Computers & Chemical Engineering最新文献_第5页

Sustainable by design: A first attempt on bioprocessing 可持续设计：生物处理的第一次尝试

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-10-15 DOI: 10.1016/j.compchemeng.2025.109454

Miriam Sarkis , Mariana Monteiro , Andrea Bernardi , Ranjith Chiplunkar , Cleo Kontoravdi , Maria M. Papathanasiou

The growing commitment of the biopharmaceutical sector to transition to Net Zero is driving the industry to embed sustainability principles across its entire pipeline of operations from early-stage process development to manufacturing. In this context, a key challenge for process design is the prediction of the impact of upstream variability on downstream process performance and, therefore, design, with effects on process economics and sustainability. In this work, we focus on the economic and sustainability analysis of antibody-producing bioprocess designs in the presence and absence of downstream process performance constraints. Specifically, we introduce a kinetic model of upstream processing that predicts the profile of critical cell-derived and product-associated impurities and their variability based on culture conditions. Upstream model simulation results are then used to inform a superstructure optimization that maximizes monoclonal antibody (mAb) throughput under purity constraints. Flowsheet simulation models of the candidate designs are developed and process performance is evaluated through techno-economic and life cycle assessment. As expected, results show that purity constraints can lead to more complex downstream configurations, with higher nominal costs and footprint, and improved capacity to withstand feedstock variability. Although intuitive, the results highlight the significance of uncertainty quantification and impurity modeling for informing end-to-end process design. The digitally-enabled holistic approach proposed herein comprehensively enables cost-effective, eco-efficient, and uncertainty-aware design decisions in bioprocessing.

生物制药行业越来越多地致力于向净零排放过渡，这推动了该行业从早期工艺开发到制造的整个运营管道中嵌入可持续性原则。在这种情况下，工艺设计的一个关键挑战是预测上游可变性对下游工艺性能的影响，从而预测设计对工艺经济性和可持续性的影响。在这项工作中，我们专注于在存在和不存在下游工艺性能限制的情况下，抗体生产生物工艺设计的经济和可持续性分析。具体来说，我们介绍了一个上游处理的动力学模型，该模型预测了关键细胞衍生和产品相关杂质的分布及其基于培养条件的可变性。然后使用上游模型仿真结果来告知上层结构优化，在纯度限制下最大化单克隆抗体（mAb）的吞吐量。开发了候选设计的流程仿真模型，并通过技术经济和生命周期评估对工艺性能进行了评估。正如预期的那样，结果表明，纯度限制可能导致更复杂的下游配置，具有更高的名义成本和足迹，并提高了承受原料变化的能力。虽然直观，但结果突出了不确定性量化和杂质建模对通知端到端工艺设计的重要性。本文提出的数字化整体方法全面实现了生物处理中具有成本效益，生态效率和不确定性意识的设计决策。

{"title":"Sustainable by design: A first attempt on bioprocessing","authors":"Miriam Sarkis , Mariana Monteiro , Andrea Bernardi , Ranjith Chiplunkar , Cleo Kontoravdi , Maria M. Papathanasiou","doi":"10.1016/j.compchemeng.2025.109454","DOIUrl":"10.1016/j.compchemeng.2025.109454","url":null,"abstract":"<div><div>The growing commitment of the biopharmaceutical sector to transition to Net Zero is driving the industry to embed sustainability principles across its entire pipeline of operations from early-stage process development to manufacturing. In this context, a key challenge for process design is the prediction of the impact of upstream variability on downstream process performance and, therefore, design, with effects on process economics and sustainability. In this work, we focus on the economic and sustainability analysis of antibody-producing bioprocess designs in the presence and absence of downstream process performance constraints. Specifically, we introduce a kinetic model of upstream processing that predicts the profile of critical cell-derived and product-associated impurities and their variability based on culture conditions. Upstream model simulation results are then used to inform a superstructure optimization that maximizes monoclonal antibody (mAb) throughput under purity constraints. Flowsheet simulation models of the candidate designs are developed and process performance is evaluated through techno-economic and life cycle assessment. As expected, results show that purity constraints can lead to more complex downstream configurations, with higher nominal costs and footprint, and improved capacity to withstand feedstock variability. Although intuitive, the results highlight the significance of uncertainty quantification and impurity modeling for informing end-to-end process design. The digitally-enabled holistic approach proposed herein comprehensively enables cost-effective, eco-efficient, and uncertainty-aware design decisions in bioprocessing.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"205 ","pages":"Article 109454"},"PeriodicalIF":3.9,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145360246","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}

