Due to the inherent structural complexity, dynamic behavior, and strong nonlinearities of interconnected systems, conventional control and existing data-driven methods such as T–S fuzzy models and SINDy often struggle to simultaneously ensure modeling accuracy, structural interpretability, and real-time adaptability under dynamic operation modes. To address these limitations, this paper proposes an affine-structured performance-controllable neural network guided backstepping predictive control framework. Unlike conventional black-box data-driven models, the proposed approach embeds the affine nonlinear system structure into the neural network and adopts a Lyapunov-based training strategy, enabling both accurate dynamic approximation and explicit control law design. Meanwhile, a backstepping predictive control scheme is developed to effectively handle interconnection-induced coupling and multivariable constraints with reduced computational burden. Furthermore, a performance-controllable integrated neural network adaptive update method is introduced by reformulating model adaptation as a control problem, allowing near-real-time model updating using only finite data and guaranteeing stable and rapid convergence of prediction errors. Rigorous theoretical analysis and extensive experimental results demonstrate that the proposed method achieves superior control performance under dynamic operation modes.

The application of autonomous systems plays a crucial role in ensuring the safe and stable operation of industrial processes. Control methods grounded in artificial intelligence (AI) present new prospects for this application. However, the implementation of AI-based control methods typically depends on complete observation data. When unobservable states exist, it becomes challenging to achieve autonomous control. On the other hand, the process generally operates under dynamic working conditions, and the model needs to be updated in time to avoid model mismatch, which further increases the difficulty of autonomous control. To address these challenges, an autonomous control method based on a joint spatial-temporal model (AC-JSTM) is proposed to achieve autonomous control of unobservable point states of industrial processes under dynamic working conditions. Specifically, to solve the problem of model mismatch and poor control effect caused by the dynamic conditions of industrial processes, a condition identifier based on orthogonal test design (CI-OTD) is first proposed. The data set is constructed through typical condition parameter design, and the conditions are distinguished based on the correspondence between data distribution and condition labels. Then, considering the spatial distribution characteristics between unobservable and observable points and the dynamic correlation of observable points in the time dimension, a JSTM is established. The features of spatial and dynamic dimensions are integrated by a joint training method to achieve the prediction of unobservable points based on observable points. Finally, combining CI-OTD and JSTM, a dynamic working condition control (DWCC) framework is established to achieve autonomous control of unobservable points under dynamic working conditions. To verify the superiority and effectiveness of the proposed method, control experiments are designed for both catalytic rods and tubular reactors. The results show that the proposed method can achieve accurate control of unobservable points under dynamic conditions, and the control accuracy is improved by 18.69% compared with the control framework based on the spatial-temporal model.

Image data serve as a vital information carrier in industrial systems, but closed-set recognition methods falter in dynamic online environments with continually emerging classes. Online learning adapts rapidly but suffers from catastrophic forgetting, while continual learning alleviates forgetting through regularization yet depends on offline training, limiting adaptability to rapid task shifts. Resource-constrained edge devices demand real-time model updates, where streaming few-shot novel classes introduce feature bias, reducing class separability and accelerating model degradation. To this end, this work proposes the Biscale Storage and bilevel contrast-based continual feature evolution learning (Bi2CFEL) for industrial online recognition. By integrating biscale storage and bilevel contrast, Bi2CFEL efficiently memorizes and captures intraclass and interclass information, enabling accurate and robust image recognition in industrial online scenarios. First, spatio-temporal biscale guided storage strategy is innovatively proposed, constructing a representative and discriminative historical storage through class prototype distance screening criterion and prototype calibration mechanism. Second, to address overfitting risks in limited storage, distribution-aware memory reactivation method reactivates old-class memory based on stored sample distributions, effectively suppressing feature drift and forgetting. Finally, bilevel contrastive dictionary learning framework optimizes high-dimensional sparse feature spaces via instance-prototype cross-level contrast mechanisms, enhancing intraclass cohesion and interclass separation to decouple old and new class features. Experimental results demonstrate that Bi2CFEL achieves state-of-the-art performance in generalization capability, forgetting rate, and overall process performance.

