Computers & Chemical Engineering最新文献_第2页

Energy and temperature management in buildings through Multi-Objective Model Predictive Control on a chip 通过芯片上的多目标模型预测控制实现楼宇能源和温度管理

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

Computers & Chemical Engineering

Pub Date : 2024-10-30 DOI: 10.1016/j.compchemeng.2024.108903

Uthraa K. Ramesh , Styliani Avraamidou , Hari S. Ganesh

Climate control in buildings involves multiple conflicting objectives, such as energy consumption and occupant comfort, which have to be considered simultaneously during the operation of the climate control system. In this work, the Multi-Objective Model Predictive Control (MOMPC) solution method is further developed through the multiparametric programming approach (mpMOMPC). The MOMPC optimal control problem is reformulated according to the

ϵ

-constraint method, and the

ϵ

vector is treated as unknown parameters to generate the control law expressions offline. This reduces online calculations to point location followed by function evaluation, enabling the controller to be implemented through a chip or low-cost hardware. To demonstrate the potential and versatility of the developed mpMOMPC algorithm, three case studies are conducted. Numerical simulation results show that the extreme-value case is the same as the rule-based MPC case and the preference function case results in maximum energy reduction by 20.1% compared to the rule-based MPC case.

建筑物中的气候控制涉及多个相互冲突的目标，如能源消耗和居住舒适度，在气候控制系统运行期间必须同时考虑这些目标。本研究通过多参数编程方法（mpMOMPC）进一步发展了多目标模型预测控制（MOMPC）求解方法。根据ϵ-约束方法重新表述了 MOMPC 优化控制问题，并将ϵ 向量视为未知参数，离线生成控制法则表达式。这将在线计算减少为点定位，然后进行函数评估，使控制器可以通过芯片或低成本硬件实现。为了证明所开发的 mpMOMPC 算法的潜力和多功能性，我们进行了三个案例研究。数值模拟结果表明，极值案例与基于规则的 MPC 案例相同，而偏好函数案例与基于规则的 MPC 案例相比，最大可降低 20.1%的能耗。

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引用次数: 0

Multiproduct pipeline scheduling: A comprehensive bibliometric analysis and a systematic literature review 多产品流水线调度：综合文献计量分析和系统文献综述

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

Computers & Chemical Engineering

Pub Date : 2024-10-28 DOI: 10.1016/j.compchemeng.2024.108911

Mouad Sidki , Nikolay Tchernev , Pierre Féniès , Libo Ren , Selwa Elfirdoussi

Multiproduct pipeline scheduling problem has garnered increasing attention over the past few decades due to potential economic savings and increased operational efficiency. This paper presents a comprehensive bibliometric analysis and systematic literature review on multiproduct pipeline scheduling, focusing on works published from 2000 to 2023. Using three reputable bibliographic databases, the research gathers and examines metadata to provide an understanding of research evolution, key themes, relevant authors, and journals, as well as the country-wise distribution of authors’ affiliations. The selected articles were refined for a systematic literature review. A research framework was developed to classify the literature into common themes for easier navigation. The classification criteria include industry sectors, pipeline systems’ topologies, scheduling accuracy, parameter certainty, operation constraints, time and volume representations, solution strategies, and objectives. The results provide a comprehensive understanding of the field's current state and highlight promising directions for future research.

由于潜在的经济节约和运营效率的提高，多产品流水线调度问题在过去几十年里受到越来越多的关注。本文对多产品流水线调度进行了全面的文献计量分析和系统的文献综述，重点关注 2000 年至 2023 年期间发表的作品。研究利用三个著名的文献数据库，收集并检查元数据，以了解研究演变、关键主题、相关作者和期刊，以及作者所属国家的分布情况。对所选文章进行了提炼，以便进行系统的文献综述。我们制定了一个研究框架，将文献归类为共同主题，以便于浏览。分类标准包括行业部门、管道系统拓扑结构、调度准确性、参数确定性、操作限制、时间和流量表示、解决策略和目标。研究结果提供了对该领域现状的全面了解，并强调了未来有前景的研究方向。

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引用次数: 0

Surrogate modeling based uncertainties analysis for the determination of safe and optimal operating conditions in batch reactors 基于代用模型的不确定性分析，用于确定间歇式反应器的安全和最佳运行条件

