Corresponding author’s email address: chijiokeugwuodo@mouau.edu.ng 529 ARID ZONE JOURNAL OF ENGINEERING, TECHNOLOGY & ENVIRONMENT ORIGINAL RESEARCH ARTICLE RESPONSE SURFACE MODELING OF SOURSOP RICH SEED PYROLYSIS FOR OPTIMUM BIO-OIL PRODUCTION C. B. Ugwuodo 1*, H. U. Itiri 1, L. A. Enyinnaya1, I. N. Emmanuel2, S. E. Agbokwor3, V.C. Uzoma1, R.U. Daniel1, and L.C. Ezeocha1 1Department of Chemical Engineering, College of Engineering and Engineering Technology, Michael Okpara University of Agriculture, Umudike 2Department of Chemical Engineering, Nazarbayev University, Astana, Kazakstan 3 Department of Mechanical Engineering, University of Nigeria Nsukka *Corresponding author’s email: chijiokeugwuodo@mouau.edu.ng ARTICLE INFORMATION ABSTRACT This research investigates bio-oil production from soursop seed through pyrolysis, with process modelling and optimization conducted using RSM via Box-Benkhen design. Soursop seeds were oven-dried, ground, and sieved into various particle sizes within 1.0 to 6 mm range before undergoing pyrolysis at temperatures between 400 to 600 °C under inert nitrogen gas flow rates between 1.0 to 1.5 L/min. A Box-Behnken design under Response Surface Methodology (RSM) was used to model and optimize the effects of temperature, particle size, and inert gas flow on bio-oil yield. Proximate and ultimate analyses characterized the feedstock, while SEM revealed a porous structure favorable for pyrolysis. Bio-oil was characterized using FTIR and GC-MS to identify key functional groups and fatty acid composition. Proximate analysis showed the seeds had high volatile matter and fixed carbon, indicating good potential for pyrolysis. Ultimate analysis revealed carbon, hydrogen, nitrogen, oxygen, and sulphur contents of 51.29%, 5.90%, 0.50%, 42.30%, and 0.01%, respectively. Scanning Electron Microscopy (SEM) showed a rough, porous structure with oil-like droplets on the surface, which enhanced pyrolysis efficiency by providing a larger reactive surface area and improving devolatilization rates. The experimental design considered temperature, particle size, and gas flow rate combinations, with the bio-oil yield as the response. Results showed that increases in these parameters significantly affected bio-oil production. The maximum observed yield of 33.1% was achieved at 500°C, 6 mm particle size, and 1.0 L/min gas flow. The RSM model showed a high degree of fit with an R² value of 0.9875, adjusted R² of 0.9715, and predicted R² of 0.8007. Optimization predicted a maximum yield of 31.43% under conditions of 461.84°C, 3.84 mm particle size, and 1.02 L/min flow rate, with a desirability of 1.0. Experimental results closely matched these predictions, validating the model. Similarly, FTIR analysis indicates that the predominant monounsaturated fatty acid made up 45.55% of the total fatty acid content, which depicts that the oil belongs to the linoleic acid group. Furthermore, the FTIR analysis reveals that the alkene group contributes to increased reactivity and combustion efficiency, boosts the octane number of the bio-oil, and decreases the boiling point of the oil. Therefore, the FTIR and GC-MS analysis findings confirm that the bio-oil was within ASTM specifications. Received: 19th March 2025 Revised: 1st May 2025 Accepted: 2nd May 2025 Keywords: Bio-oil Cellulose Box benkhen design Response surface methodology (RSM) Soursop seed Ultimization © 2025 Faculty of Engineering, University of Maiduguri, Nigeria. All rights reserved. 1.0 Introduction Biomass pyrolysis is a thermal decomposition process conducted in the absence of oxygen, converting organic materials into biochar, bio-oil, and syngas (Bridgwater, 2019). This process has gained significant attention due to its potential for producing renewable energy and high-value byproducts, which can mitigate the reliance on fossil fuels and contribute to a circular economy (Biswas et al., 2022). Fixed-bed pyrolytic reactors are AZOJETE June 2025. Vol.21(2):529-544 Published by the Faculty of Engineering, University of Maiduguri, Maiduguri, Nigeria. Print ISSN: 1596-2490, Electronic ISSN: 2545-5818 https://doi.org/10.63958/AZOJETE/2025/21/02/018 www.azojete.com.ng about:blank about:blank http://www.azojete.com.ng/ Arid Zone Journal of Engineering, Technology and Environment, June 2025; Vol. 21(2): 529-544. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: chijiokeugwuodo@mouau.edu.ng 530 prominent among the various pyrolysis technologies due to their simplicity, efficiency, and adaptability to different biomass feedstocks (Tripathi et al., 2021). Several methods are used today to create liquid fuel from biomass, including fermentation to produce ethanol (Hoover and Abraham, 2019), transesterification to produce biodiesel (Muley and Boldor, 2021) and pyrolysis to produce bio-oil (Goyal et al., 2018). In a fixed- bed reactor, the biomass is placed in a stationary bed and heated, allowing for better control over the pyrolysis conditions (Mohan et al., 2021). This setup is particularly advantageous for studying the pyrolysis behaviour of specific biomass types, such as soursop seed (Wang et al., 2022). The fixed bed configuration ensures uniform heating and consistent product distribution, which are crucial for