Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5, 578-597 2024 Publisher: Learning Gate DOI: 10.55214/25768484.v8i5.1719 © 2024 by the authors; licensee Learning Gate © 2024 by the authors; licensee Learning Gate * Correspondence: prakash.ramakrishnan@vit.ac.in Experimental and numerical analysis of particulate matter deposition in DPF for different blends of Algae Bio diesel Jayant Nalawade1, Prakash Ramakrishnan2* 1,2School of Mechanical Engineering Vellore Institute of Technology, Vellore, Tamil Nadu, India – 632 014; prakash.ramakrishnan@vit.ac.in (P.R). Abstract: The stringent emission norms have compelled to use Diesel particulate filter (DPF) to reduce particulates emission in automotive Diesel engine. The back pressure developed in the DPF due to the Particulates matter (PM) deposition in porous zone degrades the diesel engine’s performance. So, there is a need of a fuel which should produce less particulates on combustion as well as its produced particulates should be easy to get regenerated inside the DPF. Three different Algae biodiesel blends B20, B40 and B60 were selected for the study, while the results are compared with the diesel fuel. A numerical study has been done with diesel and Algae biodiesel blends to investigate the effect of PM deposition on the DPF performance. The results of PM concentration and the pressure drop has been predicted for t = 2000s. and compared with the results predicted for diesel fuel. The summarized velocity contours and the PM concentration plots show that a maximum of the PM concentration was found in the diesel fuel case, while the lower found in B60 algae blend. Also, the pressure drop was found to increase with the increase in PM deposition in each case of fuel. The SEM-EDS analysis is done after collecting PM samples after combustion shows a higher percentage of Oxygen and lower Carbon with an increment of Biodiesel in the blend. This work provides a brief idea about the PM deposition in DPF while using Diesel and Algae Biodiesel blends and highlighting the better fuel option for Diesel Engine. Keywords: Diesel particulate filter (DPF), Energy dispersive X-ray spectroscopy (EDS), Particulate matter (PM) deposition, Particulate matter oxidation, Regeneration, Scanning electron microscopy (SEM). 1. Introduction There are rising concerns about the emission from automobiles due to their contrary health effects and global climate change. Diesel engines are commonly used for heavy-duty vehicles as a power source due to their low fuel consumption, reliable performance and durability [1]. Unfortunately, these vehicles can produce a considerable number of emissions which causes the air pollution. The exhaust emissions of high-power diesel vehicles include the harmful nanoparticles of carbon monoxide, nitrates, hydrocarbons NOx, and particulate matter (PM), which are detrimental for human beings [2]. PM contains a fraction of soluble organic particles and soot. Particulate matter from the exhaust of diesel engines vehicles is carcinogenic in nature, which can threaten human life [3]. Diesel Particulate Filter (DPF) is commonly measured as the most effective after-treatment device to reduce and control harmful emissions particles. At present, many emission control methods like alternative fuels and electric vehicle are used to resolve the environmental pollution problem with stringent emission standards [4]. Many studies reported that, alternative fuels such as hydrogen, ethanol and biodiesel are less emission producing fuels [5]. Bio-diesel produced from vegetable oils, animal fat, waste oil are renewable fuels and contains methyl esters of fatty acids. A life cycle analysis indicates that biodiesel can reduce up to 19% of life cycle petroleum consumption [6]. It is well-known that neat biodiesel or its blends in 579 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 578-597, 2024 DOI: 10.55214/25768484.v8i5.1719 © 2024 by the authors; licensee Learning Gate various percentages have a great oxygen content or higher air entrainment due to a higher boiling range [7]. Hence, bio-diesel can reduce the level of PM mass emission. Nowadays, the DPF technology, an after-exhaust treatment technique, is an effective method for Particulate matter control to meet current and future government regulations [4]. The diesel engine furnished with DPF arrangement has lower PM, as it has the maximum filtration efficiency. However, the continuous deposition of particles inside the wall of DPF leads to the engine's poor performance due to increased engine back pressure [8]. The engine will ultimately stop its function once the back pressure extends its limit. Hence, there is a need to take action for removing the accumulated particles. The process of eliminating accumulated particles from the DPF is known as oxidation of PM or regeneration. Three