Pub Date : 2024-05-10DOI: 10.1016/j.compchemeng.2024.108721
Christian Zibunas , Raoul Meys , Arne Kätelhön , André Bardow
Rapid demand growth would double GHG emissions of fossil-based chemicals and plastics production by 2050. In contrast, recycling, biomass utilization, and electrification enable pathways to net-zero GHG emissions. Such pathways often compare the costs of fossil and renewable technologies based on the next 30 years. However, this assumption contrasts the timeframes of legislative periods and investors desiring fast returns, leading to myopic (i.e., short-term) investment decisions. Therefore, this study compares pathways based on long-term with myopic decision-making. While a 20-year foresight still achieves net zero by 2050, a 10-year foresight fails the net-zero target and increases cumulated GHG emission by 43%. Moreover, the chemical industry would invest +307 bn-USD (+3.2%) in additional fossil and, thus, potentially stranded assets. Therefore, industry and investors should account for the environmental and economic impacts of myopic decision-making and practice long-term decision-making to mitigate carbon lock-ins, stranded assets, and financial risks for investors.
{"title":"The cost and climate impact of myopic investment decisions in the chemical industry","authors":"Christian Zibunas , Raoul Meys , Arne Kätelhön , André Bardow","doi":"10.1016/j.compchemeng.2024.108721","DOIUrl":"10.1016/j.compchemeng.2024.108721","url":null,"abstract":"<div><p>Rapid demand growth would double GHG emissions of fossil-based chemicals and plastics production by 2050. In contrast, recycling, biomass utilization, and electrification enable pathways to net-zero GHG emissions. Such pathways often compare the costs of fossil and renewable technologies based on the next 30 years. However, this assumption contrasts the timeframes of legislative periods and investors desiring fast returns, leading to myopic (i.e., short-term) investment decisions. Therefore, this study compares pathways based on long-term with myopic decision-making. While a 20-year foresight still achieves net zero by 2050, a 10-year foresight fails the net-zero target and increases cumulated GHG emission by 43%. Moreover, the chemical industry would invest +307 bn-USD (+3.2%) in additional fossil and, thus, potentially stranded assets. Therefore, industry and investors should account for the environmental and economic impacts of myopic decision-making and practice long-term decision-making to mitigate carbon lock-ins, stranded assets, and financial risks for investors.</p></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S009813542400139X/pdfft?md5=d5efda23f48fda9295be6fc0908df018&pid=1-s2.0-S009813542400139X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141043005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-09DOI: 10.1016/j.compchemeng.2024.108714
Chuan Wang, Minglei Yang, Xin Dai, Chen Fan, Wenli Du
The scheduling of crude oil operations holds critical significance in the petrochemical industry due to its profound influence on downstream unit operations and, consequently, overall business profitability. This work presents a novel multi-operation sequencing formulation considering operations with restricted overlapping constraints. This operation-centric model also distinguishes itself by incorporating constraints posed by refinery practice such as downstream product management, multiparcel unloading, brine settling, and changeover detection. A new symmetry-breaking strategy is applied to remove symmetric solutions properly and expedite problem solving while ensuring solution quality. Case studies of various examples are conducted to validate the efficacy of the proposed model. Comparative analyses are performed with the unit-specific event-based formulation, which enables multiple time grids tailored to different units and is widely used in continuous-time modeling. Results further demonstrate the great potential of the presented formulation in generating tighter bounds and accelerating convergence.
