Sustainable Processing of Zea Mays peel ash (Methylene adsorbed) biosorbent as a novel green heterogeneous catalyst for biodiesel production

Sustainable Processing of Zea Mays peel ash (Methylene adsorbed) biosorbent as a novel green heterogeneous catalyst for biodiesel production | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sustainable Processing of Zea Mays peel ash (Methylene adsorbed) biosorbent as a novel green heterogeneous catalyst for biodiesel production Akshay Prakash, Jerold Manuel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4710073/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Jun, 2025 Read the published version in Chemical Papers → Version 1 posted 5 You are reading this latest preprint version Abstract The escalating energy crisis, propelled by extensive fossil fuel consumption, necessitates sustainable and environmentally friendly alternatives. Biodiesel, derived from renewable sources, has emerged as a promising solution. This study explores using methylene blue (M.B., which is a significant water pollutant in several parts of the world) dye-adsorbed biochar, a waste-derived green catalyst, for biodiesel production. The catalyst was synthesized from Zea Mays peels, demonstrating a circular economy approach. The optimization of transesterification reactions is achieved using the Taguchi method, considering factors including reaction time, temperature, catalyst load, and methanol-to-oil ratio. The resulting biodiesel was purified and characterized through various analyses, including Gas Chromatography and Nuclear Magnetic Resonance. Adsorption studies reveal the catalyst's potential, and structural analyses (FESEM, XRD, FTIR) provide insights into its composition. The synthesized biodiesel, identified through GC-MS, exhibited qualities that align with the findings of the literature. Overall, the study presents a sustainable and economically viable pathway for biodiesel production using a novel green catalyst derived from waste resources. Ash Methylene Blue Adsorbent Green Catalyst Biodiesel Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Practitioner points Methylene blue dye adsorbs on Zea Mays ash best at pH 8 and 10, achieving over 89% efficiency with minimal pH adjustments. Calcined Zea Mays dye-adsorbed ash (ZMDA) is highly porous; FESEM, XRD show increased surface area, EDS reveals high carbon, oxygen. Taguchi method optimized biodiesel production: 65°C, 240 minutes, 1.5% catalyst, 4:1 methanol-oil ratio; GRA confirms key factors. Zea Mays ash is an inexpensive, sustainable adsorbent and catalyst for biodiesel, wastewater treatment; promotes circular economy. The method scales easily, requiring minimal pH adjustments; integrates into existing biodiesel and wastewater systems efficiently. 1. Introduction The energy crisis is an alarming global issue due to rapid urbanization and industrialization. Fossil fuels such as coal, crude oil, petroleum, and natural gas are widely used in the transport and industrial sectors. The excessive usage of fossil fuels resulted in environmental and ecological problems such as global warming due to the emission of carbon dioxide. In addition, fossil fuels are non-renewable resources that get depleted and cannot be replenished. The annual global diesel consumption is 934 million tons, and around 97.6% of oil resources used in transportation are obtained from fossil fuels (Baskar and Aiswarya, 2016 ) (Prajapati et al., 2023 ). Therefore, there is a high need to develop renewable alternative energy sources to resolve the increase in global energy consumption and socio-economic and environmental concerns. Biodiesel is a clean and renewable alternative bioenergy that replaces fossil fuels because it is sustainable, environment-friendly, and non-toxic. The salient feature of biodiesel is that it can be used as such in internal combustion (I.C.) engines (as used by Rudolph Diesel in 1900) or blended with fossil diesel. Biodiesel can be produced via transesterification of triglycerides in the presence of an appropriate catalyst and alcohol (Ghosh et al., 2024). Various catalysts, such as homogeneous, heterogeneous, and biocatalysts, are employed in the transesterification reaction for biodiesel production. In the current scenario, homogeneously catalyzed (basic or acidic) transesterification is widely used for biodiesel production. Indeed, the reaction rate is relatively fast in the homogeneously catalyzed (K.O.H. and NaOH) transesterification process. However, some drawbacks include soap formation, additional separation, purification step, excess wastewater generation, low-grade glycerine by-product generation, difficulty reusing the catalysts, and equipment corrosion. Thus, homogeneous catalysts are undesirable for the production of biodiesel (Sulaiman and Syakirah Talha, 2016 ). In light of these limitations, researchers focus on heterogeneous catalysts to overcome the drawbacks of homogeneous catalysts. Interestingly, heterogeneous catalysts have the essential characteristics of reuse and separation, which reduce the catalyst losses and the cost of biodiesel production (Mandari and Devarai, 2022) Biomass-derived heterogeneous catalysts have recently have been widely explored for biodiesel production as they can overcome conventional catalysts' obstacles. The biochar-based heterogeneous catalyst is characterized by various compounds such as alkalized and alkaline metal oxides or carbonates (like K 2 CO 3 , CaCO 3 , K 2 O, CaO, MgO, etc.), including some of the transition metal oxides that enhance the catalytic activity in transesterification process (Chutia et al., 2023 ). Biomass is an abundant biomaterial available in the environment at low cost, which can significantly increase the economy of biodiesel production. Moreover, biochar-based heterogeneous catalysts obtained from biomass are eco-friendly, non-corrosive, non-toxic, biodegradable, and eliminate wastewater production. (Chutia et al., 2023 ) have mentioned various agricultural or forestry waste such as leaves of moringa, pineapple, Hetropanax fragrans , brassica nigra , Tectona grandis , waste of banana peels, oranges, papaya, waste husk of rice, coconut pod, Tamarindus indica , and peanut have been utilized as a green heterogeneous catalyst for the production of biodiesel. Biomass-based adsorption is gaining more attention than conventional adsorption due to the significant source of biomass resources, renewability, simple operation, and low and high sorption capacity. Various biomass, such as walnut shells, oak fruit shells, and potato peels, were effectively used to prepare biochar-based adsorbents(Shi et al., 2023 ) (Soudani et al., 2022 ). Though adsorption seems eco-friendly, after many cycles of operation containing toxic pollutants, the exhausted adsorbent is discarded as waste into the environment, creating secondary pollution. To date, no effort has been taken to discard the spent adsorbent loaded with organic or inorganic pollutants in an environment-friendly way. Circular economy and waste Valorization emphasize utilizing waste resources to produce value-added products. Accordingly, the present study is focused on the Valorization of spent adsorbent as a green catalyst for biodiesel production. Most of the ash obtained from biomass resources consists of metal oxides and carbonates. K 2 O, CaO, MgO, SiO 2 , etc., enhance the catalytic activity during the transesterification reaction(M. Sharma et al., 2012 ). Therefore, the present work is to investigate methylene blue (M.B.) dye-adsorbed biochar as a renewable green catalyst for converting waste cooking oil into biodiesel. M.B. is a synthetic dye often used for dyeing fabrics, papers, and leather, which can significantly cause water pollution and is highly toxic and carcinogenic. Zea Mays , is an extensively utilized, leading, and versatile crop cultivated in diverse climates and agricultural landscapes across nations such as India, the U.S.A., China, Brazil, Argentina, Indonesia, Mexico, South Africa, Nigeria, and Ukraine. Despite variations in weather, soil conditions, and agricultural practices among these significant producers, they collectively contribute significantly to the global production of maize. Recognized internationally as the "queen of cereals," maize exhibits unparalleled adaptability, boasting cereal crops' highest genetic yield potential. Its versatility is evident in various types, including regular yellow/white grain, sweet corn, baby corn, popcorn, waxy corn, high amylase corn, high oil corn, and quality protein maize. Beyond its role as a food source, maize serves as a vital industrial raw material, offering opportunities for value addition. Notably, the ashes of Zea Mays peels contain essential elements like potassium, calcium, phosphorus, and magnesium, making them valuable for agricultural applications such as fertilizer, soil amendment, pH adjustment, and as an aid in plant nutrition. Maize's global significance extends beyond its agricultural contributions; it plays a crucial role in agri-food systems, encompassing direct human consumption and indirect pathways through animal-sourced foods. With a surge in global maize production driven by increasing demand, it is poised to become the most widely grown crop globally and the most traded cereal internationally. The challenges and opportunities associated with maize necessitate substantial investments in international agri-food system research and development, particularly in the Global South. An integrated and inclusive approach is essential to maximize maize's developmental potential, contributing to food and nutrition security while ensuring environmental sustainability and resilience in agri-food systems, aligning with the goals of the 2030 Agenda (Erenstein et al., 2022 ) In our current investigation, we have focused on addressing two significant environmental impacts: wastewater treatment utilizing Zea Mays ashes and the transesterification process of waste cooking oil. Waste cooking oil poses a substantial threat to water quality. It is often improperly disposed of, with large quantities being drained into the sewage system instead of recycled through designated centers. This improper disposal can result in various environmental and plumbing issues, including clogs, ecological harm, damage to infrastructure, and legal consequences. As previously discussed, the adsorption of chemical dyes and heavy metals through biomass can create secondary pollution when there is no documented use for spent ash biomass. In our study, we have taken spent biomass saturated with dye, subjected it to calcination, and utilized it in the transesterification of waste cooking oil to produce fatty acid methyl ester. Commercializing this process could serve as an exemplary model of a circular economy, contributing to achieving some of the 17 sustainable development goals. Furthermore, the study involves the characterization of ashes derived from Zea Mays peels, dye-saturated ashes, and calcinated dye-saturated ashes to comprehend the physical and chemical alterations on the catalyst's surface. Techniques such as XRD, FTIR, and FE-SEM are employed to characterize and study the morphological features of the catalyst. N.M.R. and GCMS analyses of the prepared samples confirm the presence of biodiesel. To optimize the reaction, Taguchi-based Grey Relational Analysis, a mathematical and statistical method assessing the degree of relationship between process parameters, is applied. 2. Experimental 2.1 Materials The National Institute of Technology, Warangal, Telangana, India, canteen provided waste cooking oil (W.C.O.). MERCK Life Science Private Ltd. supplied high-performance liquid chromatography (HPLC) grade methanol. Finar Limited, India, sourced methylene blue. A street vendor outside the NITW campus provided the peels of Zea Mays . 2.2 Acid Value Determination and Pre-treatment of Waste Cooking Oil (W.C.O.) The collected W.C.O. underwent a settling period to allow the sedimentation of debris and other impurities. Subsequently, an Acid Value test was conducted to assess the percentage of Free Fatty Acids (% F.F.A.) present, indicative of the likelihood of glycerol formation. The acid value test was performed in triplicate, yielding an average % F.F.A. of 1.692. According to ASTM standards, this value slightly exceeded the recommended 1% threshold for transesterification. To address this, the W.C.O. underwent pre-treatment involving the addition of sulfuric acid and methanol, aiming to reduce the F.F.A. content as per the equation: For 100g of oil with x% F.F.A., 2.25 × x = y ml of methanol (1) 0.05 × x = y ml of sulfuric acid (2) The sulfuric acid and methanol quantities were calculated with the W.C.O. and agitated for 60 minutes at 60°C. Following agitation, we transferred the pre-treated W.C.O. to a 250 ml separating funnel and allowed it to settle overnight. The subsequent day, we observed two distinct layers. The bottom layer containing impurities was discarded, while the upper layer was retained for future use. 2.3 Dye adsorption study In the adsorption experiment, methylene blue was poured into five flasks, each featuring a distinct pH ranging from 2 to 10, all with a constant concentration of 100 mg/L. A consistent quantity of 5 grams of Zea Mays ashes was added to each flask, allowing them to adsorb the methylene blue with periodic shaking until the blue colour disappeared. Upon completion of the allotted time, the mixture of Zea Mays ashes and methylene blue solution was filtered through filter paper to obtain a clear solution. Subsequently, the optical density of the final solution was measured to determine the percentage of adsorption by the Zea Mays ashes. According to optical density, a graph was plotted to observe the effect of pH and adsorption. 