引用次数: 0

Multi-stage model predictive control for slug flow crystallizers using uncertainty-aware surrogate models 基于不确定性感知代理模型的段塞流结晶器多级模型预测控制

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-10-15 DOI: 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.

本文提出了一种新的段塞流结晶器动态模型，该模型解决了空间分布的挑战，没有回混或扩散，有可能实现先进的基于模型的控制。所建立的模型可以准确地描述段塞流结晶器的主要特性，包括段塞间的可变性，但由于考虑了偏微分方程和种群平衡方程，计算复杂度较高。因此，该模型不能直接用于过程优化和控制。为了解决这一挑战，我们提出了两种不同的方法，共形分位数回归和贝叶斯最后一层神经网络，以开发具有不确定性量化能力的代理模型。这些代理输出系统状态的预测以及这些预测的不确定性，以解释过程可变性和模型不确定性。我们利用预测的不确定性制定了一种鲁棒模型预测控制方法，实现了对段塞流结晶器的鲁棒实时高级控制。

{"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}

引用次数: 0

From titer to quality: Exploring reinforcement learning for bioprocess control in silico 从滴度到质量：探索强化学习在生物过程控制中的应用

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-10-15 DOI: 10.1016/j.compchemeng.2025.109452

Mariana Monteiro, Konstantinos Flevaris, Cleo Kontoravdi

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.

哺乳动物细胞中单克隆抗体的产生是一个高度复杂和非线性的过程。控制该过程的行业标准未能捕获其复杂的动态，导致批次到批次的可变性。这种固有的复杂性使得生物过程难以纯粹机械地建模，而缺乏丰富的实验数据集和对控制政策可解释性的需求进一步阻碍了完全数据驱动解决方案的使用。我们提出了一种混合方法来优化营养喂养策略，利用强化学习（RL）与细胞代谢和糖基化的机制模型。RL代理使用off-policy方法来训练数据效率，并且能够从状态的部分观察中学习，这允许改进泛化。该控制器适用于有或没有附加产品质量考虑因素的过程，例如糖基化。我们证明，考虑产品糖基化产生不同的控制策略，而忽视它，只关注滴度会损害产品质量。该方法的持续学习能力确保了对过程变化的适应性，同时在环境中包含一个机制模型有助于学习控制动作的可解释性。

{"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}

引用次数: 0

A multistage stochastic programming approach for renewable ammonia supply chain network design 可再生氨供应链网络设计的多阶段随机规划方法

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-10-14 DOI: 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.

本文考虑了氨气市场价格的不确定性对现有氨气供应链网络中可再生氨气生产设施部署的影响。我们使用氨供应链过渡优化模型来研究这种不确定性的影响。具体而言，我们制定了一个多阶段随机规划问题，以确定在满足氨需求和总供应链成本最小化的多年过渡期内，新的可再生氨生产的最优投资政策。该方法用于分析明尼苏达州氨供应链的转型。结果表明，价格随时间的变化轨迹决定了可再生氨生产设施的采用程度。从广义上讲，通过多阶段随机问题考虑高于平均水平的常规氨市场价格的可能性，相对于只考虑经济上最优供应链转型中的平均市场价格的确定性问题，可再生能源生产的采用范围更广。与先前研究的两阶段随机规划方法相比，考虑价格随时间变化的不确定性可使成本降低4.4%。对于完全过渡到可再生能源生产，由于包括低于平均市场价格的情景，多阶段随机框架的结果平均略慢于确定性问题的过渡。

{"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}

引用次数: 0

A grid-scale study of demand bidding by large industrial users 大型工业用户需求竞价的电网规模研究

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-10-14 DOI: 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.