{"title":"A novel multi-operation sequencing formulation for the crude oil scheduling problem with restricted overlapping constraints","authors":"Chuan Wang, Minglei Yang, Xin Dai, Chen Fan, Wenli Du","doi":"10.1016/j.compchemeng.2024.108714","DOIUrl":"10.1016/j.compchemeng.2024.108714","url":null,"abstract":"<div><p>The scheduling of crude oil operations holds critical significance in the petrochemical industry due to its profound influence on downstream unit operations and, consequently, overall business profitability. This work presents a novel multi-operation sequencing formulation considering operations with restricted overlapping constraints. This operation-centric model also distinguishes itself by incorporating constraints posed by refinery practice such as downstream product management, multiparcel unloading, brine settling, and changeover detection. A new symmetry-breaking strategy is applied to remove symmetric solutions properly and expedite problem solving while ensuring solution quality. Case studies of various examples are conducted to validate the efficacy of the proposed model. Comparative analyses are performed with the unit-specific event-based formulation, which enables multiple time grids tailored to different units and is widely used in continuous-time modeling. Results further demonstrate the great potential of the presented formulation in generating tighter bounds and accelerating convergence.</p></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141054342","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 : 2024-05-09DOI: 10.1016/j.compchemeng.2024.108723
Benjamin Decardi-Nelson , Abdulelah S. Alshehri , Akshay Ajagekar , Fengqi You
This review article explores how emerging generative artificial intelligence (GenAI) models, such as large language models (LLMs), can enhance solution methodologies within process systems engineering (PSE). These cutting-edge GenAI models, particularly foundation models (FMs), which are pre-trained on extensive, general-purpose datasets, offer versatile adaptability for a broad range of tasks, including responding to queries, image generation, and complex decision-making. Given the close relationship between advancements in PSE and developments in computing and systems technologies, exploring the synergy between GenAI and PSE is essential. We begin our discussion with a compact overview of both classic and emerging GenAI models, including FMs, and then dive into their applications within key PSE domains: synthesis and design, optimization and integration, and process monitoring and control. In each domain, we explore how GenAI models could potentially advance PSE methodologies, providing insights and prospects for each area. Furthermore, the article identifies and discusses potential challenges in fully leveraging GenAI within PSE, including multiscale modeling, data requirements, evaluation metrics and benchmarks, and trust and safety, thereby deepening the discourse on effective GenAI integration into systems analysis, design, optimization, operations, monitoring, and control. This paper provides a guide for future research focused on the applications of emerging GenAI in PSE.
这篇综述文章探讨了新兴的生成式人工智能(GenAI)模型(如大型语言模型(LLM))如何能够增强流程系统工程(PSE)中的解决方案方法。这些前沿的 GenAI 模型,尤其是在广泛的通用数据集上预先训练的基础模型 (FM),为广泛的任务提供了多功能的适应性,包括响应查询、图像生成和复杂决策。鉴于 PSE 的进步与计算和系统技术的发展之间的密切关系,探索 GenAI 与 PSE 之间的协同作用至关重要。我们首先简要概述了经典和新兴的 GenAI 模型(包括调频模型),然后深入探讨了它们在关键 PSE 领域的应用:合成与设计、优化与集成以及流程监控。在每个领域,我们都探讨了 GenAI 模型如何有可能推进 PSE 方法,为每个领域提供了见解和前景。此外,文章还指出并讨论了在 PSE 中充分利用 GenAI 所面临的潜在挑战,包括多尺度建模、数据要求、评估指标和基准以及信任和安全,从而深化了有关将 GenAI 有效集成到系统分析、设计、优化、运营、监测和控制中的讨论。本文为未来研究提供了指南,重点关注新兴 GenAI 在 PSE 中的应用。
{"title":"Generative AI and process systems engineering: The next frontier","authors":"Benjamin Decardi-Nelson , Abdulelah S. Alshehri , Akshay Ajagekar , Fengqi You","doi":"10.1016/j.compchemeng.2024.108723","DOIUrl":"https://doi.org/10.1016/j.compchemeng.2024.108723","url":null,"abstract":"<div><p>This review article explores how emerging generative artificial intelligence (GenAI) models, such as large language models (LLMs), can enhance solution methodologies within process systems engineering (PSE). These cutting-edge GenAI models, particularly foundation models (FMs), which are pre-trained on extensive, general-purpose datasets, offer versatile adaptability for a broad range of tasks, including responding to queries, image generation, and complex decision-making. Given the close relationship between advancements in PSE and developments in computing and systems technologies, exploring the synergy between GenAI and PSE is essential. We begin our discussion with a compact