Introduction

In contrast to solar thermal energy, which provides a more environmentally friendly alternative, production of essential oil in aromatherapy and medicine historically and conventionally relies on conventional energy sources. The usual methods for extracting volatile oils from aromatic plants use processes, including solvent extraction, solvent distillation, cold pressing, and maceration, each best suited for specific plants and results. The concentrated extracts are widely used in various industries and are considered of great importance as export products for most develo** countries. In recent times, there has been an increasing demand for sustainable industrial processes involving renewable energy sources. The conventional methodologies, which primarily need fossil energy for their operation, pose a significantly negative environmental impact. The present study aims to address the challenge of reducing dependency on fossil energy by utilizing solar thermal energy in a decentralized manner to extract essential oil from the leaves of the plant. Now, according to the WHO, 21,000 plant species have therapeutic potential (Anand et al., 2019). These plants contribute to fragrances, lotions, and skincare products in the cosmetics industry by providing aromatic and medicinal properties. Essential oils are widely used in aromatherapy for their aromatic and healing properties. In addition, essential oils have been used in dentistry. The Myrtaceae family’s eucalyptus essential oil has a distinct aroma and health advantages and works as an antiseptic for dental hygiene (James & Bell, 2001). Due to its anti-cough and decongestant qualities, this oil, derived from eucalyptus leaves, is also used in respiratory products, particularly in treating bronchitis, chronic rhinitis, sinusitis, and asthma. The oil quality is determined by root structure and water movement; its primary constituents are 1–8 cineole and α-Pinene. Notably, 1–8 cineole is anti-inflammatory and inhibits tumour necrosis factor-alpha (Juergens et al., 2004; Santos and Rao, 2000).

Various studies have been conducted to develop solar distillation systems and evaluate their performance in the context of solar energy applications for extracting volatile oils from medicinal and aromatic plants. The use of solar thermal energy reduces the need for fossil fuels and omits hazardous emissions (Jabbar et al., 2015). A decentralized solar distillation system employing a fixed-focus Scheffler concentrator was developed. The system achieved focus receiver temperatures of 300–400 °C within a solar radiation range of 700–800 W/m2. The recorded power and system efficiency values were 1.55 kW and 33.21%, respectively, at a solar radiation level of 739 W/m2 (Munir & Hensel, 2010). This solar distillation setup successfully extracted essential oils from medicinal and aromatic plants. For instance, experiments with peppermint leaves (9.1 kg) obtained 28.2 ml of essential oil using 3.18 kWh of thermal energy. In comparison, Melissa leaves (11.6 kg) yielded 1.425 ml of essential oil with 3.87 kWh of thermal energy (Munir et al., 2014). A similar system employed a water-steam distillation system consisting of solar compound parabolic collectors (C.P.C.s) and distillation units. The CPC collected solar energy, which was then transferred to the distillation unit. The system could distil 5 kg of plant material per batch, and the overall thermal efficiency of the solar CPCs ranged from 27.9% to 34.3% (Kültürel & Tarhan, 2016). A hybrid system combining a steam receiver and a biomass-based boiler was developed with a different approach for extracting essential oils from herbs. The concentration of essential oils obtained was reported as 0.59% w/w for fresh Eucalyptus leaves and 0.40% w/w for peppermint leaves (Afzal et al., 2017). Furthermore, solar energy was utilized for essential oil extraction in another study. The extraction rate and yield percentage were evaluated under standard conditions using different plant samples. The results demonstrated variations in the extraction rate, with oregano showing the fastest rate (3.4 ml/day) and rose petals exhibiting the slowest rate (1.8 ml/day). The yield also varied depending on the plant sample, with sambong (Conyza balsamifera Linn) yielding the highest yield at 72%. In comparison, rose petals (Rosa damascena) had the lowest percentage at 18.5% (Pesimo, 2017). According to the surveys, most essential oil extraction processes were carried out at or below 180 °C. Due to their automatic tracking mechanism and parabolic shape, solar concentrators such as Scheffler concentrators are in demand for low-to-medium-temperature applications (Juergens et al., 2004; Kanyowa et al., 2021; Munir et al., 2010; Sareriya et al., 2022). Solar distillation systems have successfully processed peppermint, Melissa, fennel seeds, basil, lavender, rosemary, and cumin. Now, for different species of Eucalyptus, the main components of their oils varied. From fresh Eucalyptus species plant material, the % yield of essential oils varied from 0.13% to 1.87%, and the % composition of 1, 8-cineole and α-Pinene varied from 32.4% to 61.3% and 1.2% to 20.3%, respectively (Boukhatem et al., 2014). For Eucalyptus globulus, the main components were 1,8-cineole at 70.15% and α-Pinene at 3.65%. Eucalyptus globulus madidenii contained 1,8-cineole at 55.82% and α-Pinene at 3.91%. Another variation of Eucalyptus globulus madidenii had 60.29% 1,8-cineole and 15.5% α-Pinene. These variations in the composition of essential oils in different Eucalyptus species were able to impact their therapeutic properties and potential applications in various products and treatments. In Eucalyptus viminalis Labill. 1, 8-cineole was 56.43%, and α-Pinene was 3.91% (Hassani Moghaddam et al., 2020). In the leaves, the % of 1,8-cineol was 49.07–83.59%, and α-Pinene was 1.27–26.35%, as observed by Sebei et al., 2015 (Sebei et al., 2015).

In another context, the response surface methodology (RSM) optimizes any process by analyzing relationships between variables and recommending optimal conditions (James & Bell, 2001). It describes and optimizes the interactions between various input factors (also known as independent variables) and the process's output response (also known as the dependent variable). Regression analysis is used to develop a mathematical model explaining the relationship between the input factors and the response after a series of experiments. RSM aims to find the ideal input variable set that maximizes or minimizes the expected response. By applying the mathematical model, researchers can forecast the response at various points within the experimental range and pinpoint the circumstances that provide the most significant outcomes. RSM is frequently utilized in many industries and research domains to optimize processes and raise product quality while using fewer resources. The method can assist in determining the ideal parameters (such as temperature, pressure, and extraction time) for getting the best yield or desired qualities of the extracted essential oils in the context of essential oil extraction (Khuri & Mukhopadhyay, 2010; Liu et al., 2009).

The present work aimed to optimize the Eucalyptus essential oil extraction process parameters to maximize oil yield and the concentration of the key components in the conventional steam distillation process and demonstrate the feasibility of solar-driven steam distillation as an eco-friendly alternative.

The specific objectives can be summarized as

  • To select plant material and extraction methodology and prepare the traditional steam distillation and Scheffler solar concentrator experimental setups.