提出了一种大型工业用电用户参与电网运行的需求竞价机制。需求竞价是在交流最优潮流问题的基础上改进的优化问题，可以理解为电网运营商和电力用户之间的一种补偿过程。这项工作提供了第一个——据作者所知——需求投标的电网规模案例研究，在德克萨斯州电网的足迹中使用了一个合成网格结构。结果表明，需求竞价降低了总体发电成本，但随着参与者数量的增加，经济效益趋于稳定。输电线路和变压器容量约束成为制约因素，表明扩大和强化输电基础设施是扩大需求方参与的关键。当投标人数量增加时，需求投标不会实质性地改变现有投标实体的最优运行，从而支持现有投标人留在系统中，并鼓励新投标人加入。

{"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}

引用次数: 0

Applications of machine learning for decision support in biomass supply chains: A systematic review 机器学习在生物质供应链决策支持中的应用：系统综述

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-10-12 DOI: 10.1016/j.compchemeng.2025.109451

Shayan Razmi, Hossein Mirzaee, Gaurav Kumar, Taraneh Sowlati

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.

有效规划生物质供应链（BSC），包括生物产品的收集、运输、预处理、储存、转化和交付，对于确保效率和可持续性至关重要。最近，机器学习（ML）已被用于解决供应链的复杂性，以实现有效的规划。ML提供了动态和数据驱动的解决方案，可以增强决策。它已被应用于预测生物质产量，预测供需，优化物流和设施位置，以及提高转化过程的效率。本文综述了机器学习在平衡计分卡规划中的作用。本研究考虑了生物质能来源，如食品加工残留物、动物粪便（如粪便），以及基于森林和农业的生物质能，研究了从上游到下游供应链所有阶段的过程。我们根据机器学习模型的学习范式：监督学习、无监督学习和强化学习，以及执行分析的类型：预测分析、预测分析和规范分析。与数据可用性、计算需求和模型泛化相关的挑战限制了ML在bsc中的应用。未来的研究可以通过解决不确定性，将重点放在预处理、运输和收获活动的可扩展和适应性模型上。整合先进的机器学习可以显著提高生物干细胞的弹性、可持续性和效率，支持生物经济的发展和可持续发展目标的实现。

{"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}

引用次数: 0

Assessing, diagnosing, and benchmarking control loops using the input-output cross autocorrelation diagram (IO-CAD) 使用输入-输出交叉自相关图（IO-CAD）评估、诊断和基准测试控制回路

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-10-11 DOI: 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.

监测控制回路的性能对工业过程的运行效率和安全至关重要。本研究提出了一种基于输入-输出交叉自相关图（IOCAD）的控制回路性能评估新方法，这是一种已经在文献中建立的技术。在这项工作中，介绍了两个基于IOCAD的极坐标表示的新指标，补充了先前使用笛卡尔公式开发的四个现有指标。通过分析过程变量（PV）和操纵变量（MV）之间的自相关性，这些指标可以仅使用常规工厂数据进行绩效评估。与最小方差控制（MVC）等传统方法相比，基于iocad的方法对噪声和设定值变化具有更强的鲁棒性，同时还可以对性能下降的根本原因（如调优问题或过程动态变化）提供诊断见解。并提出了控制性能指标（CPI）。包括海上石油生产控制回路在内的各种控制回路的仿真验证了该方法在各种操作场景下实时监测的有效性和适用性。

{"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}

引用次数: 0

Techno-economic analysis and life cycle assessment of a novel algae-based CCUS technology 基于藻类的新型CCUS技术的技术经济分析与生命周期评估

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-10-10 DOI: 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.