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

Computers & Chemical Engineering

Pub Date : 2024-10-28 DOI: 10.1016/j.compchemeng.2024.108909

Lujie Shi , Younes Aoues , Valeria Casson Moreno , Yankai Wang , Sébastien Leveneur

In chemical process optimization, identifying conditions that balance production rate and thermal risks is crucial. This paper presents a surrogate-assisted optimization methodology that integrates parameters uncertainty, specifically focusing on synthesizing γ-valerolactone (GVL) in adiabatic and batch modes. A surrogate model was established to elucidate the relationships between input variables, production rate and risk index, which reduces the computational burden associated with complex differential equations. The Latin Hypercube Sampling method was employed to assess how uncertainties propagate through the processes. This study formulates a multi-objective optimization model that seeks to find a balance between the highest possible GVL production rate and the lowest probability of failure under deterministic and uncertain scenarios. The results in Pareto charts illustrate the possible operating conditions and determine the optimized initial conditions. This approach serves as a model for optimizing complex chemical processes, balancing production capacity and safety while considering uncertainty management.

在化学工艺优化中，确定平衡生产率和热风险的条件至关重要。本文介绍了一种代用辅助优化方法，该方法综合了参数的不确定性，特别侧重于在绝热和间歇模式下合成γ-戊内酯（GVL）。建立了一个替代模型来阐明输入变量、生产率和风险指数之间的关系，从而减轻了与复杂微分方程相关的计算负担。采用了拉丁超立方采样法来评估不确定性如何通过过程传播。本研究制定了一个多目标优化模型，力求在确定性和不确定性情景下，在尽可能高的龙胆生产率和最低的故障概率之间找到平衡。帕累托图表的结果说明了可能的运行条件，并确定了优化的初始条件。这种方法可作为优化复杂化学过程的模型，在考虑不确定性管理的同时平衡生产能力和安全性。

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引用次数: 0

Model-based workflow for sustainable production of high-quality spirits in packed column stills 在填料柱蒸馏器中持续生产优质烈酒的基于模型的工作流程

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

Computers & Chemical Engineering

Pub Date : 2024-10-28 DOI: 10.1016/j.compchemeng.2024.108910

Simón Díaz-Quezada , David I. Wilson , José R. Pérez-Correa

This study addresses water scarcity in Chilean distilleries by developing a model-based engineering workflow. Prolonged droughts, likely driven by global warming, have intensified this problem. The primary challenge is maintaining high-quality spirits while reducing cooling water and energy use during batch distillation in packed columns. This process was modeled using mass and energy balances, resulting in a system of partial differential algebraic equations (PDAE). Our workflow includes mechanistic modeling, disturbance modeling, multiobjective optimization, model predictive control, and Monte Carlo simulations. Our findings show that cooling water consumption can be reduced by up to 35 % and energy consumption by up to 14.4 % while maintaining product quality. The proposed system is robust against operational disturbances and model mismatch, ensuring consistent distillate quality. This research demonstrates the integration of model-based optimization and control strategies in batch distillation processes, which can be replicated in other fruit wine distillation processes for improved sustainability.

本研究通过开发基于模型的工程工作流程来解决智利蒸馏厂的缺水问题。全球变暖可能导致的长期干旱加剧了这一问题。主要挑战是在填料塔分批蒸馏过程中，既要保持高质量的烈酒，又要减少冷却水和能源的使用。我们利用质量和能量平衡对这一过程进行了建模，并得出了偏微分代数方程系统 (PDAE)。我们的工作流程包括机械建模、干扰建模、多目标优化、模型预测控制和蒙特卡罗模拟。我们的研究结果表明，在保持产品质量的前提下，冷却水消耗最多可减少 35%，能耗最多可减少 14.4%。所提出的系统对运行干扰和模型失配具有很强的鲁棒性，可确保蒸馏物质量的一致性。这项研究展示了基于模型的优化和控制策略在批量蒸馏过程中的整合，可在其他果酒蒸馏过程中推广，以提高可持续性。

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引用次数: 0

Quo Vadis ChatGPT? From large language models to Large Knowledge Models ChatGPT 何去何从？从大型语言模型到大型知识模型