optimizing the pyrolysis process and enhancing the quality of the derived products (Wang et al., 2022). Soursop (Annona muricata) is a tropical fruit celebrated for its nutritional and medicinal properties. Yet, its seeds are often overlooked and discarded as agricultural waste (Martínez et al., 2020). These seeds, which comprise about 20% of the fruit's total weight, contain valuable organic material that can be converted into useful products through pyrolysis (Ishaq et al., 2020). The process involves heating the biomass in a fixed bed pyrolytic reactor, where it undergoes complex chemical reactions to produce solid (biochar), liquid (bio-oil), and gaseous products. (Zhao et al., 2023). The distribution and yield of these products are influenced by several factors, including temperature, heating rate, particle size, and residence time (Sharma et al., 2021). Recent studies have highlighted the potential of pyrolyzing soursop seeds as a sustainable waste management strategy and a method for generating renewable energy (Mansur et al., 2020). The biochar produced from soursop seeds exhibits promising characteristics, such as high porosity and nutrient content, making it a potential candidate for soil amendment and carbon sequestration (Alkhalidi et al., 2023). Moreover, bio-oil derived from the seeds can serve as a valuable source of biofuels and chemicals, aligning with the global shift towards greener energy solutions (Dajanta et al., 2021). Bio-oil derived from the pyrolysis of soursop seeds is rich in various organic compounds that can be upgraded to produce biofuels and bio-chemicals (Mansur et al., 2020). The potential applications of bio-oil are extensive, ranging from energy production to the synthesis of high-value chemicals, thereby enhancing the economic viability of soursop seeds (Lehmann and Joseph, 2019). The temperature of pyrolysis plays a critical role in determining the composition and yield of the pyrolysis products (Aboelela et al., 2021). At lower temperatures (300-400°C), the process favours the production of biochar, while higher temperatures (500-700°C) increase the yield of bio-oil and syngas (Zhao et al., 2023). Soursop seed pyrolysis at varying temperatures has demonstrated this trend, where biochar yields decrease, and bio-oil yields increase with rising temperatures (Patel et al., 2021). Furthermore, the soursop seed's heating rate and particle size also influence the pyrolysis kinetics and product distribution (Dajanta et al., 2021). Faster heating rates enhance the bio-oil yield, while smaller particle sizes facilitate more efficient heat transfer and faster reaction rates (Huang et al., 2020). The pyrolysis of soursop seeds presents an environmentally friendly alternative to conventional waste disposal methods (Singh et al., 2021). By converting waste into energy and improving soil quality, pyrolysis contributes to the circular economy (Patel et al., 2021). Additionally, the process can help mitigate greenhouse gas emissions by sequestering carbon in the form of biochar (Alkhalidi et al., 2023). In search of optimal pyrolysis conditions, researchers explore modeling, predicting, and optimizing process parameters utilizing Responses Surfaces Methodology (RSM), (Ingie et al., 2023; Nwosu-Obieogu et al., 2024). RSM evaluates linear, interaction, and quadratic effects to identify ideal operating conditions for processes (Ude et al., 2020; Fakhari, 2023). While RSM has been used for pyrolysis of agricultural waste, there is currently a lack of literature on comprehensive investigations and optimization employing RSM, tailored explicitly for soursop seeds pyrolysis (Okeleye and Betiku, 2019; Belmajdoub and Abderaft, 2023). Onaifo et al. (2023) investigated the co-pyrolysis of soursop and mango (Mangifera indica) seeds to produce high-quality bio-oil. Utilizing slow pyrolysis at temperatures between 350–500 °C with a heating rate of 5 °C/min and a residence time of 60 minutes, they achieved optimal bio-oil yields at 400 °C: 36.48% for soursop seeds, 20.54% for mango seeds, and 26.16% for the combined feedstock. Gas chromatography-mass spectrometry (GC-MS) analysis revealed that the co-pyrolyzed bio-oil was rich in hydrocarbons (41.16%), carboxylic acids (21.89%), ethers (13.60%), esters (7.99%), and phenolics (5.20%). The study concluded that co-pyrolysis suppressed ether formation, resulting in bio-oil with potential applications as a biodiesel substitute and in the industrial production of resins and adhesives. Schroeder et al. (2017) analyzed the liquid fractions obtained from the slow pyrolysis of soursop seed cake. The research focused on the chemical and physical properties, providing insights into their potential applications. Although specific details are limited, the study contributes to understanding the composition and utility of pyrolysis products derived from soursop seeds. Kan et al. (2016) reviewed the pyrolysis of lignocellulosic biomass, focusing on product properties and the influence of various pyrolysis parameters. Although not specific to soursop seeds, the study provides valuable insights into how factors such as temperature, heating rate, and residence time affect the yield and composition http://www.azojete.com.ng/ about:blank Arid Zone Journal of Engineering, Technology and Environment, June 2025; Vol. 21(2): 529-544. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: chijiokeugwuodo@mouau.edu.ng 531 of biochar, bio-oil, and syngas, which can be applied to optimizing soursop seed pyrolysis processes. Sukumar et al. (2015) conducted rapid pyrolysis tests on sweet lime empty fruit bunches in a fixed bed reactor using an electric furnace to generate bio-oil. The impact of particle size, temperature and nitrogen gas flow rate on the end product yields were examined. These pyrolysis parameters are improved by Respond Surface Methodology (RSM) with a Box-Behnken Design (BBD). The findings demonstrated that the bio-oil yield is 28.3% for the experimental and 28.2% for the statistical approach at the optimal temperature of 550°C, 4mm of particle size, and 300 cm3min-1 of gas flow rate. Therefore, this work aims to optimize via RSM, implementing Box-Behnken on some selected parameters (temperature, particle size, and inert gas flow rate) that affect the pyrolysis of soursop seed. Furthermore, there is a need for more comprehensive studies on the environmental impacts of discarding this agricultural waste on the surroundings. 2. Materials and Method 2.1 Preparation of Feedstock The soursop seed used in this study was obtained from a plantation in Ndoro village in the Ikwuano local government area of Abia State, Nigeria. The sample was thoroughly washed to remove all foreign materials and ensure it was free of impurities. The soursop seed was oven-dried for 48 hours at a controlled temperature (60-70°C) to a constant weight. The dried soursop seed was ground in a high-speed rotary cutting mill. The samples of ground soursop seeds were separated into various particle size distributions (1-6 mm) using a standard sieve and stored in different desiccators until needed. 2.1.1 Equipment used The equipment used was an integration of different units and instruments. A stainless-steel reactor served as the primary vessel for thermal processing, securely sealed using a pair of flanges and spiral-wound gaskets to ensure leak-tight operation. Thermal insulation was provided by fiberglass wrapping, while precise temperature regulation was achieved using a K-type thermocouple connected to a digital temperature controller. Condensation of volatile products was facilitated through a water-cooled condenser connected downstream of the reactor. Additional components included nitrogen gas supplied from a pressurized cylinder to maintain an inert atmosphere, electric heaters for uniform heating, and fasteners (bolts and nuts) for secure assembly. Auxiliary tools such as a stopwatch, test sieves, and analytical balance were used for process timing, particle size classification, and mass measurements, respectively. 2.2 Characterization of the Feedstock 2.2.1 Proximate analysis The proximate analysis is defined as the loss in weight of the soursop seed sample heated under the test condition specified. The proximate analysis was performed according to ASTM E871 and E872 to determine the soursop seed's moisture content (MC), volatile matter (VM), fixed carbon (FC), and ash content (AC). Equations 1, 2, 3 and 4 are used to deduce MC, VM, FC and AC respectively. Moisture content (wt %) = ( Wo−W1 Wo ) × 100 1 Volatile matter (wt %) = ( W1−W2 W1 ) × 100 2 Fixed carbon (wt %) = ( W2−W3 W2 ) × 100 3 where, Wo is the weight of sample in g before heating, W1 is the weight of sample in g after heating at 1150C, W2 is the weight of sample in g after heating at 7500C, W3 is the weight of sample in g after heating at 9000C. The mass of the ash will be calculated as a percentage of the original sample using Equation 4. Ash, mass % = ( w W ) × 100 4 where, w = mass of ash (g) and W = mass of sample (g) 2.2.2 Ultimate analysis http://www.azojete.com.ng/ about:blank Arid Zone Journal of Engineering, Technology and Environment, June 2025; Vol. 21(2): 529-544. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: chijiokeugwuodo@mouau.edu.ng 532 This test determines the element percent of carbon, hydrogen, nitrogen, sulphur, and oxygen in the soursop seed. The ultimate analysis was carried out in accordance with ASTM standards E777 and E778 for the determination of carbon, hydrogen, and nitrogen, respectively, as validated in previous studies (Demirbaş, 2004). 2.2.3 SEM (Scanning Electron Microscopy) The morphological structure of the soursop seed was evaluated before paralyzing it using a scanning electron microscope (SEM) (JEOL-JSM 760F model, Japan) to investigate surface features at high magnification. The procedure involved collecting and cleaning mature seeds, air-drying them, sectioning them, drying them in a laboratory oven, mounting them on aluminum SEM stubs, applying a thin layer of gold to prevent charging, and imaging them under high vacuum. High-resolution micrographs were captured at various magnifications, observing surface features like texture, cracks, pores, and fibrous structures. The images were analyzed using SEM image analysis software to support further interpretation of the seed's structure and potential influence on pyrolysis behavior. 