Modern industrial processes are highly complex and dynamically evolving, with new modes emerging due to variations in raw materials, production environments, and other factors. Traditional monitoring methods often suffer from catastrophic forgetting during updates, where old knowledge is overridden. Although continual learning offers an effective solution, existing methods tend to overprotect historical knowledge, limiting the adaptability to new modes. To address the above problems, we propose orthogonal decoupled continual dictionary learning (ODCDL), which orthogonally decouples the dictionary space into stability and plasticity subspaces. The stability space preserves representations of historical modes to mitigate forgetting, while the plasticity space allows flexible updates for learning new modes. To balance the tradeoff between stability and plasticity, we impose dynamic constraints with varying strengths on the two subspaces. This design enhances both retention of old knowledge and learning of new knowledge, ensuring accurate monitoring across all operating conditions. Extensive experiments have demonstrated that the proposed method outperforms several state-of-the-art methods, exhibiting superior continual learning capability and process monitoring performance.

Modern industrial systems typically operate in a multimode environment due to continuous changes in raw materials and production schedules. Traditional static monitoring methods often struggle to accurately capture the intricate relationships between different modes, leading to model mismatches and a significant decline in monitoring performance. On the other hand, emergence of a new mode often prompts model updates that focus on the new data, potentially causing the model to forget historical knowledge, which is a phenomenon known as catastrophic forgetting. To address these issues, we propose a zero forgetting lifelong dictionary learning (ZFLDL) method for multimode process monitoring. ZFLDL integrates a low-rank basis matrix growth mechanism with an adaptive basis matrix selector, enabling effective representation of knowledge across both new and historical modes. Specifically, a novel low-rank decomposition method is first proposed to address feature redundancy in data representation with an overcomplete dictionary. This method utilizes a low-rank matrix, i.e. a “basis matrix”, as the fundamental representation unit, which can accurately capture the core data features and enhance the dictionary's representation capability. Then, to address the emergence of new modes, a zero forgetting continual learning framework based on low-rank basis matrix growth is proposed. By adaptively adding basis matrices for each new mode while freezing those associated with historical modes, the dictionary efficiently assimilates new knowledge while ensuring zero forgetting of historical knowledge. Additionally, since only the basis matrices related to the new mode are updated, this approach significantly reduces the number of parameters requiring training, thus enhancing the model's learning efficiency. Finally, an adaptive basis matrix selector is proposed. By evaluating the contribution of each basis matrix to different modes, it dynamically selects the basis matrices that best match the specific mode, ensuring accurate mode recognition and reliable process monitoring. Extensive experiments have demonstrated that the proposed method outperforms several state-of-the-art methods, exhibiting superior continual learning capability and enhanced process monitoring performance.

In response to the challenge of strongly nonlinear and multimode systems control, this paper introduces a weighted deep learning based adaptive predictive control method. This approach integrates LSTM networks for different operating modes using a set of weighting coefficients. These coefficients are dynamically updated during online control via an error-guided scheduling strategy to adapt to changing operation modes. Compared to offline identification based methods, the proposed method eliminates the need for mode recognition or model switching strategies and can adapt to drifted operation modes. In contrast to online methods, it achieves rapid model convergence and reduced computational cost, requiring only minimal data to update the weighting coefficients without necessitating the retraining of the LSTM networks. Theoretical convergence and stability analysis ensure the reliability of the proposed method. Numerical simulations and industrial control experiments demonstrate that the proposed approach exhibits favorable control performance across both known and drifted operation modes. Note to Practitioners—Considering the changing operation modes in complex industrial processes and the detrimental effect of slow or unstable control during system operation, this paper proposes a weighted LSTM based predictive control method for strongly nonlinear and multimode systems. Extensive experiments demonstrate that compared to other state-of-the-art methods, this method can rapidly adapt to changes in operating modes with a small amount of data, meeting both real-time and stability requirements for online control.

