Computers & Chemical Engineering最新文献

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 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2024-11-12 DOI: 10.1016/j.compchemeng.2024.108916

Xuluo Yin, Wenting Tang

The bullwhip effect widely exists in supply chains and shows its significance for the competitiveness of enterprises in supply chains. In this study, we analyze the bullwhip effect in two parallel supply chains with competing products, each one consisting of a supplier and a retailer. A model is detailed for measuring the bullwhip effect in which the demand of retailers follows a similar vector autoregressive model (VAR-like) process. The results show that the bullwhip effect can be characterized as a quadratic function of the standard deviation ratio. The impact of market competition on the bullwhip effect of the supply chain may have the opposite result, which depends on some parameters, including lead time and market competition in the parallel supply chain. The parameters have asymmetric influence on bullwhip effect. Compared with VAR(1) and AR(1) model, the empirical results show that our VAR(1)-like model is closer to reality. Furthermore, we discuss the conclusion of research and its inspiration for supply chain management.

牛鞭效应广泛存在于供应链中，对供应链中企业的竞争力具有重要影响。在本研究中，我们分析了两条具有竞争产品的平行供应链中的牛鞭效应，每条供应链都由一个供应商和一个零售商组成。在该模型中，零售商的需求遵循类似向量自回归模型（VAR-like）的过程。结果表明，牛鞭效应可以表征为标准偏差率的二次函数。市场竞争对供应链牛鞭效应的影响可能会产生相反的结果，这取决于一些参数，包括并行供应链中的提前期和市场竞争。这些参数对牛鞭效应的影响是不对称的。与 VAR(1) 模型和 AR(1) 模型相比，实证结果表明我们的类 VAR(1) 模型更接近现实。此外，我们还讨论了研究结论及其对供应链管理的启示。

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 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

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

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.

本研究介绍了 CADET-Julia，这是一款开源、多功能、快速的色谱求解器，采用 Julia 编程语言实现。该软件为一系列色谱模型（包括通用速率模型 (GRM)）的快速原型设计和数值改进提供了平台。间隙柱质量平衡采用强形式非连续伽勒金谱元法（DGSEM）进行空间离散化，而颗粒质量平衡则采用广义空间伽勒金谱元法（GSM）。三个不同的基准测试展示了 CADET-Julia 的计算效率：通过比较 Julia 实现与采用相同数学方法和时间积分器的 C++ 实现（CADET-DG），建立了基准测试。对各种 Julia 时间积分器进行了测试，并使用性能最佳的设置，将 Julia 实现与 CADET-DG 和基于有限体积 (FV) 的 C++ 实现 (CADET-FV) 进行了基准比较。总体而言，Julia 实现的性能优于 C++ 实现，Galerkin 方法总体上优于有限体积方法。

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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 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

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

In this work, real-time optimization (RTO) schemes are proposed and applied on a continuous pharmaceutical manufacturing process which consists of three units: synthesis unit, hot melt extrusion unit and direct compaction line. The developed RTO strategies calculate the operating conditions by optimizing the considered objective functions while satisfying the specific constraints. Moreover, the RTO schemes can cope with intentional changes in the process and unintentional changes such as disturbances. Results from simulations and experiments are presented in this work. An advantageous performance is achieved when using the developed schemes.

本研究提出了实时优化（RTO）方案，并将其应用于由合成单元、热熔挤出单元和直接压制生产线三个单元组成的连续制药过程。所开发的 RTO 策略在满足特定约束条件的同时，通过优化所考虑的目标函数来计算运行条件。此外，RTO 方案还能应对流程中的有意变化和非有意变化（如干扰）。本文介绍了模拟和实验结果。在使用所开发的方案时，可以获得良好的性能。

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 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

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

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.

