Taguchi Optimization on the Biooil Extraction from Fresh Water Algae (<i>Spirogyra</i>) Using Soxhlet Apparatus

Aravind, S.; Barik, Debabrata

doi:https://doi.org/10.1155/2023/6213851

International Journal of Energy Research

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Abstract Introduction Materials and Methods Results Conclusion Data Availability Conflicts of Interest Authors’ Contributions Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2023 | Article ID 6213851 | https://doi.org/10.1155/2023/6213851

Taguchi Optimization on the Biooil Extraction from Fresh Water Algae (Spirogyra) Using Soxhlet Apparatus

S. Aravind¹and Debabrata Barik¹

Academic Editor: Andrey Kuzmin

Received04 Jan 2023

Revised08 May 2023

Accepted14 Aug 2023

Published24 Aug 2023

Abstract

The present investigation is aimed at maximizing the biooil yield (BOY) from the algal species Spirogyra by solvent extraction method with a nonpolar solvent n-hexane using a Soxhlet apparatus. Also, this present work is aimed at acquiring optimal physiochemical properties such as kinematic viscosity and iodine value of the biooil produced from the algae. To carry out this work, the Taguchi technique with the L9 orthogonal array (OA) was used. With the L9 OA, nine sets of experiments were directed as per the design matrix, and the results were analyzed using the Taguchi method with the help of Minitab 16 software. The results generated from this optimization process showcased that the BOY from the Spirogyra was found to be a maximum of 26.86% with the particle size (PS) being 0.462 μm, the response time (RT) of 60 minutes, the solvent ratio (SR) of 1 : 10, and the extraction temperature (ET) of 75°C. The optimal set of input parameters for the optimal output response kinematic viscosity of 1.92 mm²/s was found to be achieved with the PS being 0.462 μm, 60 minutes of RT, the SR of 1 : 15, and the ET of 75°C. Similarly, the optimal set of input parameters for the optimal output response iodine value was found to be at 0.322 μm PS, 120 minutes of RT, an SR of 1 : 5, and an ET of 75°C. The signal-to-noise ratio (SNR) for the output responses exposed that the PS exhibited a major impact on the BOY and kinematic viscosity and the contribution levels of 76.68 and 51.7%, respectively.

