Journal of Process Control最新文献_第9页

Adversarial relationship graph learning soft sensor via negative information exclusion 基于负信息排除的对抗关系图学习软传感器

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-01-01 DOI: 10.1016/j.jprocont.2024.103354

Mingwei Jia , Chao Yang , Zhouxin Pan , Qiang Liu , Yi Liu

引用次数: 0

Partially precise instrument measurements-aided deep learning for industrial quality prediction 用于工业质量预测的部分精密仪器测量辅助深度学习

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-01-01 DOI: 10.1016/j.jprocont.2024.103346

Zhiyi Ji, Xiang Lei, Sijia Wang, Kai Wang, Chunhua Yang

{"title":"Partially precise instrument measurements-aided deep learning for industrial quality prediction","authors":"Zhiyi Ji, Xiang Lei, Sijia Wang, Kai Wang, Chunhua Yang","doi":"10.1016/j.jprocont.2024.103346","DOIUrl":"10.1016/j.jprocont.2024.103346","url":null,"abstract":"<div><div>Material composition is a kind of important quality index in the process industry. Even though instruments for online measuring these compositions have been widely applied, the precision of material composition measurements is suspicious due to corrosion, scaling and other factors. Laboratory values are more convinced, while these instruments are largely idle in real applications. Nevertheless, despite suspicious precision, partially precise trends exist in these measurements, which are also useful for indicating the variation in quality. This means that a wealth of information directly related to quality variables can provide positive guidance for quality prediction. Enlightened by the requirement of information utilization, a long short-term memory network with embedded trend consistency criteria (TCC-LSTM) is proposed for industrial quality prediction through extremely efficient utilization of partially precise quality instrument data. Specifically, based on the property that the trends of the measured values for quality variable are similar to that of the corresponding laboratory values over time, six trend consistency criteria are designed to evaluate the reliability of instrument data, so as to determine the contribution weights of these data in deep learning-based quality prediction. Moreover, in the neural network structure, the space-wise and time-wise attention mechanisms are designed for capturing important variables and time information. Extensive experiments on an actual alumina digestion process demonstrate the efficiency of TCC-LSTM, whose correlation coefficient is averagely improved by 0.2247 and mean absolute error is as low as 0.008079.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"145 ","pages":"Article 103346"},"PeriodicalIF":3.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135884","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

Multi large language model collaboration framework for few-shot link prediction in evolutionary fault diagnosis event graphs 基于演化故障诊断事件图的多语言模型协作框架

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-01-01 DOI: 10.1016/j.jprocont.2024.103342

Tian Wang , Ping Wang , Feng Yang , Shuai Wang , Qiang Fang , Meng Chi

{"title":"Multi large language model collaboration framework for few-shot link prediction in evolutionary fault diagnosis event graphs","authors":"Tian Wang , Ping Wang , Feng Yang , Shuai Wang , Qiang Fang , Meng Chi","doi":"10.1016/j.jprocont.2024.103342","DOIUrl":"10.1016/j.jprocont.2024.103342","url":null,"abstract":"<div><div>Fault-tolerant control is crucial for ensuring flight safety in aircraft. However, existing methods for fault diagnosis in nonlinear systems face challenges such as data sparsity, limited generalization, and lack of explainability. To address these challenges, this paper proposes a multi-large language model (LLM) collaboration framework for few-shot link prediction in evolutionary fault diagnosis event graphs. The framework consists of two modules: the Clustering Language Model (LMc) and the Prediction Language Model (LMP). LMc utilizes the semantic understanding capabilities of LLMs to cluster entities and decompose large-scale graph data into smaller subgraphs, mitigating the impact of data sparsity on link prediction. LMP leverages the reasoning capabilities of LLMs to perform link prediction within each subgraph and fuses the prediction results to enhance accuracy and generalization. The completion of the link serves as a means to an end, which is to conduct fault diagnosis reasoning on a more detailed knowledge graph, thereby significantly improving the accuracy of fault diagnosis. Experimental results demonstrate that the proposed framework outperforms traditional embedding models and existing meta-learning methods on multiple datasets, particularly for sparse and background-rich datasets. This approach offers a novel solution for fault diagnosis in nonlinear systems, with significant theoretical and practical value.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"145 ","pages":"Article 103342"},"PeriodicalIF":3.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096540","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-tolerant safe control for water networks: A backstepping neural control barrier function approach 水网容错安全控制：一种后退神经控制障碍函数方法

