Markov Chain Monte Carlo simulation-based optimization for a production scale milk drying process

Adrián  Ferrari; Soledad Gutiérrez; Gürkan Sin

doi:10.1016/B978-0-443-15274-0.50047-0

Markov Chain Monte Carlo simulation-based optimization for a production scale milk drying process

Adrián Ferrari^*
, Soledad Gutiérrez
, Gürkan Sin

^*Corresponding author for this work

Universidad de la República

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

Spray drying is widely used for dehydration of dairy products and among, the most energy-intensive unit operation in this field. An optimization problem for a production scale milk drying system was implemented and solved, considering plant capacity (maximization) and energy consumption (minimization) as the objective. Decision variables were inputs to the spray unit namely the inlet dry bulb air temperature, concentrate moisture, and dry solids flow rate. Product stickiness conditions and moisture content were the main constraints, which were modeled using mass and energy balances. A non-deterministic derivative free based optimization technique, namely Markov Chain Monte Carlo (MCMC) algorithm was chosen to solve the problem. The results showed that throughput maximization is achieved at the expense of a relative energy consumption penalization in the spray and revealed that the structure of the problem seems to be convex. This study shows a promising non-conventional use of MCMC algorithms in optimization studies.

Original language	English
Title of host publication	Proceedings of the 33rd European Symposium on Computer Aided Process Engineering
Editors	Antonis Kokossis, Michael C. Georgiadis, Efstratios N. Pistikopoulos
Volume	52
Publisher	Elsevier
Publication date	2023
Pages	291-296
ISBN (Print)	978-0-443-23553-5, 978-0-443-15274-0
DOIs	https://doi.org/10.1016/B978-0-443-15274-0.50047-0
Publication status	Published - 2023
Event	33rd European Symposium on Computer Aided Process Engineering - Athens, Greece Duration: 18 Jun 2023 → 21 Jun 2023

Conference

Conference	33rd European Symposium on Computer Aided Process Engineering
Country/Territory	Greece
City	Athens
Period	18/06/2023 → 21/06/2023

Series	Computer Aided Chemical Engineering
Volume	52
ISSN	1570-7946

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

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Ferrari, A., Gutiérrez, S., & Sin, G. (2023). Markov Chain Monte Carlo simulation-based optimization for a production scale milk drying process. In A. Kokossis, M. C. Georgiadis, & E. N. Pistikopoulos (Eds.), Proceedings of the 33rd European Symposium on Computer Aided Process Engineering (Vol. 52, pp. 291-296). Elsevier. https://doi.org/10.1016/B978-0-443-15274-0.50047-0

Ferrari, Adrián ; Gutiérrez, Soledad ; Sin, Gürkan. / Markov Chain Monte Carlo simulation-based optimization for a production scale milk drying process. Proceedings of the 33rd European Symposium on Computer Aided Process Engineering. editor / Antonis Kokossis ; Michael C. Georgiadis ; Efstratios N. Pistikopoulos. Vol. 52 Elsevier, 2023. pp. 291-296 (Computer Aided Chemical Engineering, Vol. 52).

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title = "Markov Chain Monte Carlo simulation-based optimization for a production scale milk drying process",

abstract = "Spray drying is widely used for dehydration of dairy products and among, the most energy-intensive unit operation in this field. An optimization problem for a production scale milk drying system was implemented and solved, considering plant capacity (maximization) and energy consumption (minimization) as the objective. Decision variables were inputs to the spray unit namely the inlet dry bulb air temperature, concentrate moisture, and dry solids flow rate. Product stickiness conditions and moisture content were the main constraints, which were modeled using mass and energy balances. A non-deterministic derivative free based optimization technique, namely Markov Chain Monte Carlo (MCMC) algorithm was chosen to solve the problem. The results showed that throughput maximization is achieved at the expense of a relative energy consumption penalization in the spray and revealed that the structure of the problem seems to be convex. This study shows a promising non-conventional use of MCMC algorithms in optimization studies.",

