Artificial neural network models for forecasting the extracted yield of essential oils from Curcuma longa L. by hydro-distillation
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Paniz Salimi Babamiri
and
Maryam Khajenoori
Abstract
Turmeric is commonly acknowledged in traditional medical practices for its strong healing properties. In the present work, hydro-distillation was employed to extract essential oils from turmeric powder. The response surface methodology (RSM) was applied to investigate the effects of various parameters, including evaporation rate (0.23, 0.5, 0.8, 0.97, 1.36, 2 ml/min), solid/liquid ratio (4:100, 6:100, 8:100, 1:10, 11:100 g/ml), and extraction duration (13–250 min) on the yield of essential oils. The central composite design (CCD) proved to be an effective tool for evaluating the extraction yield of essential oils. A three-layer artificial neural network (ANN) was utilized to develop the extraction model, employing the Levenberg–Marquardt (LM) optimization algorithm. The neural network’s input layer comprised the solid/liquid ratio, evaporation rate, and extraction time, while the output layer indicated the yield of essential oil extraction. The most appropriate model included a hidden layer with 16 neurons, achieving R 2 and MSE values of 0.9989 and 0.0013, respectively. This investigation indicates that an artificial neural network prediction model serves as an effective method for estimating essential oil yield.
Acknowledgments
The authors acknowledge the Semnan University staff for their beneficial and professional help.
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Research ethics: We declare that this article is original, has not been published before, and is not currently considered for publication elsewhere. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us. Written informed consent for participate was obtained from all participants.
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Author contributions: P. S. B.: Methodology, Investigation, Software, Writing-original draft; B. Z.: Conceptualization, Methodology, Validation, Investigation, Supervision, Project administration, Writing-review & editing; M. Kh.: Conceptualization, Methodology, Validation, Investigation, Supervision, Writing-review & editing.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors do not have any competing interests.
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Research funding: No funding.
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Data availability: The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.
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Articles in the same Issue
- Frontmatter
- Research Articles
- Energy cost prediction for chromium removal by nanofiltration membrane
- Forecasting gasification sustainability through enhanced K-nearest neighbour models for hydrogen and nitrogen amount
- Applying machine learning for biomass gasification prediction: enhancing efficiency and sustainability
- Enhancing prediction of elemental composition through machine learning decision tree models for biomass gasification optimization
- Nonlinear model predictive controller of hydrogenation of dimethyl oxalate for ethylene glycol production
- Dynamic optimization of boiler for minimizing energy consumption in the intentionally transient process operation: effect of different interval number
- Heat transfer efficiency in gas–solid fluidized beds with flat and corrugated walls
- Ant lion based optimization for performance improvement of methanol production
- Boundary Element Method for Viscous Flow through Out-phase Slip-patterned Microchannel under the Influence of Inclined Magnetic Field
- Artificial neural network models for forecasting the extracted yield of essential oils from Curcuma longa L. by hydro-distillation
Articles in the same Issue
- Frontmatter
- Research Articles
- Energy cost prediction for chromium removal by nanofiltration membrane
- Forecasting gasification sustainability through enhanced K-nearest neighbour models for hydrogen and nitrogen amount
- Applying machine learning for biomass gasification prediction: enhancing efficiency and sustainability
- Enhancing prediction of elemental composition through machine learning decision tree models for biomass gasification optimization
- Nonlinear model predictive controller of hydrogenation of dimethyl oxalate for ethylene glycol production
- Dynamic optimization of boiler for minimizing energy consumption in the intentionally transient process operation: effect of different interval number
- Heat transfer efficiency in gas–solid fluidized beds with flat and corrugated walls
- Ant lion based optimization for performance improvement of methanol production
- Boundary Element Method for Viscous Flow through Out-phase Slip-patterned Microchannel under the Influence of Inclined Magnetic Field
- Artificial neural network models for forecasting the extracted yield of essential oils from Curcuma longa L. by hydro-distillation
