Computers & Chemical Engineering最新文献

{"title":"Mathematical modelling of a sustainable energy system for restaurant communities: Waste-to-H2 conversion, CO2 sequestration, clean fuel production, and power generation","authors":"Syed Muhammad Aun Rizvi,&nbsp;Khurram Kamal,&nbsp;Tahir Abdul Hussain Ratlamwala","doi":"10.1016/j.compchemeng.2025.109038","DOIUrl":"10.1016/j.compchemeng.2025.109038","url":null,"abstract":"<div><div>This study presents a comprehensive mathematical simulation using Simulink software for a novel hybrid waste-to-energy sustainable system tailored for restaurant communities. The system integrates a microbial fuel cell with subsystems for biodiesel production, anaerobic biogas digestion, methane reforming, and methanol production. The hybrid system aims to convert 500 kg of waste cooking oil, 2000 kg of food waste, and wastewater produced daily by the community into valuable resources. Results revealed that the system can produce 319,376 kWh of electricity, 14.6 t of H₂ gas, 116.8 t of CO₂ and 525 m³ of purified water annually. These outputs provide a net saving/profit of $245,530 with a return on investment of just 6 months. Additionally, the system demonstrates environmental benefits by reducing annual emissions by 200 tCO₂ and 27.450 tCH_4. The findings highlight the hybrid system's effectiveness in mitigating environmental impact, generating clean energy and valuable fuels, and advancing sustainable waste management practices within restaurant communities.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"199 ","pages":"Article 109038"},"PeriodicalIF":3.9,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769139","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}

{"title":"Data-driven alarm parameter optimization","authors":"Tayfun Eylen,&nbsp;P. Erhan Eren,&nbsp;Altan Koçyiğit","doi":"10.1016/j.compchemeng.2025.109041","DOIUrl":"10.1016/j.compchemeng.2025.109041","url":null,"abstract":"<div><div>Most manufacturing sector businesses utilize advanced control mechanisms to sustain their ongoing operations. An alarm management system is one of these control mechanisms that works as a safety barrier, and it contains alarm messages indicating abnormal situations to operators. The causes of alarms mainly result in a harmful state of operations that should be eliminated as quickly as possible to minimize possible negative results. However, the size of the system, lack of people directing the system, and process-dependent peak conditions may lead operators to miss some critical alarms. Quality and quantity of products, job safety, and operational costs are some of the features negatively affected by these missing alarms. The proposed work aims to combine a well-established alarm management philosophy with advanced data analytics techniques to optimize decision variables in alarm management processes. This study introduces a novel data-driven optimization method that leverages the Tennessee Eastman Process as a benchmark to validate its effectiveness. The proposed method aims to ensure continuous alarm system health by contributing to the automation of the parameter optimization process in the life cycles of alarm management systems. Key contributions include the development of a method to associate disturbances with alarms, the creation of an alarm simulation platform, and the improvement of alarm parameters through a unique optimization approach. The results show that there is a trade-off between alarm reaction delay, which refers to the time between disturbances and the first relevant alarm and number of alarms and alarm on times. This trade-off can be evaluated in the desired direction by taking into account the priorities of the process.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"196 ","pages":"Article 109041"},"PeriodicalIF":3.9,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422194","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}

{"title":"Economic Nonlinear Model Predictive Control for cyclic gas pipeline operation","authors":"Lavinia Marina Paola Ghilardi ,&nbsp;Sakshi Naik ,&nbsp;Emanuele Martelli ,&nbsp;Francesco Casella ,&nbsp;Lorenz T. Biegler","doi":"10.1016/j.compchemeng.2025.109039","DOIUrl":"10.1016/j.compchemeng.2025.109039","url":null,"abstract":"<div><div>This study presents an economic Nonlinear Model Predictive Control for optimizing gas pipeline operation. The operation of gas networks is governed by dynamic gas transport equations, compressor performance characteristics, and control valve modeling. Given the daily fluctuations in demand, these systems often do not operate under steady-state conditions. To address this, we propose a controller formulation designed for cyclic steady-state systems, incorporating stabilizing and terminal constraints to ensure asymptotic stability. The application of this approach to real-world, complex branched pipelines involves dealing with non-smoothness and switching conditions, which we tackle through smoothing and complementarity reformulations. The effectiveness of our method is demonstrated in a test network as well as the nationwide Italian gas network, showcasing its practicality for large-scale applications.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"196 ","pages":"Article 109039"},"PeriodicalIF":3.9,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422049","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}

