Knowledge-based hybrid modelling of a batch crystallisation when accounting for nucleation, growth and agglomeration phenomena

Georgieva, Pétia; Meireles, Magda; Azevedo, S. Feyo de

doi:10.1016/s0009-2509(03)00260-4

Cited by 79 publications

(85 citation statements)

References 19 publications

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“…Sugar production is characterized by strongly non-linear and non-stationary dynamics and goes naturally through a sequence of relatively independent stages: charging, concentration, seeding, setting the grain, crystallization (the main phase), tightening and discharge (Georgieva et al, 2003). Therefore the operation strategy is formulated as a cascade of individual control loops for each of the stages (Fig.…”

Section: Problem Solutionmentioning

confidence: 99%

“…• Case 1 (Generated data): Randomly generated bounded inputs ( i u ) are introduced to a simulator of a general evaporative sugar crystallization process introduced in Georgieva et al, 2003. It is a system of nonlinear differential equations for the mass and energy balances with the operation parameters computed based on empirical relations (for no stationary parameters) or keeping constant values (for stationary parameters).…”

Section: Neural Network Model Predictive Controlmentioning

confidence: 99%

“…The output predictions are provided either by a simplified discrete model (with the main operation parameters kept constant) or by a trained ANN model (5-8). A process simulator was developed based on a detailed phenomenological model (Georgieva et al, 2003). Realistic disturbances and noise are introduced substituting the analytical expressions for the vacuum pressure, brix and temperature of the feed flow, pressure and temperature of the steam with original industrial data (without any preprocessing(Scenario-2)).…”

Section: Control Loop 1 Controlmentioning

confidence: 99%

“…• Scenario -1: The simulation uses, like process, the set of equations differentials proposed in (Georgieva et al 2003) with empirical operation parameters.…”

Section: Control Loop 1 Controlmentioning

confidence: 99%

“…• Scenario -2: The simulation uses, like process, the set of equations differentials proposed in (Georgieva et al 2003), but are used like operation parameter e real industrial data batch not used in neural network training. Time trajectories of the controlled and the manipulated variables for the control loop 1, 2 and 4 of one batch (Batch 1) are depicted in Figs.…”

Section: Control Loop 1 Controlmentioning

confidence: 99%

See 4 more Smart Citations

Model Predictive Control Strategies for Batch Sugar Crystallization Process

Suarez¹,

Georgieva²,

Azevedo³

2011

Advanced Model Predictive Control

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Section: Problem Solutionmentioning

confidence: 99%

Section: Neural Network Model Predictive Controlmentioning

confidence: 99%

Section: Control Loop 1 Controlmentioning

confidence: 99%

“…• Scenario -1: The simulation uses, like process, the set of equations differentials proposed in (Georgieva et al 2003) with empirical operation parameters.…”

Section: Control Loop 1 Controlmentioning

confidence: 99%

Section: Control Loop 1 Controlmentioning

confidence: 99%

See 3 more Smart Citations

Model Predictive Control Strategies for Batch Sugar Crystallization Process

Suarez¹,

Georgieva²,

Azevedo³

2011

Advanced Model Predictive Control

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Enhancing decanter centrifuge process design with data‐driven material parameters in multi‐compartment modeling

Zhai,

Ehret,

Rhein

et al. 2024

J Adv Manuf & Process

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Predicting the separation performance of decanter centrifuges is challenging due to dynamic events within the apparatus. Current methods for designing decanter centrifuges rely on simplified models, often leading to inaccuracies. Consequently, manufacturers must perform time‐intensive pilot scale experiments to derive their own correction factors. Growing computing power sparks interest in alternative modeling strategies. Grey box models (GBM) combine mechanistic white box models (WBM) and data‐driven black box models (BBM), with the optimal structure (parallel or serial) varying by application. For modeling decanter centrifuges, we propose a serial GBM that comprises an artificial neural network that outputs unknown material parameters into a first‐principle multi‐compartment model. Comparing this approach to alternative data‐driven modeling strategies (pure BBM, parallel GBM), we conclude that the serial GBM excels in terms of extrapolation, prediction ability, and transparency while also enabling a better comprehension of the separation process.

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Hybrid Semi‐parametric Modeling in Separation Processes: A Review

McBride

Medina

Sundmacher

2020

Chemie Ingenieur Technik

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This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. Separations of mixtures play a critical role in chemical industries. Over the last century, the knowledge in the area of chemical thermodynamics and modeling of separation processes has been substantially expanded. Since the models are still not completely accurate, hybrid models can be used as an alternative that allows to retain existing knowledge and augment it using data. This paper explores some of the weaknesses in the current knowledge in separations design, simulation, optimization, and operation, and presents many examples where data-driven and hybrid models have been used to facilitate these tasks.

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Knowledge-based hybrid modelling of a batch crystallisation when accounting for nucleation, growth and agglomeration phenomena

Cited by 79 publications

References 19 publications

Model Predictive Control Strategies for Batch Sugar Crystallization Process

Model Predictive Control Strategies for Batch Sugar Crystallization Process

Enhancing decanter centrifuge process design with data‐driven material parameters in multi‐compartment modeling

Hybrid Semi‐parametric Modeling in Separation Processes: A Review

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