Full Factorial Experimental Design Research Articles

Surface generation on titanium alloy through powder-mixed electric discharge machining with the focus on bioimplant applications

The inflammation around poorly osseointegrated bioimplant is one of the root causes of its failure. Therefore, the biomedical industry constantly strives for new ways to develop bioactive surfaces in permanent implants to enhance the service life. In this regard, implant surface modification at micro/nanoscales is carried out to enrich substrate with higher engineering attributes and biocompatibility. Considering the complexities of post-processing of implants, this study evaluates the potentiality of an integrated process of implant machining and surface modification, namely, powder-mixed electric discharge machining (PMEDM). Ti6Al4V ELI implant material, as substrate, is machined under two distinct (Si, SiC) mixed additive conditions using a full factorial design of experiments. The surface quality, surface morphology, recast layer depth, surface chemistry, and work hardening have been holistically investigated. The bioactivity analysis of machined surfaces shows more porosity in the case of Si powder particles (200 to 400 nm) compared to SiC (100 to 250 nm). Furthermore, the study optimized the process parameters for minimum roughness and recast layer depth considering 5 g/L powder concentration, 5A pulse current, 50 µs pulse on time for Si, and 100 µs pulse on time for SiC. A comprehensive review of surface features based on process physical science is established, and nanoscale surface topography influencing protein absorption is analyzed.

Optimization in the Resistant Spot-Welding Process of AZ61 Magnesium Alloy

In this paper, an integrated artificial neural network (ANN) and multi-objective genetic algorithm (GA) are developed to optimize the resistance spot welding (RSW) of AZ61 magnesium alloy. Since the stability and strength of a welded joint are strongly dependent on the size of the nugget and the residual stresses created during the welding process, the main purpose of the optimization is to achieve the maximum size of the nugget and minimum tensile residual stress in the weld zone. It is identified that the electrical current, welding time, and electrode force are the main welding parameters affecting the weld quality. The experiments are carried out based on the full factorial design of experiments (DOE). In order to measure the residual stresses, an X-ray diffraction technique is used. Moreover, two separate ANNs are developed to predict the nugget size and the maximum tensile residual stress based on the welding parameters. The ANN is integrated with a multi-objective GA to find the optimum welding parameters. The findings show that the integrated optimization method presented in this study is effective and feasible for optimizing the RSW joints and process.

Optimisation of mechanical properties of polyethylene terephthalate fibre/fly ash hybrid concrete composite

The study focuses on the prediction and optimisation using modelling with respect to the fresh and hardened properties of polyethylene terephthalate (PET) fibre reinforced concrete containing partial cement replacement with fly ash. Full factorial experimental design methodology was employed to fabricate the test specimens by simultaneously varying the independent factors to develop a model for overall response variation. Numerical optimisation was carried out concerning the fibre reinforced concrete's fresh and hardened mechanical properties. Predictive modified quadratic equations were developed for slump value, compressive, flexural, split tensile strength and total cost. Analysis of variance test was carried out for all the responses indicated and the developed model could predict the slump value and mechanical properties of the fibre reinforced concrete correctly and effectively with a coefficient of determination in the range of 0.5–0.9467. The optimum constituent combination for maximum mechanical strength at the lowest possible cost was found to be 15.76 % fly ash and 0.32 % PET fibre. These optimum values corresponded to responses of 25.65 MPa, 3.69 MPa, 2.36 MPa, 31.48 mm, R2744.17 for compressive strength, flexural strength, tensile strength, slump value and total cost, respectively. These predictions were validated experimentally, and a good correlation was observed between the actual and predicted values based on the observed standard deviations of 0.1335, 0.031, 0.005, 0.676, 0.02 for compressive strength, flexural strength, tensile strength, slump value and cost, respectively. Concrete slabs were optimised for various possible end uses, and the optimum PET fibre % and fly ash % were ascertained. The optimum combination for suspended slabs was 19.09 % for fly ash and 0.34 % PET fibre. These values corresponded to responses of R2742.03/kg/m3, 2.30 MPa, 3.68 MPa, 24.77 MPa, 30.60 mm for the total cost, split tensile, flexural, compressive strength and slump value. For foundation slabs, the optimum combination was 3.94 % fly ash and 0.65 % PET fibre, and the corresponding response values are R 2760.47 /kg/m3, 1.82 MPa, 3.26 MPa, 19.32 MPa and 10.78 mm for a total cost, split tensile, flexural, compressive strength and slump value, respectively. For paving slabs, the optimum combination was 0.65 % PET fibre giving a response of R 2775.13 kg/m3, 1.41 MPa, 3.29 MPa, 19.11 MPa and 9.92 mm for total cost, split tensile, flexural, compressive strength and slump value, respectively.

