Computers & Chemical Engineering最新文献_第2页

Prediction of internal corrosion rate for gas pipeline: A new method based on transformer architecture

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-03-13 DOI: 10.1016/j.compchemeng.2025.109084

Li Tan , Yang Yang , Kemeng Zhang , Kexi Liao , Guoxi He , Jing Tian , Xin Lu

{"title":"Prediction of internal corrosion rate for gas pipeline: A new method based on transformer architecture","authors":"Li Tan , Yang Yang , Kemeng Zhang , Kexi Liao , Guoxi He , Jing Tian , Xin Lu","doi":"10.1016/j.compchemeng.2025.109084","DOIUrl":"10.1016/j.compchemeng.2025.109084","url":null,"abstract":"<div><div>Accurate assessment of internal corrosion rates in steel natural gas pipelines is a critical process in oil and gas pipeline integrity management. However, the existing models used for predicting internal corrosion rates often suffer from various issues, such as low accuracy, poor generalization, and a lack of interpretability. In order to appropriately address these challenges, we propose CNN-BO-Transformer, and employ DeepSHAP for enhancing the interpretability of the model. The proposed CNN-BO-Transformer is used to predict the corrosion rate in natural gas pipelines, while DeepSHAP is utilized to analyze the causal relationships between input variables and model's predictions. The proposed model is validated by using a real pipeline excavation dataset obtained from a gas field located in Northwest China, achieving an average error of 0.21mm/y. This represents reductions of 69.74 % and 66.67 % as compared to the errors of support vector regression (SVR) and the Transformer model, respectively. The proposed method significantly improves the accuracy and reliability of corrosion rate predictions in natural gas gathering and transportation pipelines, thus providing an effective approach for predictive maintenance and repair of steel gathering in transmission pipelines in gas fields.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109084"},"PeriodicalIF":3.9,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685170","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

Application of a temporal multiscale method for efficient simulation of degradation in PEM Water Electrolysis under dynamic operating conditions 应用时间多尺度方法高效模拟 PEM 水电解在动态运行条件下的降解过程

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-03-10 DOI: 10.1016/j.compchemeng.2025.109083

Dayron Chang Dominguez , An Phuc Dam , Shaun M. Alia , Thomas Richter , Kai Sundmacher

{"title":"Application of a temporal multiscale method for efficient simulation of degradation in PEM Water Electrolysis under dynamic operating conditions","authors":"Dayron Chang Dominguez , An Phuc Dam , Shaun M. Alia , Thomas Richter , Kai Sundmacher","doi":"10.1016/j.compchemeng.2025.109083","DOIUrl":"10.1016/j.compchemeng.2025.109083","url":null,"abstract":"<div><div>Hydrogen is emerging as a vital energy carrier, driven by the need to reduce carbon emissions. Proton Electrolyte Membrane Water Electrolysis (PEMWE) enables hydrogen production under fluctuating renewable power conditions but requires improved understanding and stability of the anode catalyst layer under dynamic operating conditions, especially with low noble metal loadings. Long-term degradation experiments are both time-consuming and costly; therefore, a systematic, model-aided approach is essential. In the present work, a temporal multiscale method is applied to reduce the computational effort of simulating long-term degradation processes in PEMWE, with an exemplary focus on catalyst dissolution. A mechanistic model incorporating the oxygen evolution reaction, catalyst dissolution, and hydrogen permeation from the cathode to the anode was hypothesized and implemented. In this way, the local periodicity of transport and reaction processes in dynamic PEMWE operation, which influence the gradual degradation of the catalyst layer, is captured. The temporal multiscale method significantly reduces the computational effort of simulation, decreasing processing time from hours to mere minutes. This efficiency gain is attributed to the limited evolution of Slow-Scale variables during each period of time P of the Fast-Scale variables. Consequently, simulation is required only until local periodicity is achieved within each Slow-Scale time step. Hence, the fully resolved dynamic problem is decoupled into these two scales, employing a heterogeneous multiscale technique. The developed approach effectively accelerates parameter estimation and predictive simulations, supporting systematic modeling of PEMWE degradation under dynamic conditions.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109083"},"PeriodicalIF":3.9,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628985","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}

引用次数: 0

Predicting the temperature-dependent CMC of surfactant mixtures with graph neural networks

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-03-10 DOI: 10.1016/j.compchemeng.2025.109085

Christoforos Brozos , Jan G. Rittig , Elie Akanny , Sandip Bhattacharya , Christina Kohlmann , Alexander Mitsos

