This article investigates the optimal operational control (OOC) problem for a class of industrial systems consisting of multiple unit devices with fast dynamics and an unknown operational process ...with slow dynamics. First, the OOC problem is formulated as a noncascade optimal control problem of two-time-scale systems with a novel performance function. Second, using singular perturbation theory, a decentralized composite control scheme is proposed by decomposing the original optimal problem into reduced-order fast and slow subsystem problems. Then, in the framework of reinforcement learning, an online controller design method for the slow subsystem is proposed by using the online measurement, and an offline controller design for the fast subsystem is proposed by using the unit device models. The obtained decentralized composite optimal controller achieves both the desired operational index tracking and disturbance rejection without requiring the dynamics of the operational process. Different from the existing cascade design methods, the proposed approach regulates the unit devices and operational process simultaneously, as well as overcomes the potential high dimensionality and ill-conditioned numerical issues. Finally, a mixed separation thickening process and a numerical example are given to illustrate the presented results.
Sensors are ubiquitous in automatized industrial systems. To ensure the safety of the process control, the fault diagnosis and fault-tolerant control of sensors is necessary. This article proposes ...subspace-aided sensor fault diagnosis and compensation control approaches based on the data-driven stable kernel representation (SKR) and stable image representation (SIR) identified by the process data decompositions. First, this article obtains data-driven SKR and SIR through the mapping relationship of the subspaces of signals and proposes a series of fault diagnosis and compensation approaches. Furthermore, considering the accuracy and timeliness, this article presents an accurate online fault diagnosis and compensation approach by the online updating <inline-formula><tex-math notation="LaTeX">LQ</tex-math></inline-formula> decomposition. These approaches can perform fault diagnosis, fault estimation, and fault compensation for the multiple and different types of additive sensor faults. The effectiveness of the strategies has been verified by the numerical study and the three-tank experimental system, which has a specific engineering significance.
To characterize the traditional systems of small pig producers in Jipijapa (Manabí, Ecuador) and to classify farms into representative categories, we interviewed fifty-five farmers from seven ...communities considering four dimensions: social, organizational, production methods, and local food resources. Multiple correspondence analyses and hierarchical clusters were carried out using the Ward method. The analysis differentiated communities based on social, productive, and local resource variables, showing three factors that accounted for 85.3% of the total variance: the socioeconomic dimension, related to the welfare of families, explained 34.4% of the variation, the care provided to animals explained 30.9%, and the management practices for the supply of food explained 20%. We identified five clusters that shared common characteristics: Group 1 included farmers from Albajacal, wage workers, and Creole pig breeders, Group 2 included farmers raising pigs under lockdown conditions, Group 3 typified traditional farms from the La Cuesta community, Group 4 included landowners, and Group 5 included professionalized farmers in Colón Alfaro. We also studied the supplied alternative food formulations made up of crop surpluses. The role of small pig farmers is a social activity linked to the location, the crops of each area, and the specific practices for the care of animals.
In industrial areas, understanding the preference of customers is one of the important considerations for establishing profitable product manufacturing plans. As one of the approaches in pattern ...mining, high utility pattern mining has been employed to find a set of products creating high profits by considering the purchase quantity and price of each product. In this regard, high utility pattern mining can be useful to establish profitable product manufacturing plans that allow a corporation to maximize its revenue. For establishing manufacturing plans, we also need to understand the recent preference of customers from stream data, which are continually generated without limitations. In this paper, we propose a novel algorithm and list structure for finding high utility patterns over data streams on the basis of a sliding window mode. Unlike existing algorithms, the proposed algorithm does not consume huge computational resources for verifying candidate patterns because it can avoid the generation of candidate patterns. Therefore, the algorithm efficiently works in complex dynamic systems. Experimental results obtained from various tests using real-world dataset show that the proposed algorithm outperforms state-of-the-art methods in terms of runtime, memory usage, and scalability.
Optimizing maintenance procedures is essential in today's industrial settings to reduce downtime and increase operational effectiveness. To improve predictive maintenance in industrial settings, this ...article investigates the combination of machine learning (ML) techniques and the Industrial Internet of Things (IIoT). The goal of this research is to advance predictive maintenance in industrial settings by integrating ML with IIoT in a seamless manner. Addressing the complexities of industrial systems and limitations of traditional maintenance methods, this study presents a methodology leveraging four distinct ML models. The technique includes a thorough assessment of these models' correctness, revealing differences that highlight the significance of a careful model selection procedure. The current investigation analysis finds the most effective model for predictive maintenance activities using thorough data analysis and visualization. Our work offers a potential path forward for the industrial sector and provides insights into the complex interactions between IIoT and ML. This study lays the groundwork for future developments in predictive maintenance, which will reduce downtime and extend the life of industrial equipment.