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

Computers & Chemical Engineering

Pub Date : 2024-10-28 DOI: 10.1016/j.compchemeng.2024.108895

Venkat Venkatasubramanian, Arijit Chakraborty

The startling success of ChatGPT and other large language models (LLMs) using transformer-based generative neural network architecture in applications such as natural language processing and image synthesis has many researchers excited about potential opportunities in process systems engineering (PSE). The almost human-like performance of LLMs in these areas is indeed very impressive, surprising, and a major breakthrough. Their capabilities are very useful in certain tasks, such as writing first drafts of documents, code writing assistance, text summarization, etc. However, their success is limited in highly scientific domains as they cannot yet reason, plan, or explain due to their lack of in-depth mechanistic domain knowledge. This is a problem in domains such as chemical engineering as they are governed by fundamental laws of physics and chemistry (and biology), constitutive relations, and highly technical knowledge about materials, processes, and systems. Although purely data-driven machine learning has its immediate uses, the long-term success of AI in scientific and engineering domains would depend on developing hybrid AI systems that combine first principles and technical knowledge effectively. We call these hybrid AI systems Large Knowledge Models (LKMs), as they will not be limited to only NLP-based techniques or NLP-like applications. In this paper, we discuss the challenges and opportunities in developing such systems in chemical engineering.

ChatGPT 和其他使用基于变压器的生成神经网络架构的大型语言模型 (LLM) 在自然语言处理和图像合成等应用领域取得了惊人的成功，这让许多研究人员对流程系统工程 (PSE) 的潜在机遇感到兴奋。LLM 在这些领域中几乎与人类无异的表现确实令人印象深刻，令人惊讶，是一项重大突破。它们的能力在某些任务中非常有用，如撰写文档初稿、协助编写代码、文本摘要等。然而，由于缺乏深入的机械领域知识，它们还不能进行推理、规划或解释，因此在高度科学的领域中，它们的成功是有限的。这在化学工程等领域是个问题，因为这些领域受物理和化学（以及生物学）基本规律、构成关系以及有关材料、过程和系统的高技术知识的制约。尽管纯数据驱动的机器学习有其直接用途，但人工智能在科学和工程领域的长期成功将取决于能否开发出有效结合第一原理和技术知识的混合人工智能系统。我们称这些混合人工智能系统为大型知识模型（LKM），因为它们将不仅仅局限于基于 NLP 的技术或类似 NLP 的应用。在本文中，我们将讨论在化学工程领域开发此类系统所面临的挑战和机遇。

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引用次数: 0

A leap forward in chemical process design: Introducing an automated framework for integrated AI and CFD simulations 化学工艺设计的飞跃：引入集成人工智能和 CFD 模拟的自动化框架

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

Computers & Chemical Engineering

Pub Date : 2024-10-24 DOI: 10.1016/j.compchemeng.2024.108906

Dela Quarme Gbadago , Sejin Go , Sungwon Hwang

Despite the numerous possibilities of integrating AI and CFD simulations for chemical process design, researchers often rely on manual techniques, resulting in suboptimal models and time-consuming processes. To address these challenges, we propose an automated framework that combines high-fidelity AI modeling with hyperparameter optimization, automated CFD simulations using OpenFOAM, and effortless post-processing for data extraction. This framework was tested on a reactor scale-up process involving 51 different configurations for butadiene synthesis, achieving 98.8 % accuracy in CFD validation and over 99 % accuracy in AI models. The automation pipeline streamlines geometry generation, meshing, simulation, data extraction, and AI-driven optimization, significantly reducing manual effort. Our framework is versatile, customizable for various types of process equipment design, and employs open-source software for ease of adoption and reproducibility. This approach not only enhances accuracy and efficiency but also opens up AI and CFD integration to a broader range of researchers.

尽管将人工智能和 CFD 模拟集成到化学工艺设计中具有多种可能性，但研究人员往往依赖于人工技术，从而导致模型不理想和过程耗时。为了应对这些挑战，我们提出了一个自动化框架，它将高保真人工智能建模与超参数优化、使用 OpenFOAM 的自动 CFD 模拟以及轻松的数据提取后处理结合在一起。该框架在一个涉及 51 种不同丁二烯合成配置的反应器放大过程中进行了测试，其 CFD 验证准确率达到 98.8%，AI 模型准确率超过 99%。自动化管道简化了几何生成、网格划分、模拟、数据提取和人工智能驱动的优化过程，大大减少了人工操作。我们的框架用途广泛，可针对各种类型的工艺设备设计进行定制，并采用开源软件，便于采用和复制。这种方法不仅提高了准确性和效率，还向更多研究人员开放了人工智能和 CFD 集成。