1. Introduction

The utilization of mineral-based fuels drastically increased with modern technological development. The extinction of fossil fuels is going to occur soon, and this led the entire research community to find alternative energy sources. Alternative fuels like biodiesel were produced from various edible and nonedible oil sources. Similarly, the algae were found to be a better source for producing biodiesel [1–3]. The reason would be the production rate of algae is very high when compared to other food crops. The algal oil extracted was around 20 to 80 kilo liter per acre per year, which is found to be around 31 times larger than the oil gained from viable crops grown [4, 5]. There are many varieties of algal species available in this world. Out of which, the Spirogyra is one genus type that belongs to the order Zygnematales, and it has around 400 different species. The Spirogyra algae have its chloroplast in the spiral form, and they grow in water by using carbon dioxide for their growth [6]. Due to its higher rate of production, it can be considered a rich source to extract biooil from which biodiesel production can be performed. To extract biooil from the biomass, two methods are used: the one is cold pressing and another one is solvent extraction techniques. The cold pressing technique was found to be perfect for biomass having bigger PSs [7, 8]. However, the solvent extraction process has the capability of reacting with smaller size particles to extract the biooil content from them [9]. To extract biooil from biomass using a solvent extraction process, the selection of a suitable solvent type is very important. The solvents in general are subdivided into polar and nonpolar solvents [10]. The polar solvents highly react with the polar lipid phase of the biomass. The dielectric constant of such solvents is very high, whereas solvents like n-hexane, diethyl ether, benzene, and toluene have low dielectric constants and are considered to be nonpolar [11]. The solvent extraction process is influenced by process parameters such as ET, RT, SR, and PS. The biooil production from the different algal species and biomass using different solvents was reported in many works. The extraction of biooil from the algal species Chlorella vulgaris by a solvent extraction process using the Soxhlet apparatus with heptane as solvent at optimal conditions resulted in higher BOY [12]. In a similar pattern, biooil was extracted from the algal species Nannochloropsis salina, C. vulgaris, Scenedesmus obliquus, Acutodesmus obliquus, Cladophora glomerata, Dunaliella sp., and Botryococcus braunii using solvent extraction process with different types of solvents and found the optimal settings for improved BOY. The major factors like solid-to-SR, RT, ET, and PS which affect the biooil extraction process through the solvent extraction method are considered. These factors prominently showed much influence on the efficiency of the biooil production process. The quantity of biooil extracted from the algal species was mainly affected by these parameters [7, 13–16]. The optimization of the input parameters for extraction of biooil content from the algal source is very essential. To maximize the BOY from the algal species, the process parameters such as ET, RT, solvent quantity, and PS are to be optimized. There are many types of optimization techniques available, in which the Taguchi technique is found to be used widely [17–21]. The Taguchi technique uses the design of experiment concept to minimize the number of experiments to be conducted and helps in finding the optimal solution by the SNR. Chen et al. [22] used the Taguchi optimization technique to optimize the BOY from ginger. The optimal conditions were found to be 60°C of temperature, 4 bar pressure, 595 μm ginger PS, and 90 minutes of RT resulting in a maximum yield of ginger oil. Kim et al. [23] used the Taguchi method with the L9 OA to optimize the lipid extraction from the species Scenedesmus. The optimal settings were identified to be 5 hours of RT, a temperature of 35°C, and a solvent ratio of 1 : 20 resulting in a maximum BOY of 20.55% which contained 96% of the overall lipid content of the algal species. There were no other works presented in the optimization of BOY by a solvent extraction process using the Taguchi technique. A comparison between cold pressing and solvent extraction techniques for the production of algal biooil is given in Table 1.

From Table 1, it can be observed that the extraction of biooil from algae carried out by the cold pressing method makes use of mechanical force to disrupt the algal cells to release the lipid content. This method is simple and provides environment-friendly and chemical-free biooil [24, 25]. The cost of extraction of the biooil by this method is cheaper, and the quality of the biooil extracted is also high [26, 27]. However, the main disadvantage of this method is the low oil yield, since the cells of the algae are complex in nature and some lipid contents are found inside the cytoplasm and are strongly bound by hydrogen bonds and protein membranes, which cannot be broken down by the cold pressing method. Also, the pressing process may take a longer time, and the scaling up of the process for larger quantities is very challenging and time-consuming [28–30]. Some parts of the biomass may be wasted during the cold pressing process. The main reason for using the solvent extraction method is that the solvents used have the ability to break down the cells of the algae to the complete level and, in turn, increase the biooil yield to a higher level. The other advantages of the solvent extraction method are the scaling up of the process is easier and the process is very quick, making it a time-efficient process [10, 31, 32].

In the present work, the Taguchi method with L9 OA was employed to maximize the BOY from the algal species Spirogyra by the method of solvent extraction in a Soxhlet apparatus using the solvent n-hexane. The optimization was carried out to find the optimal process parameters such as the SR, RT (reaction time), and PS of the algal powder for maximizing the BOY. Also, the influence of the input parameters on biooil extraction was investigated along with the physiochemical properties of the produced biooil to justify the usage of the yielded biooil from the Spirogyra as a substitute fuel in the compression ignition engine.

