The process industry is a continuous manufacturing system that comprises intricate physical and chemical reactions. Given the increasing constraints on resources and energy, it is urgent to optimize process indicators by maintaining an efficient reaction atmosphere. Reinforcement learning (RL), using trial and error to learn control strategies, has become a topic of interest in the control community. However, practical implementation reveals that the mapping between observed state variables and the reaction atmosphere is subject to uncertain disturbances, which seriously affect the reliability of process indicator control. To address these issues, a robust RL (RRL) control method based on knowledge-constrained adversarial perturbation is proposed. It applies the adversary to perturb the observed state to characterize the uncertain disturbance. First, the insight of composite modeling for the process industry is presented to factorize the inherent and external uncertainties. Based on this insight, a reaction atmosphere indicator surrogate model is built to quantify the inherent uncertainty. Second, by leveraging the variation boundary information of the surrogate model, a dynamic state perturbation set and its update policy are proposed to ensure the rationality of the state perturbation. Last, an external uncertain time series generation method with continuity constraints is proposed to incorporate reasonable external uncertainty in the training process. Case validation in zinc electrowinning demonstrates that the proposed method effectively enhances control performance in uncertain scenarios.

Systematical measurement errors in industrial sensors should be avoided or rectified, as severe discrepancies in data can impede control, operation, and evaluation. However, some systematical errors are hard to estimate or experiment with. These errors often arise due to the change in external conditions. This means the measurement errors can be ignored when the external conditions remain within the designed scope. Generally, the relationship between the external condition and the systematical error is of complex nonlinearity, which may not be analytically tractable so rectifying the error is challenging. Nonetheless, a general assumption can be made in typical industrial scenarios: most external conditions fall within the design scope so that the corresponding systematical errors are 0. This assumption holds because the sensor is generally installed and calibrated under the most common operating mode. Based on these rationales, we first propose an intermediate sample-enhanced clustering strategy to identify the majority pattern, aiding in figuring out the zero systematical measurement error points. Then, leveraging the zero systematical measurement error information and partly known labels, a semi-supervised learning method is employed for estimating the complex nonlinear mapping from the external condition and the measurement, thereby rectifying the errors. The effectiveness of our approach is demonstrated through the rectification of a density meter in a real industrial aluminum oxide process, validated by the comparison with the laboratory assay outcomes. Note to Practitioners—Measurement instruments in industrial systems are designed to meet the precision requirement under specific conditions. Once the condition x is out of scope, the measurement y will be accompanied by the systematical measurement errors which is the function of x, denoted by$f(x)$. Thus the measurement model is$ y=y_{t}+f(x)+e$, where$y_{t}$is the true value and e is the random measurement error. However, for many scenarios, the errors are complex.$f(x)$is nonlinear and even intractable. From the perspective of engineering practice, the condition x in most of the running period should remain within the design scope to ensure normal use. However, it is also common for the running status to drift from the original designed working points over time. Then, an intolerant systematical measurement error occurs, causing the instrument which can be expensive lose their function. To estimate the systematical measurement error and implement the rectification of the deviated measurement, this paper proposes a data-driven strategy when the condition x is measurable and part of the label for y is available. We demonstrated the effectiveness of the strategy using a real industrial application example.

Precise control of roasting temperature is paramount for optimizing production efficiency in the zinc smelting process. However, existing research mainly focuses on average temperature control, and there is little research on temperature distribution control. To achieve this, a roasting temperature distribution model is first established based on the principles of heat transfer. Second, accounting for modeling errors and environmental disturbances, a discrete sliding mode control with disturbance compensation is proposed. Besides, continuous reaching law is implemented to address issues related to chattering, so as to ensure stable roasting temperature. Finally, the quasi-sliding-mode domain of the proposed method is obtained by boundary analysis. The simulation results of roasting temperature distribution control substantiate the efficacy of the proposed approach.Note to Practitioners—Roasting temperature is the most critical temperature that directly determines product quality and stable production during the roasting process. Currently popular schemes all use average temperature as the control target. However, the average temperature does not represent the actual temperature inside the roaster. This paper aims to achieve the temperature distribution of the roaster, thereby ultimately improving product quality and ensuring safe production. This paper proposes a roasting temperature control scheme based on discrete sliding mode control. During the implementation of this method, the current temperature error distribution is used as input to adjust the zinc concentrate feeding rate in real time. Experimental simulations verified the feasibility of this method, but it has not yet been applied in actual production.