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-01-01 DOI: 10.1016/j.jprocont.2024.103344

Qingkai Meng, Milad Shahvali, Stelios Vrachimis, Marios M. Polycarpou

{"title":"Fault-tolerant safe control for water networks: A backstepping neural control barrier function approach","authors":"Qingkai Meng, Milad Shahvali, Stelios Vrachimis, Marios M. Polycarpou","doi":"10.1016/j.jprocont.2024.103344","DOIUrl":"10.1016/j.jprocont.2024.103344","url":null,"abstract":"<div><div>As a typical nonlinear process control infrastructure, the safety and reliability of drinking water transport systems (DWTS) are affected by various factors, including their complex interconnected structures and external environments. This paper proposes a fault-tolerant control scheme for DWTS that ensures their states remain within safe boundaries despite the presence of disturbances, uncertainties and faults. Firstly, considering the impacts of random consumer behavior, unpredictable process and actuator faults, the DWTS is modeled as an interconnected stochastic nonlinear system. Secondly, combining the backstepping technique with control barrier functions, a sufficient and necessary condition for guaranteeing system safety is derived. Thirdly, by minimizing a loss function constructed based on dynamic programming, we synthesize a distributed controller using neural networks and theoretically prove the safety guarantees provided by our approach. Lastly, simulations are conducted to validate the effectiveness of the proposed approach on our benchmark water transport system.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"145 ","pages":"Article 103344"},"PeriodicalIF":3.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096451","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

Machine learning enabled uncertainty set for data-driven robust optimization 机器学习实现了数据驱动鲁棒优化的不确定性集

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-12-01 DOI: 10.1016/j.jprocont.2024.103339

Yun Li , Neil Yorke-Smith , Tamas Keviczky

{"title":"Machine learning enabled uncertainty set for data-driven robust optimization","authors":"Yun Li , Neil Yorke-Smith , Tamas Keviczky","doi":"10.1016/j.jprocont.2024.103339","DOIUrl":"10.1016/j.jprocont.2024.103339","url":null,"abstract":"<div><div>The way how the uncertainties are represented by sets plays a vital role in the performance of robust optimization (RO). This paper presents a novel approach leveraging machine learning (ML) techniques to construct data-driven uncertainty sets from historical uncertainty data for RO problems. The proposed method integrates Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Gaussian Mixture Model (GMM), and Principle Component Analysis (PCA) systematically to eliminate the influence of uncertainty scenarios with low occurrence probability and generate a nonconvex uncertainty set that is a union of multiple basic subsets (box or ellipsoid) without sacrificing its computational tractability. In addition to presenting a comprehensive algorithm for uncertainty set development, this paper offers detailed guidelines for parameter tuning and performance analysis. By harnessing the well-established ML packages <span>scikit-learn</span>, a Python-based toolkit for implementing the proposed approach is also provided. Furthermore, a computationally efficient solution for a two-stage linear RO problem with the proposed data-driven uncertainty set is derived, alongside establishing a probabilistic guarantee of constraint satisfaction for out-of-sample uncertainties. Extensive numerical experiments, conducted on both synthetic and real-world datasets as well as an optimization-based control problem, are performed to demonstrate the efficacy of the proposed methodology.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"144 ","pages":"Article 103339"},"PeriodicalIF":3.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744901","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

Fault detection for T–S fuzzy systems with unmeasurable premise variables based on a two-step interval estimation method 基于两步区间估计法的具有不可测前提变量的 T-S 模糊系统的故障检测

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-11-26 DOI: 10.1016/j.jprocont.2024.103341

