism and glycosylation. The RL agent is trained using an off-policy method for data efficiency and is capable of learning from partial observations of the state, which allows for improved generalization. The controller is adaptable to processes with or without additional product quality considerations, such as glycosylation. We demonstrate that accounting for product glycosylation yields different control strategies whereas neglecting it to focus on titer alone can compromise product quality. The continuous learning abilities of the proposed method ensure adaptability in response to process changes, while the inclusion of a mechanistic model in the environment aids in the interpretability of the learned control actions.
{"title":"From titer to quality: Exploring reinforcement learning for bioprocess control in silico","authors":"Mariana Monteiro, Konstantinos Flevaris, Cleo Kontoravdi","doi":"10.1016/j.compchemeng.2025.109452","DOIUrl":"10.1016/j.compchemeng.2025.109452","url":null,"abstract":"<div><div>The production of monoclonal antibodies in mammalian cells is a highly complex and nonlinear process. The industry standard for controlling this process fails to capture its complex dynamics, leading to batch-to-batch variability. This inherent complexity makes bioprocesses challenging to model purely mechanistically, while the lack of rich experimental datasets and the need for interpretability in control policies further prevent the use of fully data-driven solutions. We propose a hybrid methodology for optimising the nutrient feeding strategy that leverages Reinforcement Learning (RL) with mechanistic models of cellular metabolism and glycosylation. The RL agent is trained using an off-policy method for data efficiency and is capable of learning from partial observations of the state, which allows for improved generalization. The controller is adaptable to processes with or without additional product quality considerations, such as glycosylation. We demonstrate that accounting for product glycosylation yields different control strategies whereas neglecting it to focus on titer alone can compromise product quality. The continuous learning abilities of the proposed method ensure adaptability in response to process changes, while the inclusion of a mechanistic model in the environment aids in the interpretability of the learned control actions.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"205 ","pages":"Article 109452"},"PeriodicalIF":3.9,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145322888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-14DOI: 10.1016/j.compchemeng.2025.109443
Ilias Mitrai , Matthew J. Palys , Prodromos Daoutidis
This paper considers the effect of ammonia market price uncertainty across multiple years on the deployment of renewable ammonia production facilities in existing ammonia supply chain networks. We use an ammonia supply chain transition optimization model to investigate the effect of this uncertainty. Specifically, we formulate a multistage stochastic programming problem to determine the optimal investment policy for new renewable ammonia production over a multi-year transition horizon such that ammonia demand is satisfied and the total supply chain cost is minimized. The proposed approach is used to analyze the transition of the ammonia supply chain for the state of Minnesota. The results show that the trajectory of the price over time determines the degree to which renewable ammonia production facilities are adopted. In a broad sense, considering the possibility of higher-than-average conventional ammonia market prices through a multistage stochastic problem leads to a wider adoption of renewable production relative to a deterministic problem, which only considers the average market price in an economically optimal supply chain transition. Comparison with a two-stage stochastic programming approach from prior work shows that accounting for price uncertainty across time leads to 4.4% reduction in the cost. For a full transition to renewable production, the multistage stochastic framework results, on average, in a slightly slower transition than the deterministic problem due to scenarios which include lower-than-average market prices.