2.4 Preparation of catalyst Zea Mays peels were collected and washed with tap and distilled water. The washed peels were then dried at 65°C in an incubator for several days. Subsequently, the dried peels underwent controlled burning in a safe, open environment between the temperatures of 600℃ to 900℃ for ash formation at one atmospheric pressure. After sufficient cooling for safe handling, the ashes derived from Zea Mays were stored for catalyst synthesis. In a parallel process, 100 mg of methylene blue was weighed and mixed with 100 ml of water to create a 1 mg/ml solution. In a separate conical flask, 5 grams of Zea Mays ashes were suspended in 100 ml of the prepared methylene blue solution and mixed until the blue color disappeared. After mixing, the solution containing methylene blue dye and Zea Mays ashes was filtered using filter paper, and the adsorbed dye residues obtained were collected, dried, and subjected to calcination at 400 ℃ for 1 hour. 2.5 Transesterification of W.C.O. using dye-adsorbed ash catalyst The transesterification process involving waste cooking oil (W.C.O.) and methanol utilized a ZMDA catalyst. The biodiesel was prepared in a 250 ml three-neck round-bottom flask placed on a magnetic stirrer at 60°C and 400 rpm. The round-bottom flask was equipped with a condenser to prevent the escape of methanol from the system, as shown in Fig. 1 (Yesilyurt and Cesur, 2022 ). Initially, 25 g of oil was weighed and heated to 60°C to 90°C according to the experiment run. A mixture containing 4-32wt% methanol and 0.5-2.0wt% dye-adsorbed catalyst was agitated in a separate flask. This methanol and catalyst mixture was then transferred into a 250 ml conical flask containing 25 g of oil. The resulting mixture was stirred for 60–240 minutes. After the reaction, the biodiesel, glycerol, and catalyst solution were moved to a 250 ml separator funnel. Glycerol separation from biodiesel occurred after 24 hours of settling through gravity. The obtained biodiesel underwent washing with warm water and was subsequently characterized. The percentage yield of fatty acid methyl esters (FAME) was calculated using Eq. ( 3 ). $$\:\%conversion=\frac{2\times\:peak\:at\:3.6\:\left(methoxy\:proton\right)}{3\times\:peak\:at\:2.3\left(methylene\:proton\right)}$$ 3 2.6 Purification of biodiesel The FAME purification process employed the water wash method, starting with the transesterification reaction. After the reaction, the mixture containing FAME, glycerol, and catalyst was transferred to a 250ml separator funnel. The catalyst, adsorbed with dye, was separated via filtration using filter paper. The remaining mixture was allowed to settle for 24 hours, utilizing gravitational force to form distinct layers. Subsequently, the catalyst and glycerol layer were drained. The remaining biodiesel, still containing some contaminants, underwent an additional purification step through water wash using warm water. Characterization of the resulting FAME product involved 1 H-NMR spectroscopy and GC-MS analyses. During transesterification, glycerol, one of the by-products, needs removal for pure biodiesel. A polar hydroxyl group in methanol endows it with emulsifying properties, contributing to the challenge of separating the methyl ester layer from water. This characteristic hydroxyl group promotes emulsification, leading to the formation of stable emulsions. The emulsification phenomenon hinders the gravitational separation of the methyl ester layer and water, resulting in difficulties in achieving a clear and distinct separation between the two phases. The polar nature of the hydroxyl group in methanol enhances its affinity for water, intensifying the emulsification effect and posing challenges in the purification process of separating methyl ester from water (Y. C. Sharma et al., 2008 ). The reaction mixture was mixed with warm water and left to sediment based on density through gravitational force. Water washing involves four stages: emulsion formation, partitioning of impurities, phase separation, and drainage of the water phase—water-soluble impurities, such as glycerol and soap, formed during transesterification. The presence of emulsifying agents, like soap, led to emulsion formation when biodiesel was mixed with water. Impurities migrated from the biodiesel phase to the water phase in the emulsion. After separating the layers, the impurities dissolved in the water phase were drained, producing the final purified biodiesel product (di Bitonto and Pastore, 2020 ) 2.7 Analytical methods 2.7.1 Catalyst characterization XRD, FESEM, and FTIR have been employed to characterize the catalyst. XRD (Rigaku Ultima III XRD) analysis is used to find the atomic arrangement, phase composition, surface orientation, and structure. Field emission scanning electron microscope (Model: JSM-IT800; make: Joel) is employed to observe the morphological change on the surface of the catalyst due to the adsorption of dye followed by calcination. Elemental mapping of the prepared catalyst was done using Energy-Dispersive X-ray (E.D.S.). Lastly, the catalyst was subjected to FTIR (Perkin Elmer 100 spectrometer) analysis to investigate functional groups present on the catalyst's surface. 2.7.2. Biodiesel characterization 1 HNMR (Make: Bruker; Model: Avance IIIHD 400 MHz) is utilized to find the conversion of triglyceride into fatty acid methyl ester. GCMS and 1 HNMR confirm the presence of FAME in the biodiesel sample. 2.8. Statistical analysis 2.8.1 optimization using the Taguchi method Cost is more important than quality, but quality is the best way to reduce costs (Genichi Taguchi). Taguchi method is one of the tremendous investigational methodologies employed to find the best optimum conditions with minimal experiments to be performed. This methodology minimizes the required number of experimental sets while furnishing complete insights into the performance parameters influenced by various factors. Unlike the conventional "single process factor at a time" experiments, the Taguchi method is preferred because it offers extensive information about the interactions among process parameters, enabling optimized conditions with a relatively limited number of experiments (Phadke, 1995 ). In the present study, a four-level design has been employed with four parameters. Table 1 shows the selected parameters that influenced the yield of the reaction, which were reaction time (A), temperature (B), catalyst load (C), and methanol-to-oil ratio (D). Table 1 Optimization of transesterification reaction using Taguchi method Process Parameters Levels 1 2 3 4 Reaction time, min A 60 120 180 240 Reaction temperature, °C B 50 65 75 90 Catalyst Load, wt% C 0.5 1.0 1.5 2.0 Methanol-to-oil molar ratio D 4 8 16 32 3. Result and Discussion 3.1 Effect of pH on adsorption A standard curve with R 2 value 0.9977 was plotted by taking optical densities of different dye concentrations of 20 mg/L, 40 mg/L, 60 mg/L, 80 mg/L, and 100 mg/L, as shown in Fig. 2 . Effects of pH on the dye adsorption capacity of ash biomass were measured using a standard curve as shown in Fig. 3 . The impact of pH on methylene blue adsorption was investigated by measuring optical density after 180 minutes of shaking and subsequent dye filtration using filter paper. Maximum adsorption was observed at pH 10 and 8, with 89.54% and 89.94%, respectively. Similarly, a comparable level of adsorption, 87.54%, was observed at pH 2. In contrast, lower adsorption rates of 80.94% and 79.34% were noted at pH 4 and 6, making them the least favourable conditions, according to Fig. 3 . The results indicated that adsorption is more effective at highly acidic or alkaline pH, suggesting that Zea Mays ash performs well across a wide pH range. Since the adsorption percentage is relatively consistent across all pH levels, there is no need to adjust the pH. The residual product after dye adsorption is termed Zea Mays dye-adsorbed ash (ZMDA), which is subsequently calcined for transesterification purposes. 3.2 Catalyst characterization 3.2.1 FESEM analysis The morphological analysis of Zea Mays peel ashes (Fig. 4 a), dye absorbed Zea Mays peels ashes (Fig. 4 b), and calcined dye absorbed Zea Mays peel ashes (ZMDA) (Fig. 4 c) was done by Field Emission Electron Microscope (FESEM). The surface's morphology change, especially the porosity, demonstrates an efficient catalyst synthesis (Georgogianni et al., 2009 ). The surface of Zea Mays peel ashes was intact, but some pore-like structures were observed after the adsorption of methylene blue dye on its surface. These pore-like structures appeared more significant after the calcination of the dye absorbed by Zea Mays Peels. The presence of illuminated areas in the FESEM images could be a substantial source of oxides and carbonates of different metals. The porous surface observed in Fig. 4 c may be due to the release of water molecules from the catalyst's surface due to calcination, making the catalyst more efficacious (Ngamcharussrivichai et al., 2010 ). Changes in the material's porosity can also be confirmed using XRD data. The area of all peaks of Zea Mays ashes, dye-adsorbed ashes, and calcined dye-adsorbed ashes increases from 2021.26 ( Zea Mays ashes) to 7427 (dye-adsorbed ashes). The surface area of the peak after calcination has risen to 8788.45, signifying the effect of calcination on the surface porosity, providing better contact between the surface of the catalyst and reaction mixture. The change in porosity can also be identified on the sample surface with a change in the process step. 3.2.2 Energy-Dispersive X-ray (E.D.S.) Carbon, oxygen, sodium, and magnesium were abundant compared to other elements like silicon, phosphorus, chlorine, potassium, calcium, and copper. The distribution study of elements utilizes potassium as a reference element owing to its abundance in the earth's crust, possession of stable isotopes, homogeneous distribution, ease of analysis, accessibility of K-lines (characteristic X-rays in the K-shell region that are easily detectable and quantifiable), moderate atomic number, and biological significance. Figure 5 , 6 , & 7 show the elemental mapping on the surface of ashes, dye-adsorbed ashes, and calcined dye-adsorbed ashes of Zea Mays , respectively. Carbon, nitrogen, sodium, magnesium, silicon, phosphorous, chlorine, potassium, and calcium are the several elements on the catalyst's surface, as shown in Table 2 , stating the basicity of the heterogeneous catalyst. Among all the elements, carbon was found to be most abundant, followed by oxygen, nitrogen, potassium, calcium, magnesium, silicon, and sodium. Observing the FE-SEM images reveals that dye adsorption and changes in pH cause the ash surface to become porous, followed by the calcination of dye-adsorbed ash. The shift in crystallinity from 56–6.42% indicates that the prepared catalyst is amorphous, confirming the increased porosity on the surface. The same can also be approved by XRD data, which suggests that, as shown in Figs. 8 , 9 & 10 , the distribution of active elements tends to provide enough active sites responsible for the reaction to increase the conversion of triglycerides to fatty acid methyl ester (Jamil et al., 2010). 3.2.3 XRD analysis of ZMDA The structural alterations on the Zea Mays peel ashes catalyst surface were investigated using X-ray diffraction (XRD) analysis. The peaks in Figs. 8 , 9 , and 10 clearly illustrate changes in the shape of the peels. Upon comparison across all three Figures, the crystal indices have become more intricate, indicating modifications resulting from dye adsorption by Zea Mays peel ashes and their calcined form. In Fig. 8 , sharp peaks suggest the crystalline nature of the ashes with simple indices such as (011), (020), (022), and (121). As the ashes adsorb the dye, the crystalline nature becomes more amorphous. Therefore, calcination is essential to shift the crystalline nature of the ashes towards unstructured arrangements of the plane. The structural complexity is evident in Figs. 9 and 10 , characterized by indices like (040), (131), (132), (155), (044), and (442). The presence of sharp and narrow peaks in the Figs. 9 and 10 show the changes in the catalyst's structural arrangement, indicating the increased porosity, hence providing more surface area for the reaction. The average particle size of the CNDA catalyst was calculated from diffraction peaks using the Debye-Scherrer formula (Abu-Ghazala et al., 2023 ). $$\:D=\frac{K\lambda\:}{\beta\:cos\theta\:}$$ 4 The biosynthesized catalyst exhibited an average size of 53.25 nm, determined using the Scherrer formula, where K is the Scherrer constant (0.94), λ is the wavelength of the X-ray source (0.15406 nm), β is the full width at half maximum in radians (FWHM), and θ is the diffraction angle in radians. The crystals of the ashes measured 32.58 nm, which increased to 42.8 nm upon the adsorption of methylene blue dye. A similar pattern was observed in the spacing between the atoms of crystals. The Zea Mays ashes displayed an average d-value of 2.08 nm, which increased to 2.4 nm after the adsorption of methylene blue dye. Furthermore, the calcinated Zea Mays ashes, following dye adsorption, had an average d-value of 2.54 nm. The peak area of Ashes of Zea Mays peel was determined to be 2021.56 nm and 3607.87 nm; the total peak area was measured using X'Pert HighScore Plus software. The overall percentage of crystallinity was calculated to be 56.034% with the Eq. ( 4 ), indicating that the arrangement of atoms in Zea Mays peel ashes is crystalline, while the remaining 43.966% is amorphous. Similarly, the percentage crystallinity of dye-absorbed Zea Mays peel ashes and calcined dye-absorbed Zea Mays peel ashes were determined to be 3.31% and 6.42%, respectively. The change in the percentage of crystallinity indicates the adsorption of methylene blue dye by Zea Mays peel ashes, and the increase in crystallinity from 3.31–6.42% demonstrates the effect of calcination on dye-absorbed Zea Mays peel ashes. Morphological changes on the catalyst's surface in the FE-SEM analysis of all three samples can further support the numerical data. 3.2.4 FTIR analysis of ZMDA Figures 11 and 12 depict the FTIR spectrum of uncalcined and calcined ZMDA, respectively. The ZMDA transitions to a more rigid state after calcination, as evidenced by a decrease in peak intensity from Fig. 11 to 12 . Figure 11 shows a broad peak in the functional group region at 3415.47 cm − 1 , and 3383.81 cm − 1 condenses into a single, shorter peak in Fig. 12 . This change signifies that calcination has resulted in a closer hydrogen bonding arrangement between elements, leading to decreased light transmittance and a shorter, sharper peak in Fig. 12 . The light intensity throughout the spectra diminishes, indicating a significant decrease in peak transmittance due to calcination. A peak at 2369 cm − 1 in Fig. 11 corresponds to the C-H absorption peak stretching vibration, suggesting the residual saturated hydrocarbon group after the combustion of Zea Mays husk/peels into ashes (Xu et al., 2019). The absence of the 2369 cm − 1 peak in Fig. 12 suggests the removal of these residual hydrocarbons through the calcination process. The change in the shape of peaks and new peak formation defines the change in the morphology of the catalyst with a change in the process. Sharp peaks indicate a rigid structure, signifying that the catalyst's atoms have come closer, making the catalyst immiscible in the reaction mixture of triglyceride and alcohol. Change in the shape of FTIR peaks also signifies the formation of pores on the catalyst's surface, making it more efficient since more space is available on the catalyst's surface, providing more contact. In many literatures, it has been found that the peak around 3415 cm − 1 and 3383 cm − 1 indicates the presence of water molecules in the sample. It signifies the presence of the -O.H. group (Nath et al., 2020 )(Basumatary et al., 2021 ). Since transesterification is reversible, the -O.H. group or water molecule diverges the reaction pathway towards soap or glycerol formation instead of fatty acid methyl ester. The shortened width and height of this peak in Fig. 12 indicate a decrease in these unwanted groups, making the reaction converge more towards the fatty acid methyl ester or biodiesel. 