overview of both classic and emerging GenAI models, including FMs, and then dive into their applications within key PSE domains: synthesis and design, optimization and integration, and process monitoring and control. In each domain, we explore how GenAI models could potentially advance PSE methodologies, providing insights and prospects for each area. Furthermore, the article identifies and discusses potential challenges in fully leveraging GenAI within PSE, including multiscale modeling, data requirements, evaluation metrics and benchmarks, and trust and safety, thereby deepening the discourse on effective GenAI integration into systems analysis, design, optimization, operations, monitoring, and control. This paper provides a guide for future research focused on the applications of emerging GenAI in PSE.</p></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140909826","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 : 2024-05-09DOI: 10.1016/j.compchemeng.2024.108727
Sergei Kucherenko, Nannapat Sopittakamol, Nilay Shah
The design space (DS) is defined as the combination of materials and process conditions that guarantees the assurance of quality. This principle ensures that as long as a process operates within DS, it consistently produces a product that meets specifications. It was originally developed for a single unit system. Many industrial processes frequently involve multiple unit operations. Assessing the interaction of Critical Process Parameters (CPPs) with Critical Quality Attributes (CQAs) across stages enables informed decision-making and the capacity to balance different requirements. Analysis and visualization of the complex, multi-dimensional DS is a challenging task. This paper presents a framework for identification such DSs, considering both joint and decoupled strategies using a detailed analysis of a two-stage batch reactor case study. We assess and discuss the practicality and relevance of these methods.
{"title":"Design space identification of a coupled two-stage batch reactor system","authors":"Sergei Kucherenko, Nannapat Sopittakamol, Nilay Shah","doi":"10.1016/j.compchemeng.2024.108727","DOIUrl":"https://doi.org/10.1016/j.compchemeng.2024.108727","url":null,"abstract":"<div><p>The design space (DS) is defined as the combination of materials and process conditions that guarantees the assurance of quality. This principle ensures that as long as a process operates within DS, it consistently produces a product that meets specifications. It was originally developed for a single unit system. Many industrial processes frequently involve multiple unit operations. Assessing the interaction of Critical Process Parameters (CPPs) with Critical Quality Attributes (CQAs) across stages enables informed decision-making and the capacity to balance different requirements. Analysis and visualization of the complex, multi-dimensional DS is a challenging task. This paper presents a framework for identification such DSs, considering both joint and decoupled strategies using a detailed analysis of a two-stage batch reactor case study. We assess and discuss the practicality and relevance of these methods.</p></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0098135424001455/pdfft?md5=778b3bdfc7151cf7a5c302341e0e8e07&pid=1-s2.0-S0098135424001455-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140918530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-09DOI: 10.1016/j.compchemeng.2024.108717
Hesam Hassanpour , Amir H. Hamedi , Prashant Mhaskar , John M. House , Timothy I. Salsbury
The building sector, primarily through heating, ventilation, and air conditioning (HVAC) systems, accounts for over 30% of global final energy consumption and 26% of energy-related emissions, highlighting the urgency for efficient energy management and effective fault detection. Optimizing HVAC system performance is crucial for energy conservation and sustainability. This study introduces a hybrid modeling methodology to enhance HVAC systems’ fault detection and isolation (FDI). Using feature extraction through principal component analysis (PCA) and autoencoder (AE), the proposed approach integrates first-principles knowledge with data to improve the performance of different clustering algorithms (K-means, density-based spatial clustering of applications with noise (DBSCAN), and ordering points to identify the clustering structure (OPTICS)) to distinguish datasets of different operating conditions (normal and faulty conditions). The proposed approach is applied to detect common faults in HVAC systems, demonstrating superior performance compared to purely data-driven methods.