计算机辅助配方设计是一种利用领域知识和选定的适合计算机应用的方法和工具来辅助配方（产品）设计的方法。本文将分子动力学模拟和贝叶斯神经网络算法与众所周知的工程模型相结合，帮助加快基于配方的洗涤剂产品的开发和优化，以期提高产品质量和性能。其中特别强调了聚合物（产品中的一种活性成分）在香味及其停留时间方面改善产品质量的行为机理。应用分子动力学模拟研究分子相互作用机理的结果表明，聚合物对香味分子具有吸引作用，可以吸附更多的香味分子，使其停留在衣物表面。此外，聚合物还能减弱香味分子的扩散，延长香味扩散的整个过程，这是聚合物能够延缓香味释放的本质所在。通过贝叶斯神经网络（BNN）建立了成分比例与香味扩散之间的定量结构-属性关系（QSPR）模型，并根据该模型对产品配方进行了优化。在聚合物和香水成分不变的情况下，优化表面活性剂的用量，以提高产品质量。

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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 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2024-11-06 DOI: 10.1016/j.compchemeng.2024.108912

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

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,

\hat{Q}

-learning and

β

-pessimistic

Q

-learning, are examined against conventional

Q

-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

Q

-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

β

-pessimistic

Q

-learning can achieve good performance under normal conditions and maintain robustness against moderate environment inconsistencies, making it suitable for risk-averse decision-making.

当环境偏离训练条件时，经典强化学习（RL）的性能可能会下降，从而限制了其在规避风险的供应链管理中的应用。这项研究探索了在供应链运作中使用鲁棒强化学习来对冲环境的不一致性和变化。针对传统的 Q-learning 和基线订单到库存策略，研究了两种鲁棒 RL 算法，即 Q-learning 和 β-pessimistic Q-learning。此外，这项工作还将 RL 应用从前瞻性供应链扩展到闭环供应链。使用基于代理建模开发的供应链模拟器进行了两个案例研究。研究结果表明，Q-learning 在正常条件下的表现优于基准策略，但在环境偏差的情况下，Q-learning 的性能会明显下降。相比之下，稳健的 RL 模型倾向于做出更保守的库存决策，以避免出现较大的短缺惩罚。具体来说，经过微调的 β-悲观 Q-learning 在正常条件下可以获得良好的性能，并能在中等程度的环境不一致情况下保持稳健性，因此适用于规避风险的决策。

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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 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

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

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.

尽管最近在大规模深度神经网络应用方面取得了令人惊叹的进展，但我们对其微观结构、"能量 "函数和优化设计的理解仍不全面。在这里，我们提出了一个新的博弈论框架，称为统计远程动力学，揭示了对这些关键特性的重要见解。此类网络的最佳稳健设计本质上涉及计算效益-成本权衡，而物理启发模型并不能充分捕捉到这一点。在训练过程中，神经元和连接会在资源限制下竞相提高其有效效用，从而产生这些权衡。在一个经过充分训练的网络中，这会导致一种套利平衡状态，即给定层中的所有神经元都具有相同的有效效用，而通向给定层的所有连接都具有相同的有效效用。这种平衡状态的特点是，连接权重和神经元输出出现了两个对数正态分布，这是大型深度神经网络的普遍微观结构。我们将这种网络称为杰恩斯机器。七个大型深度神经网络的经验数据证明了我们的理论预测。我们还证明，Hopfield 网络和玻尔兹曼机是杰恩斯机的相同特例。

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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 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2024-11-02 DOI: 10.1016/j.compchemeng.2024.108907

Jaeyeon Kim , Luthfan Adhy Lesmana , Muhammad Aziz

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 ex-situ characterization for better electrochemical performance.

本研究的重点是颗粒的球形度对电化学设备关键的多孔材料特性的影响。我们生成了带有球形和圆柱形颗粒的三维结构，以模拟多孔颗粒和纤维材料。构建的颗粒几何形状如下：球形和圆柱形，其长宽比（高度与直径之比）分别为 0.1、0.5、1.0、2.5、5.0、10 和 20。每个模型的孔隙率为 0.500 ± 0.001，以排除孔隙率的影响。用 200×200×200 的无量纲体素对结构进行了二值化处理，分析了比表面积、晶粒和孔隙尺寸分布、几何迂回度、电导率以及贯穿面和内部面的扩散率。结果表明，颗粒几何形状对扭曲度、电导率和扩散率有显著影响，斯皮尔曼相关系数的绝对值高达 1。

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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 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

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

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.