  • Apply response surface methodology (RSM) to identify the optimal conditions for leaf size, extraction temperature, solid/solvent ratio, and extraction time to maximize essential oil yield from Eucalyptus leaves obtained through the traditional steam distillation process and determine the effects on the concentrations of the major oil constituents, 1–8 cineole and α-Pinene.

  • To perform experiments according to the given run sheet of RSM with the conventional distillation setup and then implement the optimal extraction conditions in a Scheffler concentrator-driven solar steam distillation setup to evaluate the efficiency and oil yield under ambient conditions.

  • To compare the conventional and solar distillation methods by assessing the differences in oil yield and component concentrations in order to shed light on the potential benefits and drawbacks of implementing solar steam distillation in practical applications.

The demonstration of the applicability of the decentralized solar thermal essential oil extraction process was thus aimed in the present work. It can be a sustainable alternative to conventional methods supporting the goals of environmental conservation and sustainable development.

Materials and methods

System description

Conventional electric-powered steam distillation system

The conventional electric-powered steam distillation system comprised an electric boiler, extraction column, condenser, and oil–water separator. The photograph and schematic diagram of the unit are shown in Fig. 1a, b. The electric boiler had a power rating of 2000 W and was used to heat 40 L of water. It served as the source of steam for the distillation process. The extractor column could store 5 kg of raw material and 25 l of water. It was made from food-grade stainless steel with an opening of 0.012 m for drainage. A 0.003 m nozzle was installed inside the extractor for sparging. At the bottom of the extractor, column was a 0.005 m perforated stainless-steel wire mesh to hold the raw material during the steam distillation process. The separator was a glass tube with a capacity of 100 ml. It was used to separate the extracted oil from the condensate water. The condenser was a shell and tube type made from stainless steel. It was used to condense the steam vapour back into liquid form during the distillation process. The condenser was sealed to prevent any leaks using gaskets and silicone sealants.

Fig. 1
figure 1

a Conventional electric-powered steam distillation system. b Schematic diagram of conventional electric-powered steam distillation system

Full size image

Solar steam distillation system

The solar steam distillation system consisted of a solar baby boiler with a capacity of 4 L. It was designed to collect solar energy efficiently and then used to generate steam for the distillation process. The Scheffler concentrator was mounted on a two-axis tracking system that allowed it to follow the sun’s movement throughout the day. This tracking system ensured the concentrator remained oriented towards the sun, maximizing the sunlight it captured. As the concentrator tracked the sun, it reflected and concentrated sunlight onto the solar boiler, which was placed at the focal point. The Scheffler concentrator had an aperture area of 2.5 m2. The solar steam distillation system was integrated with the distillation assembly, as in Fig. 1a, to extract the oil. The photograph and schematic diagram of the integrated system are shown in Fig. 2a, b.

Fig. 2
figure 2

a Solar steam distillation system. b Schematic diagram of solar steam distillation

Full size image

Experimental procedure

Fresh leaves Eucalyptus leaves (E. hybrid) were collected from Ambaji (24° 33 N, 72°0.850 E') in the northern part of Gujarat, a state in western India. Eucalyptus hybrid (E. hybrid) was introduced in Gujarat, India in 1961. This tree species is known for its rapid growth, which allows it to overtop and outcompete weeds, ensuring its dominance in various environments. It can regrow from its stump after being cut, which makes it sustainable for repeated harvesting. The species can thrive in a wide range of soil and climatic conditions, making it a versatile option for various regions. These characteristics make E. hybrid a valuable tree for afforestation, land rehabilitation, and sustainable forestry practices (Palanna, 1996).

In addition, the species contains significant amounts of 1–8 cineole or eucalyptol, which is highly valued for its medicinal and antimicrobial properties, making the oil ideal for pharmaceutical, cosmetic, and therapeutic applications. In addition, it contains α-Pinene, a compound known for its anti-inflammatory and bronchodilator effects, enhancing the oil's utility in respiratory treatments. The output oil quantity through conventional steam distillation ensures that the extraction process is economically viable and can reliably meet industrial standards. The species is well-adapted to various climates, facilitating extensive cultivation. Using solar thermal energy for the extraction process further aligns with sustainable practices, reducing the carbon footprint compared to conventional methods. From other species of Eucalyptus, e.g., E. camaldulensis and E. citriodora leaves essential oil yields of 0.61% and 1.16% were obtained using a solar distillation system (Hussain et al., 2022). In addition, 16.2% of 1,8-cineole and 15.6% of α-Pinene were identified in E. camaldulensis (Gakuubi, 2016). In Eucalyptus camaldulensis leaves, 1,8-cineole (eucalyptol) was found to be 50.9% (Afzal. et al., 2017).

The leaves were thoroughly washed with water, and the cleaned leaves were then kept at room temperature for 2–3 h, partially air-drying them before further processing. The leaves were then sorted according to their size, as in Fig. 3. This ensured uniformity and consistency in the experimental setup, as leaves of different sizes have varying characteristics that could affect the results. The organized leaves of the same size were then used for the experiments.

Fig. 3
figure 3

Eucalyptus leaves with normal and different sizes

Full size image

Experiments with conventional electric-powered steam distillation system

According to the run-sheet of RSM, Eucalyptus leaves were prepared for the 27 runs. The independent variables (input parameters) were selected as implemented by other researchers (Basma. et al., 2013; Galadima, 2012; Kabuba, 2009; Rao. et al., 2006) as well as trial runs were performed to decide the range of independent variables for better outcomes. The ranges of input parameters were

Leaf Size: Ranging from 0.02 to 0.05 m.

Extraction Temperature: Ranging from 92 ℃ to 98 ℃.

Solid/Solvent Ratio: Ranging from 0.4545 to 0.6250.

Extraction Time: Ranging from 150 to 210 min.

The selected range for leaf size ensured an optimal balance between exposing enough surface area and maintaining ease of handling. The temperature range of 92–98 ℃ was chosen with the aim to maximize the efficiency of steam generation and oil extraction without degrading heat-sensitive components of the essential oil. Temperatures below 92 ℃ might not produce sufficient steam for effective distillation, while temperatures above 98 ℃ risk degrading valuable oil constituents. The solid/solvent ratio of 0.4545–0.6250 ratio reflected the quantity of leaves in relation to the water used in the extraction process. A lower ratio might have resulted in insufficient steam for efficient extraction, while a higher ratio could have led to saturation, reducing extraction efficiency. The range for an extraction time of 150–210 min was to ensure thorough oil extraction. Shorter times might not have allowed for complete extraction, while excessively long times might have led to unnecessary energy use. For each experimental run, 2.5 kg of Eucalyptus leaves was used. The leaves were kept on the wire mesh installed at the bottom of the extractor. To reach the extraction temperature range, the steam from the boiler was introduced to the extractor through a sparger. The sparger ensured equal steam distribution on leaves throughout the experimental run. Electric heaters were used in the electric-powered conventional steam distillation system to keep it within the intended temperature range of 92–98 °C. Throughout the experimental runs, temperature sensors and control systems were used to monitor and modify the heaters to reach and maintain the desired temperature. The first drop of oil was collected after an hour, and the quantity of the collected oil was recorded at regular intervals. The water vapours and the volatile essence of the leaves flowed through the extractor and were then condensed. Oil samples were separated from the water layer in the separator based on their density. The separated oil samples were filtered and dried for 12 h using anhydrous sodium sulphate (Na2SO4). The dried samples were stored in a refrigerator at 4 ℃. The characteristics of the oil samples were determined through qualitative analysis by gas chromatography–mass spectrometry (GC–MS). A similar procedure was followed for all 27 runs. The rated power of the electric boiler was 2000 W. The total run time for each run was observed and found to be 3.5 h. These experiments aimed to optimize the conventional electric-powered steam distillation process and identify the best combination of input parameters that lead to a higher oil yield from the Eucalyptus leaves.