{"title":"A hybrid clustering approach integrating first-principles knowledge with data for fault detection in HVAC systems","authors":"Hesam Hassanpour , Amir H. Hamedi , Prashant Mhaskar , John M. House , Timothy I. Salsbury","doi":"10.1016/j.compchemeng.2024.108717","DOIUrl":"https://doi.org/10.1016/j.compchemeng.2024.108717","url":null,"abstract":"<div><p>The building sector, primarily through heating, ventilation, and air conditioning (HVAC) systems, accounts for over 30% of global final energy consumption and 26% of energy-related emissions, highlighting the urgency for efficient energy management and effective fault detection. Optimizing HVAC system performance is crucial for energy conservation and sustainability. This study introduces a hybrid modeling methodology to enhance HVAC systems’ fault detection and isolation (FDI). Using feature extraction through principal component analysis (PCA) and autoencoder (AE), the proposed approach integrates first-principles knowledge with data to improve the performance of different clustering algorithms (K-means, density-based spatial clustering of applications with noise (DBSCAN), and ordering points to identify the clustering structure (OPTICS)) to distinguish datasets of different operating conditions (normal and faulty conditions). The proposed approach is applied to detect common faults in HVAC systems, demonstrating superior performance compared to purely data-driven methods.</p></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0098135424001352/pdfft?md5=579b7698006cdbefee1c15cd8aebee50&pid=1-s2.0-S0098135424001352-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140950685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-09DOI: 10.1016/j.compchemeng.2024.108720
Federico M. Mione , Lucas Kaspersetz , Martin F. Luna , Judit Aizpuru , Randolf Scholz , Maxim Borisyak , Annina Kemmer , M. Therese Schermeyer , Ernesto C. Martinez , Peter Neubauer , M. Nicolas Cruz Bournazou
To foster self-driving experimentation and address the reproducibility crisis in bioprocess development in a collaborative environment, a modular Workflow Management System (WMS) is required. In this work, a WMS based on Directed Acyclic Graphs that offers a modular and flexible design for plug-and-play integration of computational tools is presented. A case study is used to demonstrate that the implementation of a computational WMS in robotic experimental facilities promotes the application of Findable, Accessible, Interoperable and Re-usable principles, allowing researchers to readily share protocols, models, methods and data. As a proof of concept, we integrated three different computational workflows for online re-design of feeding rates in 24 parallel E. coli fed-batch cultivations producing elastin-like proteins. This approach provides a solid foundation for increasing scientific data generation in robotic experimental facilities, fostering open collaboration, and addressing the challenges of reproducibility in research.
{"title":"A workflow management system for reproducible and interoperable high-throughput self-driving experiments","authors":"Federico M. Mione , Lucas Kaspersetz , Martin F. Luna , Judit Aizpuru , Randolf Scholz , Maxim Borisyak , Annina Kemmer , M. Therese Schermeyer , Ernesto C. Martinez , Peter Neubauer , M. Nicolas Cruz Bournazou","doi":"10.1016/j.compchemeng.2024.108720","DOIUrl":"10.1016/j.compchemeng.2024.108720","url":null,"abstract":"<div><p>To foster self-driving experimentation and address the reproducibility crisis in bioprocess development in a collaborative environment, a modular Workflow Management System (WMS) is required. In this work, a WMS based on Directed Acyclic Graphs that offers a modular and flexible design for plug-and-play integration of computational tools is presented. A case study is used to demonstrate that the implementation of a computational WMS in robotic experimental facilities promotes the application of Findable, Accessible, Interoperable and Re-usable principles, allowing researchers to readily share protocols, models, methods and data. As a proof of concept, we integrated three different computational workflows for online re-design of feeding rates in 24 parallel <em>E. coli</em> fed-batch cultivations producing elastin-like proteins. This approach provides a solid foundation for increasing scientific data generation in robotic experimental facilities, fostering open collaboration, and addressing the challenges of reproducibility in research.</p></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0098135424001388/pdfft?md5=d04b68f05251b23b6272f0586e98bed3&pid=1-s2.0-S0098135424001388-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141057385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-07DOI: 10.1016/j.compchemeng.2024.108722
Angan Mukherjee, Debangsu Bhattacharyya
This paper presents the development of algorithms for mass-constrained neural network models that can exactly satisfy mass conservation laws of chemical process systems, even if the training data violates the same. As opposed to approximately satisfying mass balance constraints of a system by considering additional penalty terms in the objective function, algorithms have been developed to solve an equality-constrained optimization problem, thus ensuring the exact satisfaction of the overall mass conservation laws. For developing dynamic mass-constrained networks, hybrid series and parallel all-nonlinear static-dynamic neural network models are leveraged. The proposed algorithms for solving both the inverse and forward problems are tested by considering both steady-state and dynamic data in presence of varieties of noise characterizations. The proposed structures and algorithms are applied to the development of data-driven models of two nonlinear dynamic chemical processes, namely the Van de Vusse reactor system as well as a solvent-based post-combustion CO2 capture process.
