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Qingkai Wang - One of the best experts on this subject based on the ideXlab platform.

  • Bayesian Network-Based Modeling and Operational Adjustment of Plantwide Flotation Industrial Process
    Industrial & Engineering Chemistry Research, 2020
    Co-Authors: Hao Yan, Fuli Wang, Luping Zhao, Qingkai Wang
    Abstract:

    The operational variables of the flotation industrial process (FIP) are controlled by the operators and are usually not adjusted in time. This makes it difficult to control the technical indexes such as Concentrate Grade within acceptable ranges. To resolve this problem, a Bayesian network (BN)-based modeling and operational adjustment method is investigated. Considering the complexity of modeling, a modular BN modeling framework for plantwide FIP is proposed. First, the plantwide FIP is decomposed into several related submodules, and corresponding local BNs are established through structure learning and parameter learning. Then, on the basis of the process knowledge and associated variables, each local BN is fused into the global BN. In the application, a novel operational adjustment framework for plantwide FIP is proposed. First, a global operational adjustment is inferred by setting the index of Concentrate Grade. Then, according to the obtained operational adjustment of each submodule from local to global, the Concentrate Grade is further predicted. Once the predicted result meets a certain condition, the current operational adjustment will be implemented right away. Data experiments evaluate the performance of the proposed method in the decision-making of operational adjustment.

  • Bayesian Network-based Technical Index Estimation for Industrial Flotation Process under Incomplete Data
    2020 Chinese Control And Decision Conference (CCDC), 2020
    Co-Authors: Fuli Wang, Dakuo He, Qingkai Wang
    Abstract:

    Due to the lack of detection instruments or long measurement cycles in the industrial flotation process, accurate and real-time estimation of the technical index is of great significance for optimizing flotation performance and operational adjustment. In the real-world flotation process, incomplete data is a widespread phenomenon owing to hardware sensor failures and other reasons. To this end, this paper proposes a Bayesian network (BN)-based Concentrate Grade estimation method under incomplete data. The real-time froth image information and the Concentrate Grades of the previous periods are taken as the input of the BN model, and the current Concentrate Grade is the output of the BN model. The expectation maximum (EM) algorithm is used to estimate the model parameters. The application results show the proposed method can accurately estimate the Concentrate Grade even if some data are missing.

  • An Operational Adjustment Framework for a Complex Industrial Process Based on Hybrid Bayesian Network
    IEEE Transactions on Automation Science and Engineering, 2020
    Co-Authors: Hao Yan, Fuli Wang, Qingkai Wang
    Abstract:

    The operational variables used to adjust the control level of the copper cleaner flotation process have a noticeable impact on the object variables, e.g., the copper Concentrate Grade. Currently, due to the complexity of the flotation process, the operational variables, which are controlled by operators, are often not adjusted properly in time. Hence, this article investigates an intelligent operational adjustment framework based on a hybrid Bayesian network (BN). The offline BN model structure and the parameters are established based on process knowledge and real industrial data, respectively. After receiving the expected value of the copper Concentrate Grade as evidence, an operational adjustment can be obtained online by BN reasoning. To ensure its credibility, the copper Concentrate Grade after operational adjustment is further predicted. According to the predicted value, the operators can determine whether to implement the operational adjustment or not. Finally, the experimental results show the effectiveness and practical significance of the proposed method. Note to Practitioners— This article was motivated by the problem of operational adjustment in the copper cleaner flotation process. Because of the current manual operational adjustment, controlling the Concentrate Grade within its acceptable range is difficult. This article suggests a hybrid Bayesian network (BN) approach to address this problem. The hybrid BN model is built offline and used to make inferences online. Compared with a conventional discrete BN, this approach can provide operators with specific adjustment values to qualify the Concentrate Grade. For the evaluation, data experiments are used. This evaluation shows the efficiency and superiority of the proposed approach in terms of automation, intelligence, and decision-making.