3.3 Biodiesel characterization 3.3.1 Optimization of reaction by Taguchi method Using the Taguchi method, we optimized the transesterification reaction by considering four controlled factors: Time, Temperature (Temp), Catalyst load (CatL), and Methanol-to-oil ratio (MTOR). We treated Oil quality as an uncontrolled factor in the optimization process. Upon completing all the experiments, we found that we achieved the maximum yield of 95.77% at a temperature of 65℃, with a duration of 240 minutes, using 1.5 g of catalyst and a methanol to oil ratio of 4:1. According to the Taguchi analysis, the signal-to-response ratio indicated that the methanol ratio and time are the primary influencing parameters, followed by temperature and catalyst load. In this Taguchi analysis aimed at optimizing yield, the investigation focused on signal-to-noise ratios (S/N ratios), means, and standard deviations for different factor levels—MTOR, Time, Temp, and CatL. The results revealed that MTOR at Level 1 exhibited the highest S/N ratio and mean, suggesting its paramount importance in achieving higher yield and robust performance. They followed closely in rankings for time at Level 4, emphasizing its significant contribution to favourable outcomes. CatL at Level 3 secured the third position, while Temp at Level 2 held the lowest rank in both S/N ratios and means. Unfortunately, specific standard deviation values were not provided, but the uniform rank assignment indicates equal desirability across all factor levels. Overall, this analysis guides the recommendation to set MTOR to Level 1, Time to Level 4, CatL to Level 3, and Temp to Level 2 for optimal yield and robustness, aligning with the objectives of the Taguchi method in achieving consistent and high-performance outcomes. 3.3.2 Grey relational analysis The Grey Relational Analysis (G.R.A.) technique, established by Deng (Deng, 1982.), is a widely employed multi-objective decision-making method across various engineering disciplines due to its simplicity and efficiency in response evaluation. This technique is particularly valuable for analyzing uncertainties within systems. In the G.R.A. approach, all output responses undergo normalization, scaling them between 0 and 1. This normalization facilitates the interpretation of data and subsequent analysis. The normalized values are then utilized to compute the Grey Relational Coefficient (G.R.C.) from the entire output data set. The Grey Relational Grade (G.R.G.), essential for assessing the performance of the experimental trials, is obtained by averaging the G.R.C. values. A higher G.R.G. value signifies an optimal solution for the given response. The G.R.A. procedure outlined here is crucial in understanding system dynamics comprehensively, making it a valuable tool for scientific research and experimentation. Firstly, the entire experimental data is normalized as per the required conditions, normalization for higher the better $$\:{x}_{i}\left(q\right)=\frac{{v}_{\dot{I}}\left(q\right)-\text{min}{v}_{\dot{i}}\left(q\right)}{\text{max}{v}_{i}\left(q\right)-\text{min}{v}_{i}\left(q\right)}$$ 5 Normalization for lower-the-better $$\:{x}_{i}\left(q\right)=\frac{\text{max}{v}_{i}\left(q\right)-{v}_{\dot{I}}\left(q\right)}{\text{max}{v}_{i}\left(q\right)-\text{min}{v}_{i}\left(q\right)}$$ 6 where v i (q) is the normalized value for output response; \(\:\text{min}{v}_{\dot{i}}\left(q\right)\) Is the minimum value of \(\:{v}_{\dot{I}}\left(q\right)\) for q th response; \(\:\text{max}{v}_{i}\left(q\right)\) Is the maximum value of \(\:{v}_{\dot{I}}\left(q\right)\) for q th response. The Grey Relational Coefficient (G.R.C.) is calculated in the second step to create a correlation between ideal and actual normalized values. $$\:\gamma\:\left(k\right)=\frac{{\varDelta\:}_{\text{min}}+\:\omega\:{\varDelta\:}_{max}}{{\varDelta\:}_{oi}+\:\omega\:{\varDelta\:}_{max}}$$ 7 where, \(\:{\varDelta\:}_{oi}\) (k) = | x o ( k ) – x i ( k )|, \(\:\omega\:\) is the distinctive coefficient, which is utilized to either enlarge or decrease the range of the grey relation coefficient. The value of \(\:\omega\:\) is between 1 and 0. Through a literature survey, the preferred value of \(\:\omega\:\) = 0.5 is taken. \(\:{\varDelta\:}_{\text{m}\text{i}\text{n}}\) is the minimum of \(\:{\varDelta\:}_{oi}\) and \(\:{\varDelta\:}_{max}\) is the maximum value of \(\:{\varDelta\:}_{oi}\) The third step calculates Grey Relational Grade (G.R.G.) using the equation below. $$\:\delta\:\left(i\right)=\frac{1}{n}\sum\:_{i=1}^{n}\gamma\:\left(k\right)$$ 8 Where \(\:\delta\:\left(i\right)\) is the grey relational grade, n is the number of output responses. The parameters of the transesterification process used to convert waste cooking oil using dye-adsorbed Zea Mays catalyst into fatty acid methyl esters are investigated using the Taguchi-based Grey Relational Analysis (G.R.A.). G.R.G., G.R.C., and normalized values are calculated using equations ( 5 ), ( 6 ), (7), and (8) as shown in Table 3 and 4 . Table 4 GRG and normalized data of process parameters Time Temp CatL MTOR Time Temp CatL MTOR Level GRG Normalized Value 0.14174 0.0903 0.10039 0.15126 0.38192 0.88427 0.7451 0.32637 1 0.25 0.10212 0.25 0.25 0 0.72404 0 0 2 0.23365 0.08333 0.1663 0.08333 0.03499 1 0.25163 1 3 0.08333 0.25 0.08333 0.09093 1 0 1 0.87467 4 It is considered the best since experiment number 14 has been ranked 1 in all 16 experiments. It has a high yield with 95.5% conversion in biodiesel with parameters time 240 minutes, 50℃ temperature, 0.5 wt% catalyst load, and 8:1 methanol to oil. Although the boiling point of methanol is 65 ℃, and so above that, the circulation of methanol in the system becomes rapid, and the system temperature cannot reach above 90 ℃. Suppose all the values related to temperature 120℃ are neglected. In that case, rank 4, an experiment run 4 will become optimal condition with a maximum yield conversion of 94% at 65 ℃ temperature, 60 minutes of reaction time, 1.5 wt% catalyst load, and 32:1 methanol to oil. A comparison between rank 1 and 4 shows an inverse relation between the amount of methanol and reaction time. When time is increased, a low amount of methanol can also give better results; similarly, time decreases when the amount of methanol increases. Both factors have a significant economic effect on the end product's cost. Time is considered an economic factor related to the amount of energy consumed during the reaction time. This can be confirmed by Taguchi's analysis, which ranked the methanol-to-oil ratio and temperature above other parameters. In the Grey Relational Analysis (G.R.A.) conducted with a small positive constant ε = 0.1, the sequences representing different factor levels were assessed for their relationship with a reference sequence. The deviation values for each sequence were calculated, considering the normalized values of Time, Temp, CatL, and MTOR. Subsequently, the Grey Relational Coefficient (G.R.C.) was determined for each sequence, indicating the strength of its relationship with the reference. Higher G.R.C. values suggest a stronger relationship. Upon ranking the sequences based on their G.R.C. values, Sequence 2 (corresponding to Level 2) emerged with the highest G.R.C., signifying the most robust relationship with the reference. Following this, Sequence 3 (Level 3) was ranked second, followed by Sequence 1 (Level 1), and Sequence 4 (Level 4) was ranked last. Therefore, the Grey Relational Analysis suggests that, in terms of robustness and relationship with the reference sequence, the factor level corresponding to Sequence 2 holds the highest significance, followed by Levels 3, 1, and 4, as shown in Fig. 14 . The effect of the experimental variable is given in Fig. 13 (x-axis- variable parameters; y-axis- means of yield); in the case of time duration of the reaction, it can be observed that yield was highest at 240 mins. Similarly, the highest yield was observed at 75 ℃. Since the boiling point of methanol is near about 64℃, methanol starts escaping the system at boiling temperature and condenses back into the reaction environment, giving enough time to mix in the reaction mixture. This phenomenon was also observed during the experimentation. The lowest yield at 90℃ indicates that as soon as methanol condenses back, it again starts escaping due to other incoming methanol fumes and not allowing the methanol to mix back in the reaction mixture. 3.3.2 Gas Chromatography of Biodiesel Qualitative analysis of synthesized biodiesel samples from waste cooking oil was investigated by GC-MS analysis using Zea Mays dye-adsorbed catalyst. Gas chromatography was conducted on a Clarus 680 GC (PerkinElmer), employing a rapid injection-to-injection cycle. The initial oven temperature was set at 40°C for 2 minutes, followed by a linear increase of 10°C per minute until reaching 140°C. After a 2-minute hold at 140°C, the temperature was further raised to 300°C at 40°C per minute. Subsequently, a 2-minute hold was maintained at 300°C, followed by a gradual increase to 300°C at a rate of 7°C per minute, with a 5-minute hold. The sample was injected in split mode with a split/column flow ratio 20:1, utilizing a sample volume of 1 µl. Peak identification for the synthesized biodiesel was achieved by comparing mass spectra with entries in the NIST libraries. Compounds matching the samples, their respective molecular weights, and retention times are presented in the G.C. chromatogram in Fig. 15 , and compounds are listed in Table 5 . The identified compounds align with literature findings, confirming the presence of biodiesel components or fatty acid methyl esters (Gandure et al., 2013). The gas chromatograph of FAMEs showed that the mixture comprises several methyl ester groups of fatty acid with retention times 26.36, 29.65, 31.60, and 37.1 minutes. Searching these peaks in the database gave several esters, which are listed below with reference. 3.3.3 NMR analysis of FAME (Biodiesel) Figure 16 shows the NMR spectrum of the synthesized biodiesel in which methyl ester's methoxy proton (-OCH3) (-COOCH3) is visible at 3.66 ppm. Notably, the triplet signals of the bis-allylic proton (-C = C-CH2-C = C-) in the polyunsaturated fatty acid chain appear at 2.77 ppm, while the α-methylene protons (α-CH2) of the ester (-CH2-COOMe) resonate at 2.30 ppm. A multiplet signal between 2.04 to 2.06 ppm corresponds to the alpha-methylene protons (α-CH2) in the bond (-CH2-C = C-), and the signal at 1.62 ppm represents the β-methylene proton of the ester (-CH2-CH = COO-Me). The multiplet signal between 5.32 to 5.37 ppm is also associated with olefinic protons (-CH = CH=) bonded to carbon atoms. Furthermore, a triplet signal at 0.88 ppm indicates the presence of terminal methyl protons (-C-CH3). Finally, the peaks at 1.25, 1.27 and 1.30 ppm are attributed to the protons in the methylene (-(CH2)n=) backbone of long fatty acid chains (Moawia et al., 2019 )(Hassan and Nageswara, 2018 )(G Knothe, 2006 )(Morgenstern et al., 2006 ). The overall conversion of triglyceride into fatty acid methyl ester, also known as biodiesel, was 95.5% at 65℃, with a catalyst load of 1.5 grams, and a 4:1 methanol to oil ratio for 240 minutes. Conclusion This comprehensive study demonstrates the multifaceted potential of Zea Mays ash as both an adsorbent and catalyst in biodiesel production. Optimal adsorption performance for methylene blue dye was achieved at pH 10 and 8, with efficiencies of 89.54% and 89.94%, respectively. The resulting product, Zea Mays dye-adsorbed ash (ZMDA), underwent calcination, resulting in a versatile catalyst with a porous surface, enhancing catalytic activity. Morphological analyses via FESEM revealed significant surface structure changes in Zea Mays peel ashes, dye-absorbed ashes, and calcinated dye-absorbed Zea Mays peel ashes, indicating increased surface area and improved catalytic activity post-calcination. E.D.S. analysis highlighted prominent elemental distribution, with carbon, oxygen, sodium, and magnesium identified. XRD analysis demonstrated structural alterations, with increased porosity observed post-dye absorption and calcination, yielding an average particle size of 53.25 nm. Optimization using the Taguchi method and G.R.A. confirmed the transesterification reaction's optimal conditions, leading to a maximum biodiesel yield of 95.77%. G.C. analysis confirmed the presence of FAMEs, while N.M.R. analysis provided a detailed spectrum of the final biodiesel product. The conversion of triglycerides into FAMEs reached 95.5% under optimized conditions, highlighting the potential of Zea Mays ash in sustainable biodiesel production, offering environmentally friendly and economically viable energy solutions. Abbreviations ASTM - American Society for Testing and Materials EDS - Energy-Dispersive X-ray Spectroscopy FESEM - Field Emission Scanning Electron Microscope FAME - Fatty Acid Methyl Ester FTIR - Fourier Transform Infrared Spectroscopy GC-MS - Gas Chromatography-Mass Spectrometry GRA - Grey Relational Analysis GRG - Generalized Grey Relational Grade HPLC - High-Performance Liquid Chromatography M.B. - Methylene Blue MTOR - Methanol-to-oil ratio NMR - Nuclear Magnetic Resonance WCO - Waste Cooking Oil XRD - X-ray diffraction ZMDA - Zea Mays dye-adsorbed ash 1 H-NMR - Proton Nuclear Magnetic Resonance Declarations Acknowledgement: The authors thank the Central Instrumentation Research Facility, National Institute of Technology, Warangal, for providing an analytical facility for our research work in our publication. Also, the authors show their gratitude to the CAIF facility of Guwahati Biotech Park Incubation Centre for providing the needed facility. Declaration of competing interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding Agency: This research received no funding from public, commercial, or non-profit organizations. Authorship contribution statement: Akshay Prakash : Conceptualization, Methodology, Formal analysis, Writing – original draft, Validation. M Jerold : Writing – review & editing, Validation, Supervision, Resources . References Abu-Ghazala, A. H., Abdelhady, H. H., Mazhar, A. A., & El-Deab, M. S. (2023). Exceptional room temperature catalytic transesterification of waste cooking oil to biodiesel using environmentally-benign K2CO3/γ-Al2O3 nano-catalyst. Chemical Engineering Journal , 474 , 145784. Baskar, G.; Aiswarya, R. (2016) Trends in catalytic production of biodiesel from various feedstocks. Renewable and Sustainable Energy Reviews , 57 , 496–504. 