Browsing by Author "Mansouri, M."

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    DEM-LBM approach to the onset of sand boiling
    (AIP, 2009) Mansouri, M.; Delenne, J.-Y.; El Youssoufi, M.S.; Seridi, A.
    We present a 3D numerical model applied to study the onset of the boiling phenomenon in a polydisperse granular material by computing the critical hydraulic gradient. The sample is constructed by means of the Discrete Element Method (DEM), then subjected to an upward increasing hydraulic gradient until the intergranular forces vanish. The hydrodynamic forces applied on solid grains are computed by means of the Lattice Boltzmann Method (LBM). To reduce calculation time, bi-periodic boundaries are implemented in both horizontal directions and for both fluid and solid (grains) materials. The results are in good agreement with the classical values of the critical hydraulic gradient
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    Machine Learning-Based Reduced Kernel PCA Model for Nonlinear Chemical Process Monitoring
    (Springer, 2020) Harkat, M.-F.; Kouadri, A.; Fezai, R.; Mansouri, M.; Nounou, H.; Nounou, M.
    Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and fault detection. Kernel PCA (KPCA) is the nonlinear form of the PCA, which better exploits a complicated spatial structure of high-dimensional features, where a kernel function implicitly defines a nonlinear transformation into a feature space wherein standard PCA is performed. Despite its success and flexibility, conventional KPCA might not perform properly because the use of KPCA for a large-sized training dataset imposes a high computational load and a significant storage memory space since the required elements used for modelling have to be saved and used for monitoring, as well. To address this problem, a reduced KPCA (RKPCA) for fault detection of chemical processes is developed. RKPCA is a novel machine learning tool which merges dimensionality reduction, supervised learning as well as kernel selection. This novel method is used to reduce the size of recorded measurements while maintaining the most relevant data features. The removed observations, including redundant samples that are linearly correlated in the collected measurements, are described by only one sample. The obtained uncorrelated observations via PCA technique are then employed to identify the reduced KPCA model by which Hotelling T2 and squared predictive error or Q statistics are extracted for detection purposes. Besides, their combination is also used as a detection index. The performance of the proposed process monitoring technique is illustrated through its application to Tennessee Eastman process. The obtained results demonstrate the effectiveness of the developed RKPCA technique in detecting various faults with remarkably reduced computation time and memory storage space
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    Numerical model for the computation of permeability of a cemented granular material
    (Elsevier, 2011) Mansouri, M.; Delenne, J.-Y.; Seridi, A.; El Youssoufi, M.S.
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    Reliable fault detection and diagnosis of large-scale nonlinear uncertain systems using interval reduced kernel PLS
    (Institute of Electrical and Electronics Engineers, 2020) Fezai, R.; Abodayeh, K.; Mansouri, M.; Kouadri, A.; Harkat, M.-F.; Nounou, H.; Nounou, M.
    Kernel partial least squares (KPLS) models are widely used as nonlinear data-driven methods for faults detection (FD) in industrial processes. However, KPLS models lead to irrelevant performance over long operation periods due to process parameters changes, errors and uncertainties associated with measurements. Therefore, in this paper, two different interval reduced KPLS (IRKPLS) models are developed for monitoring large scale nonlinear uncertain systems. The proposed IRKPLS models present an interval versions of the classical KPLS model. The two proposed IRKPLS models are based on the Euclidean distance between interval-valued observations as a dissimilarity metric to keep only the more relevant and informative samples. The first proposed IRKPLS technique uses the centers and ranges of intervals to estimate the interval model, while the second one is based on the upper and lower bounds of intervals for model identification. These obtained models are used to evaluate the monitored interval residuals. The aforementioned interval residuals are fed to the generalized likelihood ratio test (GLRT) chart to detect the faults. In addition to considering the uncertainties in the input-output systems, the new IRKPLS-based GLRT techniques aim to decrease the execution time when ensuring the fault detection performance. The developed IRKPLS-based GLRT approaches are evaluated across various faults of the well-known Tennessee Eastman (TE) process. The performance of the proposed IRKPLS-based GLRT methods is evaluated in terms of missed detection rate, false alarms rate, and execution time. The obtained results demonstrate the efficiency of the proposed approaches, compared with the classical interval KPLS