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dc.contributor.author
Morales Gonzalez, Humberto  
dc.contributor.author
Di Sciascio, Fernando Agustín  
dc.contributor.author
Aguirre Zapata, Estefanía  
dc.contributor.author
Amicarelli, Adriana Natacha  
dc.date.available
2023-11-28T15:59:37Z  
dc.date.issued
2023-09  
dc.identifier.citation
Morales Gonzalez, Humberto; Di Sciascio, Fernando Agustín; Aguirre Zapata, Estefanía; Amicarelli, Adriana Natacha; A model-based supersaturation estimator (inferential or soft-sensor) for industrial sugar crystallization process; Elsevier; Journal of Process Control; 129; 103065; 9-2023; 1-19  
dc.identifier.issn
0959-1524  
dc.identifier.uri
http://hdl.handle.net/11336/218711  
dc.description.abstract
The degree of supersaturation of the mother liquor is a key factor in improving the monitoring and control of the final stage of industrial sugar crystallization. However, the difficulty of obtaining online supersaturation measurements is one of the challenges associated with monitoring and controlling sugar crystallization. There is no direct method or single instrument for measuring supersaturation. It can only be calculated or inferred from other measurements. In the literature, estimators of mother liquor supersaturation are reported, typically focused on the first stage of crystallization. The SeedMaster series transmitters are the sole industrial instruments that provide online supersaturation information by calculating it from external measurements. The purpose of this study is to design a first-principles model-based soft-sensor as a practical alternative to obtain real-time information about supersaturation in the last stage of sugar crystallization. The proposed estimator relies on two models: a supersaturation model and a second simplified model of the last stage of crystallization. The parameters of both models were estimated based on real industrial data. The estimation is performed in three steps: 1. An Unscented Kalman Filter estimates the states of the crystallization model and their variance. 2. The estimated supersaturation value is obtained by substituting the estimated states into the supersaturation model. 3. The estimator's bias, and variance are calculated to establish error bounds. The main characteristics of the obtained estimator are: practical unbiasedness, nearly minimum variance and robustness. The performance and behavior of the supersaturation estimator are contrasted using real data from an industrial crystallization plant (Urbano Noris factory, Holguín, Cuba). Regardless of its initial conditions, the estimator converges to the three standard deviation error band in less than three minutes. The exact time may vary depending on how much the estimator's initial conditions deviate from those of the process. After this time (Reach Time), the estimates remain within the calculated error limits of three standard deviations. The maximum absolute errors obtained were less than 0.019 units, corresponding to a maximum relative error of less than 1.5%. These values are favorable since they are well below critical values (0.125 units of absolute error). Moreover, the error bands are much smaller than the operating zone width (approximately 0.25 units), which is a necessary condition for any supersaturation estimator to be useful. Finally, it should be noted that the errors have been reduced compared to the values reported in previous research focused on the sugar industry using other techniques.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Elsevier  
dc.rights
info:eu-repo/semantics/restrictedAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/  
dc.subject
ERROR BOUNDS  
dc.subject
ESTIMATORS  
dc.subject
INFERENTIAL OR SOFT-SENSOR  
dc.subject
SUGAR CRYSTALLIZATION PROCESS  
dc.subject
SUPERSATURATION  
dc.subject
UNSCENTED KALMAN FILTER  
dc.subject.classification
Sistemas de Automatización y Control  
dc.subject.classification
Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información  
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS  
dc.title
A model-based supersaturation estimator (inferential or soft-sensor) for industrial sugar crystallization process  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2023-11-28T14:35:40Z  
dc.journal.volume
129  
dc.journal.number
103065  
dc.journal.pagination
1-19  
dc.journal.pais
Países Bajos  
dc.journal.ciudad
Amsterdam  
dc.description.fil
Fil: Morales Gonzalez, Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina  
dc.description.fil
Fil: Di Sciascio, Fernando Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina  
dc.description.fil
Fil: Aguirre Zapata, Estefanía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina  
dc.description.fil
Fil: Amicarelli, Adriana Natacha. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina  
dc.journal.title
Journal of Process Control  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S095915242300152X  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1016/j.jprocont.2023.103065