author = "Adri{\'a}n Ferrari and Soledad Guti{\'e}rrez and G{\"u}rkan Sin",

year = "2023",

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Ferrari, A, Gutiérrez, S & Sin, G 2023, Markov Chain Monte Carlo simulation-based optimization for a production scale milk drying process. in A Kokossis, M C. Georgiadis & E N. Pistikopoulos (eds), Proceedings of the 33rd European Symposium on Computer Aided Process Engineering. vol. 52, Elsevier, Computer Aided Chemical Engineering, vol. 52, pp. 291-296, 33rd European Symposium on Computer Aided Process Engineering, Athens, Greece, 18/06/2023. https://doi.org/10.1016/B978-0-443-15274-0.50047-0

Markov Chain Monte Carlo simulation-based optimization for a production scale milk drying process. / Ferrari, Adrián ; Gutiérrez, Soledad; Sin, Gürkan.
Proceedings of the 33rd European Symposium on Computer Aided Process Engineering. ed. / Antonis Kokossis; Michael C. Georgiadis; Efstratios N. Pistikopoulos. Vol. 52 Elsevier, 2023. p. 291-296 (Computer Aided Chemical Engineering, Vol. 52).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - Markov Chain Monte Carlo simulation-based optimization for a production scale milk drying process

AU - Ferrari, Adrián

AU - Gutiérrez, Soledad

AU - Sin, Gürkan

PY - 2023

Y1 - 2023

N2 - Spray drying is widely used for dehydration of dairy products and among, the most energy-intensive unit operation in this field. An optimization problem for a production scale milk drying system was implemented and solved, considering plant capacity (maximization) and energy consumption (minimization) as the objective. Decision variables were inputs to the spray unit namely the inlet dry bulb air temperature, concentrate moisture, and dry solids flow rate. Product stickiness conditions and moisture content were the main constraints, which were modeled using mass and energy balances. A non-deterministic derivative free based optimization technique, namely Markov Chain Monte Carlo (MCMC) algorithm was chosen to solve the problem. The results showed that throughput maximization is achieved at the expense of a relative energy consumption penalization in the spray and revealed that the structure of the problem seems to be convex. This study shows a promising non-conventional use of MCMC algorithms in optimization studies.

AB - Spray drying is widely used for dehydration of dairy products and among, the most energy-intensive unit operation in this field. An optimization problem for a production scale milk drying system was implemented and solved, considering plant capacity (maximization) and energy consumption (minimization) as the objective. Decision variables were inputs to the spray unit namely the inlet dry bulb air temperature, concentrate moisture, and dry solids flow rate. Product stickiness conditions and moisture content were the main constraints, which were modeled using mass and energy balances. A non-deterministic derivative free based optimization technique, namely Markov Chain Monte Carlo (MCMC) algorithm was chosen to solve the problem. The results showed that throughput maximization is achieved at the expense of a relative energy consumption penalization in the spray and revealed that the structure of the problem seems to be convex. This study shows a promising non-conventional use of MCMC algorithms in optimization studies.

U2 - 10.1016/B978-0-443-15274-0.50047-0

DO - 10.1016/B978-0-443-15274-0.50047-0

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SN - 978-0-443-23553-5

SN - 978-0-443-15274-0

VL - 52

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BT - Proceedings of the 33rd European Symposium on Computer Aided Process Engineering

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A2 - C. Georgiadis, Michael

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PB - Elsevier

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Ferrari A, Gutiérrez S, Sin G. Markov Chain Monte Carlo simulation-based optimization for a production scale milk drying process. In Kokossis A, C. Georgiadis M, N. Pistikopoulos E, editors, Proceedings of the 33rd European Symposium on Computer Aided Process Engineering. Vol. 52. Elsevier. 2023. p. 291-296. (Computer Aided Chemical Engineering, Vol. 52). doi: 10.1016/B978-0-443-15274-0.50047-0

Markov Chain Monte Carlo simulation-based optimization for a production scale milk drying process

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