{"title":"A multistage stochastic programming approach for renewable ammonia supply chain network design","authors":"Ilias Mitrai , Matthew J. Palys , Prodromos Daoutidis","doi":"10.1016/j.compchemeng.2025.109443","DOIUrl":"10.1016/j.compchemeng.2025.109443","url":null,"abstract":"<div><div>This paper considers the effect of ammonia market price uncertainty across multiple years on the deployment of renewable ammonia production facilities in existing ammonia supply chain networks. We use an ammonia supply chain transition optimization model to investigate the effect of this uncertainty. Specifically, we formulate a multistage stochastic programming problem to determine the optimal investment policy for new renewable ammonia production over a multi-year transition horizon such that ammonia demand is satisfied and the total supply chain cost is minimized. The proposed approach is used to analyze the transition of the ammonia supply chain for the state of Minnesota. The results show that the trajectory of the price over time determines the degree to which renewable ammonia production facilities are adopted. In a broad sense, considering the possibility of higher-than-average conventional ammonia market prices through a multistage stochastic problem leads to a wider adoption of renewable production relative to a deterministic problem, which only considers the average market price in an economically optimal supply chain transition. Comparison with a two-stage stochastic programming approach from prior work shows that accounting for price uncertainty across time leads to 4.4% reduction in the cost. For a full transition to renewable production, the multistage stochastic framework results, on average, in a slightly slower transition than the deterministic problem due to scenarios which include lower-than-average market prices.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"205 ","pages":"Article 109443"},"PeriodicalIF":3.9,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145322885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-14DOI: 10.1016/j.compchemeng.2025.109442
Xin Tang , Cosmin G. Petra , Michael Baldea , Ross Baldick
A demand bidding mechanism for engaging large industrial electricity users in the operation of the power grid is presented. Demand bidding is formulated as an optimization problem based on a modified version of the alternating current optimal power flow problem, and can be interpreted as a tâtonnement process between the grid operator and electricity users. The work provides the first – to the authors’ knowledge – grid-scale case study of demand bidding, using a synthetic grid structure in the footprint of the grid of Texas. Results reveal that the demand bidding lowers overall power generation costs, but economic benefits plateau as the number of participants increases. Transmission line and transformer capacity constraints become the limiting factors, revealing that expanding and fortifying the transmission infrastructure is key to expanding demand-side participation. Demand bidding does not substantially alter the optimal operation of existing bidding entities when the number of bidders increases, thereby supporting existing bidders to stay in the system and encouraging new ones to join.
{"title":"A grid-scale study of demand bidding by large industrial users","authors":"Xin Tang , Cosmin G. Petra , Michael Baldea , Ross Baldick","doi":"10.1016/j.compchemeng.2025.109442","DOIUrl":"10.1016/j.compchemeng.2025.109442","url":null,"abstract":"<div><div>A demand bidding mechanism for engaging large industrial electricity users in the operation of the power grid is presented. Demand bidding is formulated as an optimization problem based on a modified version of the alternating current optimal power flow problem, and can be interpreted as a tâtonnement process between the grid operator and electricity users. The work provides the first – to the authors’ knowledge – grid-scale case study of demand bidding, using a synthetic grid structure in the footprint of the grid of Texas. Results reveal that the demand bidding lowers overall power generation costs, but economic benefits plateau as the number of participants increases. Transmission line and transformer capacity constraints become the limiting factors, revealing that expanding and fortifying the transmission infrastructure is key to expanding demand-side participation. Demand bidding does not substantially alter the optimal operation of existing bidding entities when the number of bidders increases, thereby supporting existing bidders to stay in the system and encouraging new ones to join.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"205 ","pages":"Article 109442"},"PeriodicalIF":3.9,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145360248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Effective planning of biomass supply chains (BSC), which involve collection, transportation, pre-processing, storage, conversion, and delivery of bioproducts, is essential to ensure efficiency and sustainability. Recently, machine learning (ML) has been adopted to address the supply chain’s complexities for effective planning. ML provides dynamic and data-driven solutions that enhance decision-making. It has been applied for predicting biomass yields, forecasting supply and demand, optimizing logistics and facility location, and improving the efficiency of conversion processes. This review paper highlights the role of ML in BSC planning. This study considers biomass sources such as food processing residues, animal waste (e.g., manure), in addition to forest-based and agricultural-based biomass, examining processes across all stages of a supply chain from upstream to downstream. We examine ML models in previous studies based on their learning paradigms: supervised, unsupervised, and reinforcement learning, and the type of performed analytics: predictive, and both predictive and prescriptive analytics. Challenges related to data availability, computational requirements, and model generalization limit ML applications in BSCs. Future research could focus on scalable and adaptable models for preprocessing, transportation, and harvesting activities by addressing the uncertainty. Integrating advanced ML could significantly enhance the resiliency, sustainability, and efficiency of BSCs, supporting bioeconomy advancement and the achievement of sustainability goals.