Modern industrial processes typically operate under variable conditions due to fluctuations in raw materials and production loads. Consequently, accurate monitoring across multiple operational modes is essential for process optimization. Although some global modeling based methods have been proposed, they struggle to adapt to the continual emergence of new modes. On the other hand, online updating monitoring methods often suffer from “catastrophic forgetting”, i.e., they lose the ability to represent historical modes. To address these challenges, this article proposes a multimode process monitoring method based on Incremental Rank Continual Dictionary Learning (IRCDL). The method integrates the incremental learning framework based on low-rank matrix augmentation with the adaptive weight selector, allowing the dictionary to retain the knowledge of both historical and new modes. Specifically, to address the issue of information redundancy in overcomplete dictionaries, an innovative dictionary construction method based on low-rank matrices weighting is proposed. Using low-rank matrices as the fundamental units for dictionary construction, this method accurately captures the intrinsic features of different modes while significantly reducing redundant parameters. Then, to handle the continual emergence of new modes, an incremental learning framework based on low-rank matrix augmentation is developed. This framework continually adds new low-rank matrices to learn representations of new modes, while keeping the historical low-rank matrices intact to maintain “zero-forgetting” of historical modes, thereby enabling accurate monitoring of multimode processes. Finally, an adaptive weight selector is designed to automatically optimize the weights of low-rank matrices, thereby enhancing the representation of different modes and significantly improving the performance of multimode monitoring. Extensive experiments have demonstrated that the proposed method achieves state-of-the-art continual learning capabilities (i.e., “zero forgetting”) and exceptional process monitoring performance.

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.

During the operation of a distributed parameter system (DPS), its working conditions typically undergo dynamic changes. Although online learning methods can enable models to adapt to new working conditions to some extent, they often confront the “catastrophic forgetting” problem, where the updated model forgets historical working conditions. On the other hand, only a few new samples can be collected during online operation, and the sparse samples make it difficult to establish accurate models for new working conditions. Therefore, achieving precise control under full working conditions remains a challenging problem. To address these challenges, this paper proposes an adaptive predictive control method based on continuous learning that achieves stable control under full working conditions by continuously identifying working conditions and triggering adaptive model updates in real time. Specifically, a spatial-temporal feature-based working condition identification method is first proposed to identify changes in working conditions automatically. Then, to address the challenge of limited data for model updating, a parameter transfer method is proposed. Simultaneously, to ensure that the updated model retains the ability to characterize historical working conditions, an Elastic Weight Consolidation (EWC) constraint is incorporated into the loss function, thus overcoming the catastrophic forgetting problem, and ensuring the updated model can represent both the historical and new working conditions. Finally, by incorporating this condition identification mechanism and adaptive predictive model into the model predictive control framework, continuous precise control of DPS can be achieved. To demonstrate the superiority of the proposed method, extensive experiments are designed. Experimental results show that the proposed method can accurately identify new working conditions and learn new condition models with only a few online samples while overcoming model mismatch of historical conditions, ultimately achieving high-precision control under full working conditions.Note to Practitioners—Motivated by the fact that DPS often operates in different conditions and online learning methods confront “catastrophic forgetting” and “sparse sample training” problems, this paper proposes an adaptive learning control method. The proposed method can learn new working condition features with small online samples while retaining the ability to represent historical working conditions, and thus achieves accurate control under full working conditions.

Complex systems are frequently influenced by uncertain factors, making it difficult for traditional fixed-model control schemes to achieve high-precision control. Data-driven control methods offer a solution, but they face challenges in constructing accurate models due to insufficient excitation in operational data. Moreover, mismatches between historical models and new conditions coupled with limited data accumulation under new conditions reduces the operational performance throughout the entire process. To address these issues, this paper proposes a generalized integrated fuzzy model predictive control (GIF-MPC) framework. It combines the generalization capability of fuzzy control with the precision of model predictive control to ensure highprecision control under all conditions. Specifically, a strategy switching mechanism, triggered by a mismatch characteristic parameter is first proposed, which transitions the original strategy to a fuzzy-driven excitation control method, thereby mitigating the control performance degradation caused by the mismatch between control strategies and complex systems. Then, a fuzzy control feature extraction method is proposed to balance fuzzy set activation and improve adaptability to unknown conditions. Additionally, an optimal input excitation design method is proposed to tackle insufficient data excitation, enabling effective control. Once sufficient data is accumulated, the model switches to model predictive control. The dual decision mechanism guided by the data information and triggered by the mismatch characteristic parameter effectively ensures high precision control under uncertainties. Numerical experiments demonstrate that the GIF-MPC method ensures high-precision control throughout disturbances and condition changes. The solution is also successfully deployed in an industrial setting, validating its excellent control performance under full operation conditions.