Enhanced photodegradation toward graphene–based MgFe2O4–TiO2: Investigation and optimization

In this work, graphene–based MgFe2O4–TiO2 (MFO–TiO2@rGO) nanocomposite was synthesized and conducted an efficient photo–treatment with p–nitrophenol (PNP) as a well–known pollutant in water. The ternary material was characterized by modern analysis methods including Fourier transform infrared spectroscopy, X–ray diffraction, Raman spectroscopy, Transmission electron microscopy, Energy–dispersive X–ray spectroscopy, Selected area electron diffraction, and UV–vis spectroscopy. The simultaneous effects of the volume of H2O2, catalyst dosage, and pH on the photodegradation of p–nitrophenol were also examined by full factorial experimental design according to Box–Behnken designs. As a result, the characterization has confirmed the uniform bounding of MFO and TiO2 nanosizing from 4 to 6 nm on graphene with a high crystallinity degree. The photocatalysis of this material toward PNP can reach up to ∼99.44% in only 60 min reaction under UV control with 1.12 mL H2O2 added, 38.15 mg catalyst, and pH 9. Besides, in solar condition (directly under sun), the material also expressed a relatively impressive capability in degrade PNP from water. Furthermore, the ternary nanocomposite can be easily recycled and reused with an inconsiderable change in yield, represented by the elimination yield of recovered MFO–TiO2@rGO was significantly more than 90% after ten–run cycles. Indeed, the nanocomposite suggests an efficient pathway for treating organic contaminants in wastewater treatment.

Impacts of deficit irrigation and organic amendments on soil microbial populations and yield of processing tomatoes

Altered precipitation patterns and increased water demands from urban, industrial, and environmental needs often reduce the volume of irrigation water available for agriculture. Strategies that reduce irrigation water inputs, e.g., deficit irrigation (DI), need further evaluation to determine potential impacts on yield and soil microbial communities driving critical soil biogeochemical cycles. Whether soil organic amendments can stabilize DI effects on yield and microbial communities remains unknown. Processing tomato beds were established in a full factorial experimental design of soils unamended or amended with a one-time application—four to five years before sampling—of either biochar or biochar with compost and three irrigation regimes: full (100 % of plant water demand), or DI treatments at 75 % or 50 % of full irrigation. We profiled soil bacterial and archaeal community compositions for two growing seasons and determined soil C and N metabolic potentials and biomass of microbial groups using high throughput sequencing of 16S rRNA genes and phospholipid fatty acid analysis. We tested the discrete and interactive effects of DI and soil amendments on soil chemical and biological properties, and crop yield. DI had stronger effects on the bacterial and archaeal community composition than soil organic amendments. However, 75 % DI did not strongly affect bacterial and archaeal community composition or the total and individual biomass of microbial groups, but it increased irrigation water productivity of processing tomatoes. Although soil amendments did not stabilize the compositional shifts induced by DI, their recalcitrant C still had residual effects on the bacterial and archaeal community composition years after their incorporation. Furthermore, soil moisture correlated with bacterial and archaeal community's C and N metabolic potentials, likely augmenting the amendments' C and N residence time in DI soils. Our results provide insight into water-saving mechanisms that could increase profit margins in water-scarce years without affecting microbial populations that support plant growth and productivity.

Comparing the Replication Fidelity of Solid Microneedles Using Injection Compression Moulding and Conventional Injection Moulding.

Polymer surfaces are increasingly being functionalized with micro- and nano- surface features using mass replication methods such as injection moulding. An example of these are microneedle arrays, which contain needle-like microscopic structures, which facilitate drug or vaccine delivery in a minimally invasive way. In this study, the replication fidelity of two types of solid polycarbonate microneedles was investigated using injection compression moulding and conventional injection moulding. Using a full factorial design of experiments for the injection moulding process, it was found that the volumetric injection rate had the largest positive effect on the replication fidelity. The mould temperature and holding pressure were also found to have a positive effect, while the effect of the melt temperature was found to be insignificant for the considered temperature range. For the injection compression moulding process, it was found that a larger compression stroke resulted in a better replication fidelity. A comparison between the replication fidelity for the injection moulding and injection compression moulding indicated that the injection compression moulding process resulted in a higher and more uniform replication fidelity. Using finite element flow simulations, a higher and more evenly distributed cavity pressure was observed compared to the conventional injection moulding process.