氢能作为未来前景广阔的能源形式之一，以其成本效益高、低碳环保的特点吸引了学术界和工业界的关注。与石油和天然气运输相比，氢能运输因其复杂的混合机制而更具挑战性。推断运输过程中的内部状态对于混氢天然气管道的状态监测和运营规划至关重要。考虑到非线性时空动态和有限的传感器信息，重建无限维管道状态变量具有挑战性。本文利用物理信息神经网络解决了非线性无限维氢气混合天然气管道系统的状态重建问题。所提出的设计方案将神经网络与管理管道系统的非线性偏微分方程相结合。在有限的测量条件下，训练有素的模型能够预测任何地点瞬态运输过程中氢的压力、流量和质量通量比的状态演变。通过详细的数值模拟和敏感性分析，对所提出的设计方案进行了论证。

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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 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2024-11-02 DOI: 10.1016/j.compchemeng.2024.108902

Marcello Di Martino , Patrick Linke , Efstratios N. Pistikopoulos

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.

粮食-能源-水关系（FEWN）假定，有关相互关联的粮食、能源和水资源的可持续决策必须全面考虑所有相关资源。由于其多尺度的复杂性，相互关联的 FEWN 优化仍面临建模挑战和计算难点。为了克服这些挑战，本研究提出采用基于数据驱动和模型优化技术的代用模型，同时量化因所选近似和优化方法而引入的误差。反过来，我们基于温室种植、可再生能源和反渗透海水淡化供水系统，推导出一个混合整数线性 FEWN 规划和调度优化模型，该模型最初在计算上是难以实现的。在利用松弛、模块化和凸壳重构等策略求解完整的 FEWN 供水系统之前，首先讨论并克服了能源-水关系供应系统的计算复杂性。

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引用次数: 0

Multi-criteria Forecast Combination Method with Nonlinear Programming for time series prediction models 时间序列预测模型的非线性编程多标准预测组合方法

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

Computers & Chemical Engineering

Pub Date : 2024-11-01 DOI: 10.1016/j.compchemeng.2024.108901

Oscar Generoso Gutierrez , Clara Simón de Blas , Ana E. Garcia Sipols

Improving prediction computation for time series analysis is still a challenge. Finding a method that combines the benefits of different methodologies is still an open problem. Besides the very efficient prediction combination techniques proposed, there is still a lack of procedures that jointly consider error measure combinations and model constraints. In this work, we propose a new forecast combination procedure based on multi-criteria methods that allows the assignment of weights to different error measures in the objective function and the incorporation of constraints. A real case from the pharmaceutical industry for the sale of a probiotic product is presented to illustrate the performance of the proposal. This method is capable of considering different error measures and non distance based errors, is enriched by the consideration of constraints that consider desirable properties of the solution and is robust with respect to different time series characteristics such as trends, seasonality, etc. Results shows similar accuracy to the best known forecasting methods to date.

改进时间序列分析的预测计算仍然是一项挑战。找到一种能将不同方法的优势结合起来的方法仍是一个未决问题。除了已提出的非常有效的预测组合技术外，目前仍缺乏能同时考虑误差测量组合和模型约束条件的程序。在这项工作中，我们提出了一种基于多标准方法的新预测组合程序，允许在目标函数中为不同的误差测量分配权重，并纳入约束条件。本文介绍了一个制药行业益生菌产品销售的真实案例，以说明该建议的性能。该方法能够考虑不同的误差度量和非距离误差，并通过考虑解的理想属性的约束条件而得到丰富，而且对不同的时间序列特征（如趋势、季节性等）具有鲁棒性。结果显示，其准确性与迄今已知的最佳预测方法相似。

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引用次数: 0