Industrial systems often undergo dynamic changes during operation, which presents challenges for traditional identification and control methods. These challenges arise in two aspects: variations in ...model structure and parameters, and differences in control objectives across diverse operating conditions. Traditional static predictive control methods face challenges in meeting the high-precision, real-time requirements in practice. In addition, control schemes with fixed parameters encounter difficulties in adapting to varying control objectives, resulting in suboptimal control performance. To address these problems, this article proposes a dynamic error-triggered adaptive control method, which can identify the operating conditions and objectives in real-time. Specifically, a dynamic error-triggered model updating mechanism is first established to detect changes in operating conditions and update the prediction model. To overcome the model mismatch during the transition process, a novel enhanced transition control (ETC) method is proposed, which designs a transition error corrector to decline prediction error and a high informative pseudo-random binary sequence (HIPRBS) input to enhance the excitation level. Considering the differences in control objectives under varying operating conditions, a fuzzy weight-adaptive method is proposed to balance heterogeneous indicators in different conditions. Two types of systems, high-speed and high-stability, are designed to validate the superiority of the proposed method. Extensive experimental results demonstrate that, compared to some state-of-the-art methods, the proposed method can efficiently and accurately identify emerging operating conditions, dynamically adjust optimization objectives, and achieve real-time control effects under varying operation conditions. Note to Practitioners -The motivation of this paper is to develop a high-precision and real-time control method for industrial systems that operate under frequently changing conditions. The proposed method can adapt to changes in model parameters and control objectives in multiple operating conditions processes. Compared with some state-of-the-art methods, this method significantly enhances the control performance in the transition process of mode switching, meeting the long-term stable operation requirements of industrial systems.
The aim of this study is to investigate the body of literature on digital twins, exploring, in particular, their role in enabling smart industrial systems. This review adopts a dynamic and ...quantitative bibliometric method including works citations, keywords co-occurrence networks, and keywords burst detection with the aim of clarifying the main contributions to this research area and highlighting prevalent topics and trends over time. The analysis performed on citations traces the backbone of contributions to the topic, visible within the main path. Keywords co-occurrence networks depict the prevalent issues addressed, tools implemented, and application areas. The burst detection completes the analysis identifying the trends and most recent research areas characterizing research on the digital twin topic.
Decision-making, process design, and life cycle as well as the enabling role in the adoption of the latest industrial paradigms emerge as the prevalent issues addressed by the body of literature on digital twins. In particular, the up-to-date issues of real-time systems and industry 4.0 technologies, closely related to the concept of smart industrial systems, characterize the latest research trajectories identified in the literature on digital twins. In this context, the digital twin can find new opportunities for application in manufacturing, control, and services.
•Life cycle sustainability index under uncertainties was developed.•Data uncertainties were incorporated in this sustainability ranking method.•Ambiguity and vagueness in human's judgments can be ...addressed.•Sustainability ranking of industrial systems under uncertainties can be achieved.
This study aims at developing a generic method for measuring the sustainability of industrial systems and prioritizing industrial systems under uncertainties. The interval preference relation based goal programming model which can address vagueness and ambiguity existing in human's judgments was employed to determine the weights of the criteria for life cycle sustainability assessment. A life cycle aggregated sustainability index which incorporates both the data of industrial systems with respect to the evaluation criteria and the weights of the criteria was developed to prioritize the industrial systems. An illustrative case including four electricity generation systems were studied by the proposed method, and the results were also validated by another four multi-criteria decision making methods. The results reveal that the developed life cycle aggregated sustainability index can effectively prioritizing industrial systems under data uncertainties.
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Energy efficiency improvement of industrial systems through the application of demand side management (DSM) techniques is discussed. In particular, a unified classification of efficiency of energy ...systems, namely performance efficiency, operation efficiency, equipment efficiency and technology efficiency (POET), is reviewed and further discussed to facilitate effective use of DSM methods in a selection of energy-intensive industrial processes. The operational level efficiency improvement is then focused on and the corresponding modelling and control by model predictive control (MPC) approach are presented. The modelling process is generalised to cater for a number of industrial processes. Robustness and convergence of MPC method when applied to periodic industrial processes are elaborated. The relationship between control and the POET is outlined thereafter to link the two such that one can make use of the POET concept to guide the controller design. Finally, case studies are provided to demonstrate the effectiveness of the approaches presented.
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