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(2012) Wood ash as a potential heterogeneous catalyst for biodiesel synthesis. Biomass Bioenergy , 41 , 94–106. Sharma, Y. C.; Singh, B.; Upadhyay, S. N. (2008) Advancements in development and characterization of biodiesel: A review. Fuel , 87 (12), 2355–2373. Shi, Q.; Wang, W.; Zhang, H.; Bai, H.; Liu, K.; Zhang, J.; Li, Z.; et al. (2023) Porous biochar derived from walnut shell as an efficient adsorbent for tetracycline removal. Bioresour Technol , 383 , 129213. Soudani, A.; Youcef, L.; Bulgariu, L.; Youcef, S.; Toumi, K.; Soudani, N. (2022) Characterizing and modeling of oak fruit shells biochar as an adsorbent for the removal of Cu, Cd, and Zn in single and in competitive systems. Chemical Engineering Research and Design , 188 , 972–987. Talha, N. S., & Sulaiman, S. (2016). Overview of catalysts in biodiesel production. ARPN Journal of Engineering and Applied Sciences , 11 (1), 439-442. Knothe, G. (2006). 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Supplementary Files Tables23and5.docx GraphicalAbstract.png Cite Share Download PDF Status: Published Journal Publication published 14 Jun, 2025 Read the published version in Chemical Papers → Version 1 posted Reviewers agreed at journal 08 Oct, 2024 Reviewers invited by journal 09 Aug, 2024 Editor invited by journal 10 Jul, 2024 Editor assigned by journal 10 Jul, 2024 First submitted to journal 08 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4710073","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":338243883,"identity":"45fcf26c-3a21-456c-a082-ebb965a22da0","order_by":0,"name":"Akshay Prakash","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-0430-1332","institution":"National Institute of Technology Warangal","correspondingAuthor":true,"prefix":"","firstName":"Akshay","middleName":"","lastName":"Prakash","suffix":""},{"id":338243884,"identity":"54fcfe0f-9c59-4ea8-82d8-25ece2fda165","order_by":1,"name":"Jerold Manuel","email":"","orcid":"","institution":"National Institute of Technology Warangal","correspondingAuthor":false,"prefix":"","firstName":"Jerold","middleName":"","lastName":"Manuel","suffix":""}],"badges":[],"createdAt":"2024-07-09 07:44:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4710073/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4710073/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11696-025-04083-8","type":"published","date":"2025-06-14T15:58:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64054525,"identity":"dac2f55d-5c8e-489a-b1eb-508d5d0f0a44","added_by":"auto","created_at":"2024-09-05 18:08:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":84259,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental setup using condenser [11]\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4710073/v1/f74138166ff7ed9079042cce.png"},{"id":64054718,"identity":"b9fc23c5-9df5-42fb-a306-337970048935","added_by":"auto","created_at":"2024-09-05 18:16:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":37148,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMethylene blue standard Curve\u003cbr\u003e\nConcentration (20-100 mg/L)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4710073/v1/5066944b0c759adc6e43bbc8.png"},{"id":64054529,"identity":"31556df7-e8e2-4b8e-a04e-4fc9f4407cfe","added_by":"auto","created_at":"2024-09-05 18:08:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":18296,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003epH effect on adsorption of methylene blue using \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eZea Mays \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eashes as biosorbent\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4710073/v1/906a83537b17df950e5e91e8.png"},{"id":64054527,"identity":"5eb478bd-7f7d-4975-a737-f7983655ee83","added_by":"auto","created_at":"2024-09-05 18:08:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":248435,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFE-SEM of a. \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eZea Mays \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eAshes; b. 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9","display":"","copyAsset":false,"role":"figure","size":301211,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eXRD spectra of dye-adsorbed \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eZea Mays \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003ePeel ashes\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4710073/v1/a58cc961a6816d937b8686a3.png"},{"id":64054540,"identity":"28814c3f-4377-46e8-8fba-51834582b8bc","added_by":"auto","created_at":"2024-09-05 18:08:05","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":262389,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eXRD spectra of dye-adsorbed \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eZea Mays \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003ePeel ashes\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4710073/v1/775af1b459b620f0fd738a90.png"},{"id":64054542,"identity":"5974e430-7f20-4a90-bb0d-91958d114df6","added_by":"auto","created_at":"2024-09-05 18:08:05","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":18406,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFTIR spectrum of uncalcined dye adsorbed \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eZea Mays \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003epeels ashes\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-4710073/v1/38420786e4c92a0787f04375.png"},{"id":64054543,"identity":"99572f7f-0a35-4083-9c87-f4d128216ef1","added_by":"auto","created_at":"2024-09-05 18:08:06","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":91343,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFTIR spectrum calcined dye adsorbed \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eZea Mays \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003epeels ashes\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-4710073/v1/501f4fea0d514255d9d94982.png"},{"id":64054533,"identity":"3c4995f6-cef7-4771-8cd0-563d557ea10e","added_by":"auto","created_at":"2024-09-05 18:08:04","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":46729,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMain effects plot for means\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-4710073/v1/fade7d8858fae3204b7cfec0.png"},{"id":64054530,"identity":"cd3642eb-aa3a-435a-9e29-5c834578cb3f","added_by":"auto","created_at":"2024-09-05 18:08:04","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":310190,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePlot of GRG values vs Temp\\CatL\\MTOR\\Time\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-4710073/v1/a4e2e58dc2024ff3805ac202.png"},{"id":64054721,"identity":"34a32c57-8552-405d-983b-dcf99386b59a","added_by":"auto","created_at":"2024-09-05 18:16:05","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":86401,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGC chromatogram for biodiesel/FAME\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-4710073/v1/a8829e721dc7547538ff66da.png"},{"id":64054537,"identity":"addfd26b-8e57-42fb-8235-c03d998e3e66","added_by":"auto","created_at":"2024-09-05 18:08:05","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":288808,"visible":true,"origin":"","legend":"\u003cp\u003e\u003csup\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eHNMR peaks of biodiesel with ~95 % of yeild. Time = 240 minutes; Temperature = 60℃; Catalyst Load = 1.5 wt% and Methanol to oil ratio = 4:1\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"16.png","url":"https://assets-eu.researchsquare.com/files/rs-4710073/v1/7e733077a2f6ee0501f07a6a.png"},{"id":64054720,"identity":"ffedc729-371b-4715-af06-497a20077844","added_by":"auto","created_at":"2024-09-05 18:16:04","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":403265,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProcess Flow chart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"unknown.png","url":"https://assets-eu.researchsquare.com/files/rs-4710073/v1/62a695fb4e55b0c172b8d6b2.png"},{"id":84726568,"identity":"041d30f9-31ee-4d41-a6b4-df286db3b8d9","added_by":"auto","created_at":"2025-06-16 16:07:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8781964,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4710073/v1/a0e78ffa-0ed9-4be2-8945-fd702791ebd3.pdf"},{"id":64054531,"identity":"414a01d6-9c8a-4202-94eb-47830995ad87","added_by":"auto","created_at":"2024-09-05 18:08:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":218375,"visible":true,"origin":"","legend":"","description":"","filename":"Tables23and5.docx","url":"https://assets-eu.researchsquare.com/files/rs-4710073/v1/77161f6ca27b5756173c3707.docx"},{"id":64054528,"identity":"43071718-d8df-48eb-8b6b-20ffc10d0eb5","added_by":"auto","created_at":"2024-09-05 18:08:04","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":682087,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-4710073/v1/477f649eb32f1bf869b555d1.png"}],"financialInterests":"","formattedTitle":"Sustainable Processing of Zea Mays peel ash (Methylene adsorbed) biosorbent as a novel green heterogeneous catalyst for biodiesel production","fulltext":[{"header":"Practitioner points","content":"\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eMethylene blue dye adsorbs on \u003cem\u003eZea Mays\u003c/em\u003e ash best at pH 8 and 10, achieving over 89% efficiency with minimal pH adjustments.\u003c/li\u003e\n \u003cli\u003eCalcined \u003cem\u003eZea Mays\u003c/em\u003e dye-adsorbed ash (ZMDA) is highly porous; FESEM, XRD show increased surface area, EDS reveals high carbon, oxygen.\u003c/li\u003e\n \u003cli\u003eTaguchi method optimized biodiesel production: 65\u0026deg;C, 240 minutes, 1.5% catalyst, 4:1 methanol-oil ratio; GRA confirms key factors.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eZea Mays\u003c/em\u003e ash is an inexpensive, sustainable adsorbent and catalyst for biodiesel, wastewater treatment; promotes circular economy.\u003c/li\u003e\n \u003cli\u003eThe method scales easily, requiring minimal pH adjustments; integrates into existing biodiesel and wastewater systems efficiently.\u003c/li\u003e\n\u003c/ul\u003e\n"},{"header":"1. Introduction","content":"\u003cp\u003eThe energy crisis is an alarming global issue due to rapid urbanization and industrialization. Fossil fuels such as coal, crude oil, petroleum, and natural gas are widely used in the transport and industrial sectors. The excessive usage of fossil fuels resulted in environmental and ecological problems such as global warming due to the emission of carbon dioxide. In addition, fossil fuels are non-renewable resources that get depleted and cannot be replenished. The annual global diesel consumption is 934\u0026nbsp;million tons, and around 97.6% of oil resources used in transportation are obtained from fossil fuels (Baskar and Aiswarya, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) (Prajapati et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, there is a high need to develop renewable alternative energy sources to resolve the increase in global energy consumption and socio-economic and environmental concerns.