2. Materials and Methods

2.1. Algal Collection and Groundwork

The algal species Spirogyra were obtained from the tank nearby the Karpagam University campus. The algae collected were dried under the sun and made into a powder using a domestic grinder. The algal powder was kept in the incubator at a temperature of 100°C for about 1 hour to remove the extra moisture present in the algal powder. The powder was filtered through various sized mesh sieves of 30 to 60 microns and separated for biooil production. The algal powder obtained was 0.641, 0.462, and 0.366 μm average size for mesh sizes of 30 microns, 40 microns, and 60 microns, respectively. The biooil content in 100 g of completely dry algal powder was around 28% by weight, and it was determined using the Soxhlet extraction method. Figure 1 shows the biooil extraction flow chart which explains the stages involved in biooil extraction from the Spirogyra species [33]. The solvent n-hexane (C6H14) (brand: Emsure ACS) used for the tests was purchased from the local dealer and it agrees to the standard. The technical details of the n-hexane are given in Table 2, where s.g. represents the specific gravity, is the kinematic viscosity, BP is the boiling point, ER is the evaporation rate, DC is the dielectric constant, and RI is the refractive index.

2.2. Solvent Extraction Process

The powder was kept inside the thimble along with filter paper in a Soxhlet apparatus, and the solvent of 750 ml was poured inside a round bottom flask. The algal powder is placed inside a thimble, which is then placed into the Soxhlet apparatus. The solvent is added to the flask and heated, causing the solvent to evaporate and condense in the condenser, eventually dripping onto the sample in the thimble. The solvent dissolves the oil content from the algal powder, and the resulting solution is carried back up to the flask. The process is repeated multiple times, with the fresh solvent being added each time until the desired level of extraction is achieved. The BOY process was set out according to the design of experiment. After each experiment, the biooil-solvent mixture was distilled to remove the solvent present in the biooil [33, 34]. The percentage of BOY was calculated using the following equation:

2.3. Design of Taguchi Model

The L9 orthogonal array is a commonly used design in the Taguchi optimization because it allows for the simultaneous evaluation of up to four factors, each with three levels, in a relatively small number of experiments. This makes it an efficient and cost-effective method for optimizing a process. It is an orthogonal array, and it ensures that each factor in the columns and rows is arranged in such a way that each combination of factor levels occurs exactly once. The Taguchi model of L9 OA with design at three levels was created employing Minitab 16 [18–20, 35]. The process parameters considered for the current study were the ET, RT, SR, and PS of algal powder [35]. The reason for choosing the input factors for the optimization procedure is completely based on the aspects manipulating the extraction of the biooil content from the algae. The aspects which majorly stimulated the extraction of biooil were found to be the solid-to-SR, ET, RT, and PS of the powder. The parameters used in the biooil extraction process considered for the model are given in Table 3. The optimization was done to maximize the BOY from the algal species Spirogyra. Also, the physiochemical properties like kinematic viscosity and density were analyzed and optimized. The output response parameter % of BOY is the major output parameter that decides the efficiency of the biooil extraction process by means of the solvent extraction method and the quantity of biooil extracted from the given amount of algal powder. Also, the kinematic viscosity and the iodine value define the quality of the biooil extracted from the algae. The main objective of the biooil extraction from the algae is to use the extracted biooil as an alternative source of mineral fuels. Hence, the quantity extracted and the quality of the biooil have to be optimized. In the L9 OA, the experimental design produces a total of nine experimental sets.

For analysis of the data, the Taguchi method uses the SNR as the quality defining the term. SNR is used as the quantifiable value as an alternative for standard deviation (SD) owing to the statistics that as the average decreases, the SD also declines and vice versa. In simple words, the minimization of SD could not be done in the first step; hence, the average is kept to meet the decided value. The Taguchi method implies that, for every engineering system, the factors considered for design fall under these categories, control factors that affect the process parameters, and are measured by SNR, signal factors showing no influence on SNR, and the factors which are completely out of the influence of SNR. The SNR features can be divided into three types when the characteristic is incessant: where is the mean value of the data set, is the variance of , and is the number of experiments conducted as specified in Equation (2), Equation (3), and Equation (4). For all these characteristics showing the SNR functions, the results will be better when the values of the SNR are in the higher range [20, 21].

