Journal of Process Control最新文献_第10页

Studying the effect of dynamic operation conditions on green ammonia production synthesis loop 研究动态操作条件对绿色氨生产合成回路的影响

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-04-16 DOI: 10.1016/j.jprocont.2025.103436

Raj Patel , Amin Soleimani Mehr , Jimena Incer Valverde , Günter Scheffknecht , Jörg Maier , Reihaneh Zohourian

{"title":"Studying the effect of dynamic operation conditions on green ammonia production synthesis loop","authors":"Raj Patel , Amin Soleimani Mehr , Jimena Incer Valverde , Günter Scheffknecht , Jörg Maier , Reihaneh Zohourian","doi":"10.1016/j.jprocont.2025.103436","DOIUrl":"10.1016/j.jprocont.2025.103436","url":null,"abstract":"<div><div>The global challenges meeting hydrogen demands due to limited renewable resources urge the need for low-cost imports. Green ammonia, promising for its existing infrastructure, encounters inflexibility challenges in large-scale production with renewables. This study delves into ammonia synthesis loop flexibility amid renewable intermittencies. Utilizing an Aspen Plus® model of 1223 tonnes per day ammonia production capacity, the transient behavior under varied feed flow was investigated with Aspen Dynamics™ simulations. The findings indicated that effective strategies enabled managing a minimum load of 10 % or lower under stoichiometric conditions, constrained by the electrolysis system's lower load. The study also concluded that the ammonia synthesis unit's 20 %/hr feed flow ramp rate is restricted by the thermal cycling of the reactor catalyst; the consequences of fast ramp-up and ramp-down of the operational parameters such as feed flow or stoichiometric ratio are the primary limits in green ammonia production or dynamic operation of the plant.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"150 ","pages":"Article 103436"},"PeriodicalIF":3.3,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nonlinear, robust, interval state estimation for distribution systems based on fixed-point expansion considering uncertainties 基于不确定不动点展开的配电系统非线性、鲁棒区间状态估计

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-04-16 DOI: 10.1016/j.jprocont.2025.103427

Zhengmei Lu , Hong Tan , Mohamed A. Mohamed

{"title":"Nonlinear, robust, interval state estimation for distribution systems based on fixed-point expansion considering uncertainties","authors":"Zhengmei Lu , Hong Tan , Mohamed A. Mohamed","doi":"10.1016/j.jprocont.2025.103427","DOIUrl":"10.1016/j.jprocont.2025.103427","url":null,"abstract":"<div><div>Interval state estimation (ISE) is widely used due to its ability to handle uncertainty and the simple parameters that are required. Existing ISE methods have some problems that need improvements, such as conservatism of results, lack of completeness, and limitations in the error range. Therefore, this paper proposes a nonlinear robust ISE method for distribution systems. First, the quadratic Taylor-series expansions of measurement equations are transformed into fixed-point expansions without truncation errors, which reduces errors introduced by measurement conversion and the approximation process. Second, an exponentially weighted least-squares ISE model considering power-flow constraints is proposed based on the fixed-point expansion (FPE), which avoids calculating inverse matrices of the Jacobian matrices containing interval numbers and improves the estimation accuracy. To improve the model’s robustness, an interval weight correction strategy is proposed. Then, the interval Taylor-series method is used to calculate the range of interval functions to reduce the expansion of the interval arithmetic, thereby obtaining narrower intervals for the state variables. Finally, based on an analysis of the 34-bus and the 123-bus systems, it can be seen that the proposed method has good performance for different error ranges and poor measurement ranges.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"150 ","pages":"Article 103427"},"PeriodicalIF":3.3,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-driven second-order iterative sliding mode control for cyber–physical systems under prescribed performance and DoS attacks 规定性能和 DoS 攻击下网络物理系统的数据驱动二阶迭代滑动模式控制

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-04-11 DOI: 10.1016/j.jprocont.2025.103422

Yijie Yang , Dong Liu , Xin Wang , Zhujun Wang

引用次数: 0

Temperature control system based on Active Disturbance Rejection Control and its parameter optimization in large-sized monolithic silicon epitaxy equipment reactor 大型单片硅外延设备电抗器中基于自抗扰控制的温度控制系统及其参数优化

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-04-10 DOI: 10.1016/j.jprocont.2025.103430

