Multirate industrial processes pose significant challenges for accurate forecasting due to varying sampling frequencies and missing data. This article proposes a novel hybrid deep learning framework that effectively addresses these issues. Our approach uses a combination of time series decomposition, inverted transformer (iTransformer)-based feature extraction, and a modified minimal gated unit (MGU) network. To handle missing quality variables, we introduce a robust adaptive parameter update algorithm based on dead-zone Kalman filtering. Through extensive experiments conducted on real-world industrial datasets, our method achieves a mean absolute error (MAE) reduction of 61.42%, a root-mean-square error (RMSE) reduction of 64.11%, and a high qualification rate improvement of 14.73% compared to the average performance of state-of-the-art technologies, thereby outperforming existing state-of-the-art techniques in terms of both forecasting accuracy and robustness.

To address the challenge of implementing 5G wireless networks in industrial process control, this paper integrates deep learning and reinforcement learning in AI with anti-disturbance industrial control to propose an anti-latency optimized control method for 5G wireless networks. The proposed method includes: an anti-latency controller based on data-driven signal compensators with PID controllers and the controller parameters self-optimization algorithm combining digital twin and reinforcement learning. The proposed anti-latency control method is integrated with end-edge-cloud collaboration technologies of Industrial Internet to create an intelligent control system architecture that achieves anti-latency optimized control. A control experimental system based on commercial 5G and Industrial Ethernet was established, and anti-latency control experiments are conducted on an actual heat exchange system. The experimental results indicate that the proposed anti-latency intelligent control method significantly eliminates the impact of 5G random latency on the dynamic performance of the control system.

The current control of alumina digestion process (ALDP) relies on infrequent manual assays for caustic ratio, leading to open-loop operation between assays and suboptimal performance. This paper proposes a novel cloud-edge collaboration (CEC) architecture utilizing deep learning for real-time caustic ratio prediction, enabling closed-loop operation at all times. Our method combines slow-sampled assays with fast-sampled process measurements. An autoregressive moving average with extra inputs (ARMAX) model is used for feature extraction, while an improved bidirectional gated recurrent unit (I-BiGRU) that incorporates a multi-head attention (MHA) mechanism bridges data gaps through prediction. A soft-collaboration mechanism ensures smooth model updates at the edge, enabling operators to make informed control decisions. Theoretical analysis guarantees the convergence of prediction errors. Real-world data from a large-scale alumina plant demonstrates the superior performance of our prediction method compared to the baseline methods. Additionally, industrial experiments validate the effectiveness of the CEC architecture in supporting human operators for ALDP closed-loop operation. Note to Practitioners—This paper was motivated by the challenges of achieving closed-loop operation in the alumina digestion process. The concept of cloud-edge collaboration (CEC) architecture with deep learning offers a powerful solution for various industrial scenarios that rely on infrequent manual measurements, where real-time monitoring of key performance indexes is essential. CEC can continuously predict these values, bridging the gaps between slow, accurate assays and frequent, less precise sensor data. This approach allows for closed-loop operation based on real-time predictions, potentially improving production efficiency and yield. The ability to handle data gaps makes our method applicable to processes with inherent measurement challenges. By implementing CEC in various industries, practitioners can transition from open-loop control to data-driven closed-loop systems, optimizing processes and achieving substantial benefits.

Deep learning offers promising solutions for quality prediction in industrial processes, improving decision-making and performance monitoring. In this article, we propose a novel deep learning framework that incorporates variable time delay (VTD) estimation and uncertainty quantification into quality prediction. The framework employs a collaborative method that integrates deep Q-network with random forest to estimate VTD values. It then utilizes a hybrid BMCR model, consisting of parallel bidirectional minimal gated unit and 1-D convolutional layers, along with a residual connection, specifically designed to capture both long-term and short-term features in industrial data. The framework produces prediction intervals directly to quantify the uncertainty in the prediction results. This combined method offers high-precision point predictions alongside uncertainty quantification, providing valuable insights for industrial decision-making. The effectiveness of the proposed method is validated through two numerical examples, a benchmark, and a real-world industrial case from the alumina digestion process.