In this present work, for the factor BOY, larger the better SNR is used, and for the iodine value and kinematic viscosity, the smaller the better SNR is employed. To analyze the consequence of the input parameters on the output parameters, analysis of variance (ANOVA) can be used. This helps in concluding the percentage of influence of the input parameters on the output parameters based on the proportion of contribution from the ANOVA.

3. Results and Discussions

3.1. Experimental Results

According to the design matrix, nine different sets of experiments were carried out with a 100% dried algal sample of quantity 50 g which were kept persistent for all sets of experiments conducted. The design matrix and the resulting output parameters from the nine experimental runs are given in Table 4. The BOY from the algae was found to be maximum at a 1 : 10 (g/ml) SR, 0.442 μm PS, the extraction temperature of 75°C, and an RT of 1 hour. The kinematic viscosity of the algal biooil was found to be the least for a 1 : 10 (g/ml) SR, 0.366 μm PS, the extraction temperature being 70°C, and the RT of 2 hours. The iodine value of the algal biooil was found to be within the range for all nine experiments with a maximum of 1 : 5 (g/ml) SR, 0.641 μm PS, the extraction temperature being 75°C, and the RT of 2 hours.

3.2. Effect of Process Parameters and Statistical Analysis

To analyze the outcome of the process parameters on the output parameters, an analysis of variance was performed, and the response tables for the factors BOY, kinematic viscosity, and iodine value are provided in Tables 5–7. Rank 1 shows the contribution level of the corresponding factor on the respective output response.

For BOY and kinematic viscosity, the influence of the PS was higher whereas the influence of the SR was higher for the iodine value. The mean effect plot for SNR for all three response factors is exposed in Figures 2–4. The optimal condition for the maximum BOY was found to be the PS of 0.462 μm, RT of 60 minutes, SR of 1 : 10, and ET of 75°C.

The optimal condition for the output response kinematic viscosity was found to be the PS of 0.462 μm, the RT of 60 minutes, the SR of 1 : 15, and the ET of 75°C.

Similarly, the optimal condition for the output response iodine value was found to be a PS of 0.322 μm, a RT of 120 minutes, a SR of 1 : 5, and a ET of 75°C.

From the contour plot depicted in Figures 5(a)–5(d), BOY was found to be greater for RT in the range of 60 to 80 minutes, PS in the range of 0.35 to 0.5 μm, SR in the range greater than 1 : 6 to 1 : 12.5, and ET greater than 72.5°C.

(a)

(b)

(c)

(d)

Similarly, the contour plot represented in Figures 6(a)–6(d) depicts the lower value of kinematic viscosity for RT in the range of 120 to 180 minutes, with PS less than 0.35 μm, with the SR in the range of 7.5 to 12.5, and with the ET ranging from 65 to 70°C.

(a)

(b)

(c)

(d)

The contour plots shown in Figures 7(a)–7(d) display the relationship between the iodine value of the extracted biooil and the influencing constraints of the biooil extraction process. The iodine value was found to be in its lowest limit for the RT in the range of 100 to 120 minutes, with PS greater than 0.35 μm, the SR falling in between 7.5 and 12.5, and the ET under the range of 65 to 70°C.

(a)

(b)

(c)

(d)

The PS affects the BOY since smaller PS leads to higher surface area-to-volume ratios and increases the contact of the solvent with the algal powder. The higher the contact area, the higher the BOY. And also, there may be some potential loss in the BOY with very smaller PS, since they may adhere to the surface of the thimble which will affect the process effectiveness [10, 34, 36]. The SR plays another important role in getting improved BOY; with increased SR, the BOY increases, but there is a possibility of an increase in the extraction of impurities along with desired content from the algal powder [36–38]. Hence, the SR should be kept intact so that the BOY will be out of impurities. The next process parameter under study is the RT; the higher the RT, the higher the BOY, but there is a possibility of extracting untargeted content from the algal powder other than the oil with increased RT [38–40]. Hence, it is required to maintain a balanced RT in order to get a good quality algal oil. ET plays another major role in the oil extraction process using solvent extraction methods. ET can affect the solubility of the target compound and the viscosity of the solvent, both of which can impact the extraction efficiency. Higher ET generally leads to greater solubility and faster extraction rates but may also result in the degradation of the target compound or the formation of unwanted by-products [38–40].

