Computers & Chemical Engineering最新文献

英文中文

ARRTOC: Adversarially Robust Real-Time Optimization and Control 逆向鲁棒实时优化与控制

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

Computers & Chemical Engineering

Pub Date : 2024-11-28 DOI: 10.1016/j.compchemeng.2024.108930

Akhil Ahmed, Ehecatl Antonio del Rio-Chanona, Mehmet Mercangöz

Real-Time Optimization (RTO) plays a crucial role in process operation by determining optimal set-points for lower-level controllers. However, tracking these set-points can be challenging at the control layer due to disturbances, measurement noise, and actuator limitations, leading to a mismatch between expected and achieved RTO benefits. To address this, we present the Adversarially Robust Real-Time Optimization and Control (ARRTOC) algorithm. ARRTOC addresses this issue by finding set-points which are both optimal and inherently robust to implementation errors at the control layers. ARRTOC draws inspiration from adversarial machine learning, offering a novel constrained Adversarially Robust Optimization (ARO) solution applied to the RTO layer. We present several case studies to validate our approach, including a bioreactor, a multi-loop evaporator process, and scenarios involving plant-model mismatch. These studies demonstrate that ARRTOC can improve realized RTO benefits by as much as 50% compared to traditional RTO formulations that do not account for control layer performance.

实时优化（RTO）通过确定下层控制器的最优设定点，在过程运行中起着至关重要的作用。然而，由于干扰、测量噪声和执行器的限制，在控制层跟踪这些设定点可能具有挑战性，从而导致预期和实现的RTO效益之间的不匹配。为了解决这个问题，我们提出了对抗鲁棒实时优化与控制（ARRTOC）算法。ARRTOC通过寻找对控制层的实现错误既最优又固有健壮的设定值来解决这个问题。ARRTOC从对抗性机器学习中获得灵感，提供了一种新的约束对抗性鲁棒优化（ARO）解决方案，应用于RTO层。我们提出了几个案例研究来验证我们的方法，包括一个生物反应器，一个多回路蒸发器过程，以及涉及植物模型不匹配的场景。这些研究表明，与不考虑控制层性能的传统RTO配方相比，ARRTOC可以将实际RTO效益提高50%。

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引用次数: 0

Pressure-swing heterogeneous azeotropic distillation for energy-efficient recovery of ethyl acetate and methanol from wastewater with expanded feed composition range

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

Computers & Chemical Engineering

Pub Date : 2024-11-28 DOI: 10.1016/j.compchemeng.2024.108956

Jiaxing Zhu , Ao Yang , Hao Zhang , Weifeng Shen

This article tends to address the limitations of heterogeneous azeotropic distillation (HAD) for separating Serafimov's class 2.0–2b mixtures, such as ethyl acetate/methanol/water. The feasibility of proposed HAD is constrained by a narrow feed composition range, as thoroughly analyzed through thermodynamic insights in this work. To address these limitations, we propose pressure-swing heterogeneous azeotropic distillation (PSHAD), which allows for a broader application range in feed composition and facilitates heat integration for enhanced economic performance. Thermodynamic insights explore the economic viability and feasibility of PSHAD as feed composition and operating pressure vary. The applicable feed concentration range for PSHAD is determined by liquid-liquid region area and maximum allowable pressure. A parallel genetic algorithm optimizes the processes to minimize total annual cost (TAC). Both PSHAD and the heat-integrated configuration demonstrate superior performance compared to the best process in published literature (i.e., intensified extractive distillation), achieving TAC reductions of 26.46 % and 46.22 %, respectively.

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引用次数: 0

Models, modeling and model-based systems in the era of computers, machine learning and AI

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

Computers & Chemical Engineering

Pub Date : 2024-11-28 DOI: 10.1016/j.compchemeng.2024.108957

Seyed Soheil Mansouri , Abhishek Sivaram , Christopher J. Savoie , Rafiqul Gani

Models, representing a system under study with respect to problems such as process design, process control, product synthesis and many more, are at the core of most computer-aided solution techniques. The representation of a system through a model is done in different ways, such as, symbols, data, mathematical equations, and/or some combination of these. The workflow or process of creating a proxy mathematical representation (model) of a given target system is referred to as modeling. Model-based software tools incorporate the developed models within the steps of their systematic workflow through simultaneous or decomposed solution strategies related to synthesis, design, analysis, etc., of specific systems. In this perspective paper we highlight the various ways systems can be represented by models, the different ways the required models are developed through modeling techniques, and examples of model-based software tools developed to solve different process and product engineering problems. Two types of systems - process systems and chemical systems, are considered. Important issues and challenges are highlighted and perspectives on how they can be addressed are presented.

