Modeling and Simulation of Complex Nonlinear Dynamic Processes Using Data Based Models: Application to Photo-Fenton Process

Shokry, Ahmed; Audino, Francesca; Vicente, Patricia; Escudero, Gerard; Moya, Montserrat Pérez; Graells, Moisès; Espuña, Antonio

doi:10.1016/b978-0-444-63578-5.50027-x

Cited by 10 publications

(11 citation statements)

References 6 publications

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“…More recently, semi-empirical models combining a first principles modeling approach with empirical observations have been proposed by Cabrera Reina et al (2012Reina et al ( , 2015. Finally, advanced multivariate techniques named data-based modeling (DBM) can also be found (Shokry et al 2015(Shokry et al , 2017.…”

Section: Introductionmentioning

confidence: 99%

A novel modeling approach for a generalizable photo-Fenton-based degradation of organic compounds

Audino

Pérez-Moya

Graells

et al. 2020

Environ Sci Pollut Res

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This work aims at proposing and validating a model that can be exploited for the future development of industrial applications (e.g., process design and control) of Fenton and photo-Fenton processes. Hence, a compromise modeling solution has been developed between the non-generalizable accuracy of the first principles models (FPMs) and the oversimplification of the empirical models (EMs). The work presents a novel model of moderate complexity that is simplified enough to be generalizable and computationally affordable, while retaining physical meaning. The methodology is based on a general degradation mechanism that can be algorithmically generated from the carbon number of the target compound, as well as from the knowledge of two kinetic parameters, one for the faster initial rate and the other one for the subsequent degradation steps. The contaminant degradation mechanism has been combined with an appropriately simplified implementation of the well-known Fenton and photo-Fenton kinetics. This model describes the degradation not only of the target compound and of the oxidant, but also of total organic carbon (TOC), which is used to define the overall quality of the water. Experimental design techniques were used along with a non-conventional modeling methodology of programmable process structures (PPS). This novel modeling approach was applied and validated on the degradation of three model compounds. A successful prediction of the evolution of the contaminants H 2 O 2 and TOC was confirmed and assessed by the root mean square error (RMSE).

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Section: Introductionmentioning

confidence: 99%

A novel modeling approach for a generalizable photo-Fenton-based degradation of organic compounds

Audino

Pérez-Moya

Graells

et al. 2020

Environ Sci Pollut Res

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“…mechanical and electrical components and connections, etc. ), usually are not taken into account by the FPMs, which badly affects their prediction accuracy (Jin et al, 2014;Kadlec et al, 2011;Shokry et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…However, these methods are basically developed for linear modeling and they may oversimplify the description of complex nonlinear behaviors (Jin et al, 2015;Nagy, 2007). Although some versions of these techniques have been developed to inexpensively handle nonlinearities (as kernel PCR and kernel PLS), more advanced and efficient techniques have been proposed for soft-sensing highly nonlinear processes (Kadlec et al, 2009;Nagy, 2007;Shokry et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, ANNs particularly suffer from the curse of dimensionality and require laborious tuning (selection of the network structure and configuration; as number of layers, number neurons in each layer, transfer function type, training method etc.) to achieve a reliable model fitting (Azman & Kocijan, 2007;Davis & Ierapetritou, 2007;Caballero & Grossmann, 2008;Shokry et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…The SVR model (Vapnik, 1995) consists of a subset of specific training data (support vector) that compose the margins of the simplest functional shape within which the prediction error for all training data is acceptable. SVR techniques have very good generalization properties, and rapidity of tuning (associated to the optimization problem solution time for the support vectors selection) (Yan et al, 2004;Jain et al, 2007;Kadlec et al, 2009;Shokry et al, 2015). However, the effort and the time required to select the parameters of the SVR model -prior to the optimization-, as the penalty cost, the error margin and the variance become a major limitation Shokry et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Data-driven soft-sensors for online monitoring of batch processes with different initial conditions

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Computers & Chemical Engineering

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A soft-sensing methodology applicable to batch processes operated under changeable initial conditions is presented. These cases appear when the raw materials specifications differ from batch to batch, different production scenarios should be managed, etc. The proposal exploits the capabilities of the machine learning techniques to provide practical soft-sensing approach with minimum tuning effort in spite of the fact that the inherent dynamic behavior of batch systems are tracked through other online indirect measurements. Current data modelling techniques have been also tested within the proposed methodology to demonstrate its advantages. Two simulation casestudies and a pilot-plant case-study involving a complex batch process for wastewater treatment are used to illustrate the problem, to assess the modelling approach and to compare the modelling techniques. The results reflect a promising accuracy even when the training information is scarce, allowing significant reductions in the cost associated to batch processes monitoring and control.

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Modeling and Simulation of Complex Nonlinear Dynamic Processes Using Data Based Models: Application to Photo-Fenton Process

Cited by 10 publications

References 6 publications

A novel modeling approach for a generalizable photo-Fenton-based degradation of organic compounds

A novel modeling approach for a generalizable photo-Fenton-based degradation of organic compounds

Data-driven soft-sensors for online monitoring of batch processes with different initial conditions

Multiparametric Metamodels for Model Predictive Control of Chemical Processes

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