The SOS (sum of squares) and the percentage of contribution of the various factors for the output response factors are given in Table 8. The percentage of contribution for the PS was higher with 76.7% and 51.7% for the BOY and the kinematic viscosity, respectively. For the iodine value, the percentage of contribution was higher for the process parameter SR with 68.3%. The effect of the PS and RT was inversely proportional for the BOY and kinematic viscosity; that is, smaller PS with higher order RT results in a low value of kinematic viscosity whereas, for the BOY, the PS in the range of 0.35 to 0.5 μm and RT in the lower order range of 60 to 80 minutes resulted in maximum BOY. Similarly, the other factors showed their level of influence on the output response factor [41, 42].

With the optimum parameters, the validation experiment was conducted. The results of the validation experiment are shown in Table 9. The confirmation test revealed that the optimum parameters found using the Taguchi optimization technique are showing the optimum output parameters of the BOY, kinematic viscosity, and iodine value.

4. Conclusion

The current work investigated the optimization of the parameters controlling the procedure of biooil extraction by solvent extraction using n-hexane as a solvent in a Soxhlet device. The optimization was carried out using the Taguchi technique with an L9 OA with 4 factors at three levels as input, and three factors were analyzed for output response. The BOY was found to be greater for RT in the range of 60 to 80 minutes, PS in the range of 0.35 to 0.5 μm, SR in the range greater than 1 : 6 to 1 : 12.5, and ET greater than 72.5°C. Finally, a confirmation test was carried out to validate the optimum parameters obtained by the Taguchi optimization technique. For a PS of 0.462 μm, RT of 75 minutes, SR of 10 g/ml, and ET of 65°C, the BOY was found to be around 26.58%, the kinematic viscosity of 2.01 mm²/s, and the iodine value of 64 g of iodine/100 g of biooil. The influence of the process parameter PS with a percentage of contribution of 76.7% and 51.7% had a greater influence on the BOY and kinematic viscosity, respectively. For the iodine value, the SR achieved a percentage of contribution of 68.3%. Further, the biooil extracted using the algal biomass Spirogyra species can be cultivated in large quantities in large tanks. And the biooil extracted can be blended with inorganic fuels in a desired ratio and can be used as a substitute fuel in the CI diesel engine.

Nomenclature

BOY:	Biooil yield
OA:	Orthogonal array
PS:	Particle size
RT:	Response time
SR:	Solvent ratio
ET:	Extraction temperature
SNR:	Signal-to-noise ratio
s.g.:	Specific gravity
:	Kinematic viscosity
BP:	Boiling point
ER:	Evaporation rate
DC:	Dielectric constant
RI:	Refractive index
SD:	Standard deviation
ANOVA:	Analysis of variance.

Data Availability

The data used to support the findings of this study are included in the article.

Conflicts of Interest

The authors declare that there is no conflict of interest regarding the publication of this article.

Authors’ Contributions

S. Aravind and Debabrata Barik were responsible for conceptualization and methodology; S. Aravind was responsible for experimentation, data arrangement, and validation; S. Aravind and Debabrata Barik were responsible for writing and preparation of the original draft; Debabrata Barik was responsible for supervision; S. Aravind and Debabrata Barik were responsible for editing and reviewing.

Acknowledgments

The authors sincerely thank the Karpagam Academy of Higher Education (KAHE), Coimbatore, India, for providing facilities to carry out the research.

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Copyright © 2023 S. Aravind and Debabrata Barik. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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