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引用次数: 0

A reinforcement learning based Lagrangian relaxation algorithm for multi-energy allocation problem in steel enterprise

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

Computers & Chemical Engineering

Pub Date : 2024-11-28 DOI: 10.1016/j.compchemeng.2024.108948

Miao Chang , Shengnan Zhao , Lixin Tang , Jiyin Liu , Yanyan Zhang

The integrated iron and steel enterprises are typically characterized by the presence of multiple energy media that are highly coupled, frequent start-stop cycles of energy conversion equipment, and fluctuations in energy supply and demand. In this paper, we address the problem of byproduct gas-steam-electricity scheduling in iron and steel enterprises to achieve optimal energy distribution and conversion and reduce the energy cost. This optimization problem for the multi-period full energy chain is formulated as a mathematical programming model that considers equipment start-stop cycles, with the objective of minimizing energy system operating cost. A Lagrangian relaxation framework is employed to decouple the energy management model into several independent single schedules. To further improve the algorithm performance, a novel reinforcement learning-based Lagrangian relaxation algorithm (RL-LR) is proposed, which can dynamically set step size coefficients during the iteration process. Numerical results are presented demonstrating that the RL-LR algorithm can achieve higher optimization efficiency.

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引用次数: 0

Distributionally robust CVaR optimization for refinery integrated production–maintenance scheduling under uncertainty 不确定条件下炼油厂综合生产维护调度的分布鲁棒CVaR优化

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

Computers & Chemical Engineering

Pub Date : 2024-11-27 DOI: 10.1016/j.compchemeng.2024.108949

Ya Liu , Jiahao Lai , Bo Chen , Kai Wang , Fei Qiao , Hanli Wang

In the petroleum refining industry, efficient production planning and maintenance scheduling are crucial for economic performance and operational efficiency. Moreover, the production processes face significant uncertainties stemming from market fluctuations and equipment failures. However, traditional optimization methods often treat production and maintenance independently and neglect the risk management associated with uncertainties in the production process, leading to unreliable plans and suboptimal execution. To address these issues, this paper proposes an innovative data-driven distributionally robust conditional value-at-risk (DRCVaR) method to tackle the integrated production–maintenance optimization problem under crude oil price uncertainty. By constructing confidence sets with

L_{2}

norm constraints based on historical data, our approach directly links the model’s conservatism to the amount of available data, effectively managing risk. In addition, we propose robust linear transformation to simplify the min–max nonlinear problem into a conic constraint problem, enhancing solution efficiency and ensuring better operational stability. Refinery case studies demonstrate that the proposed DRCVaR consistently achieves a practical and acceptable solution, significantly outperforming state-of-the-art approaches.

在石油炼制工业中，高效的生产计划和维修调度对经济效益和运行效率至关重要。此外，由于市场波动和设备故障，生产过程面临很大的不确定性。然而，传统的优化方法往往将生产和维护独立对待，忽视了生产过程中不确定性带来的风险管理，导致计划不可靠，执行不优。为了解决这些问题，本文提出了一种创新的数据驱动的分布式鲁棒条件风险价值（DRCVaR）方法来解决原油价格不确定性下的综合生产维护优化问题。通过基于历史数据构建具有L2范数约束的置信集，我们的方法将模型的保守性与可用数据的数量直接联系起来，从而有效地管理风险。此外，我们提出了鲁棒线性变换，将最小-最大非线性问题简化为一个二次约束问题，提高了求解效率并保证了更好的运行稳定性。炼油厂案例研究表明，建议的DRCVaR始终如一地实现了一个实用且可接受的解决方案，显著优于最先进的方法。

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引用次数: 0

Machine learning in PEM water electrolysis: A study of hydrogen production and operating parameters PEM水电解中的机器学习：产氢和操作参数的研究

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

Computers & Chemical Engineering

Pub Date : 2024-11-27 DOI: 10.1016/j.compchemeng.2024.108954

Ibrahim Shomope , Amani Al-Othman , Muhammad Tawalbeh , Hussam Alshraideh , Fares Almomani