Regression Modeling and Process Analysis of Resistance Spot Welding on Dissimilar Steel Sheets

The resistance spot welding process is used to weld dissimilar materials. Dissimilar joining is formed by two 2mm thick sheet metals of 304L austenitic stainless steel and galvanized steel. This study investigates the effects of welding parameters such as welding current, welding time, and welding force. The welding time and the welding force were, respectively, in the range of 10-13 cycles and 7-8bar, while the welding current was in the range of 10–16kA. Tensile tests were applied to determine the resistance parameters of dissimilar joining. The experimental results showed that increasing the welding current increased the tensile shear stress of the weld coupon. Regression analysis was carried out to determine the significance of the process parameters by using the coupled of the full factorial experimental design with statistical and graphical analysis of the results. Furthermore, analysis of variance was used to determine the optimal parameters and combinations to achieve the highest strength level.

Prediction of emissions and performance of a diesel engine fueled with waste cooking oil and C8 oxygenate blends using response surface methodology

Extracting energy from waste and utilizing it as a transport fuel is a sustainable option to reduce fossil fuel dependency that could also give a solution to waste management. In the present study, used cooking oil from restaurant is collected and converted to biodiesel was later tested in a diesel engine along with three oxygenates viz., di-n butyl ether (DNBE), 2-ethyl-hexanol (TEH) and 1-octanol (OCT). This study aim to minimize the emissions of a diesel engine with these C8 oxygenates and also to provide a comparative analysis on the effects of their addition to the biodiesel individually on performance and emission of a single cylinder diesel engine. A response surface methodology (RSM) based optimization using a 3-factor × 3-level full factorial experimental design was employed to find the optimum combination of compression ratio, exhaust gas recirculation (EGR) and oxygenates with an objective to minimize NOx and smoke opacity with maximum performance. Three compression ratio (16:1, 17.5:1 and 19:1), three EGR rates (0%, 10% and 20%) and three fuel blends D80-WCOME15-DNBE5 (b) D80-WCOME15-TEH5 (c) D80-WCOME15-OCT5 were used. Multiple regression models developed using RSM for NOx, smoke opacity and BTE were found to be statistically significant. Interactive effects between compression ratio and EGR on responses for all blends were compared. From desirability approach, D80-WCOME15-OCT5 injected at compression ratio 19:1 with 10% EGR rate delivered optimum performance and emission characteristics with maximum desirability of 0.967.1-Octanol was found to be the best among di-n butyl ether and 2-ethyl-hexanol for this purpose. Confirmatory tests validated the models to be adequate with an error in prediction within 6%. With reference to diesel, D80-WCOME15-OCT5 injected at compression ratio 19:1 with 10% EGR rate reduced smoke opacity by 29.38% and increased NOx emissions by 4.07% with an improvement in BTE by 1.22%. With respect to D80-WCOME20, the same blend reduced smoke opacity by 34.7% and increased NOx emissions by 5.1% with 1.3% lower BTE.

Artificial intelligence enabled efficient power generation and emissions reduction underpinning net-zero goal from the coal-based power plants

A large power generation facility is a complex multi-criteria system associated with multivariate couplings, high dependency, and non-linearity among the operating variables which present a major challenge to ensure efficient power production. In this research, an integrated artificial intelligence (AI) and response surface methodology (AI-RSM) framework to achieve the efficient power production operation of a 660 MW coal power plant is presented. Two AI algorithms, i.e., extreme learning machine (ELM) and support vector machine (SVM) are trained comprehensively on the power plant’s operational data and are validated as well. Full factorial design of experiments on the three levels of the operating parameters are constructed and simulated from the better performing AI model which is an effective non-linear representation of the complex power plant operation. RSM analysis is carried out under three power generation scenarios to simulate the effective values of the operating variables which are tested on the power plant’s operation and a reasonable agreement is found with the experimental observations. The notable improvement in fuel consumption rate, thermal efficiency, and heat rate of the power plant under Half Load, Mid Load, and Full Load capacity of the power plant is achieved by the AI-RSM framework enabled analyses. It is estimated that annual reduction in CO2, CH4 and Hg emissions measuring 210 kg tons per year (kt/y), 23.8 t/y and 2.7 kg/y, respectively can be obtained corresponding to Mid Load operating state of the power plant. The research presents the reliable and robust utilization of AI-RSM framework for simulating the effective operating conditions for the fossil-based power plants’ operation with an eventual goal to improve the techno-environmental performance which is expected to contribute to net-zero emissions goal from the energy sector.