Meng Zhou , Yan Wu , Jing Wang , Tarek Raïssi , Vicenç Puig

{"title":"Fault detection for T–S fuzzy systems with unmeasurable premise variables based on a two-step interval estimation method","authors":"Meng Zhou , Yan Wu , Jing Wang , Tarek Raïssi , Vicenç Puig","doi":"10.1016/j.jprocont.2024.103341","DOIUrl":"10.1016/j.jprocont.2024.103341","url":null,"abstract":"<div><div>This paper proposes a fault detection strategy based on a two-step interval estimation method for T–S fuzzy systems with unmeasurable premise variables. First, an <span><math><msub><mrow><mi>L</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> observer is designed to achieve robust point estimation under Lipschitz conditions. Then, the estimated error bounds are analyzed and optimized using the <span><math><msub><mrow><mi>L</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> performance conditions to enable interval estimation. Furthermore, the residual threshold is derived from the interval estimation to achieve robust fault detection. Finally, an activated sludge process in a wastewater treatment is considered to validate the proposed method. Simulation results demonstrate that the proposed approach can provide more accurate state interval estimation and outperforms standard <span><math><msub><mrow><mi>L</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> observer design methods in addressing fault detection problems compared with existing methods.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"144 ","pages":"Article 103341"},"PeriodicalIF":3.3,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722584","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 robust optimization approach for steeling-continuous casting charge batch planning with uncertain slab weight 针对板坯重量不确定的炼钢-连铸装料批次规划的稳健优化方法

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-11-19 DOI: 10.1016/j.jprocont.2024.103338

Congxin Li , Liangliang Sun

{"title":"A robust optimization approach for steeling-continuous casting charge batch planning with uncertain slab weight","authors":"Congxin Li , Liangliang Sun","doi":"10.1016/j.jprocont.2024.103338","DOIUrl":"10.1016/j.jprocont.2024.103338","url":null,"abstract":"<div><div>The volatility of slab weight in steelmaking-continuous casting (SCC) production, attributed to factors such as flexible order demand, is addressed in this paper. A robust optimization mathematical model for charge batch planning (CBP) with uncertain slab weight is established, and a collaborative optimization method using the surrogate Lagrangian relaxation (SLR) framework and improved objective feasibility pump (IOFP) is developed to solve the problem. In the SLR method, new step-size updating conditions are developed, eliminating the need for pre-estimating the optimal dual value. Additionally, only a subset of subproblems that satisfy the optimality conditions of the surrogate needs to be solved to overcome the low optimization efficiency resulting from oscillations in the feasible domain during internal searches in traditional Lagrangian relaxation (LR) methods. The IOFP method is employed to match the structure of the subproblem model of 0–1 mixed integer programming (MIP). During the search for integer solutions, a weighted objective function is added to the auxiliary model to improve the quality of solutions. Furthermore, it combines a variable neighborhood branching method to prevent the algorithm from entering into cycles. Finally, the effectiveness of the proposed model and the performance of the algorithm are validated through simulation experiments.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"144 ","pages":"Article 103338"},"PeriodicalIF":3.3,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705161","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

Safe, visualizable reinforcement learning for process control with a warm-started actor network based on PI-control 利用基于 PI 控制的暖启动行为网络，为过程控制提供安全、可视化的强化学习

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-11-16 DOI: 10.1016/j.jprocont.2024.103340

Edward H. Bras, Tobias M. Louw, Steven M. Bradshaw

{"title":"Safe, visualizable reinforcement learning for process control with a warm-started actor network based on PI-control","authors":"Edward H. Bras, Tobias M. Louw, Steven M. Bradshaw","doi":"10.1016/j.jprocont.2024.103340","DOIUrl":"10.1016/j.jprocont.2024.103340","url":null,"abstract":"<div><div>The adoption of reinforcement learning (RL) in chemical process industries is currently hindered by the use of black-box models that cannot be easily visualized or interpreted as well as the challenge of balancing safe control with exploration. Clearly illustrating the similarities between classical control- and RL theory, as well as demonstrating the possibility of maintaining process safety under RL-based control, will go a long way towards bridging the gap between academic research and industry practice. In this work, a simple approach to the dynamic online adaptation of a non-linear control policy initialised using PI control through RL is introduced. The familiar PI controller is represented as a plane in the state-action space, where the states comprise the error and integral error, and the action is the control input. The plane was recreated using a neural network and this recreated plane served as a readily visualizable initial “warm-started” policy for the RL agent. The actor-critic algorithm was applied to adapt the policy non-linearly during interaction with the controlled process, thereby leveraging the flexibility of the neural network to improve performance. Inherently safe control during training is ensured by introducing a soft active region component in the actor neural network. Finally, the use of cold connections is proposed whereby the state space can be augmented at any stage of training (e.g., through the incorporation of measurements to facilitate feedforward control) while fully preserving the agent’s training progress to date. By ensuring controller safety, the proposed methods are applicable to the dynamic adaptation of any process where stable PI control is feasible at nominal initial conditions.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"144 ","pages":"Article 103340"},"PeriodicalIF":3.3,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664019","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