\u003c/p\u003e \u003cp\u003eBiodiesel is a clean and renewable alternative bioenergy that replaces fossil fuels because it is sustainable, environment-friendly, and non-toxic. The salient feature of biodiesel is that it can be used as such in internal combustion (I.C.) engines (as used by Rudolph Diesel in 1900) or blended with fossil diesel. Biodiesel can be produced via transesterification of triglycerides in the presence of an appropriate catalyst and alcohol (Ghosh et al., 2024). Various catalysts, such as homogeneous, heterogeneous, and biocatalysts, are employed in the transesterification reaction for biodiesel production. In the current scenario, homogeneously catalyzed (basic or acidic) transesterification is widely used for biodiesel production. Indeed, the reaction rate is relatively fast in the homogeneously catalyzed (K.O.H. and NaOH) transesterification process. However, some drawbacks include soap formation, additional separation, purification step, excess wastewater generation, low-grade glycerine by-product generation, difficulty reusing the catalysts, and equipment corrosion. Thus, homogeneous catalysts are undesirable for the production of biodiesel (Sulaiman and Syakirah Talha, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In light of these limitations, researchers focus on heterogeneous catalysts to overcome the drawbacks of homogeneous catalysts. Interestingly, heterogeneous catalysts have the essential characteristics of reuse and separation, which reduce the catalyst losses and the cost of biodiesel production (Mandari and Devarai, 2022)\u003c/p\u003e \u003cp\u003eBiomass-derived heterogeneous catalysts have recently have been widely explored for biodiesel production as they can overcome conventional catalysts' obstacles. The biochar-based heterogeneous catalyst is characterized by various compounds such as alkalized and alkaline metal oxides or carbonates (like K\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e, CaCO\u003csub\u003e3\u003c/sub\u003e, K\u003csub\u003e2\u003c/sub\u003eO, CaO, MgO, etc.), including some of the transition metal oxides that enhance the catalytic activity in transesterification process (Chutia et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Biomass is an abundant biomaterial available in the environment at low cost, which can significantly increase the economy of biodiesel production. Moreover, biochar-based heterogeneous catalysts obtained from biomass are eco-friendly, non-corrosive, non-toxic, biodegradable, and eliminate wastewater production. (Chutia et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) have mentioned various agricultural or forestry waste such as leaves of moringa, pineapple, \u003cem\u003eHetropanax fragrans\u003c/em\u003e, \u003cem\u003ebrassica nigra\u003c/em\u003e, \u003cem\u003eTectona grandis\u003c/em\u003e, waste of banana peels, oranges, papaya, waste husk of rice, coconut pod, \u003cem\u003eTamarindus indica\u003c/em\u003e, and peanut have been utilized as a green heterogeneous catalyst for the production of biodiesel.\u003c/p\u003e \u003cp\u003eBiomass-based adsorption is gaining more attention than conventional adsorption due to the significant source of biomass resources, renewability, simple operation, and low and high sorption capacity. Various biomass, such as walnut shells, oak fruit shells, and potato peels, were effectively used to prepare biochar-based adsorbents(Shi et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) (Soudani et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Though adsorption seems eco-friendly, after many cycles of operation containing toxic pollutants, the exhausted adsorbent is discarded as waste into the environment, creating secondary pollution. To date, no effort has been taken to discard the spent adsorbent loaded with organic or inorganic pollutants in an environment-friendly way. Circular economy and waste Valorization emphasize utilizing waste resources to produce value-added products. Accordingly, the present study is focused on the Valorization of spent adsorbent as a green catalyst for biodiesel production. Most of the ash obtained from biomass resources consists of metal oxides and carbonates. K\u003csub\u003e2\u003c/sub\u003eO, CaO, MgO, SiO\u003csub\u003e2\u003c/sub\u003e, etc., enhance the catalytic activity during the transesterification reaction(M. Sharma et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Therefore, the present work is to investigate methylene blue (M.B.) dye-adsorbed biochar as a renewable green catalyst for converting waste cooking oil into biodiesel. M.B. is a synthetic dye often used for dyeing fabrics, papers, and leather, which can significantly cause water pollution and is highly toxic and carcinogenic.\u003c/p\u003e \u003cp\u003e \u003cem\u003eZea Mays\u003c/em\u003e, is an extensively utilized, leading, and versatile crop cultivated in diverse climates and agricultural landscapes across nations such as India, the U.S.A., China, Brazil, Argentina, Indonesia, Mexico, South Africa, Nigeria, and Ukraine. Despite variations in weather, soil conditions, and agricultural practices among these significant producers, they collectively contribute significantly to the global production of maize. Recognized internationally as the \"queen of cereals,\" maize exhibits unparalleled adaptability, boasting cereal crops' highest genetic yield potential. Its versatility is evident in various types, including regular yellow/white grain, sweet corn, baby corn, popcorn, waxy corn, high amylase corn, high oil corn, and quality protein maize. Beyond its role as a food source, maize serves as a vital industrial raw material, offering opportunities for value addition. Notably, the ashes of \u003cem\u003eZea Mays\u003c/em\u003e peels contain essential elements like potassium, calcium, phosphorus, and magnesium, making them valuable for agricultural applications such as fertilizer, soil amendment, pH adjustment, and as an aid in plant nutrition. Maize's global significance extends beyond its agricultural contributions; it plays a crucial role in agri-food systems, encompassing direct human consumption and indirect pathways through animal-sourced foods. With a surge in global maize production driven by increasing demand, it is poised to become the most widely grown crop globally and the most traded cereal internationally. The challenges and opportunities associated with maize necessitate substantial investments in international agri-food system research and development, particularly in the Global South. An integrated and inclusive approach is essential to maximize maize's developmental potential, contributing to food and nutrition security while ensuring environmental sustainability and resilience in agri-food systems, aligning with the goals of the 2030 Agenda (Erenstein et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eIn our current investigation, we have focused on addressing two significant environmental impacts: wastewater treatment utilizing \u003cem\u003eZea Mays\u003c/em\u003e ashes and the transesterification process of waste cooking oil. Waste cooking oil poses a substantial threat to water quality. It is often improperly disposed of, with large quantities being drained into the sewage system instead of recycled through designated centers. This improper disposal can result in various environmental and plumbing issues, including clogs, ecological harm, damage to infrastructure, and legal consequences. As previously discussed, the adsorption of chemical dyes and heavy metals through biomass can create secondary pollution when there is no documented use for spent ash biomass. In our study, we have taken spent biomass saturated with dye, subjected it to calcination, and utilized it in the transesterification of waste cooking oil to produce fatty acid methyl ester. Commercializing this process could serve as an exemplary model of a circular economy, contributing to achieving some of the 17 sustainable development goals.\u003c/p\u003e \u003cp\u003eFurthermore, the study involves the characterization of ashes derived from \u003cem\u003eZea Mays\u003c/em\u003e peels, dye-saturated ashes, and calcinated dye-saturated ashes to comprehend the physical and chemical alterations on the catalyst's surface. Techniques such as XRD, FTIR, and FE-SEM are employed to characterize and study the morphological features of the catalyst. N.M.R. and GCMS analyses of the prepared samples confirm the presence of biodiesel. To optimize the reaction, Taguchi-based Grey Relational Analysis, a mathematical and statistical method assessing the degree of relationship between process parameters, is applied.\u003c/p\u003e"},{"header":"2. Experimental","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Materials\u003c/h2\u003e \u003cp\u003eThe National Institute of Technology, Warangal, Telangana, India, canteen provided waste cooking oil (W.C.O.). MERCK Life Science Private Ltd. supplied high-performance liquid chromatography (HPLC) grade methanol. Finar Limited, India, sourced methylene blue. A street vendor outside the NITW campus provided the peels of \u003cem\u003eZea Mays\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Acid Value Determination and Pre-treatment of Waste Cooking Oil (W.C.O.)\u003c/h2\u003e \u003cp\u003eThe collected W.C.O. underwent a settling period to allow the sedimentation of debris and other impurities. Subsequently, an Acid Value test was conducted to assess the percentage of Free Fatty Acids (% F.F.A.) present, indicative of the likelihood of glycerol formation.\u003c/p\u003e \u003cp\u003eThe acid value test was performed in triplicate, yielding an average % F.F.A. of 1.692. According to ASTM standards, this value slightly exceeded the recommended 1% threshold for transesterification. To address this, the W.C.O. underwent pre-treatment involving the addition of sulfuric acid and methanol, aiming to reduce the F.F.A. content as per the equation:\u003c/p\u003e \u003cp\u003eFor 100g of oil with x% F.F.A.,\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.25 \u0026times; x\u0026thinsp;=\u0026thinsp;y ml of methanol (1)\u003c/h2\u003e \u003cp\u003e \u003cem\u003e0.05 \u0026times; x\u0026thinsp;=\u0026thinsp;y ml of sulfuric acid (2)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe sulfuric acid and methanol quantities were calculated with the W.C.O. and agitated for 60 minutes at 60\u0026deg;C. Following agitation, we transferred the pre-treated W.C.O. to a 250 ml separating funnel and allowed it to settle overnight. The subsequent day, we observed two distinct layers. The bottom layer containing impurities was discarded, while the upper layer was retained for future use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Dye adsorption study\u003c/h2\u003e \u003cp\u003eIn the adsorption experiment, methylene blue was poured into five flasks, each featuring a distinct pH ranging from 2 to 10, all with a constant concentration of 100 mg/L. A consistent quantity of 5 grams of \u003cem\u003eZea Mays\u003c/em\u003e ashes was added to each flask, allowing them to adsorb the methylene blue with periodic shaking until the blue colour disappeared. Upon completion of the allotted time, the mixture of \u003cem\u003eZea Mays\u003c/em\u003e ashes and methylene blue solution was filtered through filter paper to obtain a clear solution. Subsequently, the optical density of the final solution was measured to determine the percentage of adsorption by the \u003cem\u003eZea Mays\u003c/em\u003e ashes. According to optical density, a graph was plotted to observe the effect of pH and adsorption.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Preparation of catalyst\u003c/h2\u003e \u003cp\u003e \u003cem\u003eZea Mays\u003c/em\u003e peels were collected and washed with tap and distilled water. The washed peels were then dried at 65\u0026deg;C in an incubator for several days. Subsequently, the dried peels underwent controlled burning in a safe, open environment between the temperatures of 600℃ to 900℃ for ash formation at one atmospheric pressure. After sufficient cooling for safe handling, the ashes derived from \u003cem\u003eZea Mays\u003c/em\u003e were stored for catalyst synthesis.\u003c/p\u003e \u003cp\u003eIn a parallel process, 100 mg of methylene blue was weighed and mixed with 100 ml of water to create a 1 mg/ml solution. In a separate conical flask, 5 grams of \u003cem\u003eZea Mays\u003c/em\u003e ashes were suspended in 100 ml of the prepared methylene blue solution and mixed until the blue color disappeared. After mixing, the solution containing methylene blue dye and \u003cem\u003eZea Mays\u003c/em\u003e ashes was filtered using filter paper, and the adsorbed dye residues obtained were collected, dried, and subjected to calcination at 400 ℃ for 1 hour.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Transesterification of W.C.O. using dye-adsorbed ash catalyst\u003c/h2\u003e \u003cp\u003eThe transesterification process involving waste cooking oil (W.C.O.) and methanol utilized a ZMDA catalyst. The biodiesel was prepared in a 250 ml three-neck round-bottom flask placed on a magnetic stirrer at 60\u0026deg;C and 400 rpm. The round-bottom flask was equipped with a condenser to prevent the escape of methanol from the system, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(Yesilyurt and Cesur, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Initially, 25 g of oil was weighed and heated to 60\u0026deg;C to 90\u0026deg;C according to the experiment run.