different techniques viz. passive, active and mixed passive-active regeneration, are used to oxidize particulate matters, particularly DPF [4, 9–11]. A pre-catalyst activity can be helpful for dropping the regeneration temperature in a passive type regeneration, which further reduces the DPF regeneration time [12–14]. While in active technique, support of fuel burner or external heater was employed to enhance the inlet temperature to ~550 °C or higher. The mixed type method is relevant if the filters are catalyzed and pre-heated. Different researchers are fascinated towards the blended fuels like biodiesel and ethanol with diesel to lessen the particles and gaseous emissions. On the other hand, many studies concentrated on the use of DPF with the Particulate matter regeneration process. Tsuneyoshi et al. [15] designed a hexagonal pore filter to decrease the emission concentration and, ultimately the backpressure in the DPF. They found higher regeneration efficiency compared with the quadrilateral pore structure. Fang et al. [16] examined the effect of operating parameters such as exhaust flow rate on the regeneration process of DPF. They concluded with the negative impact of exhaust flow rate on maximum temperature gradient and regeneration ratio without considering the oxygen content. Wu et al. [17] explored the flow characteristics of exhaust at the entry region of DPF. They found a higher deposition or gathering of Particulate matter particles at the center part of the DPF filter. Similarly, Gong et al. [18] specified a non-uniform Particulate matter particle distribution but diverges in various regions of DPF in filter substrate for both lower and higher particle concentrations. Fernandez et al. [12] verified different fuels experimentally in an assembly of Euro 5 engine and DPF. Four types of fuels viz. diesel, e-diesel, paraffinic and biodiesel are considered for the analysis. Results shows a better capability of Particulate matter from paraffinic and oxygenated bio-fuels lower oxidation temperature, while e-diesel Particulate matter was found less sensitive to the oxygen concentration. They also mentioned that biodiesel is more cost-effective regeneration through an active process due to its more reactive nature than the other fuel tested. Several experimental and numerical studies have been conducted to understand the influences of different exhaust conditions on the deposition of Particulate matter in DPF. Yamamoto et al. [19] did numerical simulations on Particulate matter deposition in DPF. They used a lattice Boltzmann method to investigate the flow behavior in real DPF. A 3D X-ray method was used to scan the structure of cordierite filter. Both, Particulate matter deposition for particulate matter trap and the Particulate matter combustion were examined using numerical simulations. They concluded the flow path and change in pressure inside the cordierite filter structure were depends on the non-uniformity of the pore structure. The flow field changes significantly with Particulate matter deposition, with higher back- pressure. Further, in 2012, Yamamoto and Yamauchi [20] presented a numerical study on Particulate matter deposition and regeneration. They suggested that, the temperature of the filter was kept 800 K or higher to avoid the clogging in DPF to achieve a continuous regeneration. Ben said et al. [21] did an experimental study to investigate the Particulate matter deposition in DPF channels. A geometric analysis inside the DPF in regards with Particulate matter deposition profiles and regeneration was presented. A mathematical model was used to simulate the similar phenomenon and results were validated with the experimental results. Mahadevan et al. [22] investigated effect of temperature and PM distribution on catalyzed DPF, experimentally and numerically. A multi-zone particulate filter 580 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 578-597, 2024 DOI: 10.55214/25768484.v8i5.1719 © 2024 by the authors; licensee Learning Gate (MPF) model was used for the numerical analysis to examine the temperature and PM distribution inside the DPF. They validate the model with experimental results. The above literature indicated that different researchers worked on the effect of DPF structural parameters and exhaust characteristics on the flow field, temperature field, and Particulate matter deposition rate. Also, some literature found on the use of alternative fuels such as biodiesel on the Particulate matter deposition and ultimately DPF performance. This paper aims to investigate the exhaust characteristics using different fuels (diesel and Algae bio-diesel) and their effect on the Particulate matter deposition inside the DPF. The exhaust characteristics can be found experimentally and their results were used to simulate the Particulate matter deposition phenomenon in DPF. The Particulate matter concentration and respective pressure drop were predicted numerically. The soot characterization is done using SEM-EDS to check the chemical content and morphology of PM samples. 