Jiazhe Suo , Bo Jin , Lingfeng Zhu , Wenjie Shen

{"title":"Temperature control system based on Active Disturbance Rejection Control and its parameter optimization in large-sized monolithic silicon epitaxy equipment reactor","authors":"Jiazhe Suo , Bo Jin , Lingfeng Zhu , Wenjie Shen","doi":"10.1016/j.jprocont.2025.103430","DOIUrl":"10.1016/j.jprocont.2025.103430","url":null,"abstract":"<div><div>The temperature regulation of large-sized monolithic silicon epitaxial reactors presents a significant technical challenge, primarily attributed to its nonlinearities, significant time delays, and cross-regional coupling interference within the multi-zone heating process. To speed up the heating process while minimizing overshoot, this paper proposes a temperature control system based on Active Disturbance Rejection Control (ADRC). Additionally, this paper proposes a parameter optimization method based on orthogonal experimental design. The ADRC controller's parameters were optimized in the Simulink simulation model, and the controller's performance in temperature control was compared to that of the conventional PID controller following the same parameter optimization. The simulation results demonstrate that under the condition of maintaining overshoot within 1 % of the setting value, the ADRC-based temperature control system achieves stabilization within the ± 1 % error band of the setting value in 53.42 % of the time required by the PID-based temperature control system. Finally, experiments prove that the temperature control system with ADRC controllers can implement precise temperature control and shows good temperature control performance.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"150 ","pages":"Article 103430"},"PeriodicalIF":3.3,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Satisficing Infinite-Horizon Model Predictive Control 满足无限视界模型预测控制

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-04-09 DOI: 10.1016/j.jprocont.2025.103424

Marcelo Lopes de Lima , Eduardo Camponogara , Darci Odloak , Jean Panaioti Jordanou

引用次数: 0

Robust to outlier image inpainting for interface detection in primary separation vessel 初级分离容器界面检测对离群图像的鲁棒性

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-04-09 DOI: 10.1016/j.jprocont.2025.103426

Parashara Kodati , Vamsi Krishna Puli , Ranjith Chiplunkar , Biao Huang

{"title":"Robust to outlier image inpainting for interface detection in primary separation vessel","authors":"Parashara Kodati , Vamsi Krishna Puli , Ranjith Chiplunkar , Biao Huang","doi":"10.1016/j.jprocont.2025.103426","DOIUrl":"10.1016/j.jprocont.2025.103426","url":null,"abstract":"<div><div>The Primary Separation Vessel (PSV) is integral to the bitumen extraction process in the oil sands industry. Effective control of the interface between the froth and middlings layers is critical for the PSV’s optimal operation. Computer vision techniques can monitor this interface using the images captured from the PSV sight glass. However, image-based models suffer from a lower performance when the image quality is inferior. This is evident in the case of the image data being affected by external degradations. Image inpainting addresses the task of removing unwanted objects and improving the quality of the images. Variational Autoencoder (VAE) can be trained to remove the degradations and restore the image quality. However, in the latent space of a standard VAE which uses a Gaussian distribution for the prior, the input information is spread across all the latent dimensions. This is not suitable particularly in scenarios where the input data consists of a limited number of salient features, without involving complex patterns. This under-regularization of latent space may impact the performance of inpainting when outliers are present in the training data. In this article, a Laplace VAE framework is proposed where the prior is modeled as a Laplace distribution to achieve a better regularization of the latent space and enhance robustness to the outliers in training data. Further, we demonstrate that the Laplace prior promotes sparsity in the latent representations, when there are limited features of interest in the input. This model is used to restore degraded images from a pilot-scale PSV and the interface level is predicted from the restored images using a region-based segmentation method.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"150 ","pages":"Article 103426"},"PeriodicalIF":3.3,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","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 modeling of fermentation process using hybrid support vector regression 基于混合支持向量回归的发酵过程优化建模

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-04-07 DOI: 10.1016/j.jprocont.2025.103429