2.3 Experimental Procedures of the Fixed Bed Pyrolysis System The pyrolysis of soursop seed was carried out in a lab-scale fixed-bed pyrolyzer. The reactor is made of a stainless steel tube with a height of 300 mm, an internal diameter of 94 mm, and an external diameter of 100 mm. We introduced 50 grams of soursop seeds, with particle sizes ranging from 1 mm to 6 mm, into the reactor for pyrolysis. The reactor was sealed with a lid and steam gasket to ensure gas impermeability. Nitrogen gas was purged downstream of the tubular reactor at a flow rate of 1.0, 1.25, and 1.5 L/min, respectively, to establish an inert atmosphere. We adjusted the electrical control panel to heat the heaters to 400, 500, and 600°C, respectively. A thermocouple was positioned at the reactor's apex to gauge the internal temperature. The reactor's temperature was periodically checked with a thermocouple as a safety measure. The volatile products created in the reactor were purged downward and flowed into the condenser. We established the condensation apparatus by immersing the filtering flask in cold water. The liquid oil and solid char were recovered separately, while non-condensable gas was vented into the atmosphere. This technique was repeated according to the number of runs specified in the experimental design. Each experiment ran for 30 minutes. We weighed the bio-oil and char using a digital scale, documented the results, and securely stored them in separate, well-sealed containers at room temperature. The yield percentages of pyrolysis products were determined using mass balance equations consistent with those reported in previous studies (Miandad et al., 2016; Bridgwater, 2012). % 𝑌𝑖𝑒𝑙𝑑 𝑜𝑓 𝐵𝑖𝑜 − 𝑜𝑖𝑙 = X4 − X3 weight of feedstock X 100 (5) % 𝑌𝑖𝑒𝑙𝑑 𝑜𝑓 𝐵𝑖𝑜𝑐ℎ𝑎𝑟 = X2 − X1 weight of feedstock X 100 (6) % 𝑌𝑖𝑒𝑙𝑑 𝑜𝑓 𝐵𝑖𝑜𝑔𝑎𝑠 = 100 – (% yield of Bio-oil + % yield of Biochar) (7) where X1 = Weight of empty reactor, X2 = Weight of reactor after reaction, X3 = Weight of empty measuring cylinder, X4 = Weight of measuring cylinder with bio-oil. 2.4 Design of Experiment The experimental design and statistical analysis were performed according to the RSM (Response surface methodology) using Design-Expert software (Version 13). Box-Behnken design was employed to study the combined effect of three independent variables, namely, temperature, particle diameter, and inert gas flow, on the yield and quality of the oil. A synthetic medium for bio-oil production by pyrolysis was optimized using the design of experiment and response surface methodology (RSM). The effects of components were investigated by the design of experiment. The statistical model was constructed via Box-Behnken design using the selected variables. The temperature, particle diameter, and inert gas flow was used as the independent variables and bio-oil yield was the response variables with 17 experimental runs. http://www.azojete.com.ng/ about:blank Arid Zone Journal of Engineering, Technology and Environment, June 2025; Vol. 21(2): 529-544. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: chijiokeugwuodo@mouau.edu.ng 533 2.5 Characterization of Bio-oil 2.5.1 GC-MS (Gas Chromatography-Mass Spectrometry) Gas chromatography-mass spectroscopy analysis was carried out on a Perkin Elmer Turbo Mass Spectrophotometer (Norwalk, CTO6859, and USA) incorporating a Perkin Elmer Auto sampler XLGC. The column utilized was Perkin Elmer Elite -5 capillary column measuring 30m × 0.25mm with a film thickness of 0.25mm consisting of 95% Dimethyl polysiloxane. The carrier gas employed was helium at a flow rate of 0.5 mL/min. A sample injection volume of 1μl was employed. The inlet temperature was sustained at 250°C. The oven temperature was initially set to 110°C for 4 minutes, followed by a rise to 240°C. Subsequently, set to escalate to 280°C at a rate of 20°C, concluding with a duration of 5 minutes. The total duration was 90 minutes. The MS transfer line was sustained at a temperature of 200°C. The source temperature was sustained at 180°C. GCMS was analyzed using electron impact ionization at 70 eV, and data was reviewed using total ion count (TIC) for compound identification and quantification. The spectra of the components were compared with the database of known component spectra recorded in the GC-MS library. Peak area measurements and data processing were conducted using Turbo-Mass OCPTVS-Demo SPL software. 2.5.2 FTIR (Fourier Transform Infrared Spectroscopy) This study conducted a functional group analysis of the bio-oil utilizing Fourier transform infrared (FTIR) spectroscopy, Magna-IR550 (Nicolet, Madison). An online pen plotter was utilized with FTIR to generate the obtained liquids' infrared (IR) spectra. A minimal bio-oil will be applied to a potassium bromide (KBr) disc. The FTIR spectrum will be measured and recorded in the 500-3500 cm-1 region. The absorption frequency spectrum is recorded and plotted. The standard IR spectra of 61 organic compounds will be utilized to identify the functional groups present in the components of the generated bio-oil (Rajia et al., 2023). 