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA mixture containing 4-32wt% methanol and 0.5-2.0wt% dye-adsorbed catalyst was agitated in a separate flask. This methanol and catalyst mixture was then transferred into a 250 ml conical flask containing 25 g of oil. The resulting mixture was stirred for 60\u0026ndash;240 minutes. After the reaction, the biodiesel, glycerol, and catalyst solution were moved to a 250 ml separator funnel. Glycerol separation from biodiesel occurred after 24 hours of settling through gravity. The obtained biodiesel underwent washing with warm water and was subsequently characterized. The percentage yield of fatty acid methyl esters (FAME) was calculated using Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\%conversion=\\frac{2\\times\\:peak\\:at\\:3.6\\:\\left(methoxy\\:proton\\right)}{3\\times\\:peak\\:at\\:2.3\\left(methylene\\:proton\\right)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Purification of biodiesel\u003c/h2\u003e \u003cp\u003eThe FAME purification process employed the water wash method, starting with the transesterification reaction. After the reaction, the mixture containing FAME, glycerol, and catalyst was transferred to a 250ml separator funnel. The catalyst, adsorbed with dye, was separated via filtration using filter paper. The remaining mixture was allowed to settle for 24 hours, utilizing gravitational force to form distinct layers. Subsequently, the catalyst and glycerol layer were drained. The remaining biodiesel, still containing some contaminants, underwent an additional purification step through water wash using warm water. Characterization of the resulting FAME product involved \u003csup\u003e1\u003c/sup\u003eH-NMR spectroscopy and GC-MS analyses. During transesterification, glycerol, one of the by-products, needs removal for pure biodiesel. A polar hydroxyl group in methanol endows it with emulsifying properties, contributing to the challenge of separating the methyl ester layer from water. This characteristic hydroxyl group promotes emulsification, leading to the formation of stable emulsions. The emulsification phenomenon hinders the gravitational separation of the methyl ester layer and water, resulting in difficulties in achieving a clear and distinct separation between the two phases. The polar nature of the hydroxyl group in methanol enhances its affinity for water, intensifying the emulsification effect and posing challenges in the purification process of separating methyl ester from water (Y. C. Sharma et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The reaction mixture was mixed with warm water and left to sediment based on density through gravitational force. Water washing involves four stages: emulsion formation, partitioning of impurities, phase separation, and drainage of the water phase\u0026mdash;water-soluble impurities, such as glycerol and soap, formed during transesterification. The presence of emulsifying agents, like soap, led to emulsion formation when biodiesel was mixed with water. Impurities migrated from the biodiesel phase to the water phase in the emulsion. After separating the layers, the impurities dissolved in the water phase were drained, producing the final purified biodiesel product (di Bitonto and Pastore, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Analytical methods\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.7.1 Catalyst characterization\u003c/h2\u003e \u003cp\u003eXRD, FESEM, and FTIR have been employed to characterize the catalyst. XRD (Rigaku Ultima III XRD) analysis is used to find the atomic arrangement, phase composition, surface orientation, and structure. Field emission scanning electron microscope (Model: JSM-IT800; make: Joel) is employed to observe the morphological change on the surface of the catalyst due to the adsorption of dye followed by calcination. Elemental mapping of the prepared catalyst was done using Energy-Dispersive X-ray (E.D.S.). Lastly, the catalyst was subjected to FTIR (Perkin Elmer 100 spectrometer) analysis to investigate functional groups present on the catalyst's surface.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.7.2. Biodiesel characterization\u003c/h2\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003eHNMR (Make: Bruker; Model: Avance IIIHD 400 MHz) is utilized to find the conversion of triglyceride into fatty acid methyl ester. GCMS and \u003csup\u003e1\u003c/sup\u003eHNMR confirm the presence of FAME in the biodiesel sample.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Statistical analysis\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.8.1 optimization using the Taguchi method\u003c/h2\u003e \u003cp\u003eCost is more important than quality, but quality is the best way to reduce costs (Genichi Taguchi). Taguchi method is one of the tremendous investigational methodologies employed to find the best optimum conditions with minimal experiments to be performed. This methodology minimizes the required number of experimental sets while furnishing complete insights into the performance parameters influenced by various factors. Unlike the conventional \"single process factor at a time\" experiments, the Taguchi method is preferred because it offers extensive information about the interactions among process parameters, enabling optimized conditions with a relatively limited number of experiments (Phadke, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). In the present study, a four-level design has been employed with four parameters. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the selected parameters that influenced the yield of the reaction, which were reaction time (A), temperature (B), catalyst load (C), and methanol-to-oil ratio (D).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOptimization of transesterification reaction using Taguchi method\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eProcess Parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eLevels\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReaction time, min\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReaction temperature, \u0026deg;C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCatalyst Load, wt%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMethanol-to-oil molar ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Result and Discussion","content":"\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003e3.1 Effect of pH on adsorption\u003c/h2\u003e\n \u003cp\u003eA standard curve with R\u003csup\u003e2\u003c/sup\u003e value 0.9977 was plotted by taking optical densities of different dye concentrations of 20 mg/L, 40 mg/L, 60 mg/L, 80 mg/L, and 100 mg/L, as shown in Fig. \u003cspan\u003e2\u003c/span\u003e. Effects of pH on the dye adsorption capacity of ash biomass were measured using a standard curve as shown in Fig. \u003cspan\u003e3\u003c/span\u003e. The impact of pH on methylene blue adsorption was investigated by measuring optical density after 180 minutes of shaking and subsequent dye filtration using filter paper. Maximum adsorption was observed at pH 10 and 8, with 89.54% and 89.94%, respectively. Similarly, a comparable level of adsorption, 87.54%, was observed at pH 2. In contrast, lower adsorption rates of 80.94% and 79.34% were noted at pH 4 and 6, making them the least favourable conditions, according to Fig. \u003cspan\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThe results indicated that adsorption is more effective at highly acidic or alkaline pH, suggesting that \u003cem\u003eZea Mays\u003c/em\u003e ash performs well across a wide pH range. Since the adsorption percentage is relatively consistent across all pH levels, there is no need to adjust the pH. The residual product after dye adsorption is termed \u003cem\u003eZea Mays\u003c/em\u003e dye-adsorbed ash (ZMDA), which is subsequently calcined for transesterification purposes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003e3.2 Catalyst characterization\u003c/h2\u003e\n \u003cdiv id=\"Sec18\"\u003e\n \u003ch2\u003e3.2.1 FESEM analysis\u003c/h2\u003e\n \u003cp\u003eThe morphological analysis of \u003cem\u003eZea Mays\u003c/em\u003e peel ashes (Fig. \u003cspan\u003e4\u003c/span\u003ea), dye absorbed \u003cem\u003eZea Mays\u003c/em\u003e peels ashes (Fig. \u003cspan\u003e4\u003c/span\u003eb), and calcined dye absorbed \u003cem\u003eZea Mays\u003c/em\u003e peel ashes (ZMDA) (Fig. \u003cspan\u003e4\u003c/span\u003ec) was done by Field Emission Electron Microscope (FESEM). The surface\u0026apos;s morphology change, especially the porosity, demonstrates an efficient catalyst synthesis (Georgogianni et al., \u003cspan\u003e2009\u003c/span\u003e). The surface of \u003cem\u003eZea Mays\u003c/em\u003e peel ashes was intact, but some pore-like structures were observed after the adsorption of methylene blue dye on its surface. These pore-like structures appeared more significant after the calcination of the dye absorbed by \u003cem\u003eZea Mays\u003c/em\u003e Peels. The presence of illuminated areas in the FESEM images could be a substantial source of oxides and carbonates of different metals. The porous surface observed in Fig. \u003cspan\u003e4\u003c/span\u003ec may be due to the release of water molecules from the catalyst\u0026apos;s surface due to calcination, making the catalyst more efficacious (Ngamcharussrivichai et al., \u003cspan\u003e2010\u003c/span\u003e). Changes in the material\u0026apos;s porosity can also be confirmed using XRD data. The area of all peaks of \u003cem\u003eZea Mays\u003c/em\u003e ashes, dye-adsorbed ashes, and calcined dye-adsorbed ashes increases from 2021.26 (\u003cem\u003eZea Mays\u003c/em\u003e ashes) to 7427 (dye-adsorbed ashes). The surface area of the peak after calcination has risen to 8788.45, signifying the effect of calcination on the surface porosity, providing better contact between the surface of the catalyst and reaction mixture. The change in porosity can also be identified on the sample surface with a change in the process step.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.2.2 Energy-Dispersive X-ray (E.D.S.)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eCarbon, oxygen, sodium, and magnesium were abundant compared to other elements like silicon, phosphorus, chlorine, potassium, calcium, and copper. The distribution study of elements utilizes potassium as a reference element owing to its abundance in the earth\u0026apos;s crust, possession of stable isotopes, homogeneous distribution, ease of analysis, accessibility of K-lines (characteristic X-rays in the K-shell region that are easily detectable and quantifiable), moderate atomic number, and biological significance. Figure \u003cspan\u003e5\u003c/span\u003e, \u003cspan\u003e6\u003c/span\u003e, \u0026amp; \u003cspan\u003e7\u003c/span\u003e show the elemental mapping on the surface of ashes, dye-adsorbed ashes, and calcined dye-adsorbed ashes of \u003cem\u003eZea Mays\u003c/em\u003e, respectively. Carbon, nitrogen, sodium, magnesium, silicon, phosphorous, chlorine, potassium, and calcium are the several elements on the catalyst\u0026apos;s surface, as shown in Table \u003cspan\u003e2\u003c/span\u003e, stating the basicity of the heterogeneous catalyst. Among all the elements, carbon was found to be most abundant, followed by oxygen, nitrogen, potassium, calcium, magnesium, silicon, and sodium. Observing the FE-SEM images reveals that dye adsorption and changes in pH cause the ash surface to become porous, followed by the calcination of dye-adsorbed ash. The shift in crystallinity from 56\u0026ndash;6.42% indicates that the prepared catalyst is amorphous, confirming the increased porosity on the surface. The same can also be approved by XRD data, which suggests that, as shown in Figs. \u003cspan\u003e8\u003c/span\u003e, \u003cspan\u003e9\u003c/span\u003e \u0026amp; \u003cspan\u003e10\u003c/span\u003e, the distribution of active elements tends to provide enough active sites responsible for the reaction to increase the conversion of triglycerides to fatty acid methyl ester (Jamil et al., 2010).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec19\"\u003e\n \u003ch2\u003e3.2.3 XRD analysis of ZMDA\u003c/h2\u003e\n \u003cp\u003eThe structural alterations on the \u003cem\u003eZea Mays\u003c/em\u003e peel ashes catalyst surface were investigated using X-ray diffraction (XRD) analysis. The peaks in Figs. \u003cspan\u003e8\u003c/span\u003e, \u003cspan\u003e9\u003c/span\u003e, and \u003cspan\u003e10\u003c/span\u003e clearly illustrate changes in the shape of the peels. Upon comparison across all three Figures, the crystal indices have become more intricate, indicating modifications resulting from dye adsorption by \u003cem\u003eZea Mays\u003c/em\u003e peel ashes and their calcined form.\u003c/p\u003e\n \u003cp\u003eIn Fig. \u003cspan\u003e8\u003c/span\u003e, sharp peaks suggest the crystalline nature of the ashes with simple indices such as (011), (020), (022), and (121). As the ashes adsorb the dye, the crystalline nature becomes more amorphous. Therefore, calcination is essential to shift the crystalline nature of the ashes towards unstructured arrangements of the plane. The structural complexity is evident in Figs. \u003cspan\u003e9\u003c/span\u003e and \u003cspan\u003e10\u003c/span\u003e, characterized by indices like (040), (131), (132), (155), (044), and (442). The presence of sharp and narrow peaks in the Figs. \u003cspan\u003e9\u003c/span\u003e and \u003cspan\u003e10\u003c/span\u003e show the changes in the catalyst\u0026apos;s structural arrangement, indicating the increased porosity, hence providing more surface area for the reaction. The average particle size of the CNDA catalyst was calculated from diffraction peaks using the Debye-Scherrer formula (Abu-Ghazala et al., \u003cspan\u003e2023\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv id=\"Equ2\"\u003e\n \u003cdiv id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$\\:D=\\frac{K\\lambda\\:}{\\beta\\:cos\\theta\\:}$$\u003c/div\u003e\n \u003cdiv\u003e4\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe biosynthesized catalyst exhibited an average size of 53.25 nm, determined using the Scherrer formula, where K is the Scherrer constant (0.94), \u003cstrong\u003e\u0026lambda;\u003c/strong\u003e is the wavelength of the X-ray source (0.15406 nm), \u003cstrong\u003e\u0026beta;\u003c/strong\u003e is the full width at half maximum in radians (FWHM), and \u0026theta; is the diffraction angle in radians. The crystals of the ashes measured 32.58 nm, which increased to 42.8 nm upon the adsorption of methylene blue dye. A similar pattern was observed in the spacing between the atoms of crystals. The \u003cem\u003eZea Mays\u003c/em\u003e ashes displayed an average d-value of 2.08 nm, which increased to 2.4 nm after the adsorption of methylene blue dye. Furthermore, the calcinated \u003cem\u003eZea Mays\u003c/em\u003e ashes, following dye adsorption, had an average d-value of 2.54 nm.\u003c/p\u003e\n \u003cp\u003eThe peak area of Ashes of \u003cem\u003eZea Mays\u003c/em\u003e peel was determined to be 2021.56 nm and 3607.87 nm; the total peak area was measured using X\u0026apos;Pert HighScore Plus software. The overall percentage of crystallinity was calculated to be 56.034% with the Eq. (\u003cspan\u003e4\u003c/span\u003e), indicating that the arrangement of atoms in \u003cem\u003eZea Mays\u003c/em\u003e peel ashes is crystalline, while the remaining 43.966% is amorphous. Similarly, the percentage crystallinity of dye-absorbed \u003cem\u003eZea Mays\u003c/em\u003e peel ashes and calcined dye-absorbed \u003cem\u003eZea Mays\u003c/em\u003e peel ashes were determined to be 3.31% and 6.42%, respectively. The change in the percentage of crystallinity indicates the adsorption of methylene blue dye by \u003cem\u003eZea Mays\u003c/em\u003e peel ashes, and the increase in crystallinity from 3.31\u0026ndash;6.42% demonstrates the effect of calcination on dye-absorbed \u003cem\u003eZea Mays\u003c/em\u003e peel ashes. Morphological changes on the catalyst\u0026apos;s surface in the FE-SEM analysis of all three samples can further support the numerical data.