References of "Computers and Chemical Engineering"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailClassification of states and model order reduction of large scale Chemical Vapor Deposition processes with solution multiplicity
Koronaki, E.D.; Gkinis, P.A.; Beex, Lars UL et al

in Computers and Chemical Engineering (2018), 121

This paper presents an equation-free, data-driven approach for reduced order modeling of a Chemical Vapor Deposition (CVD) process. The proposed approach is based on process information provided by ... [more ▼]

This paper presents an equation-free, data-driven approach for reduced order modeling of a Chemical Vapor Deposition (CVD) process. The proposed approach is based on process information provided by detailed, high-fidelity models, but can also use spatio-temporal measurements. The Reduced Order Model (ROM) is built using the method-of-snapshots variant of the Proper Orthogonal Decomposition (POD) method and Artificial Neural Networks (ANN) for the identification of the time-dependent coefficients. The derivation of the model is completely equation-free as it circumvents the projection of the actual equations onto the POD basis. Prior to building the model, the Support Vector Machine (SVM) supervised classification algorithm is used in order to identify clusters of data corresponding to (physically) different states that may develop at the same operating conditions due to the inherent nonlinearity of the process. The different clusters are then used for ANN training and subsequent development of the ROM. The results indicate that the ROM is successful at predicting the dynamic behavior of the system in windows of operating parameters where steady states are not unique. [less ▲]

Detailed reference viewed: 105 (9 UL)
Full Text
Peer Reviewed
See detailUncertainty quantification and global sensitivity analysis of complex chemical process using a generalized polynomial chaos approach
Duong, Pham UL

in Computers and Chemical Engineering (2016), 90(7), 23-30

Uncertainties are ubiquitous and unavoidable in process design and modeling. Because they can significantly affect the safety, reliability and economic decisions, it is important to quantify these ... [more ▼]

Uncertainties are ubiquitous and unavoidable in process design and modeling. Because they can significantly affect the safety, reliability and economic decisions, it is important to quantify these uncertainties and reflect their propagation effect to process design. This paper proposes the application of generalized polynomial chaos (gPC)-based approach for uncertainty quantification and sensitivity analysis of complex chemical processes. The gPC approach approximates the dependence of a process state or output on the process inputs and parameters through expansion on an orthogonal polynomial basis. All statistical information of the interested quantity (output) can be obtained from the surrogate gPC model. The proposed methodology was compared with the traditional Monte-Carlo and Quasi Monte-Carlo sampling-based approaches to illustrate its advantages in terms of the computational efficiency. The result showed that the gPC method reduces computational effort for uncertainty quantification of complex chemical processes with an acceptable accuracy. Furthermore, Sobol’s sensitivity indices to identify influential random inputs can be obtained directly from the surrogated gPC model, which in turn further reduces the required simulations remarkably. The framework developed in this study can be usefully applied to the robust design of complex processes under uncertainties. [less ▲]

Detailed reference viewed: 151 (7 UL)
Full Text
Peer Reviewed
See detailAssessment of the potentials of implicit integration method in discrete element modelling of granular matter
Samiei, Kasra UL; Peters, Bernhard UL; Bolten, Matthias et al

in Computers and Chemical Engineering (2013)

Discrete element method (DEM) is increasingly used to simulate the motion of granular matter in engineering devices. DEM relies on numerical integration to compute the positions and velocities of ... [more ▼]

Discrete element method (DEM) is increasingly used to simulate the motion of granular matter in engineering devices. DEM relies on numerical integration to compute the positions and velocities of particles in the next time step. Typically, explicit integration methods are utilized in DEM. This paper presents a systematic assessment of the potentials of implicit integration in DEM. The results show that though the implicit integration enables larger time steps to be used compared to the common explicit methods, the overall speed up is overruled by higher computational costs of the implicit method. [less ▲]

Detailed reference viewed: 139 (10 UL)
Full Text
Peer Reviewed
See detailEvaluation of potassium chloride emissions applying the Discrete Particle Method (DPM)
Peters, Bernhard UL; Smula, Joanna UL

in Computers and Chemical Engineering (2011)

Detailed reference viewed: 119 (4 UL)