Maintaining an optimal alumina concentration distribution is crucial for ensuring stable operation and reducing energy consumption in aluminum electrolysis cells. Due to the strong coupling of multiple physical fields within the electrolysis cell, the rapid and accurate prediction of alumina concentration distributions has long been a formidable challenge. Furthermore, the harsh industrial environment only allows for the acquisition of alumina concentration data at limited locations, hindering the construction of precise predictive models for the alumina concentration distribution. Moreover, the presence of measurable disturbances, such as feeding, can exert additional influences on the alumina concentration, necessitating traditional methods to collect new data and reconstruct models. To address these issues, this article proposes a structural process knowledge-informed neural network for alumina concentration distribution prediction with desirable precision and high solving efficiency. Specifically, considering the alumina concentration distribution is primarily driven by electrolyte convection and secondarily by electrolysis consumption, pretraining modules for flow and concentration field are designed to explicitly modify the network structure, thereby strengthening the relationship between inputs and process state variables, which significantly enhance prediction efficiency and accuracy. Subsequently, a loss function incorporating process governing equations is designed, which effectively constrains the relationship between inputs and process state variables to adhere to physical principles. This enables accurate concentration prediction at any location in the field, even with a limited number of spatial training data. Finally, a training method for alumina concentration prediction under varying initial concentrations is introduced, incorporating the initial concentration and a fixed time step into the network inputs, thereby eliminating the need for traditional methods to reconstruct models for continuous predictions under different initial conditions. Extensive experiments demonstrate that our method achieves significant performance improvements compared to some state-of-the-art methods.

With the rapid development of modern industry and the increasing prominence of artificial intelligence, data-driven process monitoring methods have gained significant popularity in industrial systems. Traditional static monitoring models struggle to represent the new modes that arise in industrial production processes due to changes in production environments and operating conditions. Retraining these models to address the changes often leads to high computational complexity. To address this issue, we propose a multimode process monitoring method based on element-aware lifelong dictionary learning (EaLDL). This method initially treats dictionary elements as fundamental units and measures the global importance of dictionary elements from the perspective of the multimode global learning process. Subsequently, to ensure that the dictionary can represent new modes without losing the representation capability of historical modes during the updating process, we construct a novel surrogate loss to impose constraints on the update of dictionary elements. This constraint enables the continuous updating of the dictionary learning (DL) method to accommodate new modes without compromising the representation of previous modes. Finally, to evaluate the effectiveness of the proposed method, we perform comprehensive experiments on numerical simulations as well as an industrial process. A comparison is made with several advanced process monitoring methods to assess its performance. Experimental results demonstrate that our proposed method achieves a favorable balance between learning new modes and retaining the memory of historical modes. Moreover, the proposed method exhibits insensitivity to initial points, delivering satisfactory results under various initial conditions.

Complex industrial processes often run under varying operating conditions. Learning-based control methods are difficult to adapt to these unknown variations. Therefore, it is necessary to update the model and control strategy adaptively. However, in traditional control frameworks, due to the limitation of computational and storage resources of edge devices, control strategies are difficult to update once deployed, which leads to model mismatch after operating condition change and seriously reduces the control performance. To solve this problem, this article proposes a novel cloud-edge collaborative control method. Specifically, a cloud-assisted parallel subspace identification method is proposed, which fully utilizes the powerful computational capability of the distributed cluster in the cloud to achieve fast and accurate model identification. Then, an explicit control strategy is proposed, which solves the control law as a piece-wise affine function offline. The process model and explicit control law are sent down to the edge, enabling fast and precise control under limited resource constraints. An operating condition change detection method based on the process model is proposed, and the edge detects the emergence of new operating conditions by the prediction error. Meanwhile, to fully excite new operating condition characteristics, a joint control and excitation signal generator (JCESG) is designed. JCESG ensures accurate identification of new operating condition model under limited data, which in turn greatly shortens the operation condition switching process and ensures fast modeling and precise control in new operating conditions. Notably, considering that the proposed method can adaptively realize model identification and control law update, it is capable of adapting to the continuous change of operating conditions, and the sufficient excitation of JCESG greatly reduces the data volume requirement for model update, which further ensures that the method adapts to the full range of operating conditions. Finally, extensive experiments verified the superiority of the proposed method.

