Computers & Chemical Engineering最新文献_第3页

Data-driven robust optimization for pipeline scheduling under flow rate uncertainty 流量不确定情况下管道调度的数据驱动稳健优化

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-14 DOI: 10.1016/j.compchemeng.2024.108924

Amir Baghban , Pedro M. Castro , Fabricio Oliveira

引用次数: 0

The bullwhip effect, market competition and standard deviation ratio in two parallel supply chains 两条平行供应链中的牛鞭效应、市场竞争和标准偏差率

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-12 DOI: 10.1016/j.compchemeng.2024.108916

Xuluo Yin, Wenting Tang

引用次数: 0

CADET-Julia: Efficient and versatile, open-source simulator for batch chromatography in Julia CADET-Julia：高效、通用、开源的 Julia 批次色谱模拟器

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-12 DOI: 10.1016/j.compchemeng.2024.108913

Jesper Frandsen , Jan Michael Breuer , Johannes Schmölder , Jakob Kjøbsted Huusom , Krist V. Gernaey , Jens Abildskov , Eric von Lieres

{"title":"CADET-Julia: Efficient and versatile, open-source simulator for batch chromatography in Julia","authors":"Jesper Frandsen , Jan Michael Breuer , Johannes Schmölder , Jakob Kjøbsted Huusom , Krist V. Gernaey , Jens Abildskov , Eric von Lieres","doi":"10.1016/j.compchemeng.2024.108913","DOIUrl":"10.1016/j.compchemeng.2024.108913","url":null,"abstract":"<div><div>This study introduces CADET-Julia, an open-source, versatile and fast chromatography solver implemented in the Julia programming language. The software offers a platform for rapid prototyping and numerical refinement for a range of chromatography models, including the general rate model (GRM). The interstitial column mass balance was spatially discretized using a strong-form discontinuous Galerkin spectral element method (DGSEM) whereas a generalized spatial Galerkin spectral method (GSM) was applied for the particle mass balance. Three different benchmarks showcased the computational efficiency of CADET-Julia: A baseline benchmark was established by comparing the Julia implementation to a C++ implementation that employed the same mathematical methods and time integrator (CADET-DG). Various Julia time integrators were tested, and with the best-performing settings, the Julia implementation was benchmarked against CADET-DG and a finite volume (FV) based implementation in C++ (CADET-FV). Overall, Julia implementations performed better than C++ implementations and Galerkin methods were generally superior to finite volumes.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"192 ","pages":"Article 108913"},"PeriodicalIF":3.9,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663980","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

Model-based real-time optimization in continuous pharmaceutical manufacturing 基于模型的连续制药实时优化

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-09 DOI: 10.1016/j.compchemeng.2024.108915

Hazem Damiri , Martin Steinberger , Lisa Kuchler , Atabak Azimi , Stefano Martinuzzi , Peter Sagmeister , Jason D. Williams , Stefan Koch , Markus Tranninger , Jakob Rehrl , Selma Celikovic , Stephan Sacher , C. Oliver Kappe , Martin Horn

引用次数: 0

Computer aided formulation design based on molecular dynamics simulation: Detergents with fragrance 基于分子动力学模拟的计算机辅助配方设计：含香料的洗涤剂

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-09 DOI: 10.1016/j.compchemeng.2024.108919

Yan Qi , Lifeng Zhao , Haiqiu Tang , Lei Zhang , Rafiqul Gani

{"title":"Computer aided formulation design based on molecular dynamics simulation: Detergents with fragrance","authors":"Yan Qi , Lifeng Zhao , Haiqiu Tang , Lei Zhang , Rafiqul Gani","doi":"10.1016/j.compchemeng.2024.108919","DOIUrl":"10.1016/j.compchemeng.2024.108919","url":null,"abstract":"<div><div>Computer-aided formulation design is a methodology that utilizes domain knowledge and selected methods and tools suitable for computer-based applications to assist in formulation (product) design. In this paper, molecular dynamics simulation and Bayesian neural network algorithms are combined with well-known engineering models to help accelerate the development and optimization of formulation-based detergent products with a view to improve product quality and performance. In particular, the mechanism of the behavior of polymers (an active ingredient in the product) to improve the product quality in terms of the fragrance and its residence time is highlighted. Results from molecular dynamic simulation applied to study the molecular interaction mechanism show that the polymers have an attraction effect with fragrance molecules and could adsorb more to make them to stay on the surface of clothes. In addition, the polymer attenuates the diffusion of the fragrance molecules, lengthening the entire process of fragrance diffusion, which is the essence of the ability of the polymer to slow down the release of the fragrance. A Quantitative Structure-Property Relationship (QSPR) model between component proportions and fragrance diffusion is established through Bayesian Neural Network (BNN) and the product formulation is optimized based on this model. Keeping polymer and perfume ingredients unchanged, the surfactant amounts are optimized to provide improved product quality.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"192 ","pages":"Article 108919"},"PeriodicalIF":3.9,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663975","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