2. Materials and Methods 2.1. Fuels and Their Properties The exhaust characteristics of two fuels Diesel, and Algae Bio-diesel have been analyzed in experimental test. Chlorella vulgaris, green-colored eukaryotic based micro-algae is used to extract the biodiesel. Preparation of biodiesel from crude algae oil by Transesterification, it is a process of using methanol (CH3OH) in the presence of catalyst (Potassium hydroxide KOH) to chemically break their molecule of crude algae oil into ester and glycerol. This process is a reaction of the oil with an alcohol to remove the glycerin, a by-product of biodiesel production. The different properties of the neat bio- diesel and diesel were figure out with different in-house equipment’s in the lab. Table 1 explains the properties of the diesel and neat bio-diesel. Three different blends of Algae bio-diesel with different volume fraction named B20, B40 and B60 was considered for the analyses and results were compared with diesel fuel. Table 1. Fuel properties of the algae biodiesel and the diesel. Property Diesel B20 B40 B60 B100 Calorific value (MJ/kg) 45.5 44.58 43.67 42.76 40.93 Flash point (◦ C) 52.2 69.96 87.72 105.48 141 Fire point (◦ C) 96.1 106.48 116.86 127.24 148 Cloud point (◦ C) - 12 -10 -8 -6 -2 Pour point (◦C) - 18 -16.6 -15.2 -13.8 -11 Density ( kg/m3 ) 832 857.6 883.2 908.8 960 Kinematic viscosity (m2/s) 4.09× 10^-6 4.42× 10^-6 4.76× 10^-6 5.10× 10^-6 5.77× 10^-6 Dynamic viscosity (kg.m/s) 0.00335 0.00375 0.00422 0.00467 0.00554 2.2. Experimental Test Set Up An experimental test has been carried out to find exhaust characteristics and pressure drop in a DPF for different fuels used in analysis. A two-cylinderAtalon vertical inline diesel enginewith a capacity of 21kW (28 bhp at 2000 rpm), connected to a 100bhp eddy current dynamometer as shown in Figure 1 was used. Different particulate matters or smoke for different samples of fuels was observed and recorded at constant engine operating conditions (1500 rpm and 100% of peak load).A mechanical Micro Bosch fuel injection pump and a multi-hole nozzle injector is used in the test bed.Incylinder pressure and its cyclic variation data was recorded by a Kistler 701A piezoelectric pressure transducer mounted on the cylinder head and the proportional crank angle was recorded using a rotatory encoder model E50S8-360-3-T-5 made by Autonics, Korea.A standard square channel DPF was used for the 581 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 578-597, 2024 DOI: 10.55214/25768484.v8i5.1719 © 2024 by the authors; licensee Learning Gate analysis of pressure drop. The details about the specification of the DPF used are provided in table 2. A schematic of the experimental test setup was shown in figure 1.In this research work, the pressure drop analysis for DPF with 100% loading condition for the validation of numerical results.The exhaust from the diesel engine is connected through pipe to DPF inlet. The exhaust characteristics were recorded at the inlet of the DPF. The temperature and velocity of the exhaust gas were recorded at inlet and outlet of the exhaust. The pressure drop was measured across the DPF.The parameter of combustion like knocking, heat release rate etc. was measured using the EPA 1.0.1 Engine Performance analyser software. The emission parameter such as CO2 and CO are measured in terms of percentage volume and the unburnt NOx and HC are recorded in terms of parts per million (ppm) using an AVL 444 gas analyser based on the non-dispersive infrared (NDIR) principle for before and after the DPF. An AVL 432C smoke meter was used to measure the smoke opacity. The PM samples are collected with the help of Glass Micro Fibre filter from the exhaust of each fuel for SEM-EDS analysis. Figure 1. Schematic of the experimental test setup. Table 2. technical specifications of the DPF. Characteristics Details Filter diameter 140 mm Filter length 150 mm Wall thickness 0.40 mm Filter density 450 kg/m3 Thermal conductivity of DPF Substrate 1.4 W/mK 3. Numerical Methodology for Particulate Matter Deposition A CFD-based model of Particulate matter deposition and regeneration was developed for a single- channel catalytic DPF.Figure 2shows a schematic of the two-dimensionalcomputational domain consisting of inlet channel, outlet channel and porous regions (wall of the filter and cake layer).When the engine exhaust gas flows from the inlet channel into the outlet channel through the porous filter wall, the Particulate matterincluding soluble organic matter (SOF) is trapped on the filter wall in the inlet channel, eventually the cleaned exhaust gas is discharged from the exhaust pipe into the atmosphere. 