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引用次数: 0

Optimizing the design and operation of water networks: Two decomposition approaches 优化水网的设计和运行：两种分解方法

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

Computers & Chemical Engineering

Pub Date : 2024-10-24 DOI: 10.1016/j.compchemeng.2024.108897

Yijiang Li , Santanu S. Dey , Nikolaos V. Sahinidis , Naresh Susarla , Miguel A. Zamarripa , Markus G. Drouven

We consider the design and operation of water networks simultaneously. Water network problems can be divided into two categories: the design problem and the operation problem. The design problem involves determining the appropriate pipe sizing and placements of pump stations, while the operation problem involves scheduling pump stations over multiple time periods to account for changes in supply and demand. Our focus is on networks that involve water co-produced with oil and gas. While solving the optimization formulation for such networks, we found that obtaining a primal (feasible) solution is more challenging than obtaining dual bounds using off-the-shelf mixed-integer nonlinear programming solvers. Therefore, we propose two methods to obtain good primal solutions. One method involves a decomposition framework that utilizes a convex reformulation, while the other is based on time decomposition. To test our proposed methods, we conduct computational experiments on a network derived from the PARETO case study.

我们同时考虑水网的设计和运行问题。水网问题可分为两类：设计问题和运行问题。设计问题涉及确定适当的管道尺寸和泵站位置，而运行问题则涉及在多个时间段内对泵站进行调度，以考虑供需变化。我们的重点是涉及油气共生水的网络。在求解此类网络的优化公式时，我们发现获得原始（可行）解比使用现成的混合整数非线性编程求解器获得对偶边界更具挑战性。因此，我们提出了两种获得良好原始解的方法。一种方法涉及利用凸重构的分解框架，另一种方法则基于时间分解。为了测试我们提出的方法，我们在 PARETO 案例研究中得到的网络上进行了计算实验。

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引用次数: 0

Two-stage dynamic real-time optimization framework using parameter-dependent differential dynamic programming 使用参数相关微分动态编程的两阶段动态实时优化框架

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

Computers & Chemical Engineering

Pub Date : 2024-10-24 DOI: 10.1016/j.compchemeng.2024.108896

Hyein Jung , Jong Woo Kim , Jong Min Lee

The purpose of chemical process control includes proactive adjustment of the operation to make the most profit out of it. Within this context, real-time optimization (RTO) is proposed and extended to dynamic RTO (DRTO) in the hierarchical control structure, usually having model predictive control (MPC) below. However, online tractability confined the model complexity of RTO and MPC, which results in model inconsistency and, even, incompatible solutions. Here we use parameter-dependent differential dynamic programming (PDDP) to incorporate the closed-loop behavior of the controller in an RTO layer to reduce problem complexity and online computation time. The adaptive control performance of PDDP and the efficacy of closed-loop DRTO formulation with PDDP is demonstrated with the reaction–storage–separation network system control. Consequently, PDDP provides a useful parameterization method to express closed-loop system dynamics, which enables fast feedback control and integrated plant optimization.

化工过程控制的目的包括主动调整操作以获得最大利润。在此背景下，人们提出了实时优化（RTO），并在分层控制结构中将其扩展为动态 RTO（DRTO），通常下面还有模型预测控制（MPC）。然而，在线可控性限制了 RTO 和 MPC 的模型复杂性，导致模型不一致，甚至出现不兼容的解决方案。在此，我们使用参数相关微分动态编程（PDDP）将控制器的闭环行为纳入 RTO 层，以降低问题复杂度和减少在线计算时间。通过反应-存储-分离网络系统控制，证明了 PDDP 的自适应控制性能和使用 PDDP 的闭环 DRTO 方案的有效性。因此，PDDP 为表达闭环系统动力学提供了一种有用的参数化方法，可实现快速反馈控制和综合工厂优化。

引用次数: 0

Water and thermal management in PEM fuel cells using feasible humidity plots and model predictive controllers 利用可行的湿度图和模型预测控制器对 PEM 燃料电池中的水和热进行管理