Experiment with solar steam distillation system

After performing 27 runs with the leaves, experimental runs with a solar distillation system using optimum values derived using RSM were conducted. Instead of using an electric boiler, a solar baby boiler produced the steam in the setup. This change allowed to utilize solar thermal energy for steam generation, making the process more sustainable and environmentally friendly. The experimental procedure for the solar steam distillation system was the same as that for the conventional electric-powered steam distillation system. The Eucalyptus leaves were thoroughly washed, sorted based on size, and kept on a wire mesh at the bottom of the extractor. The steam from the solar baby boiler was introduced into the extractor through a sparger to ensure uniform steam distribution. In this setup, the temperature control was achieved by tracking the Scheffler solar concentrator to follow the path of the sun and by adjusting the entire configuration to maximize heat absorption by the solar baby boiler. Insulation materials were incorporated in the boiler assembly to minimize heat loss and help maintain the desired extraction temperature range despite fluctuations in solar intensity and ambient conditions. The experiments were conducted in May 2022 at the terrace of CSIR-Central Salt and Marine Chemicals Research Institute, Bhavnagar (21.7645° N, 72.1519° E). The solar intensity during the experiment varied between 93.95 W/m2 and 861.54 W/m2 throughout a typical day. The Eucalyptus leaves used for each run were maintained at 2.5 kg, and the optimum values for leaf size, extraction temperature, solid/solvent ratio, and extraction time were derived from the RSM analysis based on the conventional electric-powered steam distillation runs. These studies aimed to compare the solar steam distillation system with those produced by the conventional electric-powered steam distillation system. To comprehend the effectiveness and constraints of the solar distillation setup, the effect of solar radiation and other environmental conditions on the oil output was explored.

Instruments

A calibrated K-type thermocouple was used to measure the inside temperature of the solar baby boiler, and an RTD (Resistance Temperature Detector) with a PT 100 sensor was placed inside the Stevenson screen to measure the ambient temperature. Kipp and Zonen make pyranometer was used to measure the incident solar radiation. The ambient air velocity was measured manually using a Prova make anemometer. All these thermocouples, RTD, and the pyranometer were connected to a data logging system (Model: Datataker DT-80 series) to log and record the data continuously. The pressure inside the solar baby boiler was measured using a Micro-made dial-type pressure gauge. These measuring and instrumentation tools were essential for generating precise data throughout the solar steam distillation system trials. The reliability of the experimental data and its application for analysis and comparison of the outcomes from the conventional electric-powered steam distillation and solar steam distillation systems were both aided by the accuracy and precision of these instruments.

Uncertainty analysis

The accuracy and reliability of measurements determined from instruments are affected by various factors, such as calibration, sensitivity, measurement range, resolution, environmental factors like temperature and humidity, interference and cross-sensitivity, ageing, human faults, and many other sources of error and uncertainties. The uncertainties associated with individual instruments used for different measurements are referred to as independent uncertainties. Let us consider a function, Y, which is derived from n parameters, namely x1, x2, x3,…,xn. The uncertainty of the derived parameter W(Y) is calculated by summing the absolute values of the independent uncertainties (w1, w2, w3, …, wn) multiplied by the partial derivatives of the function Y with respect to each respective parameter (x1, x2, x3, …, xn).

If the values of uncertainties corresponding to the independent variables are known, then the uncertainty for the parameter Y can be expressed as follows (Andharia et al., 2020, 2022):

$$W\left(Y\right)= {\left[{\left(\frac{\partial Y}{{\partial x}_{1}}{w}_{1}\right)}^{2}+ {\left(\frac{\partial Y}{{\partial x}_{2}}{w}_{2}\right)}^{2}+.. .. .. .. ..+{\left(\frac{\partial Y}{{\partial x}_{n}}{w}_{n}\right)}^{2}\right]}^{1/2}$$
(1)

This method considers the cumulative impact of the individual parameter uncertainties on the generated parameter. An estimation of the uncertainty is achieved in the outcome by adding the contributions from each parameter weighted by their corresponding partial derivatives. Table 1 lists instruments, their accuracy, and operating range.

Table 1 List of instruments used during the experimentation
Full size table

Performance analysis

Performance evaluation of solar distillation system

A key parameter for assessing the effectiveness of the solar steam distillation system is overall efficiency. It functions as an indicator of how well the system can transform input solar thermal energy into meaningful output for the distillation process. The total heat energy input to the water is calculated (Patel et al., 2019) (Munir et. al., 2009).

The heat required for boiling of water

$${Q}_{S}= {m}_{w}{c}_{p}\Delta T$$
(2)

where \({Q}_{S}\) represents the sensible heat in kJ, depending on the units of the other parameters. \({m}_{w}\) is the mass of water in kg used in the distillation process, and \({c}_{w}\) is the specific heat capacity of water in kJ/kg°K. ∆T is the initial and final temperature difference in °K of the water and the final temperature of the water during the distillation process:

$${Q}_{L}=\text{xm}{L}_{fg}$$
(3)

where \({Q}_{L}\) represents the latent heat in kJ, x is the dryness fraction, m is the quantity of water evaporated in kg, \({L}_{fg}\) is the specific latent heat of vaporization in kJ/kg.

Equation 4 calculates the average power output of the system throughout the distillation process (Ait Nouth, 2017):

$${P}_{ave}=\frac{{E}_{P}}{{t}_{p}} \,(\text{where}, {E}_{P}={Q}_{S}+{Q}_{L})$$
(4)

where \({E}_{P}\) is the total energy for the distillation process measured in kWh. \({t}_{p}\) is the total time taken for the distillation process in s.