  • Estimation of copper Concentrate Grade for copper flotation
    IFAC-PapersOnLine, 2018
    Co-Authors: Hao Yan, Qingkai Wang, Zhiqiang Wang, Xu Wang
    Abstract:

    Abstract This paper develops a comparative study based on several modeling methods for estimating the copper Concentrate Grade in the copper flotation process. Back propagation neural network (BPNN) method, partial least squares (PLS) method, just-in-time learning partial least squares (JIT-PLS) method, adaptive-PLS method and window-adaptive-PLS method are proposed respectively. The prediction effects and test errors of these modeling methods are compared and analysed by using the measurement data of copper Grade from X fluorescent Grade analyzer in the dressing plant. The result is that window adaptive partial least squares method is the most accurate method, which can accurately predict the copper Concentrate Grade of the next measurement period in advance and can be used to guide operations.

Aldo Cipriano - One of the best experts on this subject based on the ideXlab platform.

  • model based predictive control of a rougher flotation circuit considering Grade estimation in intermediate cells
    Dyna, 2011
    Co-Authors: Daniel Rojas, Aldo Cipriano
    Abstract:

    Effective control of rougher flotation is important because a small increase in recovery results in a significant economic benefit. Although many flotation control strategies have been proposed and implemented over the years, none of them incorporate Concentrate Grade measurements at intermediate cells because these data are not usually available. On the other hand, there is much research on characterizing Concentrate froth on the cell surface by image processing in order to extract information on froth color, bubble size, and speed that can then be used for developing expert control strategies, and some works have shown the possibility of estimating the Concentrate Grade. This work presents two multivariable model based predictive control (MPC) strategies for a rougher circuit. The first strategy is based only on general tailings and Concentrate Grade measurements while the second one includes, beside these data, the intermediate cell Grade estimates. Both strategies are compared with a fixed control strategy. Simulation tests show that the recovery can increase by 1.7%, compared to the fixed control strategy.

  • model based predictive control of a rougher flotation circuit considering Grade estimation in intermediate cells control predictivo de un circuito de flotacion rougher considerando estimacion de leyes en celdas intermedias
    2011
    Co-Authors: Daniel Rojas, Aldo Cipriano
    Abstract:

    th , 2010, accepted March 14 th , 2010, fi nal version May, 24 th , 2010 ABSTRACT: Effective control of rougher fl otation is important because a small increase in recovery results in a signifi cant economic benefi t. Although many fl otation control strategies have been proposed and implemented over the years, none of them incorporate Concentrate Grade measurements at intermediate cells because these data are not usually available. On the other hand, there is much research on characterizing Concentrate froth on the cell surface by image processing in order to extract information on froth color, bubble size, and speed that can then be used for developing expert control strategies, and some works have shown the possibility of estimating the Concentrate Grade. This work pres- ents two multivariable model based predictive control (MPC) strategies for a rougher circuit. The fi rst strategy is based only on general tailings and Concentrate Grade measurements while the second one includes, beside these data, the intermediate cell Grade estimates. Both strategies are compared with a fi xed control strategy. Simulation tests show that the recovery can increase by 1.7%, compared to the fi xed control strategy.

  • Hierarchical hybrid fuzzy strategy for column flotation control
    Minerals Engineering, 2010
    Co-Authors: Felipe Núñez, Luis Tapia, Aldo Cipriano
    Abstract:

    Abstract Column flotation is widely used in the concentration of low Grade ores. Often column flotation Concentrate is the final product of a very complex circuit, and therefore control of the metallurgical performance has direct impact in the plant performance. Several control schemes has been implemented for the stabilization of column flotation process, including decentralized control, model predictive control and fuzzy approaches, which attempt to control froth depth, water bias and air holdup. At the same time many efforts have been oriented to improve process instrumentation, with the aim of providing better measurements for control purposes. Instrumentation improvements have made feasible the design of strategies focused on recovery and Concentrate Grade control. In this work we present the design and implementation of a new advanced controller for column flotation process. The controller was implemented in a 10 columns cleaning stage following a hierarchical scheme with two control levels: an improving level with the aim of metallurgical performance control of the whole process, and a stabilizing level in charge of the distribution of control actions in each column. The controller design was made based on a hybrid scheme with three different operation scenarios, defined by a recovery–Concentrate Grade domain partition. Results show that the controller is able to keep the process in the normal operation scenario 80% of the analyzed time; on the other hand, when the process was operated only with local control it achieved the normal operation scenario 43% of the analyzed time. Results also show that the controller is capable of increasing Concentrate Grade and recovery mean values, despite variations on feed Grade; while reducing recovery and Concentrate Grade standard deviations.

