The Experts below are selected from a list of 5022 Experts worldwide ranked by ideXlab platform
Chris Aldrich - One of the best experts on this subject based on the ideXlab platform.
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SSCI - Statistical process monitoring of a base metal Flotation Plant using a Gaussian mixture model and dissimilarity scale based singular spectrum analysis
2018 IEEE Symposium Series on Computational Intelligence (SSCI), 2018Co-Authors: Syamala Krishnannair, Chris AldrichAbstract:Singular spectrum analysis (SSA) is a data-adaptive multimodal method based on singular value decomposition or equivalently, principal component analysis, which is a promising tool for process monitoring and fault diagnosis in chemical process systems. Statistical monitoring of a base metal Flotation Plant using dissimilarity scale based singular spectrum analysis (DSSA) is considered in this paper. Monitoring of multiscale signals using principal component analysis obtained from the multilevel decomposition of process data using conventional SSA and DSSA assume the data to have a Gaussian distribution. This assumption limits the performance of SSA-based approaches when applied to the monitoring of complex nonlinear processes. To address this issue, a Gaussian mixture model was used to estimate the probability density function for the Hotelling’s T2 and the Qstatistics of the model. Application of the proposed study demonstrated that, in comparison with conventional SSA-based monitoring, the proposed process monitoring scheme is more reliable and efficient in detecting faults in a smaller number of modes.
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Statistical process monitoring of a base metal Flotation Plant using a Gaussian mixture model and dissimilarity scale based singular spectrum analysis
2018 IEEE Symposium Series on Computational Intelligence (SSCI), 2018Co-Authors: Syamala Krishnannair, Chris AldrichAbstract:Singular spectrum analysis (SSA) is a data-adaptive multimodal method based on singular value decomposition or equivalently, principal component analysis, which is a promising tool for process monitoring and fault diagnosis in chemical process systems. Statistical monitoring of a base metal Flotation Plant using dissimilarity scale based singular spectrum analysis (DSSA) is considered in this paper. Monitoring of multiscale signals using principal component analysis obtained from the multilevel decomposition of process data using conventional SSA and DSSA assume the data to have a Gaussian distribution. This assumption limits the performance of SSA-based approaches when applied to the monitoring of complex nonlinear processes. To address this issue, a Gaussian mixture model was used to estimate the probability density function for the Hotelling's T2 and the Qstatistics of the model. Application of the proposed study demonstrated that, in comparison with conventional SSA-based monitoring, the proposed process monitoring scheme is more reliable and efficient in detecting faults in a smaller number of modes.
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Rule-based characterization of industrial Flotation processes with inductive techniques and genetic algorithms
Industrial & Engineering Chemistry Research, 1996Co-Authors: F.s. Gouws, Chris AldrichAbstract:By making use of machine learning techniques, the features of Flotation froths and other Plant variables can be used as a basis for the development of knowledge-based systems for Plant monitoring and control. Probabilistic induction and genetic algorithms were used to classify different froth structures from industrial copper and platinum Flotation Plants, as well as recoveries from a phosphate Flotation Plant. Both algorithms were equally capable of classifying the different froths at least as well as a human expert. The genetic algorithm performed significantly better than the inductive algorithm but required more tuning before optimum results could be obtained. The classification rules produced by both algorithms can easily be incorporated into a supervisory expert system shell or decision support system for Plant operators and could consequently make a significant impact on the way Flotation Plants are currently being controlled.
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On-Line Image Analysis to Improve Industrial Flotation Plant Performance
IFAC Proceedings Volumes, 1995Co-Authors: D.w. Moolman, Chris Aldrich, J.s.j. Van Deventer, Jacques Eksteen, W. Stange, P Marais, C Goodall, R S VeitchAbstract:Abstract In this study image features extracted from froth images by an on-line machine vision system in an industrial platinum Flotation Plant were used to relate froth characteristics and Flotation performance by using neural networks. It has been shown that a considerable amount of data can be extracted from Flotation surface froths and that both novel feedback control procedures and feedback control as a complement to conventional feedforward systems are made possible. Feature measures such as chromatic information, average bubble size, froth texture, froth stability and mobility of surface froths were used in the on-line classification of Flotation froths. This intelligent vision system constitutes a powerful research tool for the investigation and interpretation of the effect of various Flotation parameters. This paper shows how the rapid development in computer technology and neural networks can be used to transform recently developed concepts and available technology into a new generation of intelligent automation systems.
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Monitoring and control of hydrometallurgical processes with self-organizing and adaptive neural net systems
Computers & Chemical Engineering, 1995Co-Authors: Chris Aldrich, D.w. Moolman, J.s.j. Van DeventerAbstract:Abstract Hydrometallurgical processes are difficult to describe fundamentally, owing to their largely stochastic nature and the often ill-defined chemorheology of the froth. Although these processes are consequently difficult to monitor accurately by means of classical methods, progress has recently been made with regard to the use of neural net control systems. In this paper the use of a self-organizing neural net to monitor the behaviour of an industrial platinum Flotation Plant is discussed. The net is shown to be an efficient means of detecting arbitrary small changes in process conditions. In addition to the self-organizing neural net, the performance of a fuzzy ARTMAP system is also evaluated. These types of nets are capable of robust incremental assimilation of new process knowledge, as is demonstrated in terms of the classification of flow regimes in an air-water flow system.