Complex industrial processes exhibit typical nonstationarity due to frequently fluctuating material flows and complex control loops. This poses three challenges for trustworthy process monitoring, including data drift, coordination of old and new knowledge, and interpretability. In this study, the adaptive and interpretable process monitoring problem is formulated as an online updating strategy and the spatial topology structure representation learning process monitoring problem. An incremental variational graph attention autoencoder with probabilistic inference framework is proposed, which aims to effectively learn continuously from dynamically changing industrial data to make interpretable monitoring results. First, an incremental learning strategy based on the Bayesian regularized self-organizing map is presented, which can distinguish between real faults and time-varying changes. Once normal samples are encountered, the itself and downstream model are elegantly updated with a dynamic down-sampling replay strategy without leading to catastrophic forgetting. Subsequently, a variational graph attention autoencoder with probabilistic inference is proposed, which endows interpretable spatial structural relationships through priors and effectively captures the variability of spatial latent representations suitable for nonstationary processes. Then, an incremental variational Bayesian inference is introduced to calculate the adaptive thresholds to adapt the system. In addition, an anomaly-aware graph attention localization mechanism is provided to localize fault root causes and propagation paths. Finally, the effectiveness of the proposed method is validated through two industrial applications. The results demonstrate that the proposed method can significantly enhance the performance of process monitoring, especially for reducing the false alarm rate (FAR) in process monitoring schemes. Moreover, it offers interpretable causal relationships among faults.

The process industry is a key manufacturing process that consumes a vast amount of energy consumption. On the premise of ensuring process stability, controlling process variables to operate the process close to the optimal working condition plays a critical role in reducing energy consumption. Reinforcement learning (RL), using trial and error to learn control strategies, has received much attention. However, the substantial fluctuations of process variables and the switching delay gap of the process industry result in a high-dimension state-action space, making it difficult to learn control strategies efficiently, and there is no guarantee of control stability. To get around these issues, first, a generic knowledge-extracted method for process industry RL control is proposed. It does not require laborious expert knowledge acquisition processes. Second, to improve learning efficiency, the implicit knowledge is extracted using decision trees from operation trajectory data and embedded into agent controllers. Third, an explicit knowledge-oriented reward constructing method is designed to guarantee control stability. A case of the zinc electrowinning process is provided to validate its superiority. The result shows that it can reduce power consumption while stabilizing process variables within the spec limits, without a laborious expert knowledge acquisition process.

The roasting temperature is critical for enhancing product quality, reducing air pollution, and ensuring the long term operation of the zinc roasting process. However, optimizing the roasting temperature is challenging due to complex reaction mechanisms, feed composition fluctuations, and the coupling relationship with downstream processes. In this paper, a two level decision-making system for co-optimization of the roasting temperature is proposed. In the first level, a fuzzy synthetic evaluation model with variable-weight degradation degree is established to accurately evaluate the operating performance of the zinc roasting process. The evaluation results are used to design the basic setting rules that provide the basic temperature setting values. In the second level, a concept of temperature adjustable margin is introduced via sensitivity analysis of the pro cess model to evaluate the optimality of two roasters in the zinc roasting process. Based on the temperature-adjustable margin, the collaborative setting rules are designed to reasonably allocate the basic setting value to the two zinc roasters for optimizing the operating performance of the zinc roasting process. Finally, an industrial case study is presented to demonstrate the effectiveness of the proposed two-level decision-making system.