With the development of information technologies, a large number of sensors have been deployed to obtain industrial data. As a result, data-driven approaches have become a crucial means for modeling of distributed parameter systems (DPS). However, due to harsh environments and unreliable sensors, data is often of low quality in practice, which in turn poses a challenge for data-driven modeling approaches. In order to address the challenge of inaccurate modeling of DPS induced by outliers, this paper proposes a physical-informed sparse learning method to overcome the adverse effects of outliers and achieve robust modeling of DPS by fully exploring the spatiotemporal dynamic of DPS. Specifically, this paper proposes an innovative method for robust modeling of DPS. The method incorporates the statistical features of contaminated data to restore the dynamic evolution structure of DPS, which weakens the adverse effects of outliers and address the problem of low modeling accuracy caused by contaminated observation data. Furthermore, the underlying partial differential equation (PDE) of DPS is incorporated into the constraint on the temporal deviation data, which leads to a physical-informed optimization objective and improves the reliability of outlier extraction and DPS modeling. Finally, an optimization algorithm based on the alternating direction method of multipliers (ADMM) with an adaptive penalty factor is proposed. This ensures the convergence of multivariate optimization problem and superior performance of the DPS modeling framework. Extensive experimental results have verified that the proposed method is effective in overcoming the adverse effects of outliers and achieving robust modeling of DPS.Note to Practitioners—The motivation of this paper is to develop an interpretable and robust modeling method for DPS. Considering the negative impact of outliers, the proposed method first restores the dynamic properties of the data based on the characteristics of the outliers. Then, the embedding of physical knowledge ensures the reliability of outlier removal and robustness of modeling. Extensive experimental results have verified that the proposed method can effectively overcome the adverse effects of outliers and outperform some state-of-the-art methods. Therefore, it is more suitable for real industrial systems.

Nowadays, the intelligent fault diagnosis problem of modern industrial systems has received increasing attention. However, with the increasing scale of industrial systems, the same category of faulty data often contains multiple working conditions, which leads to the multi-granularity of faulty data and the increasing complexity of data distribution. The possible existence of fine-granularity inter-class similarities in multi-granularity data can interfere with coarse-granularity recognition tasks, which brings challenges to data-driven fault diagnosis tasks. To solve the above issue, a fault diagnosis method based on multi-granularity dictionary learning is proposed in this paper. Specifically, the proposed method integrates prior knowledge into the dictionary learning framework by introducing the concept of “centroid atom”, which embeds the multi-granularity structure in the dictionary and improves the discrimination of dictionary atoms. In order to validate the proposed approach, experiments are conducted on a numerical simulation case and a zinc smelting roasting process. Compared with some state-of-the-art methods, the proposed approach performs better in the classification accuracy which shows that it can make full use of the knowledge provided by the multi-granularity structure while it is difficult to be modeled by the other methods. Therefore, it can be applied to fault diagnosis tasks more precisely and efficiently. Note to Practitioners—Motivated by the fact that industrial faults often occur in different modes, and different faults may occur in the same mode, the fault data often has multi-granularity characteristics, this paper proposes a multi-granularity dictionary learning method for complex industrial fault diagnosis. The proposed method can learn the multi-granularity structure in the data, and the obtained model contains both coarse-grained and fine-grained information, which effectively improves the fault diagnosis accuracy. Intensive experimental results show that the proposed method performs better than some state-of-the-art methods, it can make full use of the knowledge provided by the multi-granularity structure, while it is difficult to be modeled by others. Above all, it is verified as suitable for fault diagnosis of real industrial systems.

