Hybrid modeling based on mechanistic and data-driven approaches for cane sugar crystallization

Meng, Yanmei; Yu, Shuangshuang; Zhang, Jinlai; Qin, Johnny; Dong, Zhen; Lu, Guancheng; Pang, Haifeng

doi:10.1016/j.jfoodeng.2019.03.026

Cited by 53 publications

(28 citation statements)

References 22 publications

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“…The complexity of these processes is captured by the growth, nucleation, and agglomeration kinetic rate expressions. ANN and SVM have been connected in serial arrangement to mass, energy, and population balances to describe industrial crystallizers , , . The roll of the surrogates in these applications has been precisely to capture the behavior of the growth, nucleation, and agglomeration kinetics.…”

Section: Overview Of the Use Of Hybrid Models In Separation Processesmentioning

confidence: 99%

“…The roll of the surrogates in these applications has been precisely to capture the behavior of the growth, nucleation, and agglomeration kinetics. It has been shown that hybrid models are more flexible (i.e., high‐quality predictions at different operating conditions) than traditional modeling techniques (fitting empirical equations using nonlinear programming (NLP) optimization methods) and are significantly more consistent with experimental data , .…”

Section: Overview Of the Use Of Hybrid Models In Separation Processesmentioning

confidence: 99%

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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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Section: Overview Of the Use Of Hybrid Models In Separation Processesmentioning

confidence: 99%

Section: Overview Of the Use Of Hybrid Models In Separation Processesmentioning

confidence: 99%

Hybrid Semi‐parametric Modeling in Separation Processes: A Review

McBride

Medina

Sundmacher

2020

Chemie Ingenieur Technik

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“…As shown in Table 2, the performance of the hybrid model was better than that of the single model. Previous study has shown that a single model has worse performance in a complex situation [29]. A single model only focuses on a certain aspect, and it is easy to ignore some information in complex data; however, a hybrid model can combine the advantages of different single models to achieve complementary effects.…”

Section: B Overall Model Buildingmentioning

confidence: 99%

An Approach for in-Line Control of Moisture Content During Green Tea Processing

Zhao

Chen

et al. 2020

IEEE Access

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During preliminary tea processing, moisture content is an important consideration affecting the tea quality. Traditionally, the moisture content of tea leaves was manually controlled by the joint action of multiple processing units, and maintaining stability was difficult. In this paper, a multi-unit collaborative strategy was proposed for controlling moisture content in preliminary tea processing. Multivariate methods including polynomial regression, radical basis function neural network (RBFNN), and least squares support vector machine (LSSVM) were used to establish models for moisture content prediction in the first fixation, second fixation, and drying units, with minimal root mean square errors (RMSEs) of 1.34%, 0.86%, and 0.13%, respectively. The combination of RBFNN and LSSVM, with a RMSE of 0.03%, was used to model the preliminary processing of whole tea. Rough set data mining technology was used to obtain the optimum ranges of moisture content and critical process parameters. Finally, a Monte Carlo simulation experiment was carried out within the optimum range, and moisture content design spaces for the single unit and the whole processing line were obtained. With the proposed approach, the stability of the final moisture content of tea can be improved, which is of great significance for improving tea quality and accelerating the automation of tea production.

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“…Damour, Benne, Grondin‐Perez, and Chabriat (2010) designed a model‐based soft‐sensor to improve the process monitoring and control in industrial sugar crystallization. Meng, Lan, et al (2019) and Meng, Yu, et al (2019) put forward hybrid model for cane sugar crystallization process based on mechanism and data‐driven, which can be used in actual industrial production process. These are the applied method of data‐driven modeling in sugar crystallization, and show effect of data‐driven modeling method when it was difficult to build mechanism model or optimize variables directly through mechanism modeling, however, there are less model for CSD in cane sugar crystallization process to improve the quality of sugar crystal and increase production efficiency.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven modeling for crystal size distribution parameters in cane sugar crystallization process

Meng

Tao

et al. 2021

J Food Process Engineering

Self Cite

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Crystal size distribution (CSD) is important in evaluating crystal quality in cane sugar crystallization process. Due to the complex non-linearity, time-delay and strong coupling in cane sugar crystallization process, it is difficult to directly modeling in the mechanism of cane sugar crystallization process to obtain CSD parameters. In order to obtain two main CSD parameters so that to achieve better control and production of cane sugar, this article constructs a data-driven model based on least squares support vector regression (LSSVR) and particle swarm optimization (PSO). Based on LSSVR, the model takes the easy to measureable variables (massecuite brix, massecuite level, massecuite temperature, steam pressure, feeding volume, and vacuum degree) as input variables, and outputs sugar CSD parameters (crystal average size, coefficient of variation of crystal size). PSO algorithm is used to optimize the key parameters of primary model to get better modeling performance. Compared with other modeling methods such as back propagation, extreme learning machine, radial basis function, and SVR, the constructed PSO-LSSVR model has obvious advantages over other models in learning speed and predictive effect, generalization ability. This model has potential to be applied to the control system of cane sugar crystallization process and get better product of sugar.

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Hybrid modeling based on mechanistic and data-driven approaches for cane sugar crystallization

Cited by 53 publications

References 22 publications

Hybrid Semi‐parametric Modeling in Separation Processes: A Review

Hybrid Semi‐parametric Modeling in Separation Processes: A Review

An Approach for in-Line Control of Moisture Content During Green Tea Processing

Data‐driven modeling for crystal size distribution parameters in cane sugar crystallization process

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