582 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 578-597, 2024 DOI: 10.55214/25768484.v8i5.1719 © 2024 by the authors; licensee Learning Gate Figure 2. Shows the computational domain for the analysis. The 2-D single channel is considered for the DPF Exhaust gas recirculation. The Input conditions are defined by exhaust gas data obtain from the diesel engine combustion for various blends. For all cases B20, B40 and B60 blend the exhaust conditions has been analyzed experimentally, which can be used for the analysis of Particulate matter deposition and Particulate matterregeneration. Thebiodiesel is mixed with fossil-based diesel fuel with volume basis at a proportion of 20%, 40%, and 60%as per requirement. The exhaust results have been recorded for the different loading conditions of the engine (100 % loading conditions). The speed of the engine (1500 rpm) was kept constant for all the cases. Two processes are included in the simulation. One is the Particulate matter deposition for PM trap in the DPF structure using experimental results of exhaust. The other is the Particulate matter combustion to examine the filter regeneration with the catalytic effect using deposition results. 3.1. Particulate Matter Deposition Model The DPF is basically filter, which is going to filter out the PM particle in diesel exhaust. Therefore, the simple porous media simulation is preferred. The cell zone conditions are defined according to the functions of the flow. The filtration area is considered as the porous region. The inlet flow is considered as turbulent flow, exhaust from the engine. The species model is considered for modeling the flow which is consisting of the CO2, CO, NO2 and O2 with the soot. For adding the Particulate matter to the inlet mixer to DPF, Particulate matter model is selected as Soot-Moss Brook. Thevolumetric reaction is considered with thermal diffusion and Eddy-Finite rate reaction. The Reaction takes place in the DPF is Oxidation reaction with catalyst SiC.Theoxidation of the CO is takes place producing CO2 also Particulate matter deposition is going to take place in the filter region. The Realizable K-↋ Model is selected for the simulation of the turbulent flow. The pressure conditions are kept atmospheric at inlet and outlet. Soot Moss Brook model is implemented to simulate the Particulate matter in the inlet mixture Yamamoto 2009 [20]. The slow oxidation process is taking place. The Arrhenius Equation is selected to represent the oxidation reaction with pre-expiation factor and activation energy (Darcy equation). A mass fraction of the particulate matter and smoke has been calculated from the experimental exhaust analysis report. The soot, O2, CO2 and CO mass fraction has been set with different conditions of the inlet fuel (Diesel and Algae bio-diesel). Velocity inlet and temperature was implemented as inlet boundary conditions. For all cases velocity of the exhaust was kept constant at 3 m/s, while a temperature is set to 700 K. Initial conditions for the DPF is set to constant for all cases. The Initial Conditions are mentioned in Table 3. Table 3. Initial boundary conditions Initial conditions Value Temperature at the DPF 300 K Particulate matter concentration at DPF 0 kg/m3 Filter Porosity 0.5 Viscous Resistance at porous Media 2.00 x E12 583 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 578-597, 2024 DOI: 10.55214/25768484.v8i5.1719 © 2024 by the authors; licensee Learning Gate 3.2. Mathematical Modeling The Particulate matter deposition model is used with the standard flow balance equations which are mentioned follow, The Continuity equation- 𝜕(𝜀𝜌) 𝜕𝑡 + ∇⃗⃗ (𝜌�⃗� ) = 𝑆𝜌 (1) Where ε is the local porosity (void fraction), ρ is the fluid density, V is the velocity (in the porous regions, it is the superficial velocity),andSρ is the mass source term a rising as a result of the Particulate matter consumption (it only applies in the porous regions). Momentum equations: 𝜌𝑢 𝜕𝑢 𝜕𝑥 + 𝜌𝑣 𝜕𝑢 𝜕𝑦 = 𝜕 𝜕𝑥 (𝜇𝑒 𝜕𝑢 𝜕𝑥 ) + 𝜕 𝜕𝑦 (𝜇𝑒 𝜕𝑢 𝜕𝑦 ) − 𝜕𝑃 𝜕𝑥 (2) 𝜕(𝜌𝑢𝑣) 𝜕𝑥 + 𝜕(𝜌𝑣2) 𝜕𝑦 = 𝜕 𝜕𝑥 (𝜇𝑒 𝜕𝑣 𝜕𝑥 ) + 𝜕 𝜕𝑦 (𝜇𝑒 𝜕𝑣 𝜕𝑦 ) − 𝜕𝑃 𝜕𝑦 (3) Species conservation: 𝜕(𝜀𝜌𝑦𝑖) 𝜕𝑡 + ∇⃗⃗ (𝜌�⃗� 𝑦𝑖 + 𝐽𝑖⃗⃗ ) = 𝑆𝑦𝑖 (4) Where yi, Ji and Syi are the mass fraction, diffusive mass flux and volumetric mass generation at i’th species, respectively. 