Hybrid modelling of batch centrifuges as part of a generic object oriented beet Sugar Mill library

Mazaeda, Rogelio; Merino, Alejandro; Prada, César de; Acebes, Felipe

doi:10.1016/j.simpat.2011.12.003

Cited by 4 publications

(7 citation statements)

References 17 publications

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“…The task is, however, manageable using object-oriented modeling and simulation techniques that allows for the interconnection of parameterized instances of models by means of compatible ports in what is known as object-oriented (OO) or physical modeling. 44 This approach considers the hierarchical construction of models, reusing previously developed compatible objects such as valves, tubes, PID controllers, and tanks among other ancillary equipment. The circuitry conformed by product and steam conducting tubes, valves, and other connecting elements were not explicitly described above but are automatically inserted as requested by the described plant topology.…”

Section: Simulation Modelmentioning

confidence: 99%

Hierarchically coordinated economic MPC plantwide control of mixed continuous‐batch units in process industries with application to a beet sugar plant

Mazaeda

Cristea

Prada

2019

Optim Control Appl Methods

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Summary This paper deals with the optimal operation of processes that combine batch and continuous units. The proposed approach is based on a hierarchical architecture with two layers. The lower layer manages the optimal operation of the batch units in a noncentralized fashion, whereas the upper layer is concerned with the scheduling of the batch units and with their smooth integration with the continuous ones. The strategy has been applied to a challenging problem found at the interface of the fed‐batch crystallizers with the upstream evaporation section in beet sugar factories. The presented approach aims at designing a system that can effectively perform the plantwide economic optimization, specifically addressing the issue of energy efficiency, while complying with all process and operational constraints.

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Section: Simulation Modelmentioning

confidence: 99%

Hierarchically coordinated economic MPC plantwide control of mixed continuous‐batch units in process industries with application to a beet sugar plant

Mazaeda

Cristea

Prada

2019

Optim Control Appl Methods

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“…Production of sugar from sugar beet or sugar cane in the sugar industry mainly consists of these fundamental processes: sugar juice extraction (Almohammad et al, 2015; K. V. Loginova et al, 2011), juice purification (K. Loginova et al, 2012; Zhu & Mhemdi, 2016), thermal (Chantasiriwan, 2020) or nonthermal (Kujawa et al, 2015) juice concentration (Chantasiriwan, 2020), crystallization (Lauret et al, 2000; Reynoso et al, 2014), refrigeration (Smith et al, 2016), and centrifugal separation (Mazaeda et al, 2012). All these processes are carried out in the hydronic medium, where sucrose molecules are dissolved in water and undergo necessary treatments (Honig, 1963).…”

Section: Introductionmentioning

confidence: 99%

“…The two-phased combination and weakened saturated juice are called massecuite and weak runoff, respectively (Honig, 1963). Some scholars may use the word mother liquor to address the massecuite (Mazaeda et al, 2012).…”

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confidence: 99%

An enhanced hybrid model for batch sugar crystallization based on the pattern recognition for overall heat transfer coefficient using a machine learning approach

Azizi,

Mosharaf‐Dehkordi,

Fouladi

et al. 2024

J Food Process Engineering

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A hybrid model is developed and evaluated to simulate the heat and mass transfer in the crystallization unit of a sugar factory. While the mass transfer is modeled by using the kinetic growth rate model, the heat transfer is simulated by applying the energy balance to the model. Here, the overall convection heat transfer coefficient of the crystallizer's heat exchanger is considered as a temperature‐dependent function. As this makes the governing equations more realistic, it can help to increase the model accuracy. Additionally, a thorough examination of key practical equations and principles governing the sugar crystallization process is presented. A regression learner method is applied to extract the pattern of the overall heat transfer coefficient. According to our results, the regression learning model successfully predicts the heat transfer coefficient with an average 7% deviation from experimental results. For the hybrid model, an average deviation of about 10% is observed. The crystallizer's behavior is somehow linear, indicating a constant growth rate of sugar crystals. Furthermore, the heat transfer in the crystallizer is improved by increasing the working temperatures.Practical applicationsThe method and obtained results of this work could be used in the following practical purposes: to find the optimum working temperatures of crystallizers used in sugar industry and to predict total working time of batch crystallizers versus working parameters.

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“…The main cost driver in the production of sugar is the solid-liquid separation of sugar crystals from the mother liquor. This is the reason why, in the context of automation, major advances were made in modeling and optimization of centrifugal processes [3][4][5]. As one of the main influences on the results and economics of a centrifugal process is particle size and particle size distribution, it is an important quality attribute of sugar crystals [6].…”

Section: Introductionmentioning

confidence: 99%

“…For the production of sugar, the potential for optimization of extraction processes via a digital twin and the resulting increased process understanding has already been shown [9]. Furthermore, other process steps in the production of sugar have sophisticated process models [3][4][5]10]. An overview of the potential total process modeling is given in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Enabling Total Process Digital Twin in Sugar Refining through the Integration of Secondary Crystallization Influences

Vetter

Strube

2022

Processes

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Crystallization is the main thermal process resulting in the formation of solid products and, therefore, is widely spread in all kinds of industries, from fine chemicals to foods and drugs. For these high-performance products, a quality by design (QbD) approach is applied to maintain high product purity and steady product parameters. In this QbD-context, especially demanded in the foods and drugs industry, the significance of models to deepen process understanding and moving toward automated operation is steadily rising. To reach these aspired goals, besides major process influences like crystallization temperature, other impacting parameters have to be evaluated and a model describing these influences is sought-after. In this work, the suitability of a population balance-based physico-chemical process model for the production of sugar is investigated. A model overview is given and the resulting model is compared to a statistical DoE scheme. The resulting process model is able to picture the effects of secondary process parameters, alongside temperature or temperature gradients, the influences of seed crystal size and amount, stirrer speed, and additives.

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Hybrid modelling of batch centrifuges as part of a generic object oriented beet Sugar Mill library

Cited by 4 publications

References 17 publications

Hierarchically coordinated economic MPC plantwide control of mixed continuous‐batch units in process industries with application to a beet sugar plant

Hierarchically coordinated economic MPC plantwide control of mixed continuous‐batch units in process industries with application to a beet sugar plant

An enhanced hybrid model for batch sugar crystallization based on the pattern recognition for overall heat transfer coefficient using a machine learning approach

Enabling Total Process Digital Twin in Sugar Refining through the Integration of Secondary Crystallization Influences

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