Risk-averse supply chain management via robust reinforcement learning 通过稳健强化学习规避风险的供应链管理

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-06 DOI: 10.1016/j.compchemeng.2024.108912

Jing Wang , Christopher L.E. Swartz , Kai Huang

{"title":"Risk-averse supply chain management via robust reinforcement learning","authors":"Jing Wang , Christopher L.E. Swartz , Kai Huang","doi":"10.1016/j.compchemeng.2024.108912","DOIUrl":"10.1016/j.compchemeng.2024.108912","url":null,"abstract":"<div><div>Classical reinforcement learning (RL) may suffer performance degradation when the environment deviates from training conditions, limiting its application in risk-averse supply chain management. This work explores using robust RL in supply chain operations to hedge against environment inconsistencies and changes. Two robust RL algorithms, <span><math><mover><mrow><mi>Q</mi></mrow><mrow><mo>ˆ</mo></mrow></mover></math></span>-learning and <span><math><mi>β</mi></math></span>-pessimistic <span><math><mi>Q</mi></math></span>-learning, are examined against conventional <span><math><mi>Q</mi></math></span>-learning and a baseline order-up-to inventory policy. Furthermore, this work extends RL applications from forward to closed-loop supply chains. Two case studies are conducted using a supply chain simulator developed with agent-based modeling. The results show that <span><math><mi>Q</mi></math></span>-learning can outperform the baseline policy under normal conditions, but notably degrades under environment deviations. By comparison, the robust RL models tend to make more conservative inventory decisions to avoid large shortage penalties. Specifically, fine-tuned <span><math><mi>β</mi></math></span>-pessimistic <span><math><mi>Q</mi></math></span>-learning can achieve good performance under normal conditions and maintain robustness against moderate environment inconsistencies, making it suitable for risk-averse decision-making.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"192 ","pages":"Article 108912"},"PeriodicalIF":3.9,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663977","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

Jaynes machine: The universal microstructure of deep neural networks 杰恩斯机器深度神经网络的通用微观结构

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-04 DOI: 10.1016/j.compchemeng.2024.108908

Venkat Venkatasubramanian , N. Sanjeevrajan , Manasi Khandekar , Abhishek Sivaram , Collin Szczepanski

{"title":"Jaynes machine: The universal microstructure of deep neural networks","authors":"Venkat Venkatasubramanian , N. Sanjeevrajan , Manasi Khandekar , Abhishek Sivaram , Collin Szczepanski","doi":"10.1016/j.compchemeng.2024.108908","DOIUrl":"10.1016/j.compchemeng.2024.108908","url":null,"abstract":"<div><div>Despite the recent stunning progress in large-scale deep neural network applications, our understanding of their microstructure, ‘energy’ functions, and optimal design remains incomplete. Here, we present a new game-theoretic framework, called statistical teleodynamics, that reveals important insights into these key properties. The optimally robust design of such networks inherently involves computational benefit–cost trade-offs that physics-inspired models do not adequately capture. These trade-offs occur as neurons and connections compete to increase their effective utilities under resource constraints during training. In a fully trained network, this results in a state of arbitrage equilibrium, where all neurons in a given layer have the same effective utility, and all connections to a given layer have the same effective utility. The equilibrium is characterized by the emergence of two lognormal distributions of connection weights and neuronal output as the universal microstructure of large deep neural networks. We call such a network the Jaynes Machine. Our theoretical predictions are shown to be supported by empirical data from seven large-scale deep neural networks. We also show that the Hopfield network and the Boltzmann Machine are the same special case of the Jaynes Machine.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"192 ","pages":"Article 108908"},"PeriodicalIF":3.9,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663974","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

Impact analysis of particle sphericity on the properties of porous materials via particle packing method for hydrogen fuel and electrolysis cells 通过用于氢燃料电池和电解池的颗粒填料法分析颗粒球度对多孔材料性能的影响

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-02 DOI: 10.1016/j.compchemeng.2024.108907