3.3. Energy Conservation It was assumed that, in the porous regions, the fluid and solid (i.e., silicon carbide – SiC – for the wall and Particulate matter for the cake layer) phases are in local thermal equilibrium. This implies that, in any control volume, the fluid and solid have the same temperature. (𝜌𝑐𝑝𝑢) 𝜕𝑇 𝜕𝑥 + (𝜌𝑐𝑝𝑣) 𝜕𝑇 𝜕𝑦 = 𝑘 ( 𝜕2𝑇 𝜕𝑥2 + 𝜕2𝑇 𝜕𝑦2) + �̇� (5) Particulate Matter Conservation (Porous Zones): 𝜕𝑚 𝜕𝑡 = −𝜔𝑠̇ (6) Where m is the local Particulate matter concentration [kg/m3] and ωs is the Particulate matter reaction rate [kg/m3 s]. Particulate matter Moss Brook Equation for Particulate matter deposition in Porous media- The Moss-Brookes model solves transport equations for normalized radical nuclei concentration and Particulate mattermass fraction: 𝜕 𝜕𝑡 (𝜌𝑌𝑠𝑜𝑜𝑡) + ∇. (ρ𝑣 𝑌𝑠𝑜𝑜𝑡) = ∇. ( 𝜇𝑡 𝜎𝑠𝑜𝑜𝑡 ∇𝑌𝑠𝑜𝑜𝑡) + 𝜕𝑀 𝜕𝑡 (7) 𝜕 𝜕𝑡 (𝜌𝑏𝑛𝑢𝑐) + ∇. (ρ𝑣 𝑏𝑛𝑢𝑐) = ∇. ( 𝜇𝑡 𝜎𝑛𝑢𝑐 ∇𝑏𝑛𝑢𝑐) + 1 𝑁𝑛𝑜𝑟𝑚 𝜕𝑁 𝜕𝑡 (8) 𝑏𝑛𝑢𝑐 = 𝑁 𝜌𝑁𝑛𝑜𝑟𝑚 584 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 578-597, 2024 DOI: 10.55214/25768484.v8i5.1719 © 2024 by the authors; licensee Learning Gate Were, Ysootis the Particulate matter mass fraction, 𝑏𝑛𝑢𝑐 radical nuclei concentration, NParticulate matter particle number density. To establish the accuracy of the CFD solution, and to keep the computational costs low a mesh independence study of the heat sink domain is important. Prior to performing the CFD simulations, a grid independence study was conducted over three grid resolutions. A grid independency study has been performed to set the minimum computation time required for the analysis. Hexahedral types cells were generated in structured grid. The structural mesh has aids over the unstructured type cell elements. Five different size grids have been used for similar boundary conditions to analyses soot concentration. Table 4 shows the details about the mesh with respect to their soot concentration results. An academic based ANSYS ICEM software was used for generating the mesh. Convergence criteria were set to 10-4 for continuity and momentum equation while 10-6 for energy equation. A fine grid size was used with an average skewness and orthogonal quality of 0.412 and 0.0.356, respectively with 1.52 Lac numbers of elements. It is found that, there is marginal difference between the surface temperatures with all three grid sizes. Table 4. Grid dependence test results with diesel fuel case. Sr. No. Element size Number of elements Soot concentration(kg/m3) 1 0.005 864230 0.8880 2 0.015 430602 0.8880 3 0.025 151308 0.8879 4 0.035 77247 0.8873 5 0.045 46903 0.8868 3.4 Validation of DPF Simulation Model In order to ensure the accuracy of the numerical model, it is necessary to verify the results of the model with experimental test results. A pressure drop across the DPF was validated with the experimental results. The results of pressure drop from simulation and experimental test setup was shown in Figure 3. The pressure drop increases with time, as the trapped soot clogs the pores of the fibre material, reducing the void fraction. The greater the concentration of soot, the greater the pressure drop. A rather good agreement is established between the numerical data and the experimental results. Porous materials, such as fibre quartz and silicon carbide (SiC), are better suited for the monolithic catalytic filter, in which exhaust gas is driven through the porous wall.The error between the simulation model and the experimental test results is small and the path of the pressure is generally consistent with the testing values. However, the small difference in the results due to the selected DPF for experimental test setup was merely used for short time period. Hence, the test values are slightly higher than the simulated values. Hence, this model can be used to predict the pressure drop in DPF. 585 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 578-597, 2024 DOI: 10.55214/25768484.v8i5.1719 © 2024 by the authors; licensee Learning Gate Figure 3. Validation of the numerical model with experimental results. 4. Results 4.1. Emission Results of Diesel and Algae Bio-Diesel Blends The emission parameter such as CO2 and CO are measured in terms of percentage volume and the unburnt NOx and HC are recorded in terms of parts per million (ppm) using an AVL 444 gas analyser based on the non-dispersive infrared (NDIR) principle. An AVL 432C smoke meter was used to measure the smoke opacity. All the measurements were taken for 100% loading conditions and without EGR condition. The results exhaust for all fuels (diesel, B20, B40 and B60) are tabulated in Table 5. The results were used to account the inlet conditions for the numerical analysis. The Particulate matter deposition with respect to each fuel exhaust conditions were analysed in numerical analysis. The least reactive component was determined to be soot from fossil diesel fuel, as its oxidation occurred at the highest temperature levels. Due to the fact that soot particles were the incomplete products of compressed initiated combustion, they exhibited combustion characteristics and appeared later with decreasing initial temperature. The soot emissions in this case were a result of both soot production and oxidation. Under higher ambient temperature circumstances, poorer premixed combustion occurred, resulting in increased fuel richness inside