The overall efficiency of the solar distillation is calculated as (Ait Nouth, 2017)

$$\eta = \frac{{10^{3} E_{P} }}{{\int_{t = 0}^{t = tp} {\left[ {GA_{S} } \right]dt} }} \times 100\,\,\,$$
(5)

where As is the aperture area in m2, and it is calculated by multiplying the number of reflectors by the area of each reflector and taking into account the seasonal angle deviation of the sun. The denominator is the integral of the product of average solar radiation (G) and the aperture area over the distillation time (from t = 0 to t = tp). The integral represents the cumulative energy input from solar radiation during distillation.

Experimental design and data analysis

The variables for producing Eucalyptus essential oil were optimized using response surface methodology (RSM). It is a statistical technique commonly used to optimize processes by determining the best combination of input variables to achieve desired responses (output variables). In this case, the variables considered during the experimental runs were:

  1. 1.

    Leaf Size (LS) (A): The size of the Eucalyptus leaves used in the distillation process, measured in m.

  2. 2.

    Extraction Temperature (TLS) (B): The temperature, measured in ℃.

  3. 3.

    Solid/Solvent Ratio (SSR) (C): The ratio of the mass of Eucalyptus leaves to the volume of the solvent used for extraction.

  4. 4.

    Extraction Time (TC) (D): The duration of the distillation process in min.

The response variables selected to evaluate the performance of the process were:

  1. 1.

    Extraction oil yield (Y) (R1): The percentage of essential oil extracted.

  2. 2.

    1,8-Cineole Yield (C) (R2): The percentage of 1,8-cineole, a specific component in the oil.

  3. 3.

    α-Pinene Yield (P) (R3): The percentage of α-Pinene, a specific component in the essential oil.

The central composite design (CCD) is an extensively used experimental design in response surface methodology (RSM) to efficiently explore the response surface and identify the optimal combination of input variables or factors for a given process. It involves a combination of factorial, axial, and centre points. The experimental runs for this case were planned according to four-factor CCD, for 27 runs and 3 imitates at the centre point. The data sets were analyzed, and optimum operation conditions were derived using the Design Expert 8.0.6 software. The software helped fit response surfaces to the experimental data, identifying the best combination of variables for achieving the desired responses (e.g., maximum extraction oil yield or maximum yield of specific compounds). After selecting input and output variables (responses), in the analysis step, final regression equations in terms of coded factors and actual factors for all the responses were found in ANOVA, which is a statistical technique used to analyze the differences among group means in a data set.

The final regression equation for coded factors of independent process variables was developed for oil yield, 1–8 cineole and α-Pinene in Eqs. (6), (7), and (8), respectively:

$${\text{qrt}}\left( {\text{oil yield}} \right) \, = \, + \, 0.{82 } - \, 0.0{55 }*{\text{ A }} + \, 0.0{11 }* {\text{B }} + { 9}.{\text{836E}} - 00{4 }*{\text{ C }} - { 1}.{\text{119E}} - 00{3 }*{\text{ D }} - \, 0.0{19 }*{\text{ A }}*{\text{ B }} - { 6}.{\text{682E}} - 00{3 }*{\text{ A }}*{\text{ C }} + { 7}.{\text{385E}} - 00{3 }*{\text{ A }}*{\text{ D }} + \, 0.0{14 }*{\text{ B }}*{\text{ C }} - { 6}.{\text{897E}} - 00{3 }*{\text{ B }}*{\text{ D }} - {1}.0{\text{33E}} - 00{3 }*{\text{ C }}*{\text{ D }} - \, 0.0{4}0 \, *{\text{ A}}^{{2}} - { 5}.{\text{944E}} - 00{3 }*{\text{ B}}^{{2}} - { 2}.{\text{742E}} - 00{3 }*{\text{ C}}^{{2}} - \, 0.0{18 }*{\text{ D2}}$$
(6)
$${\text{Sqrt}}\left( {{1 } - {\text{ 8 cineole}}} \right) \, = \, + { 8}.{17 } - \, 0.{12 }*{\text{ A }} - \, 0.{12 }*{\text{ B }} - { 4}.0{\text{18E}} - 00{3 }*{\text{ C }} - 0.0{32 }*{\text{ D }} + \, 0.0{93 }*{\text{ A }}*{\text{ B }} - \, 0.0{62 }*{\text{ A }}*{\text{ C }} + \, 0.0{87 }*{\text{ A }}*{\text{ D }} - \, 0.0{75 }*{\text{ B }}*{\text{ C }} + \, 0.{17 }*{\text{ B }}*{\text{ D }} - \, 0.0{33 }*{\text{ C }}*{\text{ D }} + \, 0.0{78 }*{\text{ A}}^{{2}} - \, 0.0{12 }* {\text{B}}^{{2}} - \, 0.{1}0 \, *{\text{ C}}^{{2}} - \, 0.{12 }*{\text{ D}}^{{2}}$$
(7)
$${\text{Sqrt}}\,\,\left( {\text{alfa pinene}} \right) \, = \, + { 4}.{31 } - \, 0.0{29 }*{\text{ A }} + \, 0.{14 }*{\text{ B }} - { 9}.{5}0{\text{1E}} - 00{3 }*{\text{ C }} - { 2}.{63}0{\text{E}} - 00{3 }*{\text{ D }} - \, 0.0{93 }*{\text{ A }}*{\text{ B }} - \, 0.0{16 }*{\text{ A }}*{\text{ C }} + \, 0.{14 }*{\text{ A }}*{\text{ D }} - \, 0.{11 }*{\text{ B }}*{\text{ C }} - \, 0.0{39 }*{\text{ B }}*{\text{ D }} + \, 0.{18 }*{\text{ C }}*{\text{ D }} - 0.0{17 }*{\text{ A}}^{{2}} - \, 0.0{15 }*{\text{ B}}^{{2}} + \, 0.{13 }*{\text{ C}}^{{2}} + \, 0.0{88 }*{\text{ D}}^{{2}}$$
(8)

Characterisation of Eucalyptus oil

The chemical characteristics of the Eucalyptus oil were evaluated through gas chromatography–mass spectrometry (GC–MS) analysis. The GC–MS analysis allows for the identification and quantification of individual chemical components present in the extracted Eucalyptus oil. These components are typically represented as peaks in the chromatogram, and their retention times and mass spectra are used to identify them. The tests were conducted at the NFDD Department and Pharmaceutical Department of Saurashtra University, Rajkot. The test was performed on a Shimadzu model GCMS-QP2010 instrument with operating conditions as follows: injection temperature: 100 ℃, pressure: 73 kPa, linear velocity: 0.4 m/s, purge flow: 3.5 mL/min, and split ratio: 5. The column of the instrument was 30 m long having 0.25 × 10–3 m inner diameter. The sample was first dissolved in methanol and prepared with 10% W/V before testing. This preparation is typically done to ensure the proper solubility and accurate analysis of the sample.