{"title":"Predicting the temperature-dependent CMC of surfactant mixtures with graph neural networks","authors":"Christoforos Brozos , Jan G. Rittig , Elie Akanny , Sandip Bhattacharya , Christina Kohlmann , Alexander Mitsos","doi":"10.1016/j.compchemeng.2025.109085","DOIUrl":"10.1016/j.compchemeng.2025.109085","url":null,"abstract":"<div><div>Surfactants are key ingredients in various industries such as personal and home care with the critical micelle concentration (CMC) being of major interest. Predictive models for CMC of pure surfactants have been developed based on recent ML methods, however, in practice surfactant mixtures are typically used due to performance, environmental, and cost reasons. Herein, we develop a graph neural network (GNN) framework for surfactant mixtures to predict the temperature-dependent CMC. We collect data for 108 surfactant binary mixtures, to which we add data for pure species from our previous work Brozos et al. (2024). We then develop and train GNNs and evaluate their accuracy across different prediction test scenarios for binary mixtures relevant to practical applications. The final GNN models demonstrate very high predictive performance when interpolating between different mixture compositions and for new binary mixtures with known species. Extrapolation to binary surfactant mixtures where either one or both surfactant species are not seen before, yields accurate results for the majority of surfactant systems. We further find superior accuracy of the GNN over a semi-empirical model based on activity coefficients, which has been widely used to date. We then explore if GNN models trained solely on binary mixture and pure species data can also accurately predict the CMCs of ternary mixtures. Finally, we experimentally measure the CMC of 4 commercial surfactants that contain up to four species and industrial relevant mixtures and find a very good agreement between measured and predicted CMC values.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109085"},"PeriodicalIF":3.9,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611620","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}

引用次数: 0

Machine learning enabled multiscale model for nanoparticle margination and physiology based pharmacokinetics

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-03-09 DOI: 10.1016/j.compchemeng.2025.109081

Sahil Kulkarni , Benjamin Lin , Ravi Radhakrishnan

引用次数: 0

Nonlinear pinch analysis targeting inspired by options valuation and Black-Scholes-Merton model

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-03-03 DOI: 10.1016/j.compchemeng.2025.109086

Akshay U․ Shenoy, Uday V․ Shenoy

{"title":"Nonlinear pinch analysis targeting inspired by options valuation and Black-Scholes-Merton model","authors":"Akshay U․ Shenoy, Uday V․ Shenoy","doi":"10.1016/j.compchemeng.2025.109086","DOIUrl":"10.1016/j.compchemeng.2025.109086","url":null,"abstract":"<div><div>A novel function-condition product (FCP) approach, where conditions are evaluated using Boolean logic, is proposed for pinch analysis targeting with two distinct advantages. First, a direct targeting formula with Boolean expressions coerced to numeric equivalents provides a superior alternative to a multi-step targeting algorithm with surplus/deficit resource loads cascaded across intervals. Second, the targeting formula allows direct calculation at any level to generate even a nonlinear grand composite curve (GCC) rather than a piecewise-linear GCC with constant slope segments within each interval. The FCP approach is initially developed for the valuation of financial derivatives (specifically, options), where the payoff and P&L (profit and loss) diagrams for option strategies at expiry are shown to be analogs of piecewise-linear GCCs. The pre-expiry P&L curves for options valued by the Nobel prize-winning Black-Scholes-Merton (BSM) model are then shown to be analogous to nonlinear GCCs. An FCP formula for targeting the minimum utilities in heat exchanger networks (HENs) and the optimum mass separating agent flowrates in mass exchanger networks (MENs) is finally derived based on formally demonstrating that each stream in a HEN / MEN is equivalent to a spread in an option strategy. To illustrate various aspects of the new methodology, examples of a crude oil option strategy (for a bull put spread, put ratio spread and butterfly spread), of HENs for both constant and variable specific heat capacity <em>C<sub>p</sub></em>, and of a reactive MEN with a general nonlinear equilibrium function are considered in detail.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109086"},"PeriodicalIF":3.9,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611621","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

Utilization of Hydrogen Pentoxide and Hydrogen Peroxide in transportation sector: A comprehensive assessment study

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-03-03 DOI: 10.1016/j.compchemeng.2025.109088

Assem Abdurakhmanova , Ibrahim Dincer

引用次数: 0

Dynamic and stochastic optimization of algae cultivation process

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-03-01 DOI: 10.1016/j.compchemeng.2025.109087

Sercan Kivanc , Burcu Beykal , Ozgun Deliismail , Hasan Sildir

{"title":"Dynamic and stochastic optimization of algae cultivation process","authors":"Sercan Kivanc , Burcu Beykal , Ozgun Deliismail , Hasan Sildir","doi":"10.1016/j.compchemeng.2025.109087","DOIUrl":"10.1016/j.compchemeng.2025.109087","url":null,"abstract":"<div><div>This study offers a realistic representation of system dynamics which accounts for light intensity, biomass, substrate, and nitrogen concentration, by employing stochastic programming techniques to account for spatial and temporal variations for algae growth. The optimization task focuses on lipid productivity and selectivity, which are crucial factors in the context of algal biofuel production. Different scenarios from likely and unlikely cases of model parameters were evaluated. Optimal initial conditions for key variables such as nitrogen, substrate, light, biomass, lipid, and surface light intensity are calculated, considering the uncertainty of the parameters as well as other governing equations. The results show that a remarkable 11.18% increase in lipid productivity compared to a reference scenario. Furthermore, in the stochastic case, our results highlight that uncertainty has a disproportionately large effect on biomass in comparison to lipid concentration, providing valuable insights into the behavior of the system under varying conditions. This provides a comprehensive exploration of the parameter uncertainty on lipid productivity and algal growth.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109087"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611619","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