本文介绍了质量受限神经网络模型算法的开发情况,即使训练数据违反了化学过程系统的质量守恒定律,该模型也能精确地满足质量守恒定律。与通过在目标函数中考虑额外的惩罚项来近似满足系统的质量平衡约束不同,本文所开发的算法是为了解决相等约束的优化问题,从而确保精确满足整体质量守恒定律。为了开发动态质量受限网络,利用了混合串联和并联全非线性静态-动态神经网络模型。通过考虑存在各种噪声特征的稳态和动态数据,对所提出的解决逆向和正向问题的算法进行了测试。提出的结构和算法被应用于两个非线性动态化学过程的数据驱动模型的开发,即 Van de Vusse 反应器系统和基于溶剂的燃烧后二氧化碳捕获过程。
{"title":"On the development of steady-state and dynamic mass-constrained neural networks using noisy transient data","authors":"Angan Mukherjee, Debangsu Bhattacharyya","doi":"10.1016/j.compchemeng.2024.108722","DOIUrl":"https://doi.org/10.1016/j.compchemeng.2024.108722","url":null,"abstract":"<div><p>This paper presents the development of algorithms for mass-constrained neural network models that can exactly satisfy mass conservation laws of chemical process systems, even if the training data violates the same. As opposed to approximately satisfying mass balance constraints of a system by considering additional penalty terms in the objective function, algorithms have been developed to solve an equality-constrained optimization problem, thus ensuring the exact satisfaction of the overall mass conservation laws. For developing dynamic mass-constrained networks, hybrid series and parallel all-nonlinear static-dynamic neural network models are leveraged. The proposed algorithms for solving both the inverse and forward problems are tested by considering both steady-state and dynamic data in presence of varieties of noise characterizations. The proposed structures and algorithms are applied to the development of data-driven models of two nonlinear dynamic chemical processes, namely the Van de Vusse reactor system as well as a solvent-based post-combustion CO<sub>2</sub> capture process.</p></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140905525","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 : 2024-05-06DOI: 10.1016/j.compchemeng.2024.108719
Liu Zhang , Zhong Zheng , Yi Chai , Yongzhou Wang , Kai Zhang , Shipeng Huang , Sujun Chen
This study proposes a stock boundary compensation method to address the uncertainty of gaseous energy in integrated steel plants. The uncertainty disrupts the balance of temporary storage, resulting in energy waste, environmental pollution, and disturbances in steel production. The method accurately compensates for the impact of gaseous energy uncertainty on storage. Firstly, this paper takes oxygen and byproduct gas systems as the examples to describe the gas energy system and its scheduling model. The storage deviation caused by gaseous energy uncertainty is quantitatively analyzed using formulas. Subsequently, this study formulates an optimization problem to determine the optimal margin for compensating the storage boundary using a data-driven approach. Comparative experiments demonstrate that the proposed method not only significantly enhances the safety of gaseous energy storage subject to uncertainty, but also outperforms traditional robust optimization and stock fluctuation optimization methods in terms of system robustness against uncertainty and operating cost optimality.