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec20\"\u003e\n \u003ch2\u003e3.2.4 FTIR analysis of ZMDA\u003c/h2\u003e\n \u003cp\u003eFigures \u003cspan\u003e11\u003c/span\u003e and \u003cspan\u003e12\u003c/span\u003e depict the FTIR spectrum of uncalcined and calcined ZMDA, respectively. The ZMDA transitions to a more rigid state after calcination, as evidenced by a decrease in peak intensity from Fig. \u003cspan\u003e11\u003c/span\u003e to \u003cspan\u003e12\u003c/span\u003e. Figure \u003cspan\u003e11\u003c/span\u003e shows a broad peak in the functional group region at 3415.47 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and 3383.81 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e condenses into a single, shorter peak in Fig. \u003cspan\u003e12\u003c/span\u003e. This change signifies that calcination has resulted in a closer hydrogen bonding arrangement between elements, leading to decreased light transmittance and a shorter, sharper peak in Fig. \u003cspan\u003e12\u003c/span\u003e. The light intensity throughout the spectra diminishes, indicating a significant decrease in peak transmittance due to calcination. A peak at 2369 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in Fig. \u003cspan\u003e11\u003c/span\u003e corresponds to the C-H absorption peak stretching vibration, suggesting the residual saturated hydrocarbon group after the combustion of \u003cem\u003eZea Mays\u003c/em\u003e husk/peels into ashes (Xu et al., 2019). The absence of the 2369 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e peak in Fig. \u003cspan\u003e12\u003c/span\u003e suggests the removal of these residual hydrocarbons through the calcination process. The change in the shape of peaks and new peak formation defines the change in the morphology of the catalyst with a change in the process. Sharp peaks indicate a rigid structure, signifying that the catalyst\u0026apos;s atoms have come closer, making the catalyst immiscible in the reaction mixture of triglyceride and alcohol. Change in the shape of FTIR peaks also signifies the formation of pores on the catalyst\u0026apos;s surface, making it more efficient since more space is available on the catalyst\u0026apos;s surface, providing more contact. In many literatures, it has been found that the peak around 3415 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 3383 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e indicates the presence of water molecules in the sample. It signifies the presence of the -O.H. group (Nath et al., \u003cspan\u003e2020\u003c/span\u003e)(Basumatary et al., \u003cspan\u003e2021\u003c/span\u003e). Since transesterification is reversible, the -O.H. group or water molecule diverges the reaction pathway towards soap or glycerol formation instead of fatty acid methyl ester. The shortened width and height of this peak in Fig. \u003cspan\u003e12\u003c/span\u003e indicate a decrease in these unwanted groups, making the reaction converge more towards the fatty acid methyl ester or biodiesel.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\"\u003e\n \u003ch2\u003e3.3 Biodiesel characterization\u003c/h2\u003e\n \u003cdiv id=\"Sec22\"\u003e\n \u003ch2\u003e3.3.1 Optimization of reaction by Taguchi method\u003c/h2\u003e\n \u003cp\u003eUsing the Taguchi method, we optimized the transesterification reaction by considering four controlled factors: Time, Temperature (Temp), Catalyst load (CatL), and Methanol-to-oil ratio (MTOR). We treated Oil quality as an uncontrolled factor in the optimization process. Upon completing all the experiments, we found that we achieved the maximum yield of 95.77% at a temperature of 65℃, with a duration of 240 minutes, using 1.5 g of catalyst and a methanol to oil ratio of 4:1. According to the Taguchi analysis, the signal-to-response ratio indicated that the methanol ratio and time are the primary influencing parameters, followed by temperature and catalyst load.\u003c/p\u003e\n \u003cp\u003eIn this Taguchi analysis aimed at optimizing yield, the investigation focused on signal-to-noise ratios (S/N ratios), means, and standard deviations for different factor levels\u0026mdash;MTOR, Time, Temp, and CatL. The results revealed that MTOR at Level 1 exhibited the highest S/N ratio and mean, suggesting its paramount importance in achieving higher yield and robust performance. They followed closely in rankings for time at Level 4, emphasizing its significant contribution to favourable outcomes. CatL at Level 3 secured the third position, while Temp at Level 2 held the lowest rank in both S/N ratios and means. Unfortunately, specific standard deviation values were not provided, but the uniform rank assignment indicates equal desirability across all factor levels. Overall, this analysis guides the recommendation to set MTOR to Level 1, Time to Level 4, CatL to Level 3, and Temp to Level 2 for optimal yield and robustness, aligning with the objectives of the Taguchi method in achieving consistent and high-performance outcomes.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec23\"\u003e\n \u003ch2\u003e3.3.2 Grey relational analysis\u003c/h2\u003e\n \u003cp\u003eThe Grey Relational Analysis (G.R.A.) technique, established by Deng (Deng, 1982.), is a widely employed multi-objective decision-making method across various engineering disciplines due to its simplicity and efficiency in response evaluation. This technique is particularly valuable for analyzing uncertainties within systems. In the G.R.A. approach, all output responses undergo normalization, scaling them between 0 and 1. This normalization facilitates the interpretation of data and subsequent analysis. The normalized values are then utilized to compute the Grey Relational Coefficient (G.R.C.) from the entire output data set. The Grey Relational Grade (G.R.G.), essential for assessing the performance of the experimental trials, is obtained by averaging the G.R.C. values. A higher G.R.G. value signifies an optimal solution for the given response. The G.R.A. procedure outlined here is crucial in understanding system dynamics comprehensively, making it a valuable tool for scientific research and experimentation.\u003c/p\u003e\n \u003cp\u003eFirstly, the entire experimental data is normalized as per the required conditions, normalization for higher the better\u003c/p\u003e\n \u003cdiv id=\"Equ3\"\u003e\n \u003cdiv id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$$\\:{x}_{i}\\left(q\\right)=\\frac{{v}_{\\dot{I}}\\left(q\\right)-\\text{min}{v}_{\\dot{i}}\\left(q\\right)}{\\text{max}{v}_{i}\\left(q\\right)-\\text{min}{v}_{i}\\left(q\\right)}$$\u003c/div\u003e\n \u003cdiv\u003e5\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eNormalization for lower-the-better\u003c/p\u003e\n \u003cdiv id=\"Equ4\"\u003e\n \u003cdiv id=\"FileID_Equ4\" name=\"EquationSource\"\u003e$$\\:{x}_{i}\\left(q\\right)=\\frac{\\text{max}{v}_{i}\\left(q\\right)-{v}_{\\dot{I}}\\left(q\\right)}{\\text{max}{v}_{i}\\left(q\\right)-\\text{min}{v}_{i}\\left(q\\right)}$$\u003c/div\u003e\n \u003cdiv\u003e6\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e(q)\u003c/em\u003e is the normalized value for output response; \u003cspan\u003e\u003cspan\u003e\\(\\:\\text{min}{v}_{\\dot{i}}\\left(q\\right)\\)\u003c/span\u003e\u003c/span\u003e Is the minimum value of \u003cspan\u003e\u003cspan\u003e\\(\\:{v}_{\\dot{I}}\\left(q\\right)\\)\u003c/span\u003e\u003c/span\u003e for q\u003csup\u003eth\u003c/sup\u003e response; \u003cspan\u003e\u003cspan\u003e\\(\\:\\text{max}{v}_{i}\\left(q\\right)\\)\u003c/span\u003e\u003c/span\u003e Is the maximum value of \u003cspan\u003e\u003cspan\u003e\\(\\:{v}_{\\dot{I}}\\left(q\\right)\\)\u003c/span\u003e\u003c/span\u003e for \u003cem\u003eq\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e response.\u003c/p\u003e\n \u003cp\u003eThe Grey Relational Coefficient (G.R.C.) is calculated in the second step to create a correlation between ideal and actual normalized values.\u003c/p\u003e\n \u003cdiv id=\"Equ5\"\u003e\n \u003cdiv id=\"FileID_Equ5\" name=\"EquationSource\"\u003e$$\\:\\gamma\\:\\left(k\\right)=\\frac{{\\varDelta\\:}_{\\text{min}}+\\:\\omega\\:{\\varDelta\\:}_{max}}{{\\varDelta\\:}_{oi}+\\:\\omega\\:{\\varDelta\\:}_{max}}$$\u003c/div\u003e\n \u003cdiv\u003e7\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere, \u003cspan\u003e\u003cspan\u003e\\(\\:{\\varDelta\\:}_{oi}\\)\u003c/span\u003e\u003c/span\u003e(k) = |\u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003ek\u003c/em\u003e) \u0026ndash; \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e(\u003cem\u003ek\u003c/em\u003e)|, \u003cspan\u003e\u003cspan\u003e\\(\\:\\omega\\:\\)\u003c/span\u003e\u003c/span\u003e is the distinctive coefficient, which is utilized to either enlarge or decrease the range of the grey relation coefficient. The value of \u003cspan\u003e\u003cspan\u003e\\(\\:\\omega\\:\\)\u003c/span\u003e\u003c/span\u003e is between 1 and 0. Through a literature survey, the preferred value of \u003cspan\u003e\u003cspan\u003e\\(\\:\\omega\\:\\)\u003c/span\u003e\u003c/span\u003e= 0.5 is taken. \u003cspan\u003e\u003cspan\u003e\\(\\:{\\varDelta\\:}_{\\text{m}\\text{i}\\text{n}}\\)\u003c/span\u003e\u003c/span\u003e is the minimum of \u003cspan\u003e\u003cspan\u003e\\(\\:{\\varDelta\\:}_{oi}\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan\u003e\u003cspan\u003e\\(\\:{\\varDelta\\:}_{max}\\)\u003c/span\u003e\u003c/span\u003e is the maximum value of \u003cspan\u003e\u003cspan\u003e\\(\\:{\\varDelta\\:}_{oi}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eThe third step calculates Grey Relational Grade (G.R.G.) using the equation below.\u003c/p\u003e\n \u003cdiv id=\"Equ6\"\u003e\n \u003cdiv id=\"FileID_Equ6\" name=\"EquationSource\"\u003e$$\\:\\delta\\:\\left(i\\right)=\\frac{1}{n}\\sum\\:_{i=1}^{n}\\gamma\\:\\left(k\\right)$$\u003c/div\u003e\n \u003cdiv\u003e8\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere \u003cspan\u003e\u003cspan\u003e\\(\\:\\delta\\:\\left(i\\right)\\)\u003c/span\u003e\u003c/span\u003e is the grey relational grade, n is the number of output responses.\u003c/p\u003e\n \u003cp\u003eThe parameters of the transesterification process used to convert waste cooking oil using dye-adsorbed \u003cem\u003eZea Mays\u003c/em\u003e catalyst into fatty acid methyl esters are investigated using the Taguchi-based Grey Relational Analysis (G.R.A.). G.R.G., G.R.C., and normalized values are calculated using equations (\u003cspan\u003e5\u003c/span\u003e), (\u003cspan\u003e6\u003c/span\u003e), (7), and (8) as shown in Table \u003cspan\u003e3\u003c/span\u003e and \u003cspan\u003e4\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eGRG and normalized data of process parameters\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTemp\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCatL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMTOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTemp\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCatL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMTOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLevel\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eGRG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eNormalized Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIt is considered the best since experiment number 14 has been ranked 1 in all 16 experiments. It has a high yield with 95.5% conversion in biodiesel with parameters time 240 minutes, 50℃ temperature, 0.5 wt% catalyst load, and 8:1 methanol to oil. Although the boiling point of methanol is 65 ℃, and so above that, the circulation of methanol in the system becomes rapid, and the system temperature cannot reach above 90 ℃. Suppose all the values related to temperature 120℃ are neglected. In that case, rank 4, an experiment run 4 will become optimal condition with a maximum yield conversion of 94% at 65 ℃ temperature, 60 minutes of reaction time, 1.5 wt% catalyst load, and 32:1 methanol to oil. A comparison between rank 1 and 4 shows an inverse relation between the amount of methanol and reaction time. When time is increased, a low amount of methanol can also give better results; similarly, time decreases when the amount of methanol increases. Both factors have a significant economic effect on the end product\u0026apos;s cost. Time is considered an economic factor related to the amount of energy consumed during the reaction time. This can be confirmed by Taguchi\u0026apos;s analysis, which ranked the methanol-to-oil ratio and temperature above other parameters.\u003c/p\u003e\n \u003cp\u003eIn the Grey Relational Analysis (G.R.A.) conducted with a small positive constant \u0026epsilon;\u0026thinsp;=\u0026thinsp;0.1, the sequences representing different factor levels were assessed for their relationship with a reference sequence. The deviation values for each sequence were calculated, considering the normalized values of Time, Temp, CatL, and MTOR. Subsequently, the Grey Relational Coefficient (G.R.C.) was determined for each sequence, indicating the strength of its relationship with the reference. Higher G.R.C. values suggest a stronger relationship. Upon ranking the sequences based on their G.R.C. values, Sequence 2 (corresponding to Level 2) emerged with the highest G.R.C., signifying the most robust relationship with the reference. Following this, Sequence 3 (Level 3) was ranked second, followed by Sequence 1 (Level 1), and Sequence 4 (Level 4) was ranked last. Therefore, the Grey Relational Analysis suggests that, in terms of robustness and relationship with the reference sequence, the factor level corresponding to Sequence 2 holds the highest significance, followed by Levels 3, 1, and 4, as shown in Fig. \u003cspan\u003e14\u003c/span\u003e. The effect of the experimental variable is given in Fig. \u003cspan\u003e13\u003c/span\u003e (x-axis- variable parameters; y-axis- means of yield); in the case of time duration of the reaction, it can be observed that yield was highest at 240 mins. Similarly, the highest yield was observed at 75 ℃. Since the boiling point of methanol is near about 64℃, methanol starts escaping the system at boiling temperature and condenses back into the reaction environment, giving enough time to mix in the reaction mixture. This phenomenon was also observed during the experimentation. The lowest yield at 90℃ indicates that as soon as methanol condenses back, it again starts escaping due to other incoming methanol fumes and not allowing the methanol to mix back in the reaction mixture.