Kangwei Zhu, Shunyi Zhao, Xiaoli Luan, Fei Liu

{"title":"Optimal modeling of fermentation process using hybrid support vector regression","authors":"Kangwei Zhu, Shunyi Zhao, Xiaoli Luan, Fei Liu","doi":"10.1016/j.jprocont.2025.103429","DOIUrl":"10.1016/j.jprocont.2025.103429","url":null,"abstract":"<div><div>This study investigates the accuracy of concentration prediction in industrial fermentation, a critical factor for optimizing large-scale production. To address the limitations of single models in generalization, a hybrid support vector regression (H-SVR) model is proposed, combining the strengths of flexible and robust SVRs to enhance prediction accuracy. The algorithm segments fermentation data based on bacterial characteristics at different stages, emphasizing local phase-specific features, and uses weighted factors to construct the final hybrid model. The corresponding hyperparameters are optimized via grid search to ensure performance. Simulation results based on industrial penicillin fermentation data and succinic acid fermentation experiment demonstrate that the H-SVR model significantly reduces prediction error compared to models such as least squares support vector machines and some network models, while enabling real-time process monitoring. These findings highlight the potential of the H-SVR model in complex biological systems and demonstrate its effectiveness as a tool for optimizing fermentation processes.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"150 ","pages":"Article 103429"},"PeriodicalIF":3.3,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143785066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A numerically stable unscented transformation with optimal tuning parameters for high-dimensional systems 针对高维系统的数值稳定无符号变换与优化调整参数

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-04-06 DOI: 10.1016/j.jprocont.2025.103406

H.A. Krog, J. Jäschke

引用次数: 0

A robust data-driven approach for modeling industrial systems with non-stationary and sparsely sampled data streams 具有非平稳和稀疏采样数据流的工业系统建模的鲁棒数据驱动方法

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-04-02 DOI: 10.1016/j.jprocont.2025.103425

Changrui Xie, Xi Chen

{"title":"A robust data-driven approach for modeling industrial systems with non-stationary and sparsely sampled data streams","authors":"Changrui Xie, Xi Chen","doi":"10.1016/j.jprocont.2025.103425","DOIUrl":"10.1016/j.jprocont.2025.103425","url":null,"abstract":"<div><div>Data-driven modeling has been widely applied to estimate quality variables in industrial processes, while several practical challenges hinder their applications. This paper is concerned with data-driven modeling for industrial plants with non-stationary and sparsely sampled data streams. To avoid overfitting from data scarcity, a Bayesian linear model is preferred and an associated identification algorithm based on variational inference is developed. The key contribution of this work lies in the development of an adaptation mechanism using streaming variational Bayes with power priors, enabling model identification and adaptation to non-stationary and sparsely sampled data within a full Bayesian framework. To enhance robustness against outliers in streaming batches, Student’s-<em>t</em> distribution is used to account for noise. Furthermore, a posterior predictive distribution is approximately derived, allowing the model to provide not only a point estimate but also the associated predictive uncertainty. The effectiveness of the proposed method is validated through a numerical example and an industrial application.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"150 ","pages":"Article 103425"},"PeriodicalIF":3.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fault detection and identification using a novel process decomposition algorithm for distributed process monitoring 基于分布式过程监控的新型过程分解算法故障检测与识别

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-03-29 DOI: 10.1016/j.jprocont.2025.103423

Enrique Luna Villagómez, Vladimir Mahalec

{"title":"Fault detection and identification using a novel process decomposition algorithm for distributed process monitoring","authors":"Enrique Luna Villagómez, Vladimir Mahalec","doi":"10.1016/j.jprocont.2025.103423","DOIUrl":"10.1016/j.jprocont.2025.103423","url":null,"abstract":"<div><div>Recent progress in fault detection and identification increasingly relies on sophisticated techniques for fault detection, applied through either centralized or distributed approaches. Instead of increasing the sophistication of the fault detection method, this work introduces a novel algorithm for determining process blocks of interacting measurements and applies principal component analysis (PCA) at the block level to identify fault occurrences. Additionally, we define a novel contribution map that scales the magnitudes of disparate faults to facilitate the visual identification of abnormal values of measured variables and analysis of fault propagation. Bayesian aggregate fault index and block fault indices vs. time pinpoint origins of the fault. The proposed method yields fault detection rates on par with most sophisticated centralized or distributed methods on the Tennessee Eastman Process (TEP) benchmark. Since the decomposition algorithm relies on the process flowsheet and control loop structures, practicing control engineers can implement the proposed method in a straightforward manner.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"149 ","pages":"Article 103423"},"PeriodicalIF":3.3,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0