Integrating economic, environmental, and social sustainability in Power-to-Ammonia plants: A multi-objective optimization methodology

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-02-28 DOI: 10.1016/j.compchemeng.2025.109082

Andrea Isella, Davide Manca

{"title":"Integrating economic, environmental, and social sustainability in Power-to-Ammonia plants: A multi-objective optimization methodology","authors":"Andrea Isella, Davide Manca","doi":"10.1016/j.compchemeng.2025.109082","DOIUrl":"10.1016/j.compchemeng.2025.109082","url":null,"abstract":"<div><div>Consistent with actual decarbonization efforts in the ammonia industry, this work addresses the process design of Power-to-Ammonia plants (<em>i.e.</em> industrial facilities producing “green” ammonia starting from renewable energy via water electrolysis) by introducing an innovative methodology based on the multi-objective optimization of the “three pillars of sustainability”: economic, environmental, and social. Specifically, the proposed criterion performs a brute-force but exhaustive search evaluating the sizes and operating schedules of key process sections characterizing Power-to-Ammonia facilities (<em>e.g.</em>, the renewable power plant, the electrolyzer, electricity and hydrogen storage systems, <em>etc.</em>) to harmonize the three pillars (which are most often conflicting) as much as possible and identify the process configuration achieving the maximum attainable global sustainability. Indeed, thanks to the scalarization technique, the proposed methodology combines the three different objective functions into a global one by an appropriate set of user-assigned weights reflecting the relative importance among the pillars. For instance, proposing 60 %, 30 %, and 10 % weights to the economic (ECO), environmental (ENV), and social (SOC) pillars, respectively, leads to a Power-to-Ammonia plant achieving a Global Sustainability Score equal to 93 % (ECO: Ammonia production costs = 750.40 USD/t<sub>NH3</sub>; ENV: Global Warming Potential = 0.76 t<sub>CO2eq</sub>/t<sub>NH3</sub>; SOC: Fire and Explosion Index = 141.48). Valuable insights into the conceptual design of chemical processes integrating renewable energy and the associated sustainability assessment criteria are provided, and further industrial application opportunities are discussed.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109082"},"PeriodicalIF":3.9,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562995","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}

引用次数: 0

Optimal design of hybrid multigeneration systems to enhance sustainability in the residential sector

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-02-28 DOI: 10.1016/j.compchemeng.2025.109051

Patrizia Beraldi, Angelo Algieri, Gennaro Lavia

引用次数: 0

Mass-Constrained hybrid Gaussian radial basis neural networks: Development, training, and applications to modeling nonlinear dynamic noisy chemical processes

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-02-27 DOI: 10.1016/j.compchemeng.2025.109080

Angan Mukherjee , Dipendu Gupta , Debangsu Bhattacharyya

{"title":"Mass-Constrained hybrid Gaussian radial basis neural networks: Development, training, and applications to modeling nonlinear dynamic noisy chemical processes","authors":"Angan Mukherjee , Dipendu Gupta , Debangsu Bhattacharyya","doi":"10.1016/j.compchemeng.2025.109080","DOIUrl":"10.1016/j.compchemeng.2025.109080","url":null,"abstract":"<div><div>This paper develops sparse hybrid Gaussian Radial Basis Neural Networks (GRAB-NNs) for data-driven models. The proposed architectures are hidden-layered networks combining Gaussian and sigmoid hidden nodes. Efficient training algorithms are developed for solving the mixed integer nonlinear programming problem, where the optimal number of radial basis function (RBF) centers is obtained by a bidirectional branch and bound algorithm followed by optimal estimation of the coordinates of centers / widths and connection weights by minimizing the corrected Akaike Information Criterion. Algorithmic approaches are developed for exactly satisfying mass constraints both during the training and simulation problems. Sequential decomposition-based training approaches are developed by exploiting the structure of the hybrid model that facilitates use of different training algorithms for each sublayer of the hybrid structure thus leading to faster computation. The performance of the proposed network structures and training algorithms in presence / absence of constraints are evaluated for two nonlinear dynamic chemical systems.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"197 ","pages":"Article 109080"},"PeriodicalIF":3.9,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549412","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