{"title":"Applications of machine learning for decision support in biomass supply chains: A systematic review","authors":"Shayan Razmi, Hossein Mirzaee, Gaurav Kumar, Taraneh Sowlati","doi":"10.1016/j.compchemeng.2025.109451","DOIUrl":"10.1016/j.compchemeng.2025.109451","url":null,"abstract":"<div><div>Effective planning of biomass supply chains (BSC), which involve collection, transportation, pre-processing, storage, conversion, and delivery of bioproducts, is essential to ensure efficiency and sustainability. Recently, machine learning (ML) has been adopted to address the supply chain’s complexities for effective planning. ML provides dynamic and data-driven solutions that enhance decision-making. It has been applied for predicting biomass yields, forecasting supply and demand, optimizing logistics and facility location, and improving the efficiency of conversion processes. This review paper highlights the role of ML in BSC planning. This study considers biomass sources such as food processing residues, animal waste (e.g., manure), in addition to forest-based and agricultural-based biomass, examining processes across all stages of a supply chain from upstream to downstream. We examine ML models in previous studies based on their learning paradigms: supervised, unsupervised, and reinforcement learning, and the type of performed analytics: predictive, and both predictive and prescriptive analytics. Challenges related to data availability, computational requirements, and model generalization limit ML applications in BSCs. Future research could focus on scalable and adaptable models for preprocessing, transportation, and harvesting activities by addressing the uncertainty. Integrating advanced ML could significantly enhance the resiliency, sustainability, and efficiency of BSCs, supporting bioeconomy advancement and the achievement of sustainability goals.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"205 ","pages":"Article 109451"},"PeriodicalIF":3.9,"publicationDate":"2025-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145322887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-11DOI: 10.1016/j.compchemeng.2025.109438
Leonardo M. De Marco , Jorge Otávio Trierweiler , Fabio Cesar Diehl , Marcelo Farenzena
Monitoring the control loop performance is crucial for operation efficiency and safety in industrial processes. This study proposes a new methodology for control loop performance assessment based on the Input-Output Cross Autocorrelation Diagram (IOCAD), a technique already established in the literature. In this work, two novel indicators based on a polar representation of IOCAD are introduced, complementing four existing indicators previously developed using a Cartesian formulation. By analyzing the autocorrelation between the process variable (PV) and manipulated variable (MV), these indicators enable performance evaluation using only routine plant data. Compared to traditional approaches such as the Minimum Variance Control (MVC), the IOCAD-based method shows greater robustness to noise and setpoint changes, while also providing diagnostic insights into the root causes of performance degradation, such as tuning issues or changes in process dynamics. A Control Performance Indicator (CPI) was also proposed. Simulations involving various control loops, including an offshore oil production control loop, confirmed the method’s effectiveness and applicability for real-time monitoring in diverse operational scenarios.