Syamala Krishnannair - One of the best experts on this subject based on the ideXlab platform.
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SSCI - Statistical process monitoring of a base metal Flotation Plant using a Gaussian mixture model and dissimilarity scale based singular spectrum analysis
2018 IEEE Symposium Series on Computational Intelligence (SSCI), 2018Co-Authors: Syamala Krishnannair, Chris AldrichAbstract:Singular spectrum analysis (SSA) is a data-adaptive multimodal method based on singular value decomposition or equivalently, principal component analysis, which is a promising tool for process monitoring and fault diagnosis in chemical process systems. Statistical monitoring of a base metal Flotation Plant using dissimilarity scale based singular spectrum analysis (DSSA) is considered in this paper. Monitoring of multiscale signals using principal component analysis obtained from the multilevel decomposition of process data using conventional SSA and DSSA assume the data to have a Gaussian distribution. This assumption limits the performance of SSA-based approaches when applied to the monitoring of complex nonlinear processes. To address this issue, a Gaussian mixture model was used to estimate the probability density function for the Hotelling’s T2 and the Qstatistics of the model. Application of the proposed study demonstrated that, in comparison with conventional SSA-based monitoring, the proposed process monitoring scheme is more reliable and efficient in detecting faults in a smaller number of modes.
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Statistical process monitoring of a base metal Flotation Plant using a Gaussian mixture model and dissimilarity scale based singular spectrum analysis
2018 IEEE Symposium Series on Computational Intelligence (SSCI), 2018Co-Authors: Syamala Krishnannair, Chris AldrichAbstract:Singular spectrum analysis (SSA) is a data-adaptive multimodal method based on singular value decomposition or equivalently, principal component analysis, which is a promising tool for process monitoring and fault diagnosis in chemical process systems. Statistical monitoring of a base metal Flotation Plant using dissimilarity scale based singular spectrum analysis (DSSA) is considered in this paper. Monitoring of multiscale signals using principal component analysis obtained from the multilevel decomposition of process data using conventional SSA and DSSA assume the data to have a Gaussian distribution. This assumption limits the performance of SSA-based approaches when applied to the monitoring of complex nonlinear processes. To address this issue, a Gaussian mixture model was used to estimate the probability density function for the Hotelling's T2 and the Qstatistics of the model. Application of the proposed study demonstrated that, in comparison with conventional SSA-based monitoring, the proposed process monitoring scheme is more reliable and efficient in detecting faults in a smaller number of modes.
A. Zeraatkar Moghaddam - One of the best experts on this subject based on the ideXlab platform.
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Removal of the residual xanthate from Flotation Plant tailings using bentonite modified by magnetic nano-particles
Minerals Engineering, 2019Co-Authors: A. Amrollahi, M Massinaei, A. Zeraatkar MoghaddamAbstract:Abstract The residual organic reagents (including xanthates) in the Flotation tailings can cause environmental issues during their disposal, besides their deleterious influence on the Flotation performance during recycling. In this study, the adsorption characteristics of bentonite modified with copper and manganese ferrite nanoparticles (Be CuFe2O4, Be MnFe2O4) for xanthate removal from the synthetic and real solutions was studied. The characterization studies by XRF, XRD, BET, SEM, EDX, VSM and FTIR techniques confirmed the formation of magnetic nanoparticles. The adsorption capacity of Be CuFe2O4 was found to be higher than Be MnFe2O4 and at optimum conditions (adsorbent dosage: 4 mg/mL; xanthate concentration: 1000 mg/L; pH = 9.2; time = 40 min) more than 94% of the residual xanthate was removed by this adsorbent. The kinetic studies showed that rate of xanthate adsorption on both adsorbents followed the pseudo-second order model. The intraparticle diffusion was not the only rate-limiting step of the adsorption process. The equilibrium isotherms were best fitted to the Langmuir model. This suggests monolayer adsorption of xanthate on homogenous sites of the adsorbents. The thermodynamic studies revealed that the adsorption process was spontaneous, endothermic and entropy-driven. The synthesized adsorbents were capable of completely removing xanthate from the actual samples taken from the tailings of a copper Flotation Plant (Qaleh-Zari, Iran).