Process industry indicator describes the production status and is crucial to the stable process operation. Its low sampling frequency makes it difficult to meet the indicator perception needs for real-time process control. Indicator estimation is a promising alternative to improve its obtaining frequency. However, the low sampling frequency of indicators leads to observation scarcity, discouraging shortening the estimation period. Moreover, fluctuations in working conditions (WCs) result in difficulty in reliable estimation. Therefore, a variable-period estimation method is proposed to change the estimation period reliably in the absence of observations. First, the WCs are identified by extracting semantic information from logs. Second, the network group is proposed, which achieves variable-period estimation by adjusting the number of subnetworks. Moreover, two mechanism constraints and a continuous accumulation mapping are proposed to ensure the estimation credibility. A case study of the zinc electrowinning process is provided to validate the method.

The electrowinning process is a critical operation in nonferrous hydrometallurgy and consumes large quantities of power consumption. Current efficiency is an important process index related to power consumption, and it is vital to operate the electrolyte temperature close to the optimum point to ensure high current efficiency. However, the optimal control of electrolyte temperature faces the following challenges. First, the temporal causal relationship between process variables and current efficiency makes it difficult to estimate the current efficiency accurately and set the optimal electrolyte temperature. Second, the substantial fluctuation of influencing variables of electrolyte temperature leads to difficulty in maintaining the electrolyte temperature close to the optimum point. Third, due to the complex mechanism, building a dynamic electrowinning process model is intractable. Hence, it is a problem of index optimal control in the multivariable fluctuation scenario without process modeling. To get around this issue, an integrated optimal control method based on temporal causal network and reinforcement learning (RL) is proposed. First, the working conditions are divided and the temporal causal network is used to estimate current efficiency accurately to solve the optimal electrolyte temperature under multiple working conditions. Then, an RL controller is established under each working condition, and the optimal electrolyte temperature is placed into the controller's reward function to assist in control strategy learning. An experiment case study of the zinc electrowinning process is provided to verify the effectiveness of the proposed method and to show that it can stabilize the electrolyte temperature within the optimal range without modeling.

Modern industry processes are typically composed of multiple operating units with reaction interaction and energy-mass coupling, which result in a mixed time-varying and spatial-temporal coupling of process variables. It is challenging to develop a comprehensive and precise fault detection model for the multiple interconnected units by simple superposition of the individual unit models. In this study, the fault detection problem is formulated as a spatial-temporal fault detection problem utilizing process data of multiple interconnected unit processes. A spatial-temporal variational graph attention autoencoder (STVGATE) using interactive information is proposed for fault detection, which aims to effectively capture the spatial and temporal features of the interconnected unit processes. First, slow feature analysis (SFA) is implemented to extract temporal information that reveals the dynamic relevance of the process data. Then, an integration method of metric learning and prior knowledge is proposed to construct coupled spatial relationships based on temporal information. In addition, a variational graph attention autoencoder (VGATE) is suggested to extract temporal and spatial information for fault detection, which incorporates the dominances of variational inference and graph attention mechanisms. The proposed method can automatically extract and deeply mine spatial-temporal interactive feature information to boost detection performance. Finally, three industrial process experiments are performed to verify the feasibility and effectiveness of the proposed method. The results demonstrate that the proposed method dramatically increases the fault detection rate (FDR) and reduces the false alarm rate (FAR).