{"title":"Inductive graph neural network framework for imputation of single-cell RNA sequencing data","authors":"Boneshwar V K ,&nbsp;Deepesh Agarwal ,&nbsp;Bala Natarajan ,&nbsp;Babji Srinivasan","doi":"10.1016/j.compchemeng.2025.109031","DOIUrl":"10.1016/j.compchemeng.2025.109031","url":null,"abstract":"<div><div>Single-cell RNA sequencing (scRNA-seq) has transformed biological research, enabling detailed analysis of disease pathways, cellular differentiation, and immune responses at a cellular level. However, the noisy and sparse nature of scRNA-seq datasets often impedes accurate downstream analyses. Cell clustering and gene imputation serve as foundational tasks in harnessing scRNA-seq data for complex biological insights. While various graph-based methods have been developed to enhance imputation and clustering accuracy, traditional transductive models require entire graphs during training, limiting computational efficiency on large biological networks. This study introduces a novel inductive framework that efficiently learns relationships among graph nodes by utilizing subgraphs rather than full neighbor sets for node embedding generation, significantly reducing computational demands while maintaining robust performance. The proposed model achieves up to 60% improvement in Silhouette score, 14.9% in Adjusted Rand Index, 48% in runtime, and 4.5% in L<span><math><msub><mrow></mrow><mrow><mn>1</mn></mrow></msub></math></span> Median error over baseline models, validating the effectiveness of inductive graph learning. Evaluated on diverse scRNA-seq datasets—GSE75748 (progenitor cell types derived from human embryonic stem cells (hESCs)), GSE131928 (adult and pediatric IDH-wildtype glioblastomas (GBM)), and Goolam et al (blastomeres from early-stage Mus musculus (mouse) embryos collected at the 2-cell, 4-cell, 8-cell, 16-cell, and 32-cell stages of preimplantation development).—this framework demonstrates scalability and adaptability, offering a reliable approach for future applications in trajectory inference and gene pathway analysis.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"195 ","pages":"Article 109031"},"PeriodicalIF":3.9,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379076","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}

{"title":"Where to market flexibility? Integrating continuous intraday trading into multi-market participation of industrial multi-energy systems","authors":"Niklas Nolzen ,&nbsp;Alissa Ganter ,&nbsp;Nils Baumgärtner ,&nbsp;Florian Joseph Baader ,&nbsp;Ludger Leenders ,&nbsp;André Bardow","doi":"10.1016/j.compchemeng.2025.109026","DOIUrl":"10.1016/j.compchemeng.2025.109026","url":null,"abstract":"<div><div>The rising share of volatile renewable electricity generation increases the demand for flexibility. Flexibility can be offered by industrial multi-energy systems and marketed either on the continuous intraday, day-ahead, or balancing-power markets. Thus, industrial multi-energy systems face the question where to market their flexibility. We propose a two-step method to integrate trading on the continuous intraday market into a multi-market optimization for flexible industrial multi-energy systems. First, we estimate revenues from continuous trading in the intraday market, employing option-price theory. Second, a multi-stage stochastic optimization allocates the flexibility to the three markets. The case study of an industrial multi-energy system demonstrates that coordinated bidding in all three markets reduces costs the most. A sensitivity analysis reveals that the optimal split between the different markets strongly depends on the intraday market volatility. Overall, the proposed method provides a practical decision-support tool for multi-energy systems participating in short-term electricity and balancing-power markets.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"195 ","pages":"Article 109026"},"PeriodicalIF":3.9,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377067","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}