{"title":"Assessing, diagnosing, and benchmarking control loops using the input-output cross autocorrelation diagram (IO-CAD)","authors":"Leonardo M. De Marco , Jorge Otávio Trierweiler , Fabio Cesar Diehl , Marcelo Farenzena","doi":"10.1016/j.compchemeng.2025.109438","DOIUrl":"10.1016/j.compchemeng.2025.109438","url":null,"abstract":"<div><div>Monitoring the control loop performance is crucial for operation efficiency and safety in industrial processes. This study proposes a new methodology for control loop performance assessment based on the Input-Output Cross Autocorrelation Diagram (IO<img>CAD), a technique already established in the literature. In this work, two novel indicators based on a polar representation of IO<img>CAD are introduced, complementing four existing indicators previously developed using a Cartesian formulation. By analyzing the autocorrelation between the process variable (PV) and manipulated variable (MV), these indicators enable performance evaluation using only routine plant data. Compared to traditional approaches such as the Minimum Variance Control (MVC), the IO<img>CAD-based method shows greater robustness to noise and setpoint changes, while also providing diagnostic insights into the root causes of performance degradation, such as tuning issues or changes in process dynamics. A Control Performance Indicator (CPI) was also proposed. Simulations involving various control loops, including an offshore oil production control loop, confirmed the method’s effectiveness and applicability for real-time monitoring in diverse operational scenarios.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"205 ","pages":"Article 109438"},"PeriodicalIF":3.9,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-10DOI: 10.1016/j.compchemeng.2025.109409
Swaminathan Sundar, Rahul Kakodkar, Efstratios N. Pistikopoulos
The energy sector is a major contributors of greenhouse gases and thus decarbonizing this sector is pivotal towards achieving carbon neutrality. Carbon Capture, Utilization and Sequestration (CCUS) technologies offers a promising pathway in mitigation of these emissions. In particular, valorization of the captured carbon into value added products can enhance the economic viability and scalability of some of the novel CCUS processes. Among these, algae based CCUS process is one such promising solution which has the potential to feature in future energy systems. In this study, we conduct a detailed Techno-Economic Analysis (TEA) and Life Cycle Assessment (LCA) of an algae-based CCUS process at scale. Sensitivity analysis was also carried out to identify critical bottlenecks that hinder the scale up of this process. The levelized cost of biomass production was estimated to be $388 per ton of biomass and the levelized emission was found to be 1.3 kg CO2 per kg biomass. Based on a detailed Discount Cash Flow analysis, the minimum biomass selling price was estimated to be $424 per ton of biomass.
{"title":"Techno-economic analysis and life cycle assessment of a novel algae-based CCUS technology","authors":"Swaminathan Sundar, Rahul Kakodkar, Efstratios N. Pistikopoulos","doi":"10.1016/j.compchemeng.2025.109409","DOIUrl":"10.1016/j.compchemeng.2025.109409","url":null,"abstract":"<div><div>The energy sector is a major contributors of greenhouse gases and thus decarbonizing this sector is pivotal towards achieving carbon neutrality. Carbon Capture, Utilization and Sequestration (CCUS) technologies offers a promising pathway in mitigation of these emissions. In particular, valorization of the captured carbon into value added products can enhance the economic viability and scalability of some of the novel CCUS processes. Among these, algae based CCUS process is one such promising solution which has the potential to feature in future energy systems. In this study, we conduct a detailed Techno-Economic Analysis (TEA) and Life Cycle Assessment (LCA) of an algae-based CCUS process at scale. Sensitivity analysis was also carried out to identify critical bottlenecks that hinder the scale up of this process. The levelized cost of biomass production was estimated to be $388 per ton of biomass and the levelized emission was found to be 1.3 kg CO<sub>2</sub> per kg biomass. Based on a detailed Discount Cash Flow analysis, the minimum biomass selling price was estimated to be $424 per ton of biomass.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"205 ","pages":"Article 109409"},"PeriodicalIF":3.9,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145360247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-10DOI: 10.1016/j.compchemeng.2025.109444
Mohamad AlMoussaoui , Dhabia M. Al-Mohannadi
The economies of several countries depend heavily on hydrocarbon exports, which significantly contribute to their gross national income. As this sector is vulnerable to risks and uncertainties, it is necessary to enhance the resilience of these exports to secure their returns and, hence, the financial security of the national economy. This work studies resilient investment planning to secure the financial returns of the hydrocarbon export sector, considering the production and transportation stages. We develop a novel two-step resilient investment planning approach for the hydrocarbon export sector. In the first step, a portfolio optimization framework is formulated based on Modern Portfolio Theory (MPT) to enhance resilience against price fluctuations associated with hydrocarbon supply chains. In the second step, nine hydrocarbon supply chain resilience metrics are employed to develop a degree of resilience indicator (DORI). The indicator evaluates the performance of financially optimal investment portfolios determined from step one against several risks associated with hydrocarbon exports. The proposed methodology is applied to a case study, considering exporting four chemical commodities to three importers from a natural gas-based economy to determine the optimal investment portfolio. The ability of the proposed DORI to predict portfolio resilience is assessed by running several disruption scenarios. Results highlight the importance of considering resilience metrics, as MPT efficient portfolios with the highest financial returns are not necessarily the most resilient to supply chain disruptions. Results also demonstrate that incorporating a supply chain perspective into the portfolio optimization framework provides additional insights into the hydrocarbon export problem.
