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Regulating a distilled malt spirit fractioning process using Harrington’s desirability function

https://doi.org/10.20914/2310-1202-2018-4-219-224

Abstract

In the study, a relation is found that eases the regulation of a distilled malt spirit fractionation process. The reasons are given for considering both the objective indicators (impurity content) and the organoleptic properties as quality criteria, which implies the need for a single quality criterion based on expert opinion and having a numerical expression. Using the Harrington desirability function method, such a criterion was found, tied to the model process of distilled malt spirit fractionation. The spirit was obtained by distilling twice on a Doctor Guber pot still a wash made from light barley malt and fermented with reactivated DistilaMax MW dry yeast for 70 hours at 24°C. The mode of distillation of the wash was constant, but during the second fractional distillation, the first sample of alcohol was divided into fractions according to the initial (reference) regime, and the next sample was fractionated in small batches, with a time step of 20 min, to estimate the dynamic uptake of by-products: aldehydes, esters, and higher alcohols. After impurity determination with gas chromatography, a generalized criterion was derived with the linear convolution formula from the by-product concentrations and the respective significance coefficients. The coefficients were found via a set of fuzzy rules constructed with the Harrington function method. The logic of collegial decisions regarding the first (standard) and the next (small batch) spirit samples was thus fixated in a simplified model, and the relation between stillage time and the generalized criterion was found, which provided feedback from the expert evaluated spirit sensory characteristics to the mode of fractioning. With the feedback loop, it is possible to model the logic of expert evaluation of the spirit and thus regulate the fractioning process without further need for expert evaluation.

About the Authors

V. A. Romanov
ITMO University
Russian Federation
graduate student, department of biotechnology of products from vegetable raw materials, Lomonosova str., 9, St.Petersburg, 191002, Russia


N. V. Barakova
ITMO University
Cand. Sci. (Engin.), department of biotechnology of products from vegetable raw materials , Lomonosova str., 9, St.Petersburg, 191002, Russia


References

1. Сhristoph N., Bauer-Christoph C. Flavour of spirit drinks: raw materials, fermentation, distillation, and ageing. Flavours and Fragrances: Chemistry, Bioprocessing and Sustainability; ed. Berger R.G. Springer, 2007. pp. 219–240.

2. Robichaud J., Bleibaum R.N., Thomas H. Cracking the consumer code linking winemakers to consumers to increase brand loyalty. Proceedings of the 13th Australian Wine Industry Technical Conference. 2005.

3. GOST 15467–79. Upravlenie kachestvom produkcii. Osnovnye ponyatiya, terminy i opredeleniya [State Standard 15467–79. Product quality management. Basic concepts, terms and definitions]. Moscow, Standartinform, 2009. (in Russian).

4. Diligensky N.V., Dymova L.G., Sevas?yanov P.V. Nechetkoe modelirovanie i mnogokriterial'naya optimizaciya proizvodstvennyh sistem v usloviyah neopredelennosti: tekhnologiya, ehkonomika, ehkologiya [Fuzzy modeling and multicriterial optimization of production systems under uncertainty: technology, economy, ecology]. Moscow, Mashinostroyeniye­1, 2004. 488 p. (in Russian).

5. Grigor'eva A.A., Grigor'eva A.P. Mathematical background of a fuzzy set based system for innovation assessment. Innovacionnye tekhnologii i ehkonomika v mashinostroenii [Innovative technologies and economics in engineering: a collection of works of the International Scientific and Practical Conference]. Tomsk, Tomsk Polytechnic University, 2012. vol. 2. pp. 77–79. (in Russian).

6. RD 50.1.028. Metodologiya funkcional'nogo modelirovaniya IDEF0 [RD 50.1.028. Methodology of functional modeling IDEF0]. Moscow, IPK Publishing Standards, 2014. 75 p. (in Russian).

7. Volodin A.A. Sistemnyj analiz i upravlenie slozhnymi biosistemami na baze nejro-nechetkih regulyatorov [System analysis and control of complex systems with neuro-fuzzy controllers]. Stavropol, North Caucasus Federal University, 2014. 214 p. (in Russian).

8. Jaeger S.R., Cardello A.V., Chheang S.L., Beresford M.K. et al. Holistic and consumer-centric assessment of beer: A multi-measurement approach. Food Research International. 2017. vol. 99. no. 1. pp. 287–297. doi: 10.1016/j.foodres.2017.05.004

9. Vostrikov S.V., Korostelyov A.V., Kuchmenko T.A. et al. A study of whisky spirit flavour active compounds with artificial intelligence sensory systems. Proizvodstvo spirta i likerovodochnyh izdelij [Manufacture of ethanol and infusion beverages]. 2010. no. 4. pp. 46–48. (in Russian).

10. Romanov V.A., Barakova N.V. Choosing processing parameters for obtaining a distilled malt spirit with maximum sensory profile acceptability. Nauchnyj zhurnal NIU ITMO. Seriya "Processy i apparaty pishchevyh proizvodstv" [Scientific journal NRU ITMO. A series of "Processes and devices of food production"]. 2017. no. 4 (34). pp. 53–60. (in Russian).


Review

For citations:


Romanov V.A., Barakova N.V. Regulating a distilled malt spirit fractioning process using Harrington’s desirability function. Proceedings of the Voronezh State University of Engineering Technologies. 2018;80(4):219-224. (In Russ.) https://doi.org/10.20914/2310-1202-2018-4-219-224

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ISSN 2226-910X (Print)
ISSN 2310-1202 (Online)