D-Xylose and Glucose from the Sugar Industry waste

Olazábal, Glenia Rabassa; Suárez, Erenio Gónzalez; Sánchez, Amaury Pérez; Valdés, Juan Esteban Miño; Martínez, Amaury Pérez

doi:10.18004/ucsa/2409-8752/2015.002(02)006-022

Cited by 1 publication

(2 citation statements)

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“…Uncertainty is considered a property from a collection of expectations, judgments about possible outcomes for a future event (Hansen and Pearce, 2014). Natural and technological systems usually face uncertainty (Cerda Mejía, Pérez-Martínez, González-Suárez, and Concepción Toledo, 2018;Rabassa-Olazábal, Eduardo-Guerra, Pérez-Sánchez, González-Suárez, and Pérez-Martínez, 2016;Sahinidis, 2004). Product and process design is not free from it (Bortz et al, 2017;Steimel and Engell, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…In the agroindustrial processes and process design, the determining role of quality as an early stage of the process design and its consideration as a parameter, it is little documented (Phadke and Dehnad, 1988). An example of this is the process design carried out for products derived from the sugar industry, where the technological scheme is sometimes established from the experimental data, without holistically considering economic, environmental, technological, and transversal quality aspects (Aguilar-Rivera, Galindo, Fortanelli, and Contreras, 2009;Rabassa-Olazábal et al, 2016;Rabassa Olazábal, González Suárez, Pérez Sánchez, Miño Valdés, and Pérez Martínez, 2015). Moreover, the use of simulations is incipient, as well as the emerging sequential procedures that contemplate it (Yu and Kopcha, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Simulation strategy to reduce quality uncertainty in the sugar cane honey process design

et al. 2021

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This work proposes to increase the acceptability of the sensory quality attributes of sugarcane honey by simulating and decreasing the uncertainty of operational parameters. To this effect, it relies on the development of a case study through a strategy that contemplates the analysis of the initial data and performs calculations through the use of mathematical models, the determination of the Sigma quality level, and the use of the GUM methodology (Guide to the expression of uncertainty in measurement). It was demonstrated that a range with a great variability in the initial operational parameters generated a Sigma level of less than three and a decrease in revenues of 30-52% due to low product sales for the studied productions. By decreasing pH uncertainty from 3,79 to 4,21, and Brix degrees from 75,13 to 76,88, an increase in the Sigma level of quality was observed, which exceeded six, in compliance with product quality specifications, as well as a reduction in economic losses due to defective products. The importance of quality in the product design and the process is evinced, given its influence on the acceptability of the product and the feasibility of the process.

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