the biodiesel fuel jet, increased soot formation rates during the early stages of combustion, and lastly, increased time integrated soot mass. The primary results show that, the use of Algae bio-diesel along with diesel can reduce the emissions. Table 5. Exhaust results for diesel and algae bio-diesel blends. Fuel Load Speed Exhaust temp Smoke (%vol) CO (% vol) HC (ppm) CO2 (% vol) O2 (% vol) NOx (ppm) Diesel 100% 1500 420 95 0.02 11 7.3 18.23 850 B20 100% 1500 455 86 0.02 10 6.2 12.49 860 B40 100% 1500 473 76 0.04 5 4.4 14.7 971 B60 100% 1500 480 70 0.03 5 2.2 18.32 989 4.2. PM Characterization of Algae Biodiesel Blends The morphology of Algae Biodiesel has been characterized by a Scanning electron microscope (SEM) from the soot samples collected and the SEM images observed. The scanning electron 586 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 578-597, 2024 DOI: 10.55214/25768484.v8i5.1719 © 2024 by the authors; licensee Learning Gate microscopy combined with energy dispersive X-ray analysis using Zeiss EVO 50 Modal, Oxford EDS, Modal X-Max was used for the analysis. A computer operated field emission SEM equipped with an EDS detection system was used to conduct the SEM–EDS analyses. Different samples of algae bio- Diesel blends and plain diesel oil were collected on Carbon Tape & to make the samples electrically conductive; a very thin layer of Gold (Au) was deposited on the surface. The Figure 4 shows the SEM- EDS images with the inset line scan results showing the chemical compositions of those micro-scale precipitates within and along the grain boundaries of diesel and different Biodiesel blends, where the different elemental composition along with its local mass is measured with technique. SEM-EDS line scans in inset table presents in images in Figure 4 (a-c) show the results of various composition in a particular location as per the small precipitate (>10 µm) in a sample. The maximum carbon weight percentage was found 90% for the B20 blend. It is seen that, as the percentage of Algae Bio-Diesel increased into the diesel the carbon percentage will reduced and Oxygen percentage will get increased. The minimum carbon percentage was found 78.9% for B60 blend fuel. (a) SEM-EDS of B20 (b) SEM-EDS of B40 587 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 578-597, 2024 DOI: 10.55214/25768484.v8i5.1719 © 2024 by the authors; licensee Learning Gate (c) SEM-EDS of B60 Figure 4. SEM –EDSimages with the inset line scan results showing the chemical compositions of those micro-scale precipitates within and along the grain boundaries forMicroalgae Biodiesel blend fuels. Table 6 shows the exact values of composition present in each sample of algae fuel and Diesel which shows that the maximum carbon content (90%) was found in B20 blend fuel while the B60 blend has the minimum carbon percentage (~78.9%). Table 6. Chemical compositions of different PM samples. Diesel MA B20 MA B40 MA B60 C 90.2 90 83.7 78.9 O 8.1 8.9 16 20.6 Fe 0.8 0.8 0.1 0.2 Cl 0.3 0.2 0.1 0.1 Zn 0.4 0.1 0 0 S 0.1 0.1 0 0.2 Na 0.1 0 0 0 Ca 0.1 0 0 0 Al 0 0 0.1 0 Cu 0 0 0 0.1 Figure 5 shows the SEM-EDS Colour mapping of PM composition collected from Diesel and different biodiesel blends, where the various compositional elements are colour coded for identification. The analysis is particularly useful when we compare the different HAADF images taken from different fuel blends to recognise the compositional changequalitatively. Figure 3 (a) shows the high angle annular dark field (HAADF) image Microalgae B20 blend, also the corresponding element mapping of C, Fe, Mn, Cl, O and S are shown in Figure 5 (b). HAADF-STEM images are used to extract the information of the trace metals present in the collected soot. The different color combination in HAADF-STEM images represents a particular trace metal.Figure 5 (a) depicts that trace metals such as 588 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 578-597, 2024 DOI: 10.55214/25768484.v8i5.1719 © 2024 by the authors; licensee Learning Gate C, Fe, Mn, Cl, O, and S in particulate originate from the test fuels and concentration seems to be uniform distribution.The higher concentration in a specific color in figure, the higher is the presence of traces of respective metal in sample. (a) High angle annular dark field (HAADF) image Microalgae B20 blend (b) High angle annular dark field (HAADF) image for corresponding elements mapping in sample. Figure 5. (a) High angle annular dark field (HAADF) image Microalgae B20 blend, (b) corresponding element mapping of C, Fe, Mn, Cl, O and S. 589 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 578-597, 2024 DOI: 10.55214/25768484.v8i5.1719 © 2024 by the authors; licensee Learning Gate Figure 6 shows PM Agglomerate structure in which a high magnification images are collected to analyse the morphology. PM