Results and discussion

Analysis and evaluation of the fitted model

The analytical equations (Eqs. 6, 7 and 8) of RSM were used to derive the optimum operating condition for leaf size, extraction temperature, solid/solvent ratio, and extraction time. The responses were determined at the optimal conditions of these variables. The designed model was then validated by comparing the experimental and predicted values. Design expert software uses simultaneous optimization techniques (desirability function) to optimize multiple responses (Patel et al., 2018). The variables for the extraction of Eucalyptus essential oil and the operating conditions range for the implementation of RSM are shown in Table 2. The ranges define the lower and upper bounds for each factor in order to explore the response surface effectively and identify the optimal factor levels. Table 3 demonstrates the complete experimental run sheet, presenting the specific factor levels used in each of the 27 experimental runs, including the centre point replicates. The table shows the factors and corresponding responses observed during the experimental runs, Table 2.

Table 2 Actual and coded values of preparation variables used for the optimization
Full size table
Table 3 Detailed experimental run sheet and experimental data
Full size table

Regression coefficients for all three responses (Y, C, and P) were derived using the least squares method. The resulting regression coefficients for all three responses are presented in Table 4, and to determine the significance of the model, the model parameters were calculated. The acceptability of the model was evaluated based on the coefficient of variance (CV). A smaller CV indicates higher reproducibility, meaning that the data points are less scattered than the mean. This validates the reliability of the model. The goodness of fit of the regression model was evaluated based on the R-squared (R2) values for the three responses, Y, C, and P. R2 represents the proportion of the total variation in the response variable that the model explains. Higher R2 values indicate a better fit of the regression lines to the data. A value close to 1 for R2 suggests that the model can define a large portion of the variability in the response, implying a good fit. The R2 values of 0.9494 for Y, 0.9338 for C, and 0.9347 for P indicated that the regression lines reasonably fit the data. The difference between the predicted R2 and the adjusted R2 was less than 0.2, indicating reasonable agreement and reliability for all three responses. The adjusted R2 considers the number of predictors in the model and penalizes overfitting. When the difference between predicted R2 and adjusted R2 is small, it suggests that the model does not overfit the data and has a good balance between complexity and fit. For the present case, the validation process ensured that the model could be used to make accurate predictions and optimize the extraction process of essential oil from Eucalyptus leaves based on the determined regression coefficients and optimal factor levels. Adequate precision is assessed by the signal-to-noise ratio, with a value greater than 4. It is generally considered desirable as it indicates that the signal (variation explained by the model) is more than four times larger than the noise (residual variation). In this case, the values of adequate precision for all three responses (Y, C, and P) were significantly greater than 4, indicating that the existing model can effectively navigate the design space and accurately predict the responses under different conditions. Tables 4, 5 use the significance of the model terms based on the p values to justify the implications of the developed model. The p value is a statistical measure that helps determine the significance of each term in the regression model. It was applied in this situation to evaluate the significance of the independent variables (leaf size, extraction temperature, solid/solvent ratio, and extraction time) in predicting the chosen responses (Y, C, and P). A p value less than 0.05 was considered statistically significant, indicating that the corresponding term contributed significantly to the response’s variability. In contrast, a p value greater than 0.05 suggested that the term was not statistically significant and might not affect the response significantly. An insignificant p value for lack of fit, which refers to the discrepancy between the model's predictions and the actual experimental data points, was also desirable as it suggested that the model fits the data well and could adequately predict the responses within the specified range of input variables. It also indicated that the model accurately captured the underlying relationships between the factors and responses and did not suffer from systematic errors. For the response surface model for steam distillation, the ANOVA (Analysis of Variance), as in Table 5, could help assess the significance of the model terms and identify which factors and interactions significantly influenced the response variable, the yield or quality of the essential oil. The model showed significant linear, interaction, and quadratic effects of independent parameters (A, B, C, D, AB, AC, AD, BC, BD, CD, A2, B2, C2, D2), indicated by p values < 0.05 for significant terms and p values > 0.05 for non-significant terms (a, b), as discussed previously. The sum of square values for all responses was low, indicating low variability from the mean. The model also had a positive lower degree of freedom, indicating its desirability—the mean square values nearer to zero indicate better responses for the model. Furthermore, Fig. 4a, b and c shows that the predicted values were in close proximity to the experimental values for all three responses. The goodness of fit was supported by moderately lower values of the standard deviation (SD) and the closeness of the R2 value to unity (1.0).

Table 4 Regression coefficients of predicted models and model assessment parameters for the desired responses
Full size table
Table 5 ANOVA for response surface model for all responses
Full size table
Fig. 4
figure 4

Variation of predicted values with actual values a for % Y, b for % C, and c for % P

Full size image

The mechanisms influencing the effects of input variables on response variables

The significant factors that influence the effect of input variables on the responses in Eucalyptus essential oil extraction are the leaf surface area available for contact with the solvent, temperature facilitation, concentration gradient, and contact time. A higher surface area generally facilitates more significant interaction between the leaves and the medium, leading to more efficient extraction of essential oils. The extraction temperature also plays a crucial role in the volatility of essential oil components. Higher temperatures increase the kinetic energy of molecules of the components in the leaves, facilitating the release of volatile compounds into the vapour phase for extraction. Conversely, lower temperatures may result in reduced extraction efficiency. The solid/solvent ratio also influences the concentration gradient between the solvent or steam and the leaf surface. A higher ratio increases the concentration of constituents in the medium, thereby enhancing the driving force for mass transfer; however, excessively high ratios may lead to saturation, reducing extraction efficiency. An essential component of the extraction process is the duration during which the plant material and the extracting medium are in contact. Longer extraction times allow for a more thorough diffusion of essential oil components from the leaf matrix into the solvent or steam. However, there is an optimal extraction time beyond which further extraction may not be suitable due to the reach of equilibrium between the plant material and the extracting medium. The following sections discuss the effects of input variables on response variables in detail.