Modern industrial processes often exhibit complex and uncertain operating state fluctuations due to the diversification of production materials, the complexity of production processes, and the harsh production environment. To cope with the operating state fluctuations of industrial processes, fuzzy neural networks, as a form of human-inspired computing, have been introduced and widely applied. In contrast, model predictive control (MPC) typically employs a multi-model control strategy for the multimode process. However, ensuring control performance during the switching phase and meeting real-time control requirements poses challenges. To address the real-time control challenge in multimode processes, this article proposes a learning framework for fuzzy neural network explicit control for full operation conditions. The proposed framework includes two modules: the fuzzy neural network-based explicit control law (FNNECL) and the neural network-based predictive model (NNPM). By adjusting the structure and parameters of FNNECL, precise control of full operation conditions is achieved. Specifically, a novel truncated radial basis activation function is first presented, and the operation condition change measurement index of data coverage is proposed to identify the mismatch degree between current and historical operation conditions accurately. Then, for small-scale operation condition changes, an elastic weight consolidation (EWC) mechanism is introduced to ensure that FNNECL can learn control strategies for new operation conditions while maintaining control performance for historical operation conditions. Finally, to address large-scale operation condition changes, a radial basis function (RBF) neuron growth mechanism based on data coverage is proposed. This mechanism calculates the data coverage of fuzzy rules and selectively adds new neurons to the fixed structure FNNECL. This enables the explicit control law to fit large-scale changes and effectively learn control strategies for new operation conditions. It is worth noting that during the application of this method, there is no need to know the current operation conditions in advance. Based on a single FNNECL model, accurate control sequences can be rapidly obtained, overcoming the traditional online optimization problem of multiple models. This greatly expands the application scope and achieves precise control under full operation conditions. Extensive experiments including numerical simulation and industrial roasting process verified the feasibility and effectiveness of the proposed method.

With the digital transformation of process manufacturing, identifying the system model from process data and then applying to predictive control has become the most dominant approach in process control. However, the controlled plant often operates under changing operating conditions. What is more, there are often unknown operating conditions such as first appearance operating conditions, which make traditional predictive control methods based on identified model difficult to adapt to changing operating conditions. Moreover, the control accuracy is low during operating condition switching. To solve these problems, this article proposes an error-triggered adaptive sparse identification for predictive control (ETASI4PC) method. Specifically, an initial model is established based on sparse identification. Then, a prediction error-triggered mechanism is proposed to monitor operating condition changes in real time. Next, the previously identified model is updated with the fewest modifications by identifying parameter change, structural change, and combination of changes in the dynamical equations, thus achieving precise control to multiple operating conditions. Considering the problem of low control accuracy during the operating condition switching, a novel elastic feedback correction strategy is proposed to significantly improve the control accuracy in the transition period and ensure accurate control under full operating conditions. To verify the superiority of the proposed method, a numerical simulation case and a continuous stirred tank reactor (CSTR) case are designed. Compared with some state-of-the-art methods, the proposed method can rapidly adapt to frequent changes in operating conditions, and it can achieve real-time control effects even for unknown operating conditions such as first appearance operating conditions.

Multimode process monitoring plays a significant role in ensuring the stable operation of industrial processes under changing conditions. Due to the continuous emergence of new modes, some adaptive model updating methods are proposed. However, the updated model may forget important features learned in previous modes, thus reducing the monitoring performance. To address this problem, this article proposes a global information-based lifelong dictionary learning (GI-LDL) method for multimode process monitoring. Specifically, this article adopts dictionary atoms as the fundamental units for representing process data and proposes a method for measuring the importance of dictionary atoms based on global information. Then, to ensure that the dictionary retains its representational ability for both new and historical mode data during the mode updating process, a surrogate quadratic loss considering the importance is further proposed to penalize changes of important atoms. Compared with dictionary constraints, finer-grained atomic constraints ensure that the dictionary preserves important features of previous modes while learning features of new modes. Finally, considering that the number of modes in multimode industrial processes is often unknown in advance, this article explicitly derives analytical solutions for dictionary updating, thus it is capable of accommodating ever-increasing modes in real industrial processes. To verify the effectiveness and advancement of the proposed method, extensive experiments are elaborately designed, and experimental results indicate that the proposed method has precise monitoring capabilities for both historical and new modes.

Modern industrial process control suffers from various difficulties, such as multivariable, multiconstrained, multiobjective, and strong nonlinearity. Model predictive control (MPC) is an effective solution and is widely used in industrial processes. However, one limitation of MPC is that sufficient data are required to build accurate predictive models. To this end, this article proposes a knowledge-informed neural network MPC solution. First, a Hammerstein system structure knowledge extraction method based on sparse representation is proposed, which is able to extract system structure knowledge from a small amount of system operation data. Then, a knowledge-informed neural network model is designed, which combines the system structure knowledge to construct a neural network with a special structure, thus overcoming the problem of insufficient data during the model training. Finally, the knowledge-informed neural network model is embedded in the MPC framework, which can reduce the computational cost of rolling optimization while ensuring prediction performance. A numerical simulation and a pH neutralization process experiment are conducted to verify the feasibility and effectiveness of the proposed method.

