Predictive Model for Post-Seeding Super-Saturation of Sugar Massecuite in a Fed-Batch Evaporative Crystalliser (original) (raw)
Related papers
Advances in Super-Saturation Measurement and Estimation Methods for Sugar Crystallisation Process
ETP International Journal of Food Engineering, 2016
The super-saturation level of the massecuite is an important quality parameter for sugar crystallisation process, as it determines the seeding point, contributes to the quality of crystals and the cost of production. This paper critically appraises the current measurement and estimation methods for super-saturation level of sugar massecuite. On the one hand, the review shows that the current online hardware sensors lack the necessary accuracy, as the variable to be measured is a multivariable function of many unknowns. Moreover, the sensors require regular maintenance and recalibration to be able to obtain reliable readings. On the other hand, the review shows that soft (model or software-based) sensors are capable of offering solutions to some of the challenges of the online hardware sensors. However, their predictions depend on the hardware sensors for some input data and the available sugar crystallisation models are not in the form suitable for online estimation of super-saturation level of the sugar massecuite. It is concluded that the effective measurement/estimation and control of super-saturation of sugar massecuite is still a challenge in the sugar processing industry. It is therefore recommended that soft sensors should be introduced to complement the online hardware sensors.
A Regression Model for Estimating Sugar Crystal Size in a Fed-Batch Vacuum Evaporative Crystalliser
2019
Crystallisation occurs in a large group of biotechnological, food, pharmaceutical and chemical processes. These processes are usually carried out in a batch or fed-batch mode. Traditionally, in sugar industry, the crystals quality is examined at the end of the process. Consequently, lack of real time measurement of sugar crystal size in a fed-batch vacuum evaporative crystalliser hinders the feedback control and optimisation of the crystallisation process. A mathematical model can be used for online estimation of the sugar crystal size. Unfortunately, the existing sugar crystallisation models are not in the form suitable for online implementation. Therefore, based on these existing models and seven process variables namely temperature (T), vacuum pressure (Pvac), feed flowrate (Ff), steam flowrate (Fs), crystallisation time (t), initial super-saturation (S0) and initial crystal size (L0), 128 data sets which were obtained from a 2-level factorial experimental design using MINITAB 14...
Control Engineering Practice, 2010
This paper deals with the design of a model-based soft-sensor to improve the process monitoring and control in industrial sugar crystallization. This soft-sensor is based on an original model dedicated to the last stage of crystallization, avoiding the solving of the population balance. Additional information like the mass of crystals in the solution, the concentration of dissolved sucrose and the purity are relevant to improve the manufacturing process. As these physicochemical properties are not measurable on-line, a model based soft-sensor is developed. The effectiveness of the soft sensor is demonstrated using real plant data from an industrial crystallization process.
2009
Monitoring and control of cane sugar crystallization processes depend on the stability of the supersaturation (σ ) state. The most widely used information to represent σ is the electrical conductivity κ of the solutions. Nevertheless, previous studies point out the shortcomings of this approach: κ may be regarded as inappropriate to guarantee an accurate estimation of σ in impure solutions. To improve the process control efficiency, additional information is necessary. The mass of crystals in the solution ( c m ) and the solubility (mass ratio of sugar to water / s w m m ) are relevant to complete information. Indeed, c m inherently contains information about the mass balance and / s w m m contains information about the supersaturation state of the solution. The main problem is that c m and / s w m m are not available on-line. In this paper, a model based soft-sensor is presented for a final crystallization stage (C sugar). Simulation results obtained on industrial data show the rel...
On-line multivariate statistical monitoring of a fed-batch sugar crystallisation process
Elsevier eBooks, 2004
The paper describes statistical process control tools that have been applied for the online monitoring of an industrial fed-batch sugar crystallisation process. The process is characterised by distinct operating phases during operation and the presence of strong non-linear, dynamic relationships between the variables. Process performance is controlled manually by operators based on their experience. The success of each batch is determined at the end of the batch run through off-line crystal size distribution measurements. The development and application of a monitoring tool based on the online frequent process measurements could be of significant benefit, since it could realise early detection of operational changes, process faults and hence a reduction in the number of off-specification batches.