Jaeyeon Kim , Luthfan Adhy Lesmana , Muhammad Aziz

{"title":"Impact analysis of particle sphericity on the properties of porous materials via particle packing method for hydrogen fuel and electrolysis cells","authors":"Jaeyeon Kim , Luthfan Adhy Lesmana , Muhammad Aziz","doi":"10.1016/j.compchemeng.2024.108907","DOIUrl":"10.1016/j.compchemeng.2024.108907","url":null,"abstract":"<div><div>This study focuses on the impacts of particle's sphericity on the properties of porous materials crucial to electrochemical devices. Three-dimensional structures with spherical and cylindrical particles were generated to simulate porous granular and fibrous materials. The constructed particle geometries are as follows: a sphere and cylinders with different aspect ratios (height-to-diameter) of 0.1, 0.5, 1.0, 2.5, 5.0, 10, and 20. Every model exhibits a porosity of 0.500 ± 0.001 to exclude the effects of porosity. The structures were binarized with 200×200×200 dimensionless voxels, which were analyzed with the specific surface area, grain and pore size distributions, geometrical tortuosity, conductivity, and diffusivity across the through- and in-planes. As a result, the particle geometry significantly impacts on tortuosity, conductivity, and diffusivity, with the absolute value of Spearman's correlation coefficient of up to 1. It may imply the necessity to consider particle geometry as an <em>ex-situ</em> characterization for better electrochemical performance.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"192 ","pages":"Article 108907"},"PeriodicalIF":3.9,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573013","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

Physics-informed neural networks for state reconstruction of hydrogen energy transportation systems 用于氢能运输系统状态重构的物理信息神经网络

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-02 DOI: 10.1016/j.compchemeng.2024.108898

Lu Zhang , Junyao Xie , Qingqing Xu , Charles Robert Koch , Stevan Dubljevic

{"title":"Physics-informed neural networks for state reconstruction of hydrogen energy transportation systems","authors":"Lu Zhang , Junyao Xie , Qingqing Xu , Charles Robert Koch , Stevan Dubljevic","doi":"10.1016/j.compchemeng.2024.108898","DOIUrl":"10.1016/j.compchemeng.2024.108898","url":null,"abstract":"<div><div>Hydrogen energy, as one of the promising future energy forms, has attracted attentions from academia and industry due to its cost-effective and low-carbon nature. Compared with oil and gas transportation, its transportation is more challenging due to its complex blending mechanism. Inferring the internal states during transportation is essential for condition monitoring and operational planning of hydrogen-blending natural gas pipelines. Considering the nonlinear spatiotemporal dynamics and limited sensor information, reconstructing infinite-dimensional pipeline state variables is challenging. This paper addresses the state reconstruction of nonlinear infinite-dimensional hydrogen-blending natural gas pipeline systems using physics-informed neural networks. The proposed design combines neural networks with nonlinear partial differential equations that govern the pipeline systems. With limited measurements, the trained model is capable of predicting the state evolutions of pressure, flow, and mass flux ratio of hydrogen during transient transportation at any location. The proposed design is demonstrated through detailed numerical simulations and sensitivity analyses.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"192 ","pages":"Article 108898"},"PeriodicalIF":3.9,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663973","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

Overcoming modeling and computational complexity challenges in food–energy–water nexus optimization 克服粮食-能源-水关系优化中的建模和计算复杂性挑战

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-02 DOI: 10.1016/j.compchemeng.2024.108902

Marcello Di Martino , Patrick Linke , Efstratios N. Pistikopoulos

{"title":"Overcoming modeling and computational complexity challenges in food–energy–water nexus optimization","authors":"Marcello Di Martino , Patrick Linke , Efstratios N. Pistikopoulos","doi":"10.1016/j.compchemeng.2024.108902","DOIUrl":"10.1016/j.compchemeng.2024.108902","url":null,"abstract":"<div><div>The food–energy–water nexus (FEWN) postulates that sustainable decision-making regarding the interconnected resources food, energy and water must consider all involved resources holistically. Due to its multi-scale complexity, modeling challenges and computational intractability regarding the interconnected FEWN optimization remain. To overcome these challenges, this work proposes employing surrogate models based on data-driven and model optimization techniques, while quantifying the introduced errors due to both the selected approximation and optimization methods. In turn, we derive a mixed-integer linear FEWN planning and scheduling optimization model based on a greenhouse farming, a renewable energy and a reverse osmosis desalination water supply system, which is initially computationally intractable. This computational complexity is first discussed and overcome for the energy–water nexus supply system, before solving the complete FEWN supply system by utilizing strategies such as relaxation, modularization and convex hull reformulation.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"192 ","pages":"Article 108902"},"PeriodicalIF":3.9,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578938","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