{"title":"A stock border compensation technique for gaseous energy scheduling in steel enterprises under uncertainty","authors":"Liu Zhang , Zhong Zheng , Yi Chai , Yongzhou Wang , Kai Zhang , Shipeng Huang , Sujun Chen","doi":"10.1016/j.compchemeng.2024.108719","DOIUrl":"https://doi.org/10.1016/j.compchemeng.2024.108719","url":null,"abstract":"<div><p>This study proposes a stock boundary compensation method to address the uncertainty of gaseous energy in integrated steel plants. The uncertainty disrupts the balance of temporary storage, resulting in energy waste, environmental pollution, and disturbances in steel production. The method accurately compensates for the impact of gaseous energy uncertainty on storage. Firstly, this paper takes oxygen and byproduct gas systems as the examples to describe the gas energy system and its scheduling model. The storage deviation caused by gaseous energy uncertainty is quantitatively analyzed using formulas. Subsequently, this study formulates an optimization problem to determine the optimal margin for compensating the storage boundary using a data-driven approach. Comparative experiments demonstrate that the proposed method not only significantly enhances the safety of gaseous energy storage subject to uncertainty, but also outperforms traditional robust optimization and stock fluctuation optimization methods in terms of system robustness against uncertainty and operating cost optimality.</p></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140950686","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 : 2024-05-02DOI: 10.1016/j.compchemeng.2024.108713
Vasiliki E. Tzanakopoulou , Kalpa Narasinghe , Michael Pollitt , Daniel Castro-Rodriguez , Dimitrios I. Gerogiorgis
Minimisation of environmental footprint in line with sustainability goals rests at the top of the agenda of the pharmaceutical industry. Volatile Organic Compounds (VOCs), while essential to primary pharmaceutical manufacturing, are solvents whose emissions pose a risk to human health and ecosystems. Adsorption on activated carbon beds is an established technology for end of pipe emissions control, which, however, faces efficiency limitations by quick and irregular bed saturation due to complex, transient feeds. This study employs a validated nonisothermal, multicomponent adsorption model to quantitatively assess the exact effect and potential value of waste stream feed sequencing towards activated carbon bed utilisation optimisation. Our results indicate that gradual increase of dichloromethane feed concentration in combination with a low, constant inlet concentration of the strongly adsorbing component leads to the latest breakthrough onset for the dichloromethane-acetone, dichloromethane-chloroform and dichloromethane-toluene mixtures. Transient vs. constant feeds usage and their interplay clearly affects bed behaviour, paving the way to industrial VOC emission abatement scheduling and process optimisation.