Hao Yan - One of the best experts on this subject based on the ideXlab platform.

  • Bayesian Network-Based Modeling and Operational Adjustment of Plantwide Flotation Industrial Process
    Industrial & Engineering Chemistry Research, 2020
    Co-Authors: Hao Yan, Fuli Wang, Luping Zhao, Qingkai Wang
    Abstract:

    The operational variables of the flotation industrial process (FIP) are controlled by the operators and are usually not adjusted in time. This makes it difficult to control the technical indexes such as Concentrate Grade within acceptable ranges. To resolve this problem, a Bayesian network (BN)-based modeling and operational adjustment method is investigated. Considering the complexity of modeling, a modular BN modeling framework for plantwide FIP is proposed. First, the plantwide FIP is decomposed into several related submodules, and corresponding local BNs are established through structure learning and parameter learning. Then, on the basis of the process knowledge and associated variables, each local BN is fused into the global BN. In the application, a novel operational adjustment framework for plantwide FIP is proposed. First, a global operational adjustment is inferred by setting the index of Concentrate Grade. Then, according to the obtained operational adjustment of each submodule from local to global, the Concentrate Grade is further predicted. Once the predicted result meets a certain condition, the current operational adjustment will be implemented right away. Data experiments evaluate the performance of the proposed method in the decision-making of operational adjustment.

  • An Operational Adjustment Framework for a Complex Industrial Process Based on Hybrid Bayesian Network
    IEEE Transactions on Automation Science and Engineering, 2020
    Co-Authors: Hao Yan, Fuli Wang, Qingkai Wang
    Abstract:

    The operational variables used to adjust the control level of the copper cleaner flotation process have a noticeable impact on the object variables, e.g., the copper Concentrate Grade. Currently, due to the complexity of the flotation process, the operational variables, which are controlled by operators, are often not adjusted properly in time. Hence, this article investigates an intelligent operational adjustment framework based on a hybrid Bayesian network (BN). The offline BN model structure and the parameters are established based on process knowledge and real industrial data, respectively. After receiving the expected value of the copper Concentrate Grade as evidence, an operational adjustment can be obtained online by BN reasoning. To ensure its credibility, the copper Concentrate Grade after operational adjustment is further predicted. According to the predicted value, the operators can determine whether to implement the operational adjustment or not. Finally, the experimental results show the effectiveness and practical significance of the proposed method. Note to Practitioners— This article was motivated by the problem of operational adjustment in the copper cleaner flotation process. Because of the current manual operational adjustment, controlling the Concentrate Grade within its acceptable range is difficult. This article suggests a hybrid Bayesian network (BN) approach to address this problem. The hybrid BN model is built offline and used to make inferences online. Compared with a conventional discrete BN, this approach can provide operators with specific adjustment values to qualify the Concentrate Grade. For the evaluation, data experiments are used. This evaluation shows the efficiency and superiority of the proposed approach in terms of automation, intelligence, and decision-making.

  • Estimation of copper Concentrate Grade for copper flotation
    IFAC-PapersOnLine, 2018
    Co-Authors: Hao Yan, Qingkai Wang, Zhiqiang Wang, Xu Wang
    Abstract:

    Abstract This paper develops a comparative study based on several modeling methods for estimating the copper Concentrate Grade in the copper flotation process. Back propagation neural network (BPNN) method, partial least squares (PLS) method, just-in-time learning partial least squares (JIT-PLS) method, adaptive-PLS method and window-adaptive-PLS method are proposed respectively. The prediction effects and test errors of these modeling methods are compared and analysed by using the measurement data of copper Grade from X fluorescent Grade analyzer in the dressing plant. The result is that window adaptive partial least squares method is the most accurate method, which can accurately predict the copper Concentrate Grade of the next measurement period in advance and can be used to guide operations.