Industrial cyber-physical systems (ICPSs) are deployed in many high-value facilities recently, and the monitoring of ICPS is more and more important. However, the prerequisite of ICPS monitoring is how to obtain an accurate network structure. In addition, the structure of ICPS may change over time and the observations data are limited and noisy. These situations make the ICPS network structure identification more difficult. In this article, we proposed the algorithm of temporal network identification from sparse data (ATNISD) to address these two issues simultaneously. First, we established the temporal network analysis model from the aspect of state equation and observation equation. Then, we analyze the characteristics of temporal networks in both time domain and space domain and propose a general framework of temporal networks structure identification, which is a combinatorial optimization problem. To improve the accuracy and alleviate the computational complexity, we decompose the combinatorial problem into small independent simple problems, which can be solved efficiently. The performance of the proposed algorithm is verified on synthetic evolutionary game dynamics on both homogeneous and heterogeneous temporal networks. The experimental results show that the proposed method can efficiently solve the problem of temporal networks structure identification from sparse data. Note to Practitioners—This article addresses the importance of network structure identification in industrial cyber-physical systems (ICPSs). The proposed method can effectively cope with the task of ICPS network structure identification in time-varying environments by exploiting the spatial and temporal features of networks in both time domain and space domain. The proposed algorithm can be implemented in typical slowing changing ICPS with or without observation noise, and it can decompose the combinatorial problem into small independent simple problems to improve the accuracy and release the computational complexity. Extensive simulation experiments demonstrate the accuracy and robustness of the proposed method for solving the structure identification task of temporal networks.

With the development of information and communication technologies, industrial cyber–physical systems (ICPSs) have accumulated a large amount of data, which enables us to convert data into industrial insight. However, since the industrial process of ICPS is always complicated and large scale, the raw data only contain a few operation condition information, which brings challenges to process monitoring and control. Thus, an efficient operation condition division method for ICPS is necessary. Although many operation condition division methods have been proposed, they were mainly relying on the static characteristics and ignored how the industrial process varies dynamically. Meanwhile, with the industrial process running, there may exist some new operation conditions that make the operation condition division task even more difficult. In order to grasp the static and dynamic features simultaneously of the industrial process and divide operation conditions accurately, we proposed an operation condition division method based on joint static and dynamic analysis with incremental learning. In detail, the slow feature analysis (SFA) and self-organizing map (SOM) network were proposed to extract the static and dynamic features jointly. Then, a division strategy was proposed to distinguish the operation condition changing points. For the new operating condition, we designed an incremental learning method based on the SOM network, which can update the operation condition model in real time. Extensive experiments, including a numerical simulation, two benchmark processes, and an industrial roasting process demonstrate that the proposed method can identify the operation conditions of the raw data in ICPS accurately and efficiently.

Accurately predicting the short- and long-term variations of total nitrogen (TN) is vital for operating the wastewater treatment plants (WWTPs), considering the critical role TN plays in reflecting the eutrophication of wastewater. However, only a few relevant water quality parameters with limited samples can be obtained in WWTPs, which tremendously increases the difficulty in precisely predicting TN concentration. In this study, a multiphase attention-based recurrent neural network (MPA-RNN) is proposed. Benefited from its unique decomposition-summary attention structure, MPA-RNN first learns the temporal correlations and effectively excavates the useful information hidden in the historical data. Then, by designing a two-channel structure to transmit attention information, summary attention can integrate the decomposed information and learn the spatial relationships without information loss. Experimental results demonstrate that MPA-RNN achieves the best performance on both the SML2010 and practical TN datasets with the smallest root-mean-squared error, mean absolute error, and mean absolute percentage error when compared with the other state-of-the-art methods.

Depending on varying prices of electricity, an optimal power-dispatching system (OPDS) is developed to minimize the cost of power consumption in the electrochemical process of zinc (EPZ). Due to the complexity of the EPZ, the main factors influencing the power consumption are determined by qualitative analysis, and a series of conditional experiments is conducted to acquire sufficient data, then two backpropagation neural networks are used to describe these relationships quantitatively. An equivalent Hopfield neural network is constructed to solve the optimization problem where a penalty function is introduced into the network energy function so as to meet the equality constraints, and inequality constraints are removed by alteration of the Sigmoid function. This OPDS was put into service in a smeltery in 1998. The cost of power consumption has decreased significantly, the total electrical energy consumption is reduced, and it is also beneficial to balancing the load of the power grid. The actual results show the effectiveness of the OPDS. This paper introduces a successful industrial application and mainly presents how to utilize neural networks to solve particular problems for the real world.