Soft-sensor for industrial sugar crystallization: On-line mass of crystals, concentration and purity measurement

Damour, Cédric; Benne, Michel; Grondin-Pérez, Brigitte; Chabriat, Jean-Pierre

doi:10.1016/j.conengprac.2010.03.005

Cited by 19 publications

(18 citation statements)

References 18 publications

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“…Desai, Badhe, Tambe, and Kulkarni (2006) adopted a new machine learning modeling method, called support vector regression (SVR), to construct soft sensor model for batch cane sugar crystallization process. 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.…”

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

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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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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

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“…However, the corrosiveness of the desiccant solution reduces the long-term reliability of online sensors and requires frequent calibration and maintenance. The high cost also decreases the practicability of applying an online concentration sensor for monitoring, which is a driving force of some concentration soft sensor developments. , On the other hand, the properties of liquid desiccant solution used in the LDDS have complicated mathematical expressions, which increase the difficulty in deriving a solution from physical relations . Therefore, a more intelligent method is needed to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

Simplified Soft Sensing Model Applied in the Centralized Regenerator of a Distributed Operating Liquid Desiccant Dehumidification System

Cai²,

Wang³

et al. 2016

Ind. Eng. Chem. Res.

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A distributed operating Liquid Desiccant Dehumidification System (LDDS) designed for commercial building applications allows one regenerator to handle multiple dehumidifier units, which provides the potential to apply low-grade energy in a centralized form for desiccant solution regeneration. An online soft sensing model with the Adaptive Network-based Fuzzy Inference Systems (ANFIS) structure is developed to satisfy the operating scheme. The number of input variables is reduced by the regeneration mass transfer model, and the fuzzy interference is optimized by Genetic Algorithm (GA) with a constrained objective function. The accuracy of the soft sensing models with different simplicity levels are validated and compared in case studies. Results show that the proposed soft sensing model is accurate, and the simplification approaches can reduce the size of the model significantly without affecting the predictive accuracy.

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“…In the sugar crystallization process monitoring field, the vast majority of researchers focus on the advanced control techniques [3][4][5][6][7][8][9][10][11][12][13][14][15][16] like nonlinear control, fuzzy predictive control, neural network with PID and robust control. However, the detection sensor devices for process parameters like supersaturation, concentration, purity, crystal content and crystal size uniformity etc., are hard to be measured in the real world, which makes the application of all those advanced control techniques stay at theoretical stage but not an actual one [17].…”

Section: Introductionmentioning

confidence: 99%

“…Crystallization process consists of A crystallization, B crystallization and C crystallization these three phases in the cane sugar production process [16][17]. In order to…”

Section: Introductionmentioning

confidence: 99%

Soft-sensor for Mother Liquor Purity in The Industrial Cane Sugar Crystallization Based on Extreme Learning Machine and Particle Swarm Optimization

Meng¹,

Pang²,

Chen³

et al. 2014

IJCA

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Soft-sensor for industrial sugar crystallization: On-line mass of crystals, concentration and purity measurement

Cited by 19 publications

References 18 publications

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

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

Simplified Soft Sensing Model Applied in the Centralized Regenerator of a Distributed Operating Liquid Desiccant Dehumidification System

Soft-sensor for Mother Liquor Purity in The Industrial Cane Sugar Crystallization Based on Extreme Learning Machine and Particle Swarm Optimization

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