{"title":"Embedding resilience in natural gas monetization exports","authors":"Mohamad AlMoussaoui , Dhabia M. Al-Mohannadi","doi":"10.1016/j.compchemeng.2025.109444","DOIUrl":"10.1016/j.compchemeng.2025.109444","url":null,"abstract":"<div><div>The economies of several countries depend heavily on hydrocarbon exports, which significantly contribute to their gross national income. As this sector is vulnerable to risks and uncertainties, it is necessary to enhance the resilience of these exports to secure their returns and, hence, the financial security of the national economy. This work studies resilient investment planning to secure the financial returns of the hydrocarbon export sector, considering the production and transportation stages. We develop a novel two-step resilient investment planning approach for the hydrocarbon export sector. In the first step, a portfolio optimization framework is formulated based on Modern Portfolio Theory (MPT) to enhance resilience against price fluctuations associated with hydrocarbon supply chains. In the second step, nine hydrocarbon supply chain resilience metrics are employed to develop a degree of resilience indicator (DORI). The indicator evaluates the performance of financially optimal investment portfolios determined from step one against several risks associated with hydrocarbon exports. The proposed methodology is applied to a case study, considering exporting four chemical commodities to three importers from a natural gas-based economy to determine the optimal investment portfolio. The ability of the proposed DORI to predict portfolio resilience is assessed by running several disruption scenarios. Results highlight the importance of considering resilience metrics, as MPT efficient portfolios with the highest financial returns are not necessarily the most resilient to supply chain disruptions. Results also demonstrate that incorporating a supply chain perspective into the portfolio optimization framework provides additional insights into the hydrocarbon export problem.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"205 ","pages":"Article 109444"},"PeriodicalIF":3.9,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145323991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-10DOI: 10.1016/j.compchemeng.2025.109441
Hye Min Choi , Niket S. Kaisare , Jay H. Lee
Coking remains one of the most critical challenges in dry reforming of methane (DRM), causing catalyst deactivation and severe performance loss. While microkinetic modeling (MKM) can capture reaction dynamics at the elementary-step level, existing DRM models lack the ability to represent the evolving nature of coke formation and its mechanistic impact on the reaction network. This study introduces a novel coke-inclusive MKM that explicitly incorporates coke formation pathways and is experimentally validated against DRM data. To interpret the complex, time-dependent behavior of coking, we develop a novel phase-based framework that systematically segments coke accumulation into distinct temporal regimes, each characterized by unique rates and patterns of carbon buildup. Phase-specific mechanistic analysis reveals a gradual shift in the dominant reaction pathways as coking progresses. Early-stage coke formation involves a broad set of surface reactions, opening multiple opportunities for targeted intervention, whereas later stages show a concentration of coking influence in a few critical reactions, such as methane decomposition and CO2 adsorption. To enhance practicality, a reduced-order coke-inclusive MKM is constructed, retaining essential kinetic features while greatly improving computational efficiency. This integrated modeling strategy — the first to combine a coke-inclusive MKM with phase-based analysis — provides a powerful bridge between detailed reaction mechanisms and application-focused catalyst and reactor design, offering new tools to improve catalyst durability and advance the sustainability of DRM systems.