{"title":"Model-based policy optimization algorithms for feedback control of complex dynamic systems","authors":"Lucky E. Yerimah,&nbsp;Christian Jorgensen,&nbsp;B. Wayne Bequette","doi":"10.1016/j.compchemeng.2025.109032","DOIUrl":"10.1016/j.compchemeng.2025.109032","url":null,"abstract":"<div><div>Model-free Reinforcement learning (RL) has been successfully used in benchmark systems such as the Cart-Pole, Inverted-Pendulum, and Robotic arms. However, model-free RL algorithms have several limitations, including large data requirements and handling of state constraints. Model-based and hybrid RL algorithms offer opportunities to tackle these limitations. This research investigated the application of a model-based policy optimization algorithm (MBPO) for feedback control of the Van de Vusse reaction and the Quadruple tank system. MBPO-trained agents suffer from inaccuracies of the learned model and the computational burden of the online optimization neural network models and policy parameters. We propose a modified model-based policy optimization (MMBPO) algorithm that uses linear dynamic system models. This minimizes a learned model’s inaccuracies and eliminates the computational requirements of training the neural network models. Simulation results show that model-based policy optimization algorithms can track the setpoints of the dynamic systems studied.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"195 ","pages":"Article 109032"},"PeriodicalIF":3.9,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387771","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}

{"title":"Design of enhanced two-dimensional self-optimizing control system for batch process","authors":"Lingjian Ye ,&nbsp;Zeyu Yang ,&nbsp;Feifan Shen ,&nbsp;Xiaofeng Yuan","doi":"10.1016/j.compchemeng.2025.109029","DOIUrl":"10.1016/j.compchemeng.2025.109029","url":null,"abstract":"<div><div>In this paper, we design two-dimensional self-optimizing control (2D-SOC) systems for batch processes. In the framework of 2D-SOC, linear combinations of measurements are controlled along the time and batch axis, respectively, which work jointly to achieve near-optimal operation of batch process. Firstly, the global SOC approach is extended to enhance the self-optimizing performance in a wider range of disturbance space. In the presence of active-set changes, an improved solution method is presented to meet the constraint satisfactions. Then, a novel compensation algorithm is proposed to adjust the setpoints of within-batch controlled variables, which can efficiently improve the process optimality in the presence of tracking errors of batch-to-batch controlled variables and active constraint back-offs. A simple linear compensation law is optimally derived. Finally, the enhanced 2D-SOC design approach is systematically applied to two simulated batch processes, where its enhanced performances are verified.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"195 ","pages":"Article 109029"},"PeriodicalIF":3.9,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360859","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}

{"title":"Game-theoretic approach to cybersecurity risk assessment and protective strategy optimization in process industry production systems","authors":"Feilong Zhang ,&nbsp;Liangchao Chen ,&nbsp;Jianfeng Yang ,&nbsp;Pengchao Wang ,&nbsp;Jianwen Zhang ,&nbsp;Qianlin Wang ,&nbsp;Xu Diao ,&nbsp;Zhan Dou","doi":"10.1016/j.compchemeng.2025.109034","DOIUrl":"10.1016/j.compchemeng.2025.109034","url":null,"abstract":"<div><div>In the realm of process plants, the imperative to avert accidental incidents is compounded by the escalating specter of deliberate attacks, predominantly in the form of cyber intrusions. These cyber threats, with their attendant physical risks, are notoriously elusive to quantify, thereby impeding the plants’ ability to adapt swiftly to evolving risk profiles. This paper introduces a game-theoretic framework that translates cyber-assaults on industrial processes into process deviations induced by anomalous control actions, enabling the quantification of risk and the assessment of the cyberattacks’ impact on operational processes. Risk quantification serves as the foundation for the payoffs of both the attackers and the defenders, and it is used to address the probability and severity of incidents through static games characterized by incomplete information. Subsequently, complete information static game theory is employed to calculate the payoffs for both the attacker and the defender. This approach encompasses a spectrum of potential attacks and defenses, yielding optimal economic strategies for the defender across various temporal junctures. Furthermore, a risk tolerance model is integrated to refine the payoff calculation, offering a blueprint for the defender to execute enhanced defensive strategies. The efficacy of the proposed methodology in managing the physical risks emanating from cyberattacks is substantiated through a case study, which scrutinizes a steam stripper and its control system within a catalytic cracking unit.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"195 ","pages":"Article 109034"},"PeriodicalIF":3.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377190","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}