Effect of variables on Y %

The contour plots in Fig. 5 depict the interactive effects of different factors (leaf size, temperature, solid/solvent ratio, and time) on the percentage oil yield of the essential oil extraction process. The plots show how variations in these factors impact the oil yield, providing insights into the optimization of the extraction process. With the solid/solvent ratio kept at 0.54 and the extraction period set to 180 min, Fig. 5a depicts the interactive effect of leaf size (A) and temperature (B) on the leaf surface. The percentage of oil yield is shown as the response variable in the figure. The plot shows that when the temperature on the leaf surface rises, the oil yield % also increases. The rise is caused by several factors, including the increased vapour pressure of essential oil and an increase in the kinetic energy of the oil molecules, which promoted faster diffusion. Decreased oil viscosity and greater temperature volatility also affected its transfer to the solvent phase. A reduction in oil yield with larger leaves was demonstrated, with normal-sized leaves demonstrating around 70% of the maximum oil yield, assuming the same factor values (C) and (D). Smaller interfacial areas and the probability of a thicker cuticle were the reasons limiting the efficient transfer of essential oil from the larger leaf to the solvent during the steam distillation process. Figure 5b demonstrates the interactive effect of leaf size and solid/solvent ratio while kee** the temperature fixed at 95 °C and the extraction time at 180 min. The percentage oil yield increased with a decrease in leaf size and a solid/solvent ratio of 0.55. A higher oil yield resulted from the leaf's increased surface area, improved contact area, and well-optimized solid/solvent ratio. An optimized solid/solvent ratio was very much necessary and could be achieved by using the opposite leaves in relation to the volume of water. If excess quantity had been used, a hindrance to the flow of steam was possible, resulting in inadequate extraction of essential oil and lower yield would have occurred. On the other hand, not having enough leaves compared to the volume of water used would have resulted in the inability of the steam to carry away a sufficient amount of essential oil from the leaves. Figure 5c presents the interactive effect of leaf size and time for a fixed temperature of 95 °C and a solid/solvent ratio of 0.54. The percentage of oil yield increased with a decrease in leaf size and an increase in time. This suggests that smaller leaves and a longer extraction time lead to higher oil yield. A more extended extraction period during steam distillation, resulting in a higher oil yield, was due to increased contact with the plant material. This increased the likelihood of extracting more oil, ensuring that the steam could slowly and gradually dissolve the oil compounds, resulting in a thorough extraction. Figure 5d showcases the interactive effect of temperature and solid/solvent ratio while kee** the leaf size fixed at 3.50 cm and the extraction time at 180 min. The plot shows that the percentage oil yield increased with an increase in temperature and a solid/solvent ratio of 0.56. However, there was a limit to the increase in oil amount with the solid/solvent ratio, as the concentration gradient mass transfer between Eucalyptus leaves and the liquid phase played a vital role. The oil concentration in the leaves decreased naturally as the distillation progressed. A sharper initial concentration gradient facilitated a higher rate of essential oil transfer from the solid phase to the liquid phase, and as the concentration difference decreased over time, the transfer rate slowed down. Figure 5e demonstrates the effect of temperature and time while kee** the leaf size fixed at 3.50 cm and the solid/solvent ratio at 0.54. Here, the percentage of oil yield increased with both temperature and time. Higher temperatures and longer extraction times led to higher oil yield. However, a balance between the temperature and time during steam distillation to achieve the best results was needed as although higher temperatures can lead to increased mass transfer by overcoming the barriers to essential oil evaporation from the leaves, some components of the oil which are sensitive to heat tends to degrade or lose their aromatic properties at very high temperatures. The same applied to extraction time, and there was a practical limit to the distillation time, as excessively long durations did not yield a proportionate surge in oil yield and were unproductive. Figure 5f illustrates the interactive effect of the solid/solvent ratio and time while kee** the leaf size fixed at 3.50 cm and the temperature at 95 °C. The percentage oil yield increases with both an increase in the solid/solvent ratio and time.

Fig. 5
figure 5

Contour response surfaces for the collaborative effect of a LS and TLS, b LS and SSR, c LS and TC, d TLS and SSR, e TLS and TC, f SSR and TC on oil yield (Y %)

Full size image

Effect of variables on C %

The contour plots in Fig. 6 depict the interactive effects of different factors (leaf size, temperature, solid/solvent ratio, and time) on the percentage of 1–8 cineole in the essential oil extraction process. The plots show how variations in these factors impact the concentration of 1–8 cineole in the oil, providing insights into the optimization of the extraction process.

Fig. 6
figure 6

Contour response surfaces for the collaborative effect of a LS and TLS, b LS and SSR, c LS and TC, d TLS and SSR, e TLS and TC, f SSR and TC on % C

Full size image

Figure 6a shows the interactive effect of leaf size (A) and temperature on the leaf surface (B), with a fixed solid/solvent ratio of 0.54 and an extraction time of 180 min. The plot indicates that there is a maximum of 72% of 1–8 cineole for the leaves with the same factor values (C) and (D). Beyond this optimal point, further increase in the solid/solvent ratio and extraction time did not result in a proportional increase in the percentage of 1,8-cineole as the extraction became saturated. Figure 6b demonstrates the interactive effect of leaf size and solid/solvent ratio while kee** the temperature fixed at 95 °C and the extraction time at 180 min. The plot indicates that 1–8 cineole percentage increased with a decrease in leaf size and a solid/solvent ratio of 0.54. The increased surface area of the smaller leaves enabled improved contact between the plant material and the extraction medium, leading to enhanced mass transfer and a higher yield of volatile compounds like 1,8-cineole. Smaller leaves also decrease the diffusion path length for essential oil components to move from the inner part of the leaf to the surface, making it easier for the compounds to be released into the solvent. Figure 6c presents the interactive effect of leaf size and time for a fixed temperature of 95 °C and a solid/solvent ratio of 0.54. The plot suggests that the percentage of 1,8-cineole increased with decreased leaf size and a shorter extraction time. Due to the shorter extraction process, the 1,8-cineole and other essential oil constituents were extracted quickly before degrading or reacting with other compounds. This led to a higher percentage of 1,8-cineole in the final essential oil. Figure 6d showcases the interactive effect of temperature and solid/solvent ratio while kee** the leaf size fixed at 3.50 cm and the extraction time at 180 min. The percentage of 1–8 cineole decreased as the temperature increased. As the temperature increased during the steam distillation, the thermal energy facilitated the release of the compound, leading to higher concentrations. It is important to note that very high temperatures begin the degradation of heat-sensitive compounds or alterations in their chemical composition, affecting their quality. Therefore, the extraction temperature must be carefully controlled to optimize the process while preserving the desired properties of the oil. Figure 6e demonstrates the interactive effect of temperature and time while kee** the leaf size fixed at 3.50 cm and the solid/solvent ratio at 0.54. The 1–8 cineole percentage increased as the temperature decreased and the extraction time increased. Hence, it is desirable to use lower temperatures and longer extraction times to maximize the percentage of 1,8-cineole in Eucalyptus oils during steam distillation. As the extraction time increased, more stretch was available for the volatile compounds to diffuse and dissolve into the steam, leading to higher concentrations of 1,8-cineole in the essential oil. In addition, a lower temperature prevented excessive evaporation of the 1,8-cineole, leading to a higher retention of the component in the oil and an increased percentage. Figure 6f illustrates the interactive effect of the solid/solvent ratio and time while kee** the leaf size fixed at 3.50 cm and the temperature at 95 °C. The percentage of 1–8 cineole increased as both the solid/solvent ratio and the extraction time increased due to better dissolution of 1,8-cineole and other constituents in the solvent.