agglomerates collected on Glass microfiber filter from the exhaust of different biodiesel blends & neat Diesel. The SEM image analysis gives limited information due low magnification values. The bigger agglomerates can be analyzed for its form, shape and sizes. Through this analysis it’s observed that all the PM samples show powdered form and don’t have any specific shape. TEM can be used for further deep insights about morphological changes in Particulate Matter. (a) PM Agglomerate size analysis of MA B20 590 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 578-597, 2024 DOI: 10.55214/25768484.v8i5.1719 © 2024 by the authors; licensee Learning Gate (b) Agglomerate size analysis of MA B40 (c) PM Agglomerate size analysis of MA B60 Figure 6. PM Agglomerate size analysis of Diesel and Algae Biodiesel blends. 591 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 578-597, 2024 DOI: 10.55214/25768484.v8i5.1719 © 2024 by the authors; licensee Learning Gate 4.3. Particulate Matter Deposition in the DPF The Particulate matter deposition for the various types of Algaeblend (B20, B40 and B60) can be analyzed numerically through ANSYS Fluent software. The results of Particulate matter concentration and the pressure drop can be predicted for 2000 sec. and compared with the results predicted for diesel fuel. Theparticulate matter concentration plots were summarized here. Figure 7 shows the Particulate matter concentration at porous media and cake layer for different fuels for 2000 sec. From the figure it can be seen that, the Particulate matter concentration was increase with increase in time. The Particulate matter particles are flows from inlet to outlet within a porous media, Particulate matter particle are trapped at the porous media and deposited. It is observed from figure that, the diesel fuel has more Particulate matter deposition than the other fuel. The algae bio- diesel blends produces lesser Particulate matter hence the deposition was found lesser than the diesel fuel. The maximum Particulate matter deposition was found 0.8879 kg/m3 for diesel fuel while it is found maximum of 0.7871 kg/m3 for B20 blend. The minimum Particulate matter deposition at 2000 second was found 0.7072 kg/m3 for B60 blend. The physical trapping of the soot particles with porous wall in the DPF can reduce the soot emissions. The use of oxygenated alternative fuels in diesel engines may result in significant improvements when it comes to DPF regeneration, as the lower temperature required results in a more efficient regeneration process (thus minimizing the fuel consumption penalty related to active regeneration). Non-oxygenated paraffinic fuels also provide advantages, but to a lesser level.Particulate Matters has been deposited onto the surface of the porous wall and the channels, which results in increasing pressure drop. Soot particles first trapped in porous filter zone hence the porosity and the permeability of the filter reduced [21, 22]. A pressure drop across the test channel was calculated for every case of fuel, and results are shown in figure 8. It can be observed that, the pressure drop increases with increase in Particulate matter concentration. The maximum pressure drop for three cases Algae bio-diesel fuel was found 12.94 kPa for B20 blend. As Particulate matter deposited into the DPF, it can resist the flow of the exhaust gases which can create the back pressure into the system. The minimum pressure drop was found 11.28 kPa for B60 blend. Figure 7. Particulate matter concentration in porous media versus time in sec. 592 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 578-597, 2024 DOI: 10.55214/25768484.v8i5.1719 © 2024 by the authors; licensee Learning Gate Figure 8. Pressure drops with DPF versus time in sec. The Particulate matter accumulation of in porous region is shown in figure 9 for different time. As explained earlier, the Particulate matter deposition at the wall of the DPF increased with the exhaust flow into the porous region. The particulate matters with different size can accumulate the porous zone with different loading conditions of engine. The maximum Particulate matter concentration has been found at 2000 sec. At the entry region, the Particulate matter concentration was less as the flow rate is higher. The flow maldistribution due to the friction and other parameters at the filter channels was significantly influenced on soot deposition, which causes the uneven soot deposition inside the DPF channels. The ignition temperature and the oxygen level also have the impact on the soot deposition [19]. A non-uniform concentration of the soot can be seen in figure 9. Figure 9. Particulate matter depositions at different time in diesel fuel case. 593 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 578-597, 2024 DOI: 10.55214/25768484.v8i5.1719 © 2024 by