In mineral processing, the dynamic nature of industrial data poses challenges for decision-makers in accurately assessing current production statuses. To enhance the decision-making process, it is crucial to predict comprehensive production indices (CPIs), which are influenced by both human operators and industrial processes, and demonstrate a strong dual-scale property. To improve the accuracy of CPIs' prediction, we introduce the high-frequency (HF) unit and low-frequency (LF) unit within our proposed dual-scale deep learning (DL) network. This architecture enables the exploration of nonlinear dynamic mapping in dual-scale industrial data. By integrating the Cloud-Edge collaboration mechanism with DL, our training strategy mitigates the dominance of HF data and guides networks to prioritize different frequency information. Through self-tuning training via Cloud-Edge collaboration, the optimal model structure and parameters on the cloud server are adjusted, with the edge model self-updating accordingly. Validated through online industrial experiments, our method significantly enhances CPIs' prediction accuracy compared to the baseline approaches.

This paper presents a novel approach for predicting bi-level human decision-making in mineral processing. This method aims to enhance the adaptability of human decision-making support systems and reduce dependence on expert knowledge. To achieve this, it leverages a combination of evolutionary computation and neural networks. The proposed method utilizes a hierarchical neural network framework optimized through evolutionary computation. This framework takes advantage of the sequential nature of decision-making within the mineral processing system. The effectiveness of the method is validated through experiments using 1,324 real-world data entries from a mineral processing plant in China. The results demonstrate the method’s capability to improve prediction accuracy.

In this article, the method of dynamic performance monitoring and adaptive self-tuning of parameters for actual PID control systems of industrial processes in virtual reality scenes is proposed. This method combines the digital twin model of the PID control process based on system identification and adaptive deep learning and the PID tuning intelligent algorithm based on reinforcement learning with virtual reality and immersive interaction of industrial metaverse. An industrial metaverse-based intelligent PID tuning system is proposed by combining the above method with the end-edge-cloud collaboration technology of Industrial Internet. The challenging problem that the actual operating PID control system in complex industrial processes cannot be optimized online is solved. Using the energy-intensive equipment, the fused magnesium furnace, as an industrial object, we conducted comparative simulation experiments between the proposed control method and several advanced control methods, as well as industrial experiments for the proposed intelligent system. Simulation experiments demonstrate the effectiveness of the proposed control method. The industrial experimental results indicate that the performance monitoring and adaptive self-tuning of parameters for actual PID control systems of industrial processes in virtual reality scenes can be realized, which achieves excellent control effects.

The slurry concentration is a crucial factor in mineral processing production, affected by both upstream and downstream systems, including closed-loop control systems. The variability in slurry concentration presents a challenge due to its complex, nonlinear nature and the difficulty in accurately modeling this dynamic system for real-time monitoring. This article introduces an innovative approach for the real-time prediction of slurry concentration. Our method comprises two key components: a mechanistic model and a nonlinear dynamic system. The mechanism model is developed using the closed-loop input error method, effectively mitigating the influence of control systems. We have rigorously demonstrated the convergence of parameters and the stability of the identification process. Further, a hybrid deep neural network, called convolutional neural network-gated recurrent unit (GRU), is proposed to tackle challenges, such as intervariable dependencies and the inherent nonlinear dynamics of the process. The network integrates an autoregressive integrated moving average (ARIMA) model for laboratory delays. A parallel GRU network captures nonlinear dynamic characteristics after the ARIMA model. This architecture ensures real-time online concentration forecasting. The effectiveness of our approach has been substantiated with actual production data from a large-scale mineral processing facility.

This article considers the semiglobal cooperative suboptimal output regulation problem of heterogeneous multi-agent systems with unknown agent dynamics in the presence of input saturation. To solve the problem, we develop distributed suboptimal control strategies from two perspectives, namely, model-based and data-driven. For the model-based case, we design a suboptimal control strategy by using the low-gain technique and output regulation theory. Moreover, when the agents' dynamics are unknown, we design a data-driven algorithm to solve the problem. We show that proposed control strategies ensure each agent's output gradually follows the reference signal and achieves interference suppression while guaranteeing closed-loop stability. The theoretical results are illustrated by a numerical simulation example.

In large-scale process industries, one of the great challenges is to achieve optimum operation of systems with multi-time-scale property and partially unknown models. To this end, this article proposes a novel multi-rate layered operational optimal control (OOC) method, which employs lifting technique to unify the relatively fast dual-rate of basic loop layer and relatively slow single-rate of operational layer. Besides, by integrating model-based predictive control of basic loop layer with data-based actor-critic reinforcement learning (RL) of operational layer, it overcomes the difficulty of building the operational process dynamic model. The convergence of the proposed method is proved, and dense medium separation (DMS) process is taken as an application case to illustrate the effectiveness of our proposed method via a self-developed simulation platform.