On-line monitoring of a sugar crystallization process
Computers & Chemical Engineering, 2005
The present paper reports a comparative evaluation of four multivariate statistical process control (SPC) techniques for the on-line monitoring of an industrial sugar crystallization process. The process itself is challenging since it is carried out in multiple phases and there exists strong non-linear and dynamic effects between the variables. The methods investigated include classical on-line univariate statistical process control, batch dynamic principal component analysis (BDPCA), moving window principal component analysis (MWPCA), batch observation level analysis (BOL) and time-varying state space modelling (TVSS). The study is focused on issues of on-line detection of changes in crystallization process operation, the early warning of process malfunctions and potential production failures; problems that have not been directly addressed by existing statistical monitoring schemes. The results obtained demonstrate the superior performance of the TVSS approach to successfully detect abnormal events and periods of bad operation early enough to allow bad batches and related losses in amounts of recycled sucrose to be significantly reduced.
Steady state modeling and simulation of an industrial sugar continuous crystallizer
Computers & Chemical Engineering, 2001
The profile of supersaturation along a continuous crystallizer of sugar factories, is the decisive factor that determines the performance of this apparatus. In order to control this profile, a mathematical model was developed taking into account the main physicochemical phenomena involved in crystallization process. The model is based on flow pattern, which was assumed and validated against plant measurements using a tracer test. The steady state mathematical model developed describes the most important aspects of the crystallizer behavior in each compartment: supersaturation, crystal size distribution and flow rate of the product crystals. The model can also describe the undesirable behavior such as dissolution and nucleation. Validation of the developed model was performed using industrial data. A parametric sensitivity study confirmed that the syrup supply distribution is the main variable that should be manipulated to achieve good performance for the crystallizer.
Journal of Food Engineering, 2005
In Bois Rouge sugar mill, the sugar cane crystallisation process is performed in three stages. During the third stage: C crystallisation, the mother liquor exhaustion is the main function, because it is the ultimate step of transforming saccharose in liquid form into crystals. In order to build a predictive scheme to control the C crystallisation, we decided to identify a multi-step ahead predictor of the mother liquor purity. The identification is performed using databases collected in situ. The present work deals with the four different steps of classical identification, from the databases to the predictor performances. In an industrial context, the measurements are often noisy or incomplete, so we have developed a neural network model well adapted to this type of data. Three different predictors have been identified and their performances show that an adaptive form of the predictor is the most appropriate for performing this task.
Effect of selected impurities on sucrose crystal growth rate and granulated sugar quality
2008
The use of antiscale products is very common in the evaporation station of sugar factories. These products are generally water-soluble polymers like polyacrylates. Their role seems to be the prevention of formation of calcium oxalate scale. However the stability of calcium-acrylate complexes and their behaviour after evaporation are not well known. Effects of antiscale on calcium oxalate solubility on white sugar turbidity and on sucrose crystal growth rate were studied. It was demonstrated that antiscale protects evaporator from abundant calcium oxalate scale formation. Yet, they delay the problem of oxalate precipitation and cannot prevent turbidity of final sugar. The phenomenon is especially emphasized by decrease of temperature which affects both calcium oxalate solubility and antiscale sequestering efficacy. Effect of antiscales on growth rate and on morphology of sucrose crystals was determined by end-to-end laboratory crystallization and microboiler pilot methods. It was sho...
Evaluation of the Quality of Solid Sugar Samples by Fluorescence Spectroscopy and Chemometrics
Applied Spectroscopy, 2000
It has been shown that¯uorescence spectrosco py of sugar in aqueous solution carries im portant quality and process information related to beet sugar factories, which is accessible by m ultivariate analyses. A m ethod for m easuring crystalline sugar directly on-line in the process should be advantageous. In this paper we compare the solution m easurem ent tech nique with two m ethods of¯uorescen ce measurem ent on solid sugar. Surprisingly, it was possible to measure¯uorescence through the sugar crystals by using the same transmission techniques with 908 detection as with the sugar solutions. This m ethod was compared with a 458 front-surface re¯ection method. Sugar samples from six different sugar factories were examined. The spectral resp onses were reasonable, but they were inuenced by the heterogeneous sample composition and the sample geometry. It was possible with the two methods to separate sugar samples according to factory with the use of principal com ponent analysis (PCA). Seasonal tim e tren ds were found in week ly samples from the same factory. Partial least-squares regressio n (PLS) was used to predict quality parameters, where color (range: 6± 41), ash (range: 0.003± 0.018), and a -amino-N (range: 0.28± 5.07) could be modeled with errors of 2.3± 2.6, 0.0015± 0.0016, and 0.40± 0.42, respectively. Model errors for similar solution data have been determined to 2.4, 0.0012, and 0.266, respectively.