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• A hybrid fuzzy control strategy is developed for the control of the goethite process. • The hybrid fuzzy control strategy combines a T1 FLC and an IT2 FLC. • The T1 FLC with parameter tuning method is designed to control the zinc oxide additive rate. • The IT2 FLC is constructed to control the oxygen flow rate, and simultaneous perturbation gradient approximation method is used to update the parameters. • The effectiveness of the proposed control strategy is verified by the simulations using real-world production data. The goethite process is a complicated reaction system which holds a significant position in zinc hydrometallurgy process. It exhibits nonlinear behavior and time-delay nature because of the chemical reactions. To achieve the stable and real-time control performance, a hybrid fuzzy control strategy integrating a type-1 and a type-2 fuzzy logic controllers is proposed in this paper. According to the on-line measured pH, the type-1 fuzzy controller (T1 FLC) via the Takagi–Sugeno fuzzy control is employed to control the zinc oxide additive rate. A parameter tuning method that does not require the system model is designed for the T1 FLC. Because the ferrous ion concentration cannot be measured on-line, the interval type-2 fuzzy logic controller (IT2 FLC) is utilized to control the oxygen flow rate. According to the feedback information, gradient descent algorithm is used to update the parameters in the IT2 FLC. Because of the unknown system structure, gradient descent information is estimated by the simultaneous perturbation stochastic approximation. Finally, simulations are conducted using the practical production data. The simulation results show the effectiveness of the proposed strategy. The hybrid fuzzy control strategy improves the control performance of the goethite process, compared with PID controller. [ABSTRACT FROM AUTHOR]

In modern industrial processes, each subsystem interacts frequently and involves a large number of process variables with complex relations, which challenge process monitoring. In this paper, a distributed process monitoring method based on joint mutual information (JMI) and projective dictionary pair learning (DPL) is proposed for effective process monitoring in industrial systems with multimode, complex, and high-dimensional data. Firstly, considering the interactive information, redundancy and irrelevance among process variables, an automatic block division method based on JMI is proposed to divide process variables into several low dimensional blocks. Secondly, DPL-based monitoring model is established in each block of each mode. According to the multimode characteristic of industrial processes, a joint probability based on reconstruction error is proposed for mode recognition. Then, Bayesian inference method that fuses block statistics into global statistics is introduced for anomaly detection. The anomaly source is further determined by defining the block contribution coefficient and variable contribution coefficient. Finally, the effectiveness of the proposed method is demonstrated by a numerical simulation, Tennessee Eastman benchmark test, and experiments in an aluminum electrolysis industrial process. • A distributed process monitoring method via JMI-DPL is proposed. • An automatic block division method based on JMI is developed. • This method gives better performances in mode identification and anomaly detection. • An application in a real-world aluminum electrolysis process is provided. [ABSTRACT FROM AUTHOR]

Distributed anode current (DAC) is a high-dimensional spatial-distributed signal that can be measured online in the industrial aluminum electrolysis process. The difference of physicochemical properties in different spatial regions in an aluminum electrolysis cell can be obtained by spatial clustering analysis of DAC data. In this study, a dynamic spatial distributed information clustering method (DSDIC) for aluminum electrolysis cell is proposed. This method can effectively capture the complex dynamic spatio-temporal correlations in DAC. Firstly, the dynamic graph is identified to capture the complex dynamicity of the DAC. Then, the anode-spatial structure information (ASSI) extends the one-dimensional current signal generated by each carbon anode into a feature matrix to achieve the fusion of data and spatial structure knowledge. Finally, the adjacency matrix of dynamic graph performs low-pass filtering on the feature matrix to obtain low-frequency information that is beneficial to downstream learning tasks. Meanwhile, a fixed graph structure based on process mechanism knowledge is designed to capture the spatial correlation caused by external periodic operations in industrial process. The experimental results on the actual industrial aluminum electrolysis datasets show that our method improves the clustering accuracy by 3.96% compared with existing clustering methods. • A dynamic spatial distributed information clustering method is proposed. • A novel fixed graph structure based on process mechanism knowledge is proposed. • A method using adaptive dynamic graph to capture the dynamic spatial correlation is proposed. • A learning mode combining dynamic graph and static graph is proposed. [ABSTRACT FROM AUTHOR]

Antimony flotation is a complicated industrial process with an unknown system structure. Manual manipulation is still widely used by observing the froth appearance, which relies on operators' experience. Therefore, this paper proposes a reagent predictive control strategy for the antimony flotation process based on machine vision, which is an optional replacement of manual control. Froth image is a good indicator to reflect the working conditions of the process. A shape-weighted bubble size distribution (SWBSD) is developed to characterize the froth image feature. The SWBSD combines the bubble size and bubble shape, which differs from the separate use of froth features. Since lots of the froth images can be obtained, a random selection of an image from a froth video as the indicator of the working conditions is not advisable. A typical froth image selection method is proposed to choose the froth image which can best characterize the current working conditions. Due to the unknown system model, multi-output least square support vector regressor (LS-SVR) is used to predict the future image feature according to the current image feature and reagent dosages. Based on the above methods, predictive control technique is employed to calculate the optimal control inputs of the reagents. Simulations using the real-world production data verify the effectiveness of the proposed control strategy. In further, industrial experiments in the antimony flotation plant of China demonstrate that our control strategy improved the tracking performance and decreased the tailing grade, compared with the manual manipulation. [ABSTRACT FROM AUTHOR]