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec24\"\u003e\n \u003ch2\u003e3.3.2 Gas Chromatography of Biodiesel\u003c/h2\u003e\n \u003cp\u003eQualitative analysis of synthesized biodiesel samples from waste cooking oil was investigated by GC-MS analysis using \u003cem\u003eZea Mays\u003c/em\u003e dye-adsorbed catalyst. Gas chromatography was conducted on a Clarus 680 GC (PerkinElmer), employing a rapid injection-to-injection cycle. The initial oven temperature was set at 40\u0026deg;C for 2 minutes, followed by a linear increase of 10\u0026deg;C per minute until reaching 140\u0026deg;C. After a 2-minute hold at 140\u0026deg;C, the temperature was further raised to 300\u0026deg;C at 40\u0026deg;C per minute. Subsequently, a 2-minute hold was maintained at 300\u0026deg;C, followed by a gradual increase to 300\u0026deg;C at a rate of 7\u0026deg;C per minute, with a 5-minute hold. The sample was injected in split mode with a split/column flow ratio 20:1, utilizing a sample volume of 1 \u0026micro;l. Peak identification for the synthesized biodiesel was achieved by comparing mass spectra with entries in the NIST libraries. Compounds matching the samples, their respective molecular weights, and retention times are presented in the G.C. chromatogram in Fig. \u003cspan\u003e15\u003c/span\u003e, and compounds are listed in Table \u003cspan\u003e5\u003c/span\u003e. The identified compounds align with literature findings, confirming the presence of biodiesel components or fatty acid methyl esters (Gandure et al., 2013). The gas chromatograph of FAMEs showed that the mixture comprises several methyl ester groups of fatty acid with retention times 26.36, 29.65, 31.60, and 37.1 minutes. Searching these peaks in the database gave several esters, which are listed below with reference.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec25\"\u003e\n \u003ch2\u003e3.3.3 NMR analysis of FAME (Biodiesel)\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan\u003e16\u003c/span\u003e shows the NMR spectrum of the synthesized biodiesel in which methyl ester\u0026apos;s methoxy proton (-OCH3) (-COOCH3) is visible at 3.66 ppm. Notably, the triplet signals of the bis-allylic proton (-C\u0026thinsp;=\u0026thinsp;C-CH2-C\u0026thinsp;=\u0026thinsp;C-) in the polyunsaturated fatty acid chain appear at 2.77 ppm, while the \u0026alpha;-methylene protons (\u0026alpha;-CH2) of the ester (-CH2-COOMe) resonate at 2.30 ppm. A multiplet signal between 2.04 to 2.06 ppm corresponds to the alpha-methylene protons (\u0026alpha;-CH2) in the bond (-CH2-C\u0026thinsp;=\u0026thinsp;C-), and the signal at 1.62 ppm represents the \u0026beta;-methylene proton of the ester (-CH2-CH\u0026thinsp;=\u0026thinsp;COO-Me). The multiplet signal between 5.32 to 5.37 ppm is also associated with olefinic protons (-CH\u0026thinsp;=\u0026thinsp;CH=) bonded to carbon atoms. Furthermore, a triplet signal at 0.88 ppm indicates the presence of terminal methyl protons (-C-CH3). Finally, the peaks at 1.25, 1.27 and 1.30 ppm are attributed to the protons in the methylene (-(CH2)n=) backbone of long fatty acid chains (Moawia et al., \u003cspan\u003e2019\u003c/span\u003e)(Hassan and Nageswara, \u003cspan\u003e2018\u003c/span\u003e)(G Knothe, \u003cspan\u003e2006\u003c/span\u003e)(Morgenstern et al., \u003cspan\u003e2006\u003c/span\u003e). The overall conversion of triglyceride into fatty acid methyl ester, also known as biodiesel, was 95.5% at 65℃, with a catalyst load of 1.5 grams, and a 4:1 methanol to oil ratio for 240 minutes.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis comprehensive study demonstrates the multifaceted potential of \u003cem\u003eZea Mays\u003c/em\u003e ash as both an adsorbent and catalyst in biodiesel production. Optimal adsorption performance for methylene blue dye was achieved at pH 10 and 8, with efficiencies of 89.54% and 89.94%, respectively. The resulting product, \u003cem\u003eZea Mays\u003c/em\u003e dye-adsorbed ash (ZMDA), underwent calcination, resulting in a versatile catalyst with a porous surface, enhancing catalytic activity. Morphological analyses via FESEM revealed significant surface structure changes in \u003cem\u003eZea Mays\u003c/em\u003e peel ashes, dye-absorbed ashes, and calcinated dye-absorbed \u003cem\u003eZea Mays\u003c/em\u003e peel ashes, indicating increased surface area and improved catalytic activity post-calcination. E.D.S. analysis highlighted prominent elemental distribution, with carbon, oxygen, sodium, and magnesium identified. XRD analysis demonstrated structural alterations, with increased porosity observed post-dye absorption and calcination, yielding an average particle size of 53.25 nm. Optimization using the Taguchi method and G.R.A. confirmed the transesterification reaction's optimal conditions, leading to a maximum biodiesel yield of 95.77%. G.C. analysis confirmed the presence of FAMEs, while N.M.R. analysis provided a detailed spectrum of the final biodiesel product. The conversion of triglycerides into FAMEs reached 95.5% under optimized conditions, highlighting the potential of \u003cem\u003eZea Mays\u003c/em\u003e ash in sustainable biodiesel production, offering environmentally friendly and economically viable energy solutions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eASTM - American Society for Testing and Materials\u003c/p\u003e\n\u003cp\u003eEDS - Energy-Dispersive X-ray Spectroscopy\u003c/p\u003e\n\u003cp\u003eFESEM - Field Emission Scanning Electron Microscope\u003c/p\u003e\n\u003cp\u003eFAME - Fatty Acid Methyl Ester\u003c/p\u003e\n\u003cp\u003eFTIR - Fourier Transform Infrared Spectroscopy\u003c/p\u003e\n\u003cp\u003eGC-MS - Gas Chromatography-Mass Spectrometry\u003c/p\u003e\n\u003cp\u003eGRA - Grey Relational Analysis\u003c/p\u003e\n\u003cp\u003eGRG - Generalized Grey Relational Grade\u003c/p\u003e\n\u003cp\u003eHPLC - High-Performance Liquid Chromatography\u003c/p\u003e\n\u003cp\u003eM.B. - Methylene Blue\u003c/p\u003e\n\u003cp\u003eMTOR - Methanol-to-oil ratio\u003c/p\u003e\n\u003cp\u003eNMR - Nuclear Magnetic Resonance\u003c/p\u003e\n\u003cp\u003eWCO - Waste Cooking Oil\u003c/p\u003e\n\u003cp\u003eXRD - X-ray diffraction\u003c/p\u003e\n\u003cp\u003eZMDA - \u003cem\u003eZea Mays\u003c/em\u003e dye-adsorbed ash\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eH-NMR - Proton Nuclear Magnetic Resonance\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u003cbr\u003e\u0026nbsp;\u003c/strong\u003eThe authors thank the Central Instrumentation Research Facility, National Institute of Technology, Warangal, for providing an analytical facility for our research work in our publication. Also, the authors show their gratitude to the CAIF facility of Guwahati Biotech Park Incubation Centre for providing the needed facility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Agency:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no funding from public, commercial, or non-profit organizations.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthorship contribution statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAkshay Prakash\u003c/strong\u003e: Conceptualization, Methodology, Formal analysis, Writing \u0026ndash; original draft, Validation.\u0026nbsp;\u003cbr\u003e\u003cstrong\u003eM Jerold\u003c/strong\u003e:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; review \u0026amp; editing, Validation, Supervision, Resources\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbu-Ghazala, A. 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(1995). \u003cem\u003eQuality engineering using robust design\u003c/em\u003e. Prentice Hall PTR.\u003c/li\u003e\n\u003cli\u003ePrajapati, P., Shrivastava, S., Sharma, V., Srivastava, P., Shankhwar, V., Sharma, A., \u0026amp; Agarwal, D. D. (2023). Karanja seed shell ash: A sustainable green heterogeneous catalyst for biodiesel production. \u003cem\u003eResults in Engineering\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e, 101063.\u003c/li\u003e\n\u003cli\u003eSharma, M.; Khan, A. A.; Puri, S. K.; Tuli, D. K. (2012) Wood ash as a potential heterogeneous catalyst for biodiesel synthesis. \u003cem\u003eBiomass Bioenergy\u003c/em\u003e, \u003cstrong\u003e41\u003c/strong\u003e, 94\u0026ndash;106.\u003c/li\u003e\n\u003cli\u003eSharma, Y. C.; Singh, B.; Upadhyay, S. N. (2008) Advancements in development and characterization of biodiesel: A review. \u003cem\u003eFuel\u003c/em\u003e, \u003cstrong\u003e87\u003c/strong\u003e (12), 2355\u0026ndash;2373.\u003c/li\u003e\n\u003cli\u003eShi, Q.; Wang, W.; Zhang, H.; Bai, H.; Liu, K.; Zhang, J.; Li, Z.; et al. (2023) Porous biochar derived from walnut shell as an efficient adsorbent for tetracycline removal. \u003cem\u003eBioresour Technol\u003c/em\u003e, \u003cstrong\u003e383\u003c/strong\u003e, 129213.\u003c/li\u003e\n\u003cli\u003eSoudani, A.; Youcef, L.; Bulgariu, L.; Youcef, S.; Toumi, K.; Soudani, N. (2022) Characterizing and modeling of oak fruit shells biochar as an adsorbent for the removal of Cu, Cd, and Zn in single and in competitive systems. \u003cem\u003eChemical Engineering Research and Design\u003c/em\u003e, \u003cstrong\u003e188\u003c/strong\u003e, 972\u0026ndash;987.\u003c/li\u003e\n\u003cli\u003eTalha, N. S., \u0026amp; Sulaiman, S. (2016). Overview of catalysts in biodiesel production. \u003cem\u003eARPN Journal of Engineering and Applied Sciences\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(1), 439-442.\u003c/li\u003e\n\u003cli\u003eKnothe, G. (2006). Analysis of oxidized biodiesel by 1H‐NMR and effect of contact area with air\u003cem\u003e. European journal of lipid science and technology,\u003c/em\u003e 108(6), 493-500.\u003c/li\u003e\n\u003cli\u003eTalha, N. S., \u0026amp; Sulaiman, S. (2016). Overview of catalysts in biodiesel production. \u003cem\u003eARPN Journal of Engineering and Applied Sciences\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(1), 439-442.\u003c/li\u003e\n\u003cli\u003eYesilyurt, M. K.; Cesur, C. (2022) A statistical optimization attempt by applying the Taguchi technique for the optimum transesterification process parameters in the production of biodiesel from Papaver somniferum L. seed oil. \u003cem\u003eFuel\u003c/em\u003e, \u003cstrong\u003e329\u003c/strong\u003e, 125406.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables 2,3 and 5","content":"\u003cp\u003eTables 2,3 and 5 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"chemical-papers","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"chpa","sideBox":"Learn more about [Chemical Papers](http://link.springer.com/journal/11696)","snPcode":"11696","submissionUrl":"https://www.editorialmanager.com/CHPA/default.aspx","title":"Chemical Papers","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Ash, Methylene Blue, Adsorbent, Green Catalyst, Biodiesel","lastPublishedDoi":"10.21203/rs.3.rs-4710073/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4710073/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe escalating energy crisis, propelled by extensive fossil fuel consumption, necessitates sustainable and environmentally friendly alternatives. Biodiesel, derived from renewable sources, has emerged as a promising solution. This study explores using methylene blue (M.B., which is a significant water pollutant in several parts of the world) dye-adsorbed biochar, a waste-derived green catalyst, for biodiesel production. The catalyst was synthesized from \u003cem\u003eZea Mays\u003c/em\u003e peels, demonstrating a circular economy approach. The optimization of transesterification reactions is achieved using the Taguchi method, considering factors including reaction time, temperature, catalyst load, and methanol-to-oil ratio. The resulting biodiesel was purified and characterized through various analyses, including Gas Chromatography and Nuclear Magnetic Resonance. Adsorption studies reveal the catalyst's potential, and structural analyses (FESEM, XRD, FTIR) provide insights into its composition. The synthesized biodiesel, identified through GC-MS, exhibited qualities that align with the findings of the literature. Overall, the study presents a sustainable and economically viable pathway for biodiesel production using a novel green catalyst derived from waste resources.\u003c/p\u003e","manuscriptTitle":"Sustainable Processing of Zea Mays peel ash (Methylene adsorbed) biosorbent as a novel green heterogeneous catalyst for biodiesel production","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-05 18:07:59","doi":"10.21203/rs.3.rs-4710073/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-10-08T16:05:35+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-09T15:35:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Chemical Papers","date":"2024-07-10T22:33:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-10T13:00:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Chemical Papers","date":"2024-07-09T03:42:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"chemical-papers","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"chpa","sideBox":"Learn more about [Chemical Papers](http://link.springer.com/journal/11696)","snPcode":"11696","submissionUrl":"https://www.editorialmanager.com/CHPA/default.aspx","title":"Chemical Papers","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"76b4d048-3474-4e9b-91f6-bf06c8605033","owner":[],"postedDate":"September 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-16T16:02:48+00:00","versionOfRecord":{"articleIdentity":"rs-4710073","link":"https://doi.org/10.1007/s11696-025-04083-8","journal":{"identity":"chemical-papers","isVorOnly":false,"title":"Chemical Papers"},"publishedOn":"2025-06-14 15:58:04","publishedOnDateReadable":"June 14th, 2025"},"versionCreatedAt":"2024-09-05 18:07:59","video":"","vorDoi":"10.1007/s11696-025-04083-8","vorDoiUrl":"https://doi.org/10.1007/s11696-025-04083-8","workflowStages":[]},"version":"v1","identity":"rs-4710073","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4710073","identity":"rs-4710073","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}