the authors; licensee Learning Gate Figure 10 shows the contours of Particulate matter concentration for Algae B20 blend fuel for different time. It is clearly seen that; the Particulate matter deposition is increased with increase in time. At t = 500 sec, the Particulate matter concentration was little higher at the end of corner. The Particulate matter concentration for the different fuels at t =2000 sec. is shown in figure 11. As explained in figure 7, the maximum Particulate matter concentration found in case of diesel fuel, which also reflect in figure 11. The red color concentration explains the maximum Particulate matter concentration. The addition of the Algae bio-diesel in diesel fuel can help to reduce the emission. The B60 blend was found to be less exhaust emission with less Particulate matter concentration. Normally the PM is proportional to oxygen content available in fuel. Usually, a biodiesel blends have a higher mean molecular weight and the oxygen content than conventional diesel, so any unburnt fuel is more likely to condense as the exhaust cools [25, 26]. Figure 10. Particulate matter deposition at different time in B20 algae biodiesel blend fuel case. Diesel (t=2000 sec) 594 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 578-597, 2024 DOI: 10.55214/25768484.v8i5.1719 © 2024 by the authors; licensee Learning Gate B20 (t=2000 sec) B40 (t=2000 sec) B60 (t=2000 sec) Figure 11. Particulate matter concentration at t =2000 sec for different fuels. 595 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 578-597, 2024 DOI: 10.55214/25768484.v8i5.1719 © 2024 by the authors; licensee Learning Gate 5. Discussion The experimental and numerical analysis has been done to investigate the effect of different fuel & the following conclusions were drawn from the analysis.The exhaust emission like HC, CO was found lesser with addition of Algae bio-diesel blend in diesel than the pure diesel. The smoke found reduced & NOx found increased gradually with addition of the Algae bio-diesel. The Particulate matter concentration rate inside the DPF was seen to be maximum with diesel fuel used. The Particulate matter concentration is increased with increase in time as the Particulate matter particles were deposited on the filter (porous zone). Hence, the pressure drop was increased with increase in Particulate matter concentration. The maximum pressure drop was found with diesel fuel, at obvious due to the maximum Particulate matter deposition in diesel fuel case. The morphological analysis clears that all the PM samples of all the fuels don’t possess any specific shape and are in amorphous structure. The Chemical composition of PM collected from EDS analysis gives important information for the determination of their origin and development processes. SEM– EDS microanalysis clarifies the amount of Oxygen species present in PM composition which is in the order of CI B60 > CI B40 > CI B20 > neat Diesel. The presence of higher amount oxygen may contribute to increment in the reactivity of PM particles & better oxidation properties. Such PM can have better active as well as passive regeneration in DPF leading to reduced backpressure and hence minimizing pressure drop in the Engine performance due to it. So, the potential reactivity of Biodiesel blend PM is more and increases with the increase in Biodiesel content in the fuel due to which DPF performance also seen to be improved. 6. Conclusion The results showing the reduction in smoke, presence of oxygen in collected soot and reduced rate of clogging in DPF with progressive addition of Algae biodiesel signifies the excellent emission performance of Four stroke Diesel Engine. The Algae Biodiesel blend is proven to be effective for Diesel Engine which needs to be not only compliant with emission control laws but also to have the better performance features like reduced rate of clogging and regeneration dependencies and relevant performance implications of DPF. Acknowledgment All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Mr. Jayant Nalawade and Dr. R. Prakash. The first draft of the manuscript was written by Mr. Jayant Nalawade and both authors read and approved the final manuscript. Nomenclature DPF Diesel particulate matter RPM Rotation per minute CO Carbon monoxide CO2 Carbon dioxide NOx Oxides of nitrogen NO Nitric oxide PM Particulate matter VOC Volatile organic compounds MPF Multi-Zone Particulate Filter PPM Parts per million CH3OH Methanol KOH Potassium hydroxide BHP Brake horse power NDIR non dispersive infrared 596 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 578-597, 2024 DOI: 10.55214/25768484.v8i5.1719 © 2024 by the authors; licensee Learning Gate CFD Computational fluid dynamics SOF Soluble organic fraction Copyright: © 2024 by the authors. 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