{"title":"Dynamic simulation and analysis of dichloromethane-acetone, dichloromethane-trichloromethane and dichloromethane-toluene VOC mixture abatement systems under transient feed conditions","authors":"Vasiliki E. Tzanakopoulou , Kalpa Narasinghe , Michael Pollitt , Daniel Castro-Rodriguez , Dimitrios I. Gerogiorgis","doi":"10.1016/j.compchemeng.2024.108713","DOIUrl":"10.1016/j.compchemeng.2024.108713","url":null,"abstract":"<div><p>Minimisation of environmental footprint in line with sustainability goals rests at the top of the agenda of the pharmaceutical industry. Volatile Organic Compounds (VOCs), while essential to primary pharmaceutical manufacturing, are solvents whose emissions pose a risk to human health and ecosystems. Adsorption on activated carbon beds is an established technology for end of pipe emissions control, which, however, faces efficiency limitations by quick and irregular bed saturation due to complex, transient feeds. This study employs a validated nonisothermal, multicomponent adsorption model to quantitatively assess the exact effect and potential value of waste stream feed sequencing towards activated carbon bed utilisation optimisation. Our results indicate that gradual increase of dichloromethane feed concentration in combination with a low, constant inlet concentration of the strongly adsorbing component leads to the latest breakthrough onset for the dichloromethane-acetone, dichloromethane-chloroform and dichloromethane-toluene mixtures. Transient vs. constant feeds usage and their interplay clearly affects bed behaviour, paving the way to industrial VOC emission abatement scheduling and process optimisation.</p></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0098135424001315/pdfft?md5=d53ab01b4f5252bcc127876cc939a6a1&pid=1-s2.0-S0098135424001315-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141055994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-01DOI: 10.1016/j.compchemeng.2024.108715
Sadah Mohammed , Fadwa Eljack , Monzure-Khoda Kazi , Mert Atilhan
In this article, we present a novel deep learning-based group contribution framework for the targeted design of ionic liquids (ILs). This computational framework can expedite and improve the process of finding desirable molecular structures of IL via accurate property predictions in a data-driven manner. Our proposed framework consists of two essential steps: establishing a correlation between IL viscosity and CO2 solubility by merging two deep learning models (DNN-GC and ANN-GC) and utilizing this correlation to identify the optimal IL structure with maximal CO2 absorption capacity. Our model achieves high accuracy with R2 values of 95%, 94.2%, and 96.4% for DNN-GC, ANN-GC, and DNN-ANN-GC, respectively. Correlation results align with the experimental data, affirming the applicability of our framework. Finally, the algorithm is employed in a CO2 capture case study to generate and select the best-performing novel ILs, which exhibit behavior consistent with established ILs in the literature.
在本文中,我们提出了一种基于深度学习的新型群贡献框架,用于离子液体(ILs)的定向设计。该计算框架能以数据驱动的方式,通过准确的性质预测,加快并改善寻找理想离子液体分子结构的过程。我们提出的框架包括两个基本步骤:通过合并两个深度学习模型(DNN-GC 和 ANN-GC)建立离子液体粘度和二氧化碳溶解度之间的相关性,并利用这种相关性确定具有最大二氧化碳吸收能力的最佳离子液体结构。我们的模型具有很高的准确性,DNN-GC、ANN-GC 和 DNN-ANN-GC 的 R2 值分别为 95%、94.2% 和 96.4%。相关结果与实验数据一致,证明了我们框架的适用性。最后,在二氧化碳捕获案例研究中使用了该算法,以生成和选择性能最佳的新型 IL,这些 IL 与文献中已有的 IL 表现出一致的行为。
{"title":"Development of a deep learning-based group contribution framework for targeted design of ionic liquids","authors":"Sadah Mohammed , Fadwa Eljack , Monzure-Khoda Kazi , Mert Atilhan","doi":"10.1016/j.compchemeng.2024.108715","DOIUrl":"https://doi.org/10.1016/j.compchemeng.2024.108715","url":null,"abstract":"<div><p>In this article, we present a novel deep learning-based group contribution framework for the targeted design of ionic liquids (ILs). This computational framework can expedite and improve the process of finding desirable molecular structures of IL via accurate property predictions in a data-driven manner. Our proposed framework consists of two essential steps: establishing a correlation between IL viscosity and CO<sub>2</sub> solubility by merging two deep learning models (DNN-GC and ANN-GC) and utilizing this correlation to identify the optimal IL structure with maximal CO<sub>2</sub> absorption capacity. Our model achieves high accuracy with R<sup>2</sup> values of 95%, 94.2%, and 96.4% for DNN-GC, ANN-GC, and DNN-ANN-GC, respectively. Correlation results align with the experimental data, affirming the applicability of our framework. Finally, the algorithm is employed in a CO<sub>2</sub> capture case study to generate and select the best-performing novel ILs, which exhibit behavior consistent with established ILs in the literature.</p></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0098135424001339/pdfft?md5=c03ff4a15e950a54e5d559e4f48f13f9&pid=1-s2.0-S0098135424001339-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140879088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}