Parametric optimisation of shear thickening fluid treatment for ultra-high molecular weight polyethylene woven fabric

The shear thickening fluid (STF) treatment enhances the low and high velocity impact resistance performances of para-aramid woven fabrics for soft armour application. This research investigates the effects of important parameters on the STF add on and impact energy absorption with the aim to develop a hybrid soft armour. The STF add-on on ultra-high molecular weight polyethylene (UHMWPE) woven fabrics was varied by altering the silica particle size, dilution ratio of STF and padding pressure. The results of the full factorial design of experiment showed that the energy absorption is independent of the add-on. Further, a soft armour panel (SAP) was made from the STF treated woven fabric. The ballistic performance of the panel was evaluated in terms of back face signature (BFS) against a 9 × 19 mm lead core bullet and was compared to that of a weight equivalent untreated panel. A hybrid SAP with unidirectional (UD) laminates of UHMWPE as strike face layers and STF treated woven fabrics as backing layers was prepared, and the BFS was found to be equivalent to that of a SAP containing 100% UD laminates (∼30 mm). The results imply that a certain number of UD laminate layers can be replaced with STF treated flexible woven fabrics without compromising on the ballistic performance of SAP.

Agglomeration tendency and activated carbon concentration effects on activated carbon‐polysulfone mixed matrix membrane performance: A design of experiment formulation study

AbstractMixed matrix membranes (MMMs) are effective materials for emerging separation applications. While MMMs show promise, various membrane formation schemes have produced particle agglomerations, surface ruptures, and varying separation performance as a result. In this work, a replicated 2 × 23 full factorial design of experiment (DOE) and a mixture analysis was conducted to investigate the effects of activated carbon, polyethylene glycol (PEG), and solvent type, used during MMM formation. Aniline blue filtration was used as a model for performance. A thorough analysis was conducted on contact angle, agglomeration frequency, water flux, and dye rejection. Specifically, a novel and facile method to study agglomeration tendencies is presented. Among other trends, agglomeration tendencies were mitigated by the addition of PEG during the fabrication process. Water flux increased from 10 to 55 LMH when PEG was used as a pore former and dye rejection increased from 72% to 90% with the addition of AC particles.

Prediction of the geometric characteristics of the laser cladding of Inconel 718 on the Inconel 738 substrate via genetic algorithm and linear regression

Assessing the geometric characteristics of a track is the first step in indicating the results of the laser cladding process. In this research, the effect of three essential process parameters, namely scanning speed, laser power, and powder feed rate, on the geometric characteristics of the Inconel 718 track on the Inconel 738 substrate have been studied. The goal of this research was to obtain the optimal process parameters for Inconel 718 laser cladding. To investigate the impact of process parameters on the geometric characteristics, five levels of laser power, three levels of scanning velocity, and three levels of powder feed rate have been considered, and a full factorial design of experiment has been performed. Consequently, 45 tracks have been cladded, and their geometric characteristics, including penetration depth, wetting angle, track height, track width, and compound characteristics such as dilution, has been investigated. After analyzing the results, the behavior of the geometric characteristics for the same process parameters in other brackets have been predicted via linear regression and genetic optimization via MATLAB software. PαVβFγ is participating in Y=aX+b as a compound variable. The regression process is feasible via accurate estimation of exponents α, β and γ. Therefore, the genetic algorithm was used to reduce the errors of linear regression. It is concluded that all of the process parameters have quantitative and qualitative effects on the geometry of the track. Finally, based on the modeling results, a process map was drawn to predict the impact of process parameters on the track geometry.

A Comprehensive Analysis of AISI 316L Samples Printed via FDM: Structural and Mechanical Characterization

Metal Fused Deposition Modelling is a promising multi-step process able to manufacture metal parts by means of a low-cost additive technique. In this study, a metal-polymer composite filament characterized by homogenous mixture of AISI 316L sinterable metal powders and a multi-component polymeric matrix was used to fabricate samples by means of a FDM printer. A 24 full factorial design of experiments was elaborated to define the possible influence of the relevant printing parameters on dimensional shrinkage, bulk density and overall porosity of printed samples. In addition, the mechanical properties of printed AISI 316L samples were investigated by performing tensile tests, compression tests, Charpy impact tests, Rockwell B and Vickers hardness tests. An X-ray diffraction analysis was conducted to assess the crystallographic structure of the FDM AISI 316L samples.