A unified GPR model based on transfer learning for SOH prediction of lithium-ion batteries 基于迁移学习的统一 GPR 模型用于锂离子电池的 SOH 预测

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-11-16 DOI: 10.1016/j.jprocont.2024.103337

Li Cai

{"title":"A unified GPR model based on transfer learning for SOH prediction of lithium-ion batteries","authors":"Li Cai","doi":"10.1016/j.jprocont.2024.103337","DOIUrl":"10.1016/j.jprocont.2024.103337","url":null,"abstract":"<div><div>State of health (SOH) acts as a qualitative capability measure in lithium-ion batteries’ management systems. Accurate SOH prediction is a critical issue for lithium-ion batteries. Most existing techniques always extract features from the tested batteries’ historical charging/discharging curves to achieve SOH prediction. However, the charging or discharging curves may be incomplete in the real-world application. Also, it is necessary to provide effective and dependable SOH predictions for both one-step-ahead and multi-step-ahead scenarios simultaneously, catering to diverse requirements. In order to achieve a unified SOH prediction without a prediction lag, a Gaussian process regression (GPR) model based on transfer learning is proposed. In this article, a non-zero mean function along with a compound covariance function are designed to describe the capacity attenuation. The hyper-parameter set of this model can be transferred and pre-determined from some readily available batteries in the same processes. The proposed method is verified on several batteries from NASA dataset. Results illustrate that our approach with both superior prediction performance and stronger robustness outperforms the counterparts.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"144 ","pages":"Article 103337"},"PeriodicalIF":3.3,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664020","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

Control of Production-Inventory systems of perennial crop seeds 控制多年生作物种子的生产-库存系统

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-11-10 DOI: 10.1016/j.jprocont.2024.103330

Robbert van der Kruk , René van de Molengraft , Herman Bruyninckx , Eldert J. van Henten

{"title":"Control of Production-Inventory systems of perennial crop seeds","authors":"Robbert van der Kruk , René van de Molengraft , Herman Bruyninckx , Eldert J. van Henten","doi":"10.1016/j.jprocont.2024.103330","DOIUrl":"10.1016/j.jprocont.2024.103330","url":null,"abstract":"<div><div>Production planning and inventory control are essential for the logistic performance of breeding companies. In this paper, we discuss such a system for perennial crop seeds in which production during multiple years and a number of growth cycles before production starts are characteristic. Large variations in yield and demand are typical and could easily lead to shortages or excess in seed stock. Both are costly phenomena. For these reasons, production planning as currently done by seed breeders without much technical support is extremely challenging. This paper describes and models the seed production process of a breeding company and examines its impact on inventory levels. The approach involves developing a time-discrete model parameterised with historical data. Subsequently, three control schemes are formulated: a classical feedback–feedforward PID controller, a feedback–feedforward PID controller with a Smith Predictor and a Model Predictive Control scheme. The goal of this paper is to present and validate a novel seed production–inventory model. Only aged plants are destroyed after a fixed number of production cycles. The ordering of new plants is the input control variable. The model represents the multi-year seed production of perennial crop seeds and expands upon the dead-time delay model, which typically does not account for production level uncertainty in production–inventory systems. The parameters of the model create a general approach; for both annual and perennial crop seeds.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"144 ","pages":"Article 103330"},"PeriodicalIF":3.3,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664018","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