Proton exchange membrane water electrolysis (PEMWE) powered by renewable energy stands out as a promising technology for the sustainable production of high-purity hydrogen. This study employed three machine learning (ML) algorithms, random forest (RF), support vector machine (SVM), and eXtreme gradient boosting (XGBoost), to predict hydrogen production in PEMWE. Model performance was evaluated using root mean squared error (RMSE), coefficient of determination (R²), and mean absolute error (MAE) metrics. The top-performing models, RF and XGBoost, were further refined through hyperparameter tuning. The final models demonstrated high reliability in predicting hydrogen production rates, with RF consistently outperforming XGBoost. The RF model achieved a predictive accuracy of R² = 0.9898, RMSE = 19.99 mL/min, and MAE = 10.41 mL/min, while the XGBoost model achieved R² = 0.9894, RMSE = 20.43 mL/min, and MAE = 11.50 mL/min. Partial dependency plots (PDPs) emphasized the critical role of optimizing both cell voltage and current to maximize hydrogen production in PEMWE. These insights provide valuable guidance for operational adjustments, ensuring optimal system performance for high efficiency and productivity. The study suggests further research on the impact of parameters like temperature and power density on hydrogen production, incorporating them for better optimization.

以可再生能源为动力的质子交换膜电解水（PEMWE）是一种很有前途的可持续生产高纯度氢的技术。本研究采用随机森林（RF）、支持向量机（SVM）和极限梯度提升（XGBoost）三种机器学习（ML）算法来预测PEMWE的氢气产量。使用均方根误差（RMSE）、决定系数（R²）和平均绝对误差（MAE）指标评估模型的性能。表现最好的模型RF和XGBoost通过超参数调优进一步完善。最终模型在预测产氢率方面表现出很高的可靠性，其中RF的表现始终优于XGBoost。RF模型的预测精度为R²= 0.9898,RMSE = 19.99 mL/min, MAE = 10.41 mL/min； XGBoost模型的预测精度为R²= 0.9894,RMSE = 20.43 mL/min, MAE = 11.50 mL/min。部分依赖图（pdp）强调了优化电池电压和电流对最大化PEMWE制氢的关键作用。这些见解为操作调整提供了有价值的指导，确保了最佳的系统性能，以实现高效率和生产力。该研究建议进一步研究温度和功率密度等参数对氢气生产的影响，并将其纳入更好的优化。

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引用次数: 0

State and parameter estimation in closed-loop dynamic real-time optimization — A comparative study

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

Computers & Chemical Engineering

Pub Date : 2024-11-25 DOI: 10.1016/j.compchemeng.2024.108932

José Matias , Christopher L.E. Swartz

Dynamic real-time optimization (DRTO) schemes have risen in popularity as plant environments have become increasingly dynamic due to globalization and deregulated energy markets. Inclusion of the impact of the plant control system on the predicted response gives rise to closed-loop DRTO (CL-DRTO). To avoid using a potentially inaccurate nominal model in CL-DRTO, this work explores incorporating plant measurements through various model updating strategies: bias update, state estimation, and combined parameter and state estimation, the latter two utilizing moving horizon estimation. The strategies are applied to two case studies, a distillation column and a continuous stirred tank reactor. Our findings suggest that the combined state and parameter estimation approach provides improvement in economic performance and fewer constraint violations when parametric uncertainty affects system dynamics nonlinearly. Conversely, the bias update strategy achieves satisfactory economic performance when the propagation of parameter uncertainty in the dynamic model is linear or mildly nonlinear.

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引用次数: 0

Enhancing profitability in p-xylene production via toluene methylation

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

Computers & Chemical Engineering

Pub Date : 2024-11-24 DOI: 10.1016/j.compchemeng.2024.108951

Subin Jung, Yuchan Ahn

To address the growing industrial demand for para-xylene (PX), this study explores an alternative approach by employing toluene methylation (TM) to convert low-cost methanol into high-value PX. This study investigates the direct benefits of integrating TM with PX production. This study quantitatively evaluated the economic benefits of PX production and the investment costs of adding the TM process, considering the lack of toluene saleability. The process flow with a purity of 99.7% was simulated using Aspen Plus; the Aspen Energy Analyzer was used for heat integration (HI). The standalone PAREX process, PAREX integrated with TM, and PAREX with TM and HI showed levelized costs of 2,380, 2,341, and 2,325 USD/ton-PX, respectively. Furthermore, sensitivity analysis confirmed the price of the feed material (mixed xylene) to be the main factor influencing the process cost. This approach offers a promising pathway to enhance PX production capacity efficiently.