Sameer H Morar - One of the best experts on this subject based on the ideXlab platform.

  • The Use of a Colour Parameter in a Machine Vision System, SmartFroth, to Evaluate Copper Flotation Performance at Rio Tinto’s Kennecott Copper Concentrator
    2015
    Co-Authors: Sameer H Morar, L Esdaile
    Abstract:

    Traditionally, operators on mineral flotation plants have relied on visual inspection of the froth surface when making adjustments to process set-points. Machine vision systems are being developed to relate froth surface descriptors, which can be consistently measured in real time, to flotation performance. By investigating the impact of various selected operating variables on both froth surface characteristics and metallurgical performance it is possible to develop a management strategy for the control and optimisation of the process. This paper presents the results of testwork conducted at Kennecott Copper mine which investigates the relationship between the froth colour and the Concentrate Grade and discusses the factors which affect the froth colour. This work shows that the main factors that influence the froth colour in this system are molybdenum, copper and iron Concentrate Grade, pulp and Concentrate per cent solids and pulp iron Grade. It goes on to develop models to predict the Concentrate Grade based on colour information and velocity and stability information. It also shows that the most accurate prediction is made with combined colour and stability information

  • the use of a colour parameter in a machine vision system smartfroth to evaluate copper flotation performance at rio tinto s kennecott utah copper concentrator
    Centenary of Flotation Symposium 2005, 2005
    Co-Authors: Sameer H Morar, G Forbes, G S Heinrich, D J Bradshaw
    Abstract:

    Traditionally, operators on mineral flotation plants have relied on visual inspection of the froth surface when making adjustments to process set-points. Machine vision systems are being developed to relate froth surface descriptors, which can be consistently measured in real time, to flotation performance. By investigating the impact of various selected operating variables on both froth surface characteristics and metallurgical performance it is possible to develop a management strategy for the control and optimisation of the process. This paper presents the results of testwork conducted at Kennecott copper mine which investigates the relationship between the froth colour and the Concentrate Grade and discusses the factors which affect the froth colour. This work shows that the main factors that influence the froth colour in this system are molybdenum, copper and iron Concentrate Grade, pulp and Concentrate per cent solids and pulp iron Grade. It goes on to develop models to predict the Concentrate Grade based on colour information and velocity and stability information. It also shows that the most accurate prediction is made with combined colour and stability information.

Bradshaw D. J. - One of the best experts on this subject based on the ideXlab platform.

  • The use of a colour parameter in a machine vision system, SmartFroth, to evaluate copper flotation performance at Rio Tinto’s Kennecott Utah copper concentrator
    Australasian Institute of Mining and Metallurgy (AusIMM), 2005
    Co-Authors: Morar S. H., Forbes G., Heinrich G. S., Bradshaw D. J.
    Abstract:

    Traditionally, operators on mineral flotation plants have relied on visual inspection of the froth surface when making adjustments to process set-points. Machine vision systems are being developed to relate froth surface descriptors, which can be consistently measured in real time, to flotation performance. By investigating the impact of various selected operating variables on both froth surface characteristics and metallurgical performance it is possible to develop a management strategy for the control and optimisation of the process. This paper presents the results of testwork conducted at Kennecott copper mine which investigates the relationship between the froth colour and the Concentrate Grade and discusses the factors which affect the froth colour. This work shows that the main factors that influence the froth colour in this system are molybdenum, copper and iron Concentrate Grade, pulp and Concentrate per cent solids and pulp iron Grade. It goes on to develop models to predict the Concentrate Grade based on colour information and velocity and stability information. It also shows that the most accurate prediction is made with combined colour and stability information