Effect of variables on P%

The contour plots in Fig. 7 illustrate the interactive effects of different factors (leaf size, temperature, solid/solvent ratio, and time) on the percentage of α-Pinene in the essential oil extraction process. The plots provide insights into how variations in these factors influence the concentration of α-Pinene, aiding in optimizing the extraction process. Figure 7a shows the interactive effect of leaf size (A) and temperature on the leaf surface (B), with a fixed solid/solvent ratio of 0.54 and an extraction time of 180 min. The percentage of α-Pinene increased with an increase in temperature and a decrease in leaf size. Similar findings were observed in the literature (Uemura et al., 1997). Figure 7b demonstrates the interactive effect of temperature and solid/solvent ratio while kee** the leaf size fixed at 3.50 cm and the extraction time at 180 min. The percentage of α-Pinene increased as the temperature increased and the solid/solvent ratio decreased. The literature reports this trend (Kostić et al., 2013). α-Pinene, a volatile compound with a relatively low boiling point, has a tendency for vaporization with the increased thermal energy and thus is released at a higher %, resulting in a higher concentration in the oil. In addition, a lower solid/solvent ratio meant less plant material w.r.t the solvent, which offered more solvent to dissolve and carry away α-Pinene, leading to its increase. Figure 7c showcases the interactive effect of the solid/solvent ratio and time while kee** the leaf size fixed at 3.50 cm and the temperature at 95 °C. This finding is similar to previous research (Hussain et al., 2022).

Fig. 7
figure 7

Contour response surfaces for the collaborative effect of a LS and TLS, b TLS and SSR, c SSR and TC on % P

Full size image

Optimization

In the manufacturing process of essential oils, optimizing many responses at once could be difficult since improving one reaction might have a negative impact on another response. For example, lengthening the extraction time might have increased the yield while lowering the proportion of 1,8-cineole. On the other hand, reducing the extraction period would have resulted in a higher proportion of 1,8-cineole but a lower overall oil yield. To solve this problem and identify the ideal combination of process factors that maximized the overall performance, the desirability function, a powerful tool, was considered. The tool integrates many responses into a single objective function that can be maximized or minimized. In this instance, the goal was to maximize economic viability by increasing yield concurrently and increasing % 1,8-cineole. The desirability function offered a way to assign values to each response based on the desired values or acceptable ranges. For instance, a desired yield value or a range was assigned to increase yield. When the yield fell within the desired range, its attractiveness value was considered to be 1, and it decreased as it approached or crossed the range. The overall desirability function was created by integrating the individual desirability values defined for each response. Table 6 outlines the optimization constraints adopted in this study. For cost-effectiveness, the optimization goal for all design variables (except TLS) was set to minimize. TLS positively affected responses R1 and R3 but negatively affected response R2, due to variation of boiling points of 1–8 cineole and α-Pinene. TLS was hence assigned an optimization goal within a specific range. SSR, which showed different effects on the three responses with varying constraints, was also set to an optimization goal within a range.

Table 6 Optimization constraints
Full size table

Conversely, TC positively affected all three responses, and its optimization goal was set to maximize. The optimization goal was set to maximize all three responses to achieve maximum yield, maximum percentage of 1–8 cineole, and percentage of α-Pinene (Table 6). The input process variables yielding the highest desirability were considered the optimum solution. The maximum achieved overall desirability was found to be 0.652, corresponding to the following optimized input process variables: LS (A) = 0.02 m, TLS (B) = 97.76 °C, SSR (C) = 0.61, and TC (D) = 206.63 min. The model-predicted values for response R1 (yield) fell within the range between L1 (0.3%) and U1 (0.75%). Similar observations were held for responses R2 and R3. At the optimized input process variables, the model predicted an essential oil yield of 0.70%, 1–8 cineole of 63.98%, and percentage of α-Pinene of 20.81%.

The corresponding experimental values for yield, % 1–8 cineole, and % α-Pinene are 0.68%, 62.76%, and 16.63%, respectively, when using the conventional electric-powered steam distillation System. Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE) methods were used to calculate the error between the model predicted and experimental results. In this case, both MAE and RMSE for % yield, % 1–8 cineole, and % α-Pinene were calculated to be 0.02%, 1.22%, and 4.18%, respectively. The yield and % 1,8-cineole errors in this instance were relatively minor, demonstrating a good agreement between the model and experimental results. The greater inaccuracy for the % α-Pinene indicated that the model might need improvement or that additional factors could affect this response.

Eucalyptus essential oil from the solar steam distillation system with optimized input variables

The experiments for essential oil extraction from the solar steam distillation system were carried out in May 2022. The results for 4 sunny days of the experiment, when the extraction time and temperature matched the optimal values, are shown in Fig. 8a, and 8b. The Scheffler concentrator was positioned to face the sun eastward in the morning. It was equipped with a mechanical gear-based tracking mechanism, which allowed the parabolic dish to move and adjust its orientation based on the sun's position. Hence, as the sun moved across the sky from east to west during the day, the concentrator's parabolic dish followed the sun's path to maintain an optimal focus. The concentrated solar energy was directed at the focus to the spherical baby boiler, allowing for steam generation. The black-coated baby boiler had the capacity to produce a maximum of 2 kg steam/h and a maximum withstandable pressure of around 8 bar. The boiler was equipped with a pressure release valve, pipes and connections, proper insulation and a steam outlet line. A connected water tank allowed for continuous steam generation, even during periods of fluctuating solar intensity. The boiler was placed inside a box-casing with toughened glass facing the concentrated solar radiation from the Scheffler. The concentration ratio of the Scheffler was approximately 5. The tempered glass acted as a window, allowing solar radiation to pass through while protecting the interior from external factors like wind, dust, or rain. As the concentrated solar radiation from the Scheffler concentrator entered the box-casing through the toughened glass, it directly heated the baby boiler. The water inside the boiler absorbed the solar thermal energy, leading to steam generation within the boiler. The solar steam distillation system was operated with a Eucalyptus leaf size of 0.02 m. The experiments were carried out for approximately 3.5 h in the solar window to match the model predicted and conventional steam distillation values. This was the period of maximum solar irradiation to ensure efficient solar energy utilization for the steam distillation process. The plots show that the higher solar radiation generally correlated with increased steam generation, which also positively affected the boiler efficiency. The enhanced solar energy input resulted in higher temperature differences for better heat transfer, leading to higher solar baby boiler efficiency. It can also be observed that there was no strong relationship pattern between ambient temperature and boiler efficiency. The expanded uncertainty associated with the measurement of the thermal efficiency of the solar boiler was calculated to be ± 0.38%. Steam generated in larger quantities facilitated the mass transfer of essential oil components from the leaf to the vapour phase, leading to higher yields. Heat transfer also improved, ensuring the leaves under process reached the desired temperature quickly and uniformly, enhancing the release of essential oil. Table 7 compares the response variables the % yield, % 1–8 cineole, % α-Pinene for the conventional and solar steam distilled processes.