{"title":"Implementing Recurrent Neural Networks in Process Systems Engineering applications, the right way!","authors":"Aswin Chandrasekar,&nbsp;Tyler Wortley,&nbsp;Euan Bohm,&nbsp;Prashant Mhaskar","doi":"10.1016/j.compchemeng.2025.109027","DOIUrl":"10.1016/j.compchemeng.2025.109027","url":null,"abstract":"<div><div>This manuscript identifies, addresses and illustrates via comparisons an inconsistency and inaccuracy with the implementation of Recurrent Neural Networks (RNNs) on naturally occurring dynamical processes, particularly focusing on implementations that claim to identify input–output dynamic relationships through a state–space representation. While the RNN structure does lend itself to these types of problems, there are two major issues with how RNNs are typically structured and trained in this context. Firstly, the hidden states of the model are commonly reinitialized or discarded between each of the input–output sequences in the training data set, essentially leading to a framework where the initial state for each sequence is not trained. In contrast, in a typical state–space model identification framework, the model parameters along with the states are (and need to be) identified together. Secondly, the model structure of the RNN is different from a classic state space (SS) representation. While in state space representations the current state is defined to be a function of the state and input from the previous time step, RNNs use input from the same time step. In this paper, two changes are proposed to address these inconsistencies. The first step is to train the initial hidden states for the training sequences. To address the structural inconsistency between a state space model and the RNN, the list of hidden states retrieved from the RNN is formatted to represent the data and state pairings that a state space model would create. The effect of these corrections is demonstrated in the simplest of dynamical systems — data generated using a Linear Time-Invariant (LTI) state space model. The importance of both these corrections is demonstrated by implementing them one at a time. Interestingly, the model that performed the worst in testing was the model with only the trained hidden states. The model with no changes was slightly better, and the model with the correct input timing but no trained hidden states increased performance by a significant amount. Finally, the best results were found when both changes were implemented.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"195 ","pages":"Article 109027"},"PeriodicalIF":3.9,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348537","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}

{"title":"Sustainable production of fermentation-based novel proteins","authors":"Rofice Dickson ,&nbsp;Seyed Soheil Mansouri","doi":"10.1016/j.compchemeng.2025.109033","DOIUrl":"10.1016/j.compchemeng.2025.109033","url":null,"abstract":"<div><div>Feeding the growing global population sustainably while minimizing environmental impact is grand challenge for human society. Methane-based single-cell proteins through fermentation (bio-SCP) have emerged as a promising alternative to traditional protein sources (animal and crops), addressing the significant greenhouse gas emissions from livestock production. This study explores an innovative approach to bio-SCP production using synthetic natural gas derived from biogas. The process integrates biogas production via anaerobic digestion of food waste, biogas upgrading through a series of treatments, and SCP production via aerobic fermentation of methane. Detailed process modeling reveals that the proposed design consumes 25,000 kg/h (200 Mt/y) of food waste, producing 4,269.4 kg/h (34.2 Mt/y) of SCP and valuable by-products such as biofertilizer, elemental sulfur, low-pressure steam, and nitrogen. Notably, the proposed design achieves close to 100 % energy self-sufficiency. Techno-economic analysis indicates a capital investment of $733.5 million, annual operating costs of $43.96 million, and a minimum product selling price of $1.02/kg of bio-SCP, demonstrating promising economic viability, especially with nitrogen by-product sales. A cradle-to-gate life cycle assessment highlights the environmental benefits of bio-SCP, showing significant reductions in environmental impacts compared to fossil-driven SCP production. This study underscores the potential of bio-SCP in sustainable animal nutrition and greenhouse gas emission reduction.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"195 ","pages":"Article 109033"},"PeriodicalIF":3.9,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349507","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}