引用次数: 0

Gas dispersion modeling in stereoscopic space with obstacles using a novel spatiotemporal prediction network 基于时空预测网络的有障碍物立体空间气体分散模型

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

Computers & Chemical Engineering

Pub Date : 2024-11-24 DOI: 10.1016/j.compchemeng.2024.108934

Shikuan Chen, Wenli Du, Xinjie Wang, Bing Wang, Chenxi Cao, Xin Peng

Gas leakage can lead to catastrophic consequences on both the environment and human health. To mitigate these losses, it is imperative to develop accurate and efficient spatiotemporal models for gas dispersion. The gas diffusion process occurs in a 3-dimensional (3D) space, but most research has been confined to flat-plane scenarios, neglecting the stereoscopic distribution of gas concentrations. To address this issue, we propose a novel method that combines 3D convolution with a long short-term memory neural network (3DConvLSTM) to forecast the 3D spatiotemporal concentration distribution of gas leakage in obstructed scenes. The 3D convolutional filters fully operate in the spatial domain, capturing spatial features horizontally and vertically. To provide data for the experiment, ethane leak scenarios with different sources, rates and wind directions are simulated by computational fluid dynamics (CFD). The results demonstrate that the 3DConvLSTM exhibits higher accuracy and requires fewer parameters, highlighting the effectiveness of the proposed method.

气体泄漏会对环境和人类健康造成灾难性的后果。为了减少这些损失，必须开发准确有效的气体分散时空模型。气体扩散过程发生在三维空间中，但大多数研究都局限于平面场景，忽略了气体浓度的立体分布。为了解决这一问题，我们提出了一种将三维卷积与长短期记忆神经网络（3DConvLSTM）相结合的方法来预测阻塞场景中气体泄漏的三维时空浓度分布。三维卷积滤波器在空间域中完全工作，在水平方向和垂直方向捕获空间特征。为了给实验提供数据，利用计算流体力学（CFD）模拟了不同来源、速率和风向下的乙烷泄漏情景。结果表明，3DConvLSTM具有较高的精度和较少的参数要求，突出了该方法的有效性。

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Linear and neural network models for predicting N-glycosylation in Chinese Hamster Ovary cells based on B4GALT levels 基于B4GALT水平预测中国仓鼠卵巢细胞n -糖基化的线性和神经网络模型

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

Computers & Chemical Engineering

Pub Date : 2024-11-23 DOI: 10.1016/j.compchemeng.2024.108937

Pedro Seber, Richard D. Braatz

Glycosylation is an essential modification to proteins that has positive effects, such as improving the half-life of antibodies, and negative effects, such as promoting cancers. Despite the importance of glycosylation, data-driven models to predict quantitative N-glycan distributions have been lacking. This article constructs linear and neural network models to predict the distribution of glycans on N-glycosylation sites. The models are trained on data containing normalized B4GALT1–B4GALT4 levels in Chinese Hamster Ovary cells. The ANN models achieve a median prediction error of 1.59% on an independent test set, an error 9-fold smaller than for previously published models using the same data, and a narrow error distribution. We also discuss issues with other models in the literature and the advantages of this work’s model over other data-driven models. We openly provide all of the software used, allowing other researchers to reproduce the work and reuse or improve the code in future endeavors.

糖基化是对蛋白质的一种必要修饰，它有积极的影响，比如改善抗体的半衰期，也有消极的影响，比如促进癌症的发生。尽管糖基化的重要性，数据驱动的模型预测定量的n -聚糖分布一直缺乏。本文构建了线性和神经网络模型来预测n -糖基化位点上聚糖的分布。该模型使用中国仓鼠卵巢细胞B4GALT1-B4GALT4归一化水平数据进行训练。人工神经网络模型在独立测试集上的中位预测误差为1.59%，比先前发表的使用相同数据的模型的误差小9倍，并且误差分布窄。我们还讨论了文献中其他模型的问题，以及本工作模型相对于其他数据驱动模型的优势。我们公开提供所使用的所有软件，允许其他研究人员重现工作并在未来的努力中重用或改进代码。

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