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

Computers & Chemical Engineering

Pub Date : 2024-10-24 DOI: 10.1016/j.compchemeng.2024.108905

Beril Tümer , Deniz Şanlı Yıldız , Yaman Arkun

Water and thermal management are critical for the performance, efficiency and longevity of PEM fuel cells (PEMFCs). Effective water and thermal management require the design of control systems that can maintain the water balance and temperature at stable and optimal levels. In this paper, we consider a stack of PEM fuel cells integrated with a water recovery and cooling system. A mechanistic dynamic model is developed to be able to predict the water content and temperature in response to the fuel cell inputs. Water management uses a cascade arrangement of a supervisory Model Predictive Controller (MPC) and local anode and cathode PID humidity controllers to balance the membrane water content. Thermal management consists of a separate MPC controller to regulate the fuel stack temperature. One novelty of this work lies in identifying and utilizing the feasible region for the relative humidities of the anode and cathode when controlling the membrane water content. We introduce the feasible humidity plots (FHP) which define the feasible values for the anode and cathode relative humidities for a given fuel cell design and its operating conditions. This useful information helps to assign the set-point values to the local PID humidity controllers of the water management system. It is shown by simulations that the water and thermal management MPC controllers work in tandem and successfully track the desired set-point changes in humidity and temperature while rejecting external disturbances such as load changes. In addition, the control system is robust against modeling errors and possible model-plant mismatch introduced by fuel cell aging.

水和热管理对于 PEM 燃料电池 (PEMFC) 的性能、效率和寿命至关重要。要实现有效的水和热管理，就必须设计出能够将水平衡和温度保持在稳定和最佳水平的控制系统。在本文中，我们考虑了集成了水回收和冷却系统的 PEM 燃料电池堆。为了能够预测水含量和温度对燃料电池输入的响应，我们开发了一个机械动态模型。水管理使用一个级联安排的监督模型预测控制器（MPC）以及本地阳极和阴极 PID 湿度控制器来平衡膜含水量。热管理包括一个单独的 MPC 控制器，用于调节燃料堆温度。这项工作的新颖之处在于，在控制膜含水量时，确定并利用阳极和阴极相对湿度的可行区域。我们引入了可行湿度图 (FHP)，它定义了给定燃料电池设计及其运行条件下阳极和阴极相对湿度的可行值。这些有用的信息有助于为水管理系统的本地 PID 湿度控制器分配设定值。模拟结果表明，水管理和热管理 MPC 控制器协同工作，成功跟踪了湿度和温度的理想设定值变化，同时拒绝了负载变化等外部干扰。此外，该控制系统对建模错误和燃料电池老化可能导致的模型-设备不匹配具有鲁棒性。

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引用次数: 0

Knowledge-assisted hybrid optimization strategy of large-scale crude oil scheduling integrated production planning 大规模原油调度综合生产规划的知识辅助混合优化策略

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

Computers & Chemical Engineering

Pub Date : 2024-10-22 DOI: 10.1016/j.compchemeng.2024.108904

Renchu He , Yunhao Xie , Shiwei Zhang , Feng Xu , Jian Long

As modern refinery crude oil scheduling scales up, traditional manual operations and independent optimization methods struggle with complexity and dynamics. This study proposes an empirically assisted integrated planning and scheduling optimization model.The integrated model is solved using a hybrid optimization algorithm combining mathematical programming and particle swarm optimization (MP/PSO). Long-term planning aims to minimize operational and transportation costs while maximizing refinery profits; short-term scheduling, based on initial long-term plans, aims to minimize unit switchovers. In the short-term scheduling phase, heuristic rules based on empirical operational knowledge generate a high-performing initial population to accelerate convergence. This strategy is crucial for enhancing refinery emergency response capabilities, ensuring stable operations, and improving economic benefits. Experimental results show that within a reasonable time frame, MP/PSO performs better than PSO and manual scheduling in large-scale crude oil scheduling scenarios.

随着现代炼油厂原油调度规模的扩大，传统的人工操作和独立优化方法在复杂性和动态性面前举步维艰。本研究提出了一种经验辅助的综合计划和调度优化模型。该综合模型采用数学编程和粒子群优化（MP/PSO）相结合的混合优化算法求解。长期规划的目标是最大限度地降低运营和运输成本，同时最大限度地提高炼油厂的利润；短期调度基于最初的长期规划，旨在最大限度地减少装置切换。在短期调度阶段，基于经验运营知识的启发式规则会产生一个高性能的初始群体，以加速收敛。这一策略对于提高炼油厂应急响应能力、确保稳定运行和提高经济效益至关重要。实验结果表明，在合理的时间范围内，MP/PSO 在大规模原油调度场景中的表现优于 PSO 和人工调度。

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引用次数: 0