3. Results and Discussion 3.1 Results of Characterization of Soursop Seed 3.1.1 Proximate analysis result Table 1 displays the proximate analysis results for the Soursop seed sample. The samples' values for ash content, moisture content, volatile matter, and fixed carbon were 3.75, 9.40, 15.97, and 70.88 (%w/w), respectively, while the energy value was 4480 kcal/kg. The Soursop seed proximate analysis showed that it contained more fixed carbon and volatile matter. These components are essential for oil production (Siqueira et al., 2008). The presence of low concentrations of moisture content and ash content favours the pyrolysis process considerably. This result is in close agreement with Abel et al. (2020); Pedro et al. (2022); and Anas et al. (2024). Table 1: Proximate Analysis of the Soursop seed Item % value Ash content 3.75 + 0.2 Moisture content 9.40 + 0.2 Energy Value (Kcal/kg) 4480 Volatile content 15.97+ 0.3 Fixed carbon content 70.88+ 0.2 3.1.2 Ultimate analysis result The elemental compositions of the samples were evaluated by final analysis. As stated in Table 2, the values for Carbon, Hydrogen, Nitrogen, Oxygen, and Sulphur in the sample were 51.29, 5.90, 0.50, 42.30, and 0.01 mass% correspondingly. The elevated carbon and oxygen content signifies that soursop seed consists of highly polar structures, which is advantageous for the ion exchange adsorption mechanism. Similarly, the low sulfur and nitrogen content may mitigate the accumulation of hazardous compounds in the reactor compartment, reducing the pyrolysis plant's maintenance requirements. The low sulphur and nitrogen could lead to reduced SOx and NOx gases which severely impact the troposphere's ozone layer (Ayeni et al., 2018). This compared well with Ayeni et al., (2018); Anas et al. (2024); Narayan et al. (2021). http://www.azojete.com.ng/ about:blank Arid Zone Journal of Engineering, Technology and Environment, June 2025; Vol. 21(2): 529-544. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: chijiokeugwuodo@mouau.edu.ng 534 Table 2: Ultimate Analysis of Soursop seed Elements Wt % Carbon 51.29+ 0.1 Hydrogen 5.90 + 0.3 Nitrogen 0.50 + 0.2 Oxygen 42.30+ 0.1 Sulphur 0.01 + 0.2 3.1.3 Scanning electron microscopes (SEM) of Soursop seed The morphology of the raw soursop seed was investigated by SEM, as shown in Plate 1, and the micrographs using a magnification of 8000x show a pore size of 100 μm. The surface area and pore volumes are very important factors because the material's degradation efficiency depends on their surface areas. The image has a rough surface with tiny pores and oil-like droplets on the surface. The surface area and pore volumes offered more significant areas of contact during pyrolysis reactions, making the material's rate of devolution faster. The seed's high surface area and oil-like droplets indicate a rich volatile content, contributing to higher bio-oil yield during pyrolysis. Greater pore volume allows for easier diffusion of volatile gases, reducing secondary cracking reactions and preserving desirable pyrolysis products. The material's surface roughness and porosity indicate rapid heat transfer and decomposition, enhancing thermal degradation efficiency, making it ideal for fast pyrolysis systems with short reaction times. This is similar to report of Abel et al. (2020); Adekanmi and Olowofoyeku, (2020). Plate 1: SEM micrographs of soursop seed 3.2 Results of Characterization of the Bio-Oil 3.2.1 Gas chromatography and mass spectrometry (GC-MS) analysis for the bio-oil Figure 1 presents the bio-oil chemical composition achieved using GC-MS, and the concentration of these acids are as shown in Table 3. The compositions of the pyrolytic bio-oil were evaluated considering the relative peak area of identifying constituents using the obtained chromatogram from GC–MS analysis, which agrees with that presented by Laouge et al. (2020). The bio-oil has a total of eight peaks and is made up of Tetradecanoic Acid, Hexadecanoic Acid, Heptadecanoic Acid, 9- Octadecadienoic Acid, Octadecadienoic Acid, Eicosanoic Acid, Docosanoic Acid, and Heptacosanoic Acid. However, the most significant fatty acid in the bio-oil is 9, Octadecadienoic Acid (45.55%), Hexadecanoic Acid (27.80%), and Octadecadienoic Acid (24.30%), while Heptadecanoic Acid has the least concentration of 0.50%. Since linoleic is the predominant monounsaturated fatty acid and makes up 45.55% of the total fatty acid content, the oil belongs to the linoleic acid group (Sonntag, 2012). Linoleic acid is important for bio-oil applications because its chemical structure and properties contribute to fuel performance, stability, and reactivity. Its double bonds make it more prone to oxidation, contribute to lower pour points and better cold-flow properties, and during pyrolysis, linoleic acid produces a mix of hydrocarbons useful for biofuel applications, influencing bio-oil composition. This result compared well with the report of Okokpujie et al. (2023), Álvarez-Chávez et al. (2019), and Ude et al. (2023). http://www.azojete.com.ng/ about:blank Arid Zone Journal of Engineering, Technology and Environment, June 2025; Vol. 21(2): 529-544. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: chijiokeugwuodo@mouau.edu.ng 535 Figure 1: GC-MS of bio-oil compound produced from soursop seed Table 3: Fatty acid composition of Soursop seed oil S/No Retention Time Fatty Acid Present Compound Names Molecular Formular % Concentration 1. 