{"title":"Microkinetic insights into the impact of coking in dry reforming of methane","authors":"Hye Min Choi , Niket S. Kaisare , Jay H. Lee","doi":"10.1016/j.compchemeng.2025.109441","DOIUrl":"10.1016/j.compchemeng.2025.109441","url":null,"abstract":"<div><div>Coking remains one of the most critical challenges in dry reforming of methane (DRM), causing catalyst deactivation and severe performance loss. While microkinetic modeling (MKM) can capture reaction dynamics at the elementary-step level, existing DRM models lack the ability to represent the evolving nature of coke formation and its mechanistic impact on the reaction network. This study introduces a novel coke-inclusive MKM that explicitly incorporates coke formation pathways and is experimentally validated against DRM data. To interpret the complex, time-dependent behavior of coking, we develop a novel phase-based framework that systematically segments coke accumulation into distinct temporal regimes, each characterized by unique rates and patterns of carbon buildup. Phase-specific mechanistic analysis reveals a gradual shift in the dominant reaction pathways as coking progresses. Early-stage coke formation involves a broad set of surface reactions, opening multiple opportunities for targeted intervention, whereas later stages show a concentration of coking influence in a few critical reactions, such as methane decomposition and CO<sub>2</sub> adsorption. To enhance practicality, a reduced-order coke-inclusive MKM is constructed, retaining essential kinetic features while greatly improving computational efficiency. This integrated modeling strategy — the first to combine a coke-inclusive MKM with phase-based analysis — provides a powerful bridge between detailed reaction mechanisms and application-focused catalyst and reactor design, offering new tools to improve catalyst durability and advance the sustainability of DRM systems.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"205 ","pages":"Article 109441"},"PeriodicalIF":3.9,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145322889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-09DOI: 10.1016/j.compchemeng.2025.109428
San Dinh , Fernando V. Lima
This study presents the development of a dynamic operability analysis approach to determine an operable output funnel for linear time-invariant dynamic systems. Traditional operability mapping approaches are computationally expensive, limiting their application for online control. To address this challenge, a novel two-step calculation procedure is proposed in this article. The first step involves offline computation of the nominal funnel through convex hull construction of manipulated variable projections. The second step involves an online update that adapts the nominal funnel to an operable region based on current state information. The proposed method results in a dynamic funnel that can accommodate process disturbances and measurement noises in the form of transient output constraints. The obtained funnel can be effectively used for model predictive control applications. To demonstrate the effectiveness of the proposed framework, the cyber–physical fuel cell-gas turbine hybrid power system in the HYbrid PERformance (HYPER) process from NETL is used as an example in this study. The dynamic operability funnel constructed with the novel method requires a significantly smaller number of dynamic simulations when compared to the conventional operability mapping method, while maintaining similar accuracy. The results obtained using the proposed approach demonstrate its potential for improving the online control of dynamic systems.
{"title":"Linear dynamic operability analysis with state-space projection for the online construction of achievable output funnels","authors":"San Dinh , Fernando V. Lima","doi":"10.1016/j.compchemeng.2025.109428","DOIUrl":"10.1016/j.compchemeng.2025.109428","url":null,"abstract":"<div><div>This study presents the development of a dynamic operability analysis approach to determine an operable output funnel for linear time-invariant dynamic systems. Traditional operability mapping approaches are computationally expensive, limiting their application for online control. To address this challenge, a novel two-step calculation procedure is proposed in this article. The first step involves offline computation of the nominal funnel through convex hull construction of manipulated variable projections. The second step involves an online update that adapts the nominal funnel to an operable region based on current state information. The proposed method results in a dynamic funnel that can accommodate process disturbances and measurement noises in the form of transient output constraints. The obtained funnel can be effectively used for model predictive control applications. To demonstrate the effectiveness of the proposed framework, the cyber–physical fuel cell-gas turbine hybrid power system in the HYbrid PERformance (HYPER) process from NETL is used as an example in this study. The dynamic operability funnel constructed with the novel method requires a significantly smaller number of dynamic simulations when compared to the conventional operability mapping method, while maintaining similar accuracy. The results obtained using the proposed approach demonstrate its potential for improving the online control of dynamic systems.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"205 ","pages":"Article 109428"},"PeriodicalIF":3.9,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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