Yield Analysis of Essential Oils Extracted by Steam Distillation

Zlatev, Zlatin; Shivacheva, Galya

doi:10.15547/artte.2018.04.003

Cited by 1 publication

(2 citation statements)

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“…The predictive capacity of the network outperforms other more common mathematical models widely used in the literature (Zlatev and Shivacheva, 2018;Fakayode and Abobi, 2018), as observed in Table 3, with the highest R 2 for the MLP neuron network. Our MLP model shows clear advantages over other models in accurately predicting the extraction yield for the 350-g mass loading set located in the vicinity of the sample weight range (from 200 to 500 g).…”

Section: Resultsmentioning

confidence: 81%

“…A prediction model for steam distillation can even become obsolete with a variation in the essential oil source (Gawde et al, 2014). The inadequacy of a clear quality standard for a mathematical tool for predictions has led researchers to use polynomial, exponential, and logarithmic fitting tools to help at least with predictions in the locality (local space) of the data points (Zlatev and Shivacheva, 2018;Fakayode and Abobi, 2018). The local space of these basic fitting types comes at the expense of prediction capability away from the local domain-that is, extrapolation, a prediction feature required to enable the scalability of the extraction processes.…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence models for yield efficiency optimization, prediction, and production scalability of essential oil extraction processes from citrus fruit exocarps

Muñoz

Castro²,

Garzón³

et al. 2023

Front. Chem. Eng.

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Introduction: Excessive demand, environmental problems, and shortages in market-leader countries have led the citrus (essential) oil market price to drift to unprecedented high levels with negative implications for citrus oil-dependent secondary industries. However, the high price conditions have promoted market incentives for the incorporation of new small-scale suppliers as a short-term supply solution for the market. Essential oil chemical extraction via steam distillation is a valuable option for these new suppliers at a lab and small-scale production level. Nevertheless, mass-scaling production requires prediction tools for better large-scale control of outputs.Methods: This study provides an intelligent model based on a multi-layer perceptron (MLP) artificial neural network (ANN) for developing a highly reliable numerical dependency between the chemical extraction output from essential oil steam distillation processes (output vector) and orange peel mass loading (input vector). In a data pool of 25 extraction experiments, 14 output–input pairs were the in training set, 6 in the testing set, and 5 cross-compared the model’s accuracy with traditional numerical approaches.Results and Discussion: After varying the number of nodes in the hidden layer, a 1–9–1 MLP topology best optimizes the statistical parameters (coefficient of determination (R2) and mean square error) of the testing set, achieving a precision of nearly 97.6%. Our model can capture non-linearity behavior when scaling-up production output for mass production processes, thus providing a viable answer for the scalability issue with a state-of-the-art computational tool for planning, management, and mass production of citrus essential oils.

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Section: Resultsmentioning

confidence: 81%