12.75 Tetradecanoic Acid Myrisic Acid C14H28O2 0.90 2. 14.88 Hexadecanoic Acid Palmitic Acid C16H32O2 27.80 3. 15.80 Heptadecanoic Acid Margaric Acid C17H34O2 0.50 4. 16.60 9, Octadecadienoic Acid Linoleic Acid C18H31O2 45.55 5. 16.80 Octadecadienoic Acid Stearic Acid C18H34O2 24.30 6. 18.80 Eicosanoic Acid Arachidic Acid C20H40O2 12.60 7. 20.10 Docosanoic Acid Behenic Acid C22H44O2 1.05 8. 21.60 Heptacosanoic Acid Cosnic Acid C27H54O2 0.65 3.2.2 FTIR analysis result of the bio-oil Figure 2 depicts the FTIR spectrum of soursop seed oil. This research was undertaken to identify the different functional groups in the bio-oil. The investigation indicates the presence of =C-H functional groups (alkenes) at a precise frequency of 1036.2 cm-1. They all display double-bonded bending-type vibrations within the spectrum's low energy and frequency range. They are ascribed to functional groups comprised of unsaturated olefinic chemicals (alkenes). These substances could be components of the methyl esters from unsaturated fatty acids found in bio-oil. The presence of the esters enhances combustion efficiency. It reduces particulate emissions, improves the fuel's lubricating properties, and is biodegradable and sourced from renewable feedstocks. Esters have minimal sulfur and aromatic content, lowering harmful emissions. The peaks detected at 1068.2 cm-1 are attributed to the stretching vibrations of the C-O and C-O-C molecules. The band observed at 1378.2 cm-1 is associated with the bending vibrations of C=C bonds. In contrast, the band region at 1148.1 cm-1 is associated with the bending vibrations of C-H methyl groups. An assortment of fragrant chemicals is identified within the wavelength range of 1626.3cm-1. The existence of the 2926.1 cm-1 area indicates the presence of a carboxylic acid with an elongated O-H group. The peaks observed at 2968.1 cm-1 indicate the symmetric stretching vibrations of the C-H alkane groups. Both methyl (CH3) and methylene groups require substantial energy to generate stretching vibrations in their bonds. By contrast, alkene groups' usual C-H bending vibrations are detected at lower energy and frequency ranges. The peak observed at 3390.2 cm-1 is ascribed to the stretching vibration of alkene groups with =C-H bonds. The alkene group provides higher reactivity and combustion efficiency, enhances the bio-oil's octane number and lowers the oil's boiling point. This result aligns with the report of Ishaq et al. (2020). http://www.azojete.com.ng/ about:blank Arid Zone Journal of Engineering, Technology and Environment, June 2025; Vol. 21(2): 529-544. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: chijiokeugwuodo@mouau.edu.ng 536 Figure 2: FT-IR spectrum of Pyrolysis of soursop seed for bio-oil 3.3 Response Surface Methodology Modeling For Bio-Oil Yield The RSM- BBD results for the percentage yield of bio-oil from the pyrolysis process are presented in Table 4. Using design expert software version 13.0.6 USA, three variables, temperature, particle size, and gas flow rate represented by the alphabet A, B, and C, and three levels (-1, 0, and +1) were considered, resulting in seventeen experimental runs. The variation in values of the percentage yield of bio-oil indicated that the process parameters significantly affect the pyrolysis process. The maximum percentage yield of 33.1% occurred at a temperature of 5000C, particle size diameter of 6mm, and inert gas flow rate of 1.0L/min. Second-order polynomial (quadratic) equations in coded terms that defined the independence between the studied process variables (A, B, C) and the response (% yield) for bio-oil were obtained with the use of RSM and represented in Equations (8). Table 4: BBD experimental design for bio-oil yield Factor 1 Factor 2 Factor 3 Response 1 Std Run A:Temperature B:Particle size C:Inert gasflow Oil yield C Mm L/min % 10 1 500 6 1 33.1 6 2 600 4 1 30 8 3 600 4 1.5 20.9 15 4 500 4 1.25 27.4 11 5 500 1 1.5 21 14 6 500 4 1.25 27.4 9 7 500 1 1 37 13 8 500 4 1.25 27.4 7 9 400 4 1.5 16.25 3 10 400 6 1.25 22.3 12 11 500 6 1.5 30.9 2 12 600 1 1.25 27.8 17 13 500 4 1.25 27.4 5 14 400 4 1 25.9 4 15 600 6 1.25 22 16 16 500 4 1.25 27.4 1 17 400 1 1.25 17.2 3.3.1 Oil yield model statistical analysis Analysis of variance (ANOVA) was used to determine the statistical significance of the model and all the regressed coefficients of the developed quadratic RSM-BBD model for the % of bio-oil yield. The ANOVA results for measuring model adequacies for bio-oil yield are shown in Table 5. The high F – value (61.67), P- value (<0.0001), and non-significant lack of fit (p-value> 0.05) indicated that the model was significant (Hidayat et al., 2023). The fitness of the model was expressed by the coefficient of determination (R2) obtained as 0.9875 with an adjusted R2 value of 0.9715, which was in reasonable agreement with the predicted R2 value of 0.8007 for the developed model, i.e., the difference is less than 0.2. This showed that the developed quadratic model could predict the observed experimental data within the range of study (Kamarudin et al., 2019). The adequate precision value of 30.8050, a measure of the signal-to-noise ratio greater than 4, showed the model's desirability and indicated an adequate signal. These regression values agreed with the report of (Malatji et al., 4400.0 4000 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 350.0 -3.0 -2 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34.5 cm-1 %T 4336.51 4260.61 4196.00 3781.00 3409.00 3344.00 3002.00 2927.00 2861.71 2676.33 2363.66 2038.00 1725.62 1450.95 1239.00 1174.22 951.63 719.34 416.39 379.17 http://www.azojete.com.ng/ about:blank Arid Zone Journal of Engineering, Technology and Environment, June 2025; Vol. 21(2): 529-544. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: chijiokeugwuodo@mouau.edu.ng 537 2020), who suggested that a high correlation confirms the degree of fitness between the predicted and experimental responses. Therefore, this model can be used to navigate the design space. The linear terms (A and C), the quadratic terms (A2, B2, and C2), and the interactive effects of temperature: particle diameter (AB and BC) were all significant (p < 0.05) in the oil yield response model. As the process parameters increased, the positive and negative signs in Equation 8 showed an increase and decrease in values of response. Increased A, B, C, AB, AC, and BC led to increased response, while increased A2, B2, and C2 values resulted in decreased response. To check for the normality of the residuals, the normal probability versus studentized residuals plot was considered in Figure 3a. It showed that the points extended well diagonally, suggesting they dispersed in a reasonable pattern. The relationship between the predicted and observed actual experimental data in Figure 3b gave a straight line, which showed agreement with each other. Hence, the experimental data is acceptable (Reglioua et al., 2021). Table 5: ANOVA results for BBD-RSM Bio-oil Yield Source Sum of Squares df Mean Square F-value p-value Model 471.70 9 52.41 61.67 < 0.0001 significant A-Temperature 45.36 1 45.36 53.37 0.0002 B-Particle size 3.51 1 3.51 4.13 0.0816 C-Inert gasflow 170.66 1 170.66 200.80 < 0.0001 AB 29.70 1 29.70 34.95 0.0006 AC 0.0756 1 0.0756 0.0890 0.7741 BC 47.61 1 47.61 56.02 0.0001 A² 159.58 1 159.58 187.76 < 0.0001 B² 4.92 1 4.92 5.79 0.0470 C² 17.16 1 17.16 20.19 0.0028 Residual 5.95 7 0.8499 Lack of Fit 5.95 3 1.98 not significant Pure Error 0.0000 4 0.0000 Cor Total 477.65 16 Std. Dev. 0.9219 R² 0.9875 Mean 25.96 Adjusted R² 0.9715 C.V. % 3.55 Predicted R² 0.8007 Adeq Precision 30.8050 Oil Yield (%) = +27.40 + 2.38A +0.6625B - 4.62C - 2.73AB + 0.1375AC + 3.45BC - 6.16A2 + 1.08B2 + 2.02C2 (8) Figure 3: (a) Normal % probability vs. internal studentized residuals. (b) Predicted vs. Actual values 3.3.2 Interaction Effects of Process Parameters on Bio-oil Yield To better understand the pyrolysis process for oil yield, contour and 3D response surface plots were studied. These plots were used to study the interaction effects of the pair of independent variables, keeping other variables constant. The 3D response surface plots are the graphical representation of the regression equation used to observe the levels of each factor (Mabrouka et al., 2023). The interactive terms considered are a b http://www.azojete.com.ng/ about:blank Arid Zone Journal of Engineering, Technology and Environment, June 2025; Vol. 21(2): 529-544. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: chijiokeugwuodo@mouau.edu.ng 538 temperature and particle diameter (AB), temperature and inert gas flow (AC), and particle diameter and inert gas flow (BC). The interaction between temperature and particle diameter (AB) at the fixed inert gas flow on the % yield of bio-oil is shown in Figure 4. The maximum percentage (28.8%) of bio-oil yield was achieved at a temperature of 550 0C and a particle diameter of 1mm. At temperature levels >4000C, a steady, significant increase in bio-oil yield was observed from particle diameter 1 mm to 6 mm. In contrast, no significant increase in yield was observed at temperature levels > 5500C. Hence, an increase in AB up to A=5500C and B =6 mm increased by % of bio-oil yield. This may be attributed to the fact that below the point of 4000C the devolatilization of the feedstock has not started in full force. At this point, depolymerization is still occurring. A similar trend was recorded by Ganapathy et al. (2009). Figure 5 shows the contour and 3D surface response plot interaction of the combined effect of temperature and inert gas flow rate (AC) of bio-oil yield. Increase in A resulted in an increase % yield of bio-oil up to 550 0C after which the yield started decreasing gradually, while increase in B resulted in a significant decrease in the yield of the bio-oil. This may be as result of complete devolatilization of the biomass at the temperature level of 550 0C and the ability of the gas to purge the volatile oil out of the condenser before condensation takes place. This correspond to work done by Álvarez-Chávez et al. (2019). The interaction between particle diameter and inert gas flow rate (BC) was presented in Figure 6. An increase in BC resulted in a decrease in % yield of bio-oil. While the decrease is insignificant for an increase in particle size, it is significant for an increase in inert gas flow. This may be because complete devolatilization of the biomass has occurred, and at this point, secondary cracking of the biomass only leads to the formation of more gases and char, similar to the report of (Álvarez-Chávez et al., 2019). Figure 4: Contour and 3D plots on the effect of particle diameter and temperature on bio-oil yield Figure 5: Contour and 3D plots on the effect of inert gas flow and temperature on bio-yield http://www.azojete.com.ng/ about:blank Arid Zone Journal of Engineering, Technology and Environment, June 2025; Vol. 21(2): 529-544. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: chijiokeugwuodo@mouau.edu.ng 539 Figure 6: Contour and 3D plots on the effect of inert gas flow and particle diameter on bio-oil yield 3.4 Optimization of the Process Table 6 visualizes the optimization criteria set on the Design Expert software to achieve maximum yield and purity responses. The software default weighted coefficient for both independent and dependent variables was tabulated accordingly in Table 6. Temperature, particle diameter and inert gas flow were fixed within their experimental ranges of 400-600oC, 1-6 mm, and 1.5-1.5 L/min respectively, and the oil yield were optimized within the range. The optimum operating region for yield optimization was obtained using the desirability function algorithm of Box Behnken design. Figure 7 shows values of 461.84oC, 3.84mm, and 1.02L/m as the optimum mixture for temperature, particle size, and inert gas flow, respectively. These values give optimum yield responses (31.43%) with a desirability of 1. Figure 8 presents the desirability values of the optimized condition for optimization of the bio-oil and biochar yield. It shows 1, 1, 1, and 1 as desirability values of temperature, particle size diameter, inert gas flowrate, and oil yield, respectively, 1 was obtained as combined desirability. The combined desirability for n number of responses is obtained from Equation 9. D(x) = (d1 x d2 x …. dn)1/n 9 Table 7 gives the first 10 optimum conditions, responses to bio-oil and biochar yield, and the desirability for the criteria indicated in Table 7. For this current study, the first of the solutions was adopted. Table 6: Constraints for optimization of yield and purity responses. Name Goal Lower Limit Upper Limit Lower Weight Upper Weight Importance A: Temperature is in range 400 600 1 1 3 B: Particle diameter is in range 1 6 1 1 3 C: Inert Gas flow is in range 1 1.5 1 1 3 Oil yield is in range 16.25 37 1 1 3 http://www.azojete.com.ng/ about:blank Arid Zone Journal of Engineering, Technology and Environment, June 2025; Vol. 21(2): 529-544. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: chijiokeugwuodo@mouau.edu.ng 540 Figure 7: Numerical optimized value of the dependent and independent variables with desirability Figure 8: Desirability plot for the yield of bio-oil Table 7: First 10 solutions for optimization of yield and purity with desirability Number Temperature Particle size Inert gas flow Oil yield Desirability 1 461.840 3.842 1.020 31.428 1.000 Selected 2 600.000 6.000 1.250 22.644 1.000 3 500.000 3.500 1.250 27.400 1.000 4 600.000 1.000 1.250 26.769 1.000 5 500.000 1.000 1.500 21.769 1.000 6 600.000 3.500 1.000 30.125 1.000 7 400.000 3.500 1.000 25.637 1.000 8 500.000 6.000 1.000 32.331 1.000 9 400.000 1.000 1.250 16.556 1.000 10 600.000 3.500 1.500 21.163 1.000 3.4.1 Model validation Validation experiments were conducted at the optimal conditions suggested by RSM to confirm the model's reliability. Triplicate experiments were done and average bio-oil obtained was 29.6%. The experimental bio- http://www.azojete.com.ng/ about:blank Arid Zone Journal of Engineering, Technology and Environment, June 2025; Vol. 21(2): 529-544. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: chijiokeugwuodo@mouau.edu.ng 541 oil yields were in close agreement with the predicted values, with percentage errors less than 10%, demonstrating the model’s accuracy in practical applications. 4. Conclusion This study investigated the pyrolysis of soursop (Annona muricata) seeds in a fixed-bed reactor to optimize bio-oil production and assess its potential as a renewable energy source. The findings highlight that process parameters, like temperature, particle size, and inert gas flow rate, significantly influence bio-oil yield and composition. The optimal conditions for maximizing bio-oil production were determined using Response Surface Methodology (RSM), with a temperature of 461.84°C, a particle size of 3.84 mm, and an inert gas flow rate of 1.02 L/min, yielding 31.43% bio-oil. Characterization of the bio-oil through GC-MS and FTIR analysis confirmed the presence of valuable compounds, including linoleic acid, palmitic acid, and esters, making it a potential feedstock for biofuel production. The study underscores the viability of soursop seed pyrolysis as an environmentally friendly waste management strategy and a means of generating sustainable biofuels. Additionally, the biochar produced presents opportunities for soil enhancement and carbon sequestration. While this research contributes valuable insights into optimizing bio-oil yield, further studies are recommended to enhance bio-oil stability, refine upgrading techniques, and evaluate large-scale production feasibility. 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