Optimization of Friction Stir Welding Parameters in Hybrid Additive Manufacturing: Weldability of 3D-Printed Poly(methyl methacrylate) Plates

In this work, the expansion of friction stir welding (FSW) in parts made via material extrusion (MEX) 3D printing was investigated. Poly(methyl methacrylate) (PMMA) plates were joined in a full factorial experimental design. The effects of three FSW parameters (weld tool pin geometry, rotating speed, and travel speed) on the weld results were studied. The tensile strength was investigated using statistical modeling tools. A morphological characterization study was also conducted on the weld zone, with microscopy. The state of the material during the FSW process was monitored via real-time temperature measurements. The feasibility of the process was verified. The results show high industrial merit for the process. The highest tensile strength was reported for the sample welded with the frustum tool, at 1400 rpm and a 9 mm/min travel speed (the highest studied), with a welding efficiency > 1. This can be attributed to the reduced porosity of the weld area compared to the 3D printed structure, and indicates a high potential for joining 3D-printed PMMA sheets via the FSW process.

Modeling and optimization of Acid Orange 7 adsorption process using magnetite/carbon nanocomposite

The efficiency of a new magnetite/carbon nanocomposite (MCN), applied as adsorbent for the removal of Acid orange 7 (AO7) dye from aqueous solutions was investigated. The MCN was synthesized by combustion, and characterized using modern and relevant techniques. A full factorial design experiment was conducted to identify the optimum conditions for adsorption. The main effect of four process parameters: pH, dye concentration, adsorbent dose and temperature, and their interactions on the removal efficiency were established. A highly significant (p < 0.001) second-order polynomial model (R2 and R2adj of 0.99) provided great removal rates using: pH of 6–7, adsorbent dose of 1–1.5 g/L and initial dye concentration of 50–100 mg/L. The significance of the independent variables as well as their interactions was tested by ANOVA which revealed that the regression is statistically significant at 95% confidence level. Predicted data obtained for the removal efficiency were in good agreement with the experimental determinations (98%). The adsorption isotherms study demonstrated that the Sips model had the best fit with the experimental data (R2 0.9810) and a maximum adsorption capacity of 180.04 mg/g was obtained. The negative values obtained for Gibbs free energy and for enthalpy indicate that the adsorption process is spontaneous and has an exothermic character. After four consecutive cycles of adsorption/desorption, the removal efficiency of AO7 using MCN was yet 83.60%. The results demonstrated that MCN is a promising adsorbent, and response surface optimization could be successfully used to optimize the adsorption process parameters.

The Influence of Diamond Burnishing Process Parameters on Surface Roughness of Low-Alloyed Aluminium Workpieces

This study describes the determination and optimization of burnishing process parameters and their effects on surface roughness of EN AW-2011 aluminium alloy workpieces. The process has a low environmental load and the chip-free burnishing process improves the integrity of the machined surface, but to achieve this, the different burnishing parameters, for example, burnishing force, feed rate, speed and number of passes, must be properly defined according to the material of the workpiece. In our research, a full factorial experimental design method is used to plan and carry out the experiments and to determine the most appropriate parameter range for this material quality.

Implementation of factorial experimental design in chitosan - tripolyphosphate nanoparticles development by ionotropic gelation

Glimepiride (GM) is third generation sulfonylurea group and preferably used for the treatment of type -II diabetes mellitus. It belongs to BCS class II drugs according to biopharmaceutical classification system which shows low aqueous solubility. The purpose of this work was to develop and optimize glimepiride loaded chitosan- tripolyphosphate nanoparticles by ionotropic gelation method using 32 full factorial experimental designs. The developed chitosan nanoparticles were evaluated for particle size, zeta potential, entrapment efficiency, %yield and in vitro drug release etc. Factorial experimental design was implemented to optimize the independent variables like drug: polymer ratio (X1) and concentration of cross linking agent (X2) on dependent variables like particle size (Y1) and entrapment efficiency (Y2). A statistical analysis was performed using minitab 16 software with respect to ANOVA, regression analysis. The surface plots and contour plots represent the relationship between the experimental responses and the set of independent variables. The factorial experimental design was validated by checkpoint analysis and desirability function approach was applied for optimization of chitosan nanoparticle formulation. Drug entrapment efficiency and mean particle size of optimized formulation was found to be 61.25% and 222 nm respectively. Nanoparticles revealed initial burst release followed by slow drug release.

