Sami OTHMAN

Maître de Conférences

  • Systèmes Non Linéaires et Procédés

Diagnostic et commande pour les systèmes non linéaires


Bureau 1 LAGEP Université Claude Bernard Lyon 1, bât 308G ESCPE-Lyon, 43 bd du 11 Novembre 1918 G317 Villeurbanne 69622 France

Téléphone bureau 1: 04 72 43 18 88

Bio

 

 

Modelling particle growth under saturated and starved conditions in emulsion polymerization

date:2016
références bibliographiques:

Brunier, Barthélémy, Nida SHEIBAT -OTHMAN, Sami Othman, Yves Chevalier, and Elodie Bourgeat-Lami. “Modelling Particle Growth under Saturated and Starved Conditions in Emulsion Polymerization.” , 2016. doi:10.1002/cjce.22640.

The Canadian Journal of Chemical Engineering

 

Combination of a Model-Based Observer and Support Vector Machines for Fault Detection of Wind Turbines

date:2014
références bibliographiques:

Laouti, Nassim, Sami Othman, Mazen Alamir, and Nida SHEIBAT -OTHMAN. “Combination of a Model-Based Observer and Support Vector Machines for Fault Detection of Wind Turbines.” , 2014.

International Journal of Automation and Computing

 

Support vector machines combined to observers for fault diagnosis in chemical reactors

A hybrid data/model-based approach is proposed for fault detection and isolation for chemical reactions in jacketed stirred vessels. Using data-based methods in high nonlinear systems requires training data to include a wide range of varying operations to ensure correct fault isolation. If such data is not available, a model-based approach can be used to enhance fault isolation. But, observers require a relatively precise process model, which is also not always available. In this work, we propose to combine an observer with statistical data-based methods (support vector machines, SVM) for fault detection in order to avoid at the time precise process modelling (necessary for model-based approach) and great number of training data (necessary for data-based approach). An interesting case study that falls in this category is a chemical stirred tank reactor, with high nonlinear reactions. Therefore, a simplified process model is used as a starting point to develop an observer for fault isolation. The used process model is corrected using information from SVM when no fault is detected. The methodology is validated experimentally in lab-scale and pilot-scale polymerisation reactors. For processes with linear dynamics, model-free SVM classification was found sufficient to detect and isolate sensor and actuator faults.

date:2014
références bibliographiques:

SHEIBAT -OTHMAN, Nida, Nassim Laouti, Jean-Pierre Valour, and Sami Othman. “Support Vector Machines Combined to Observers for Fault Diagnosis in Chemical Reactors.” 92 (2014): 685–95. doi:10.1002/cjce.21881.
Pages:685-695

The Canadian Journal of Chemical Engineering

 

Support vector machines combined to observers for fault diagnosis in chemical teactors

date:2013
références bibliographiques:

Sheibat-Othman, Nida, Nassim Laouti, Jean-Pierre Valour, and Sami Othman. “Support Vector Machines Combined to Observers for Fault Diagnosis in Chemical Teactors.” (2013): n/a–n/a. doi:10.1002/cjce.21881.
Pages:n/a-n/a

The Canadian Journal of Chemical Engineering

 

Inferential MIMO predictive control of the particle size distribution in emulsion polymerization

A new inferential 2-step multiple input/multiple output (MIMO) model predictive control (MPC) of the particle size distribution (PSD) in emulsion polymerization processes is proposed. The bulk-like model describing the PSD is used with the material balances of initiator, radicals, monomer and surfactant. The inferential 2-step control strategy uses two measurements available online (without delay): the concentration of surfactant in the aqueous phase by conductimetry, and the concentration of monomer by calorimetry. In a first step, the optimal trajectory of surfactant concentration leading to the target PSD is calculated offline. In a second step, a multivariable model predictive control manipulates online the monomer and surfactant flow rates in order to track the precalculated surfactant concentration trajectory and to maximise the monomer concentration in the polymer particles in a constrained set-point tracking. Two control strategies are compared (nonlinear MPC and linearized MPC) with and without modelling errors.

date:2012
références bibliographiques:

Da Silva, Bruno, Pascal Dufour, Nida SHEIBAT -OTHMAN, and Sami Othman. “Inferential MIMO Predictive Control of the Particle Size Distribution in Emulsion Polymerization.” 38 (2012): 115–25. doi:10.1016/j.compchemeng.2011.11.003.
Pages:115-125

Computers & Chemical Engineering

 

Multivariable control of the polymer molecular weight in emulsion polymerization processes

date:2011
références bibliographiques:

Sheibat-Othman, Nida, Sami Othman, Olivier Boyron, and Mazen Alamir. “Multivariable Control of the Polymer Molecular Weight in Emulsion Polymerization Processes.” 21, no. 6 (2011): 861–73. doi:10.1016/j.jprocont.2011.03.010.
Pages:861-873

Journal of Process Control

 

Measurement based modeling and control of bimodal particle size distribution in batch emulsion polymerization

In this article, a novel modeling approach is proposed for bimodal Particle Size Distribution (PSD) control in batch emulsion polymerization. The modeling approach is based on a behavioral model structure that captures the dynamics of PSD. The parameters of the resulting model can be easily identified using a limited number of experiments. The resulting model can then be incorporated in a simple learning scheme to produce a desired bimodal PSD while compensating for model mismatch and/or physical parameters variations using very simple updating rules. © 2010 American Institute of Chemical Engineers AIChE J, 2010

date:2010
références bibliographiques:

Alamir, Mazen, Nida Sheibat-Othman, and Sami Othman. “Measurement Based Modeling and Control of Bimodal Particle Size Distribution in Batch Emulsion Polymerization.” 56, no. 8 (2010): 2122–36. doi:10.1002/aic.12148.
Pages:2122–2136

AIChE Journal