Accurate process monitoring plays a crucial role in thermal power plants since it constitutes large-scale industrial equipment and its production safety is of great significance. Therefore, accurate process monitoring is very important for thermal power plants. The vigorous nature of the production process requires dynamic algorithms for monitoring. Since the common dynamic algorithm is mainly based on data expansion, the online computing complexity is too high because of data redundancy. Accordingly, this article proposes an innovative, dynamic process monitoring algorithm called autocorrelation feature analysis (AFA). AFA mines the dynamic information of continuous samples by calculating the correlation between the current time and past time features. While improving the monitoring effect, the AFA algorithm also has extremely low online computational complexity, even lower than common static algorithms, such as principal component analysis. Furthermore, this study exhibits the general form of dynamic additive faults for the first time and verifies the reliability of the algorithm through fault detectability analysis. Conclusively, the superiority of the AFA algorithm is verified on a numerical example, continuous stirred tank reactor (CSTR), and real data measured from a 1000-MW ultrasupercritical thermal power plant.

Process monitoring, a typical application of industrial Internet of Things (IIOT), is crucial to ensure the reliable operation of the industrial system. In practice, due to the harsh environment and unreliable sensors and actuators, it is often difficult for IIoT to collect enough tagged and highly reliable data, which further degrades the process monitoring performance and makes the monitoring results not trustworthy. In order to reduce the negative impact of these unreliable factors, a self-weighted dictionary learning process monitoring method is proposed. In particular, a label propagation classifier is implemented from the labeled data to unlabeled data to obtain a credible label prediction. Subsequently, considering the interference of low-quality data and label information, we reweight the classification loss and label-consistency constraints to enhance the trustworthiness of feature extraction. Finally, a novel iterative optimization algorithm that combines the block coordinate descent method with the alternating direction multiplier method is developed to ensure the convergence speed of the learned classifier and dictionary. Extensive experiments indicate that the proposed method can guarantee the trustworthiness of the process monitoring results.

Actual industrial processes often show nonstationary characteristics, so nonstationary process monitoring is significant to ensure the safety and reliability of industrial processes. However, existing monitoring methods for nonstationary processes usually ignore process uncertainties, caused by random noises and unknown disturbances. It is worth noting that process uncertainties may degrade the monitoring performance for incipient faults, and result in over-fitting of model parameters. To address the problem of monitoring nonstationary industrial processes with uncertainty, a novel algorithm called probabilistic stationary subspace analysis (PSSA) is proposed in this article. PSSA explicitly models process uncertainties, and distinguishes actual process variations from the uncertainty. In view of the coupling between model parameters, the expectation maximization algorithm is used to estimate the parameters of PSSA, and the closed-form updates are derived in detail. Based on PSSA, two detection statistics are designed for process monitoring. Finally, the effective performance of the proposed method is demonstrated by three case studies, including a numerical example, a closed-loop continuous stirred tank reactor, and a real power plant at Zhejiang Provincial Energy Group of China.

Operation safety and efficiency are two main concerns in power plants. It is important to detect the anomalies in power plants, and further judge whether they affect key performance indicators (KPIs), such as the thermal efficiency. These two goals can be achieved by KPI-related nonstationary process monitoring. Although the thermal efficiency cannot be accurately measured online, it can be strongly characterized by some online measurable variables, including the exhaust gas temperature and oxygen content of flue gas. These critical variables closely related to the thermal efficiency are termed as output variables. Inspired from nonstationary common trends between input and output variables in thermal power plants, the output-relevant common trend analysis (OCTA) method is proposed, in this article, to model the input–output relationship. In OCTA, input and output variables are decomposed into nonstationary common trends and stationary residuals, and the model parameters are estimated by solving an optimization problem. It is pointed out that OCTA is a generalized form of partial least squares (PLS). The superior monitoring performance of OCTA is illustrated by case studies on a real power plant in Zhejiang Provincial Energy Group of China. Compared with the other PLS-based recursive algorithms, OCTA can effectively detect the anomalies in power plants and accurately determine whether they have an impact on the thermal efficiency or not.