Morphology Optimization of Leaflet for Surgical Reconstruction of the Aortic Valve: In Vitro Test and Simulation-Based DOE Study.

This study was to evaluate the impact of leaflet trimming strategy on the hemodynamic behaviors of the aortic valve after reconstructive surgery, and give recommendations based on design of experiment (DOE) and in vitro studies. An in vitro hemodynamic test was performed on the simulated surgical model to quantify the efficacy of conventional reconstructive surgery. The very same computational model was built and verified, on which the full factorial DOE was carried out to summarize the correlations between leaflet trimming parameters and valve hemodynamic characteristics. Hemodynamic characteristics of the valve substitute were significantly associated with leaflet trimming parameters. The total regurgitant and transvalvular regurgitant of the valve substitute were reduced by 27.44% and 13.61% after optimization of the leaflet design. Synthetic use of in vitro tests and DOE study based on computational models helped improve outcomes of the reconstruction of aortic valve by reducing free edge length and increasing commissure height and leaflet height.

Parametric design and evaluation of TPMS-like cellular solids

Triply periodic minimal surfaces (TPMS) present a starting point for designing infinitely many novel cellular solids, creating the need for a structured approach to their design and evaluation. In this study the design space is formalised using a set of input parameters and evaluation metrics to provide a structured and reproducible approach to designing a TPMS-like cellular solids. Interactions between the design parameters and evaluation metrics are identified using a full-factorial design of experiments. From this the most impactful design parameters are identified as the TPMS equation, type of structure, and volume fraction. It was found that the gyroid-surface structure provided the most flexibility, with near-isotropic mechanical properties across a wide range of volume fractions, allowing the creation of reduced mass/stiffness designs without introducing anisotropy or variation in Poisson’s ratio. The volume fraction at which the connectivity of a structure changes, “pinch-off limits” are presented for six of the most commonly investigated TPMS-like cellular solids. Additionally, the effects of common transformations are presented including: examples of how phase-shift and/or rotation can be used to modify the features which intersect the free-boundary of a design; dimensional analysis which shows the practical effects changing unit cell size; and analysis showing the effect of rotating a cellular structure on both the mechanical properties and surface orientation. These computational studies are supported with a physical case study which demonstrates how several design parameters may be modified to change the manufacturability of a TPMS-like cellular solid.

Bio-based herding and gelling agents from cholesterol powders and suspensions in organic liquids for effective oil spill clean-up

The negative impacts of an oil spill are severe and can last for decades after the incident. The development of techniques to overcome this threat is crucial to the ecosystem as well as human lives. The herder effectiveness of different cholesterol forms, including powders and suspensions, were measured and compared by using a MATLAB code to estimate the surface area of oil spill contracted/thickened. The powders were distinguished by their particle size distributions, crystal phases, and thermal properties. Formation of gel oil aggregates was achieved by all cholesterol powders. The highest herder effectiveness of 73.3 ± 2.0% was shown by CP/D, that is, the cholesterol powder that had the lowest particle sizes of 0.5–0.99 mm, anhydrous crystal phases, and lowest thermal degradation rate of 95.4%. Cholesterol suspensions were further prepared from the mixtures of CP/D and delivery liquids, namely gasoline, toluene, and biodiesel by varying CP/D-to-solvent ratio from 1:2 to 1:4. Herding of oil spill, instead of gel oil aggregation only, was achieved by cholesterol suspensions. Biodiesel was found to be the most efficient delivery liquid, resulting in the highest herder effectiveness of 79.1 ± 0.7%. CP/D-biodiesel suspension reduced the surface tension of water from 73.5 to 28 mN/m. Optimum herder effectiveness was estimated from full factorial experimental design and Response Surface Methodology at different conditions of temperature, water salinity and agent-to-oil ratio (AOR). Analysis of variance (ANOVA) confirmed that salinity and temperature had significant effects on the herder effectiveness of CP/D-biodiesel but the effect of AOR was insignificant. The optimum herder effectiveness was 79.5% and the accuracy of this prediction was obtained as 95%. This study demonstrated that CP/D-biodiesel suspension is a promising bio-based herding agent.