An adaptive threshold estimation scheme for abrupt changes detection algorithm in a cement rotary kiln

Kouadri, Abdelmalek; Bensmail, Abderazak; Kheldoun, Aissa; Refoufi, Larbi

doi:10.1016/j.cam.2013.07.039

Cited by 15 publications

(5 citation statements)

References 18 publications

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“…The measurements collected so far are specified for each data set by a n × m matrix X , which represents a series of n observations obtained by sampling from m different measurement devices. These data are recorded under the same system operating conditions as detailed in the work of Kouadri et al…”

Section: Resultsmentioning

confidence: 99%

Kernelized relative entropy for direct fault detection in industrial rotary kilns

Hamadouche

Kouadri

Bensmail

2018

Adaptive Control & Signal

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The objective of this work is to use a 1-dimensional signal that reflects the dissimilarity between multidimensional probability densities for detection. With the modified Kullback-Leibler divergence, faults can be directly detected without any normality assumption or joint monitoring of related test statistics in different subspaces such as the T 2 and SPE in principal component analysis-based methods. To relieve the difficulty associated with asymptotic high-dimensional density estimates, we have estimated the density ratio rather than the densities themselves. This can be done by approximating the density ratio with kernel basis functions and learn the weights from the available data. The developed algorithm is generic and can be applied to any industrial system as long as process historical data is available. As a case study, we apply this algorithm to a real rotary kiln in operation, which is an integral part of the cement manufacturing plant of Ain El Kebira, Algeria.

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Section: Resultsmentioning

confidence: 99%

Kernelized relative entropy for direct fault detection in industrial rotary kilns

Hamadouche

Kouadri

Bensmail

2018

Adaptive Control & Signal

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“…(iii) Real process fault with 2048 observations. It includes the normal operating conditions part that last less than 420 samples, and the rest is the faulty part which constitutes multiple faults in various variables that increase gradually [30]. (iv) 10 simulated sensor faults data sets with each one lasting for 1000 observations these faults are described in [19].…”

Section: B Ain El Kebira Cement Plantmentioning

confidence: 99%

Improvement of Kernel Principal Component Analysis-Based Approach for Nonlinear Process Monitoring by Data Set Size Reduction Using Class Interval

Kaib,

Kouadri,

Harkat

et al. 2024

IEEE Access

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Fault detection and diagnosis (FDD) systems play a crucial role in maintaining the adequate execution of the monitored process. One of the widely used data-driven FDD methods is the Principal Component Analysis (PCA). Unfortunately, PCA's reliability drops when data has nonlinear characteristics as industrial processes. Kernel Principal Component Analysis (KPCA) is an alternative PCA technique that is used to deal with a similar data set. For a large-sized data set, KPCA's execution time and occupied storage space will increase drastically and the monitoring performance can also be affected in this case. So, the Reduced KPCA (RKPCA) was introduced with the aim of reducing the size of a given training data set to lower the execution time and occupied storage space while maintaining KPCA's monitoring performance for nonlinear systems. Generally, RKPCA reduces the number of samples in the training data set and then builds the KPCA model based on this data set. In this paper, the proposed algorithm selects relevant observations from the original data set by utilizing a class interval technique (i.e. histogram) to maintain a bunch of representative samples from each bin. The proposed algorithm has been tested on three tank system pilot plant and Ain El Kebira Cement rotary kiln process. The proposed algorithm has successfully maintained homogeneity to the original data set, reduced the execution time and occupied storage space, and led to decent monitoring performance. INDEX TERMSFaul Detection and Diagnosis (FDD), data-driven techniques, time and storage space complexity, Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), Reduced KPCA (RKPCA), Histogram, cement plant, three tanks system.

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“…Industrial Rotary Alumina Kiln is a large-scale sintering equipment that broadly applied in cement, metallurgical, chemical, and environmental protection industries. Because of the complicated kinetics, multi-variable characters, nonlinear reaction dynamics, longdelayed reactions and various raw material feed features, in a rotary kiln process is naturally hard to be simulated and modeled [1][2][3]. So far "there is no mathematical model that adequately reflected the process.…”

Section: Introductionmentioning

confidence: 99%

“…So far "there is no mathematical model that adequately reflected the process. and the product quality of IRAKP is usually measured after the clinker cooling down" [3]. Long-delayed reactions seriously affect the online control, and consequently has an effect on the product quality.…”

Section: Introductionmentioning

confidence: 99%

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A Mathematical Model for Control and Optimization of Industrial Rotary Alumina Kiln Process

Peng

Mayorga

2020

Wseas Transactions on Systems

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Temperature is a crucial factor for clinker quality in the Industrial Rotary Alumina Kiln Process(IRAKP). However, the characteristic of the high temperature, complex kinetics, multivariable, non-linearreaction kinetics, long-time delayed reaction and various raw materials make it difficult to accurately controlthe temperature in IRAKP through an existing control technology. This paper proposes a dual-responsesurface-based process control (DRSPC) system for the IRAKP in a novel manner. In the DRSPC, instead ofthe more precise and complicated nonlinear equations, the dual response surface models are fitted to describethe reaction kinetics in the IRAKP and track their standard deviations for stable operation purpose. Because asimultaneous consideration of multiple control targets could address the problem of unstable operation inkilns; the objectives of the DRSPC study are designed as optimizing product quality, minimizing energyconsumption and temperature fluctuations. Therefore, the proposed DRSPC goals are to achieve a uniformquality clinker, a high fuel efficiency, and a long refractory life. A weight optimization approach is used tohandle these multiple objective functions. The proposed DRSPC can estimate the working conditions of a kilnand predict some optimal manipulated variables to the control system in each control time interval forimproving the efficiency of IRAKP. The DRSPC is applied to a real IRAKP for demonstrating itsapplicability and advantages.

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An adaptive threshold estimation scheme for abrupt changes detection algorithm in a cement rotary kiln

Cited by 15 publications

References 18 publications

Kernelized relative entropy for direct fault detection in industrial rotary kilns

Kernelized relative entropy for direct fault detection in industrial rotary kilns

Improvement of Kernel Principal Component Analysis-Based Approach for Nonlinear Process Monitoring by Data Set Size Reduction Using Class Interval

A Mathematical Model for Control and Optimization of Industrial Rotary Alumina Kiln Process

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