能源部门是温室气体的主要来源，因此使该部门脱碳对实现碳中和至关重要。碳捕获、利用和封存（CCUS）技术为减少这些排放提供了一条很有希望的途径。特别是，将捕获的碳转化为增值产品可以提高一些新型CCUS工艺的经济可行性和可扩展性。其中，基于藻类的CCUS工艺是一种很有前途的解决方案，它有可能在未来的能源系统中发挥作用。在本研究中，我们对大规模藻类CCUS过程进行了详细的技术经济分析（TEA）和生命周期评估（LCA）。还进行了敏感性分析，以确定阻碍这一进程扩大的关键瓶颈。据估计，生物物质生产的平均成本为每吨生物物质388美元，平均排放量为每公斤生物物质1.3公斤二氧化碳。根据详细的贴现现金流分析，生物质的最低销售价格估计为每吨424美元。

{"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}

引用次数: 0

Embedding resilience in natural gas monetization exports 在天然气货币化出口中嵌入弹性

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-10-10 DOI: 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.

几个国家的经济严重依赖碳氢化合物出口，这对其国民总收入作出了重大贡献。由于该部门易受风险和不确定性的影响，有必要加强这些出口的复原力，以确保它们的回报，从而确保国民经济的财政安全。这项工作研究弹性投资计划，以确保油气出口部门的财务回报，考虑到生产和运输阶段。我们为油气出口部门开发了一种新的两步弹性投资规划方法。第一步，基于现代投资组合理论（MPT）制定了投资组合优化框架，以增强对碳氢化合物供应链价格波动的抵御能力。在第二步中，采用9个油气供应链弹性指标来制定弹性程度指标（DORI）。该指标评估从第一步确定的财务最优投资组合的表现，以应对与油气出口相关的几种风险。所建议的方法应用于一个案例研究，考虑从一个以天然气为基础的经济体向三个进口商出口四种化学商品，以确定最佳投资组合。通过运行几个中断场景来评估所提出的DORI预测投资组合弹性的能力。结果强调了考虑弹性指标的重要性，因为具有最高财务回报的MPT高效投资组合不一定是对供应链中断最有弹性的。研究结果还表明，将供应链观点纳入投资组合优化框架可以为油气出口问题提供更多见解。

{"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}

引用次数: 0

Microkinetic insights into the impact of coking in dry reforming of methane 甲烷干重整过程中焦化影响的微动力学研究

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-10-10 DOI: 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.

在甲烷干重整（DRM）中，焦化是最关键的挑战之一，导致催化剂失活和严重的性能损失。虽然微动力学模型（MKM）可以在基本步骤水平上捕捉反应动力学，但现有的DRM模型缺乏表征焦炭形成演变性质及其对反应网络的机制影响的能力。本研究引入了一种新型的含焦炭MKM，该MKM明确地包含了焦炭形成途径，并通过DRM数据进行了实验验证。为了解释复杂的、依赖于时间的焦化行为，我们开发了一个新的基于相的框架，系统地将焦炭积累分成不同的时间制度，每个制度都以独特的碳积累速率和模式为特征。相特异性机理分析表明，随着焦化过程的进行，主要反应途径逐渐发生变化。早期的焦炭形成涉及一系列广泛的表面反应，为有针对性的干预提供了多种机会，而后期的焦炭在一些关键反应中表现出集中的影响，如甲烷分解和二氧化碳吸附。为了提高实用性，构建了一个包含焦炭的降阶MKM，在保留基本动力学特征的同时大大提高了计算效率。这种集成建模策略首次将包含焦炭的MKM与基于相的分析相结合，为详细的反应机制和以应用为重点的催化剂和反应器设计提供了强大的桥梁，为提高催化剂耐久性和推进DRM系统的可持续性提供了新的工具。

{"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}

引用次数: 0