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Removal of the residual xanthate from Flotation Plant tailings using modified bentonite
Minerals Engineering, 2018Co-Authors: R. Rezaei, M Massinaei, A. Zeraatkar MoghaddamAbstract:Abstract Xanthates are the most widely used collectors for Flotation of sulfide minerals. It has been estimated that about half of the organic reagents (including xanthate) added to the Flotation circuits are consumed, while the remaining half are discharged into the Plant tailing. The residual xanthate in the Flotation tailing can cause environmental issues through contaminating of the nearby water resources as a result of poor design of the tailings dams. In this communication two modified bentonite adsorbents, namely acid-activated (H-Be) and aluminum-pillared (Al-Be) bentonite, were synthesized and used for the removal of the residual xanthate (potassium amyl xanthate) from the synthetic and real solutions. Al-Be was found superior to H-Be in terms of adsorption performance and under optimum conditions (adsorbent dosage: 7500 mg/L; xanthate concentration: 2000 mg/L; pH = 12.2; time = 45 min) and more than 99% of the residual xanthate was removed. The kinetics data were best described by pseudo-second order model for the both adsorbents. The adsorption equilibrium isotherms were fitted by Freundlich, Langmuir, and Temkin models. The isotherm data were best-fitted to the Langmuir model, indicating monolayer adsorption of the xanthate on homogenous surfaces of the both adsorbents. The thermodynamics studies indicated that the adsorption was spontaneous, exothermic with the reducing entropy. The adsorption tests on the real samples taken from the tailing stream of a copper Flotation Plant (Qaleh-Zari in Iran) proved that both adsorbents are capable of removing more than 90% of the residual xanthate.
Aldo Cipriano - One of the best experts on this subject based on the ideXlab platform.
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Assessment of expert fuzzy controllers for conventional Flotation Plants
Minerals Engineering, 1999Co-Authors: D. Osorio, José Ricardo Pérez-correa, Aldo CiprianoAbstract:Many undesirable dynamic characteristics are present in conventional Flotation Plants that hinder the design and implementation of control systems. New expert and fuzzy control strategies for these processes are presented and assessed here via simulation. The algorithms are based on previous work, and they emulate current operating practices in an active copper Flotation Plant in Chile. The new controllers were able to achieve high recoveries and also avoid control saturation despite severe disturbances in the feed flowrate.
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A REAL TIME VISUAL SENSOR FOR SUPERVISION OF Flotation CELLS
Minerals Engineering, 1998Co-Authors: Aldo Cipriano, René Vidal, C. Sepúlveda, Alvaro Soto, Marcelo Guarini, Domingo Mery, H BriseñoAbstract:This paper describes an expert system for the supervision of Flotation Plants based on ACEFLOT, a real time analyzer of the characteristics of the froth that is formed on the surface of Flotation cells. The ACEFLOT analyzer is based on image processing and measures several physical variables of the froth, including colorimetric, geometric and dynamic information. On the other hand, the expert system detects abnormal operation states and suggests corrective actions, supporting operators on the supervision and control of the Flotation Plant.
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An Integrated System for Real-Time Supervision and Economic Optimal Control with Application to Mineral Processing Plants
IFAC Proceedings Volumes, 1998Co-Authors: Aldo Cipriano, J. Concha, E. Pajares, Carlos MuñozAbstract:Abstract In this article, a support system for the economic optimal control of industrial Plants, is described. The system collects and processes, in real time, the data obtained from sensors and informs the operator about the optimal values of manipulated variables to obtain maximum economic benefits. An application to a grinding-Flotation Plant for copper mineral processing is also presented.
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Expert system for supervision of mineral Flotation cells using artificial vision
ISIE '97 Proceeding of the IEEE International Symposium on Industrial Electronics, 1997Co-Authors: Aldo Cipriano, C. Sepúlveda, Marcelo GuariniAbstract:This paper describes an expert system for the supervision of Flotation Plants that uses ACEFLOT, a real-time analyzer of the dynamic characteristics of Flotation froth. The ACEFLOT analyzer, based on image processing, measures several physical parameters of the froth that forms on the cell surface. The expert system detects abnormal operation and suggests corrective actions, supporting operators on the supervision and control of the Flotation Plant.
Edelmira D Galvez - One of the best experts on this subject based on the ideXlab platform.
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the use of global sensitivity analysis for improving processes applications to mineral processing
Computers & Chemical Engineering, 2014Co-Authors: Felipe D Sepulveda, Luis A Cisternas, Edelmira D GalvezAbstract:Abstract This paper analyzes the application of global sensitivity analysis (GSA) to the improvement of processes using various case studies. First, a brief description of the methods applied is given, and several case studies are examined to show how GSA can be applied to the study to improve the processes. The case studies include the identification of processes; comparisons of the Sobol, E-FAST and Morris GSA methods; a comparison of GSA with local sensitivity analysis; an examination of the effect of uncertainty levels and the type of distribution function on the input factors; and the application of GSA to the improvement of a copper Flotation circuit. We conclude that GSA can be a useful tool in the analysis, comparison, design and characterization of separation circuits. In addition, we conclude that using the stage's recoveries of each species as input factors is a suitable choice for the GSA of a Flotation Plant.