Fig. 8
figure 8

a Ambient conditions on 4 days of the experiment. b Experimental results showing the average steam generation rate, solar boiler efficiency and essential oil yield on the experiment days

Full size image
Table 7 Comparison of the response variables, the % yield, % 1–8 cineole, % α-Pinene for the conventional steam distilled and solar steam distilled processes under the optimum extraction conditions
Full size table

Table 7 shows a considerable variation in the yields for both experimental conditions. MAE and RMSE errors of about 0.6% were obtained through model-predicted conventional (0.70%) and solar steam distillation (0.1%). The consistency and control of the indoor experimental setup, which allowed for better control over variables like temperature, pressure, and extraction time, could be ascribed to the enhanced distillation conditions. The ability to maintain specific and stable conditions in the traditional system might have led to the higher yield of essential oils from the Eucalyptus leaves compared to the variable conditions in the solar steam distillation system (Fig. 8a). There was a noteworthy variation in 1–8 cineole % between the two distillation processes (62.76% vis–a–vis 61.44%). Similar to the % yield, due to more stable temperatures achieved in conventional steam distillation by carefully controlling and regulating the energy input, a higher % of 1–8 cineole was obtained for the traditional system. The duration of distillation could have affected the yield of α-Pinene, which was 16.63% through conventional steam distillation compared to 8.9% for solar steam distillation. Apart from a controlled operation condition for the traditional system, the yields of α-Pinene could have differed because of the differences in the quality or composition of the source material between the experiments. Hence, despite the environmental and ecological advantages, there are several drawbacks to the solar thermal steam distillation process to produce Eucalyptus oil. Apart from the variation in solar radiation throughout the day, the process highly depends on the weather, reducing effectiveness on cloudy or rainy days and necessitating a large solar collector area, thus resulting in a considerable initial cost. Lower energy density than conventional sources can sometimes result in prolonged distillation times, leading to degradation of the obtained Eucalyptus oil, resulting in decreased quality and potentially altered chemical composition. Furthermore, solar thermal system design and maintenance require specific technical knowledge, which could be limited in some regions.

The essential oil yields and extraction processes, operated mainly by solar thermal energy for the various plants, are given in Table 8. Various research studies have reported different eucalyptus yields, including the present work. For peppermint and pinus, yields of 0.40% w/w and 0.31% w/w, respectively, have been reported. The observed solar distillation yields for lemon grass range from 0.358% to 1.533%. The reported solar distillation yields for lavender range from 0.5% to 6.8% w/w, showing that depending on the plant species, technique of extraction, and working conditions, a given batch of essential oil may have different % yield values. Apart from these, the effectiveness of the distillation setup and the analytical techniques used for yield determination are some of the elements that can be responsible for the variations in yield.

Table 8 Comparison of essential oil yields and extraction processes for various plants
Full size table

The findings of the study have significant implications for industrial applications of essential oil extraction, particularly in the context of implementing solar steam distillation. This work offers several practical benefits by optimizing extraction parameters and demonstrating the feasibility of solar-driven extraction. Firstly, there are environmental advantages to employing sustainable solar thermal energy since it reduces dependence on conventional energy sources. Furthermore, solar steam distillation can be applied in sunny areas, supporting local economies and providing opportunities for decentralized production. In addition, small-scale, solar thermal-driven Eucalyptus oil extraction has a wide range of specialized applications, including artisanal perfumery, high-end cosmetic manufacturing, botanical research, the production of herbal medicines, natural product chemistry, niche organic pest control services or agricultural practises, alternative veterinary care useful for animal treatments or therapies, natural dyeing processes in sustainable and eco-friendly fashion brands, and many more. However, factors including marketing strategies, techno-economic viability, and the unique requirements of each niche sector influence the demand. This is in addition to the typical uses in the flavour and food sectors as an industrial solvent in aromatherapy for its therapeutic effects or the use of 1,8-cineole for prospective medical applications. Solar-driven Eucalyptus essential oil extraction will offer a competitive edge over conventional processes by appealing to environmentally concerned customers and establishing an eco-friendly business image. However, considerations like ambient conditions and variations in solar intensity might affect the effectiveness of extraction. Successfully addressing these challenges needs appropriate and optimal system design and control.

Conclusions

The goal of the study was to maximize the yield of essential oil from Eucalyptus leaves while identifying the variables that affect both the oil yield and the yields of the two main constituents, 1–8 cineole and α-Pinene. Response surface methodology (RSM), a statistical and mathematical approach used to describe and optimize systems with various variables, was used to accomplish this. To investigate the impacts of various input factors on the oil yield and the components, the study carried out a total of 27 experimental runs in a conventional electric-powered steam distillation system. The following conditions were determined to be the most effective for obtaining essential oil from eucalyptus leaves: size of leaf: 0.02 m, temperature: 97.76 °C, ratio of solid to solvent: 0.61 and time: 206 min. According to the findings, the optimized solution yielded 0.70% oil, 63.98% 1,8-cineole, and 20.81% α-Pinene. The corresponding experimental values for yield, % 1–8 cineole, and % α-Pinene were 0.68%, 62.76%, and 16.63%, respectively. The study further applied the RSM-derived optimal operating conditions from the conventional system to a Scheffler solar steam distillation system. Several suggestions for more research might be made in light of the findings of the present work. More studies may be conducted to optimize the layout and functioning of solar steam distillation systems to increase efficiency and dependability. Improved tracking systems, better insulation, and innovative collector designs might enhance the efficiency of the solar thermal-driven extraction process. Analysing Eucalyptus leaves from different geographical regions can also reveal how environmental factors such as soil composition, climate, and altitude influence the composition and yield of essential oils. This information may influence decisions for commercial Eucalyptus oil production. In addition, the study of more input variables for the conventional steam distillation process e.g. effects of the flow rate of steam, steam quality, steam pressure, condenser efficiency, and also material loading in the distillation chamber—on the quality and yield of essential oil, could provide valuable insights into the extraction process. Subsequent investigations may examine a thorough gas chromatography–mass spectrometry (GC–MS) to methodically define the entire composition of obtained essential oils through conventional and solar steam distillation processes. In addition to promoting small enterprises aiming at specialized markets and sunny climates, this work aligns with the Sustainable Development Goals. By using renewable solar thermal energy, industries can reduce their carbon footprint and support global climate goals. Overall, the conclusions open the door for using solar thermal extraction technologies in industrial settings, providing a sustainable and ecologically friendly substitute for traditional essential oil production processes.