Response Surface Methodology Model Research Articles

Optimization of Enzyme-Assisted Extraction of Rosemary Essential Oil Using Response Surface Methodology and Its Antioxidant Activity by Activating Nrf2 Signaling Pathway.

Rosemary essential oil (REO) is widely recognized as a food flavoring and traditional herb and possesses potential antioxidant activity. However, its low yield rate and unclarified antioxidant mechanism warrant further investigation. In this study, an enzyme pretreatment-assisted extraction method with Box-Behnken design (BBD) and response surface methodology (RSM) models was employed to optimize the main factors of REO, and its antioxidant molecular mechanism under oxidative stress was elucidated in hydrogen peroxide-induced human lung carcinoma (A549) cells. The optimized yield (4.10%) of REO was recorded with the following optimum conditions: enzyme amount 1.60%, enzyme digestion pH 5.0, enzyme digestion temperature 46.50 °C, and enzyme digestion time 1.7 h. Meanwhile, 1.8-cineole (53.48%) and β-pinene (20.23%) exhibited radical scavenging activity higher than that of BHA and BHT. At the cellular level, REO (12.5-50 µg/mL) increased the levels of cell viability, CAT, SOD, and GSH significantly while reducing the contents of ROS, MDA, and GSSG, when compared to H2O2 exposure. Mechanically, REO relieved oxidative stress via activating the Nrf2 signaling pathway and enhancing the protein expression of Nrf2, NQO-1, and HO-1, which was further verified by molecular docking between the main component 1.8-cineole and the Kelch domain of KEAP1. Therefore, REO could be considered as a potent natural antioxidant with a potential strategy in the food and pharmaceutical industries.

Experimental investigation of utilizing coconut shell ash and coconut shell granules as aggregates in coconut coir reinforced concrete

In the agricultural industry, coconut shells are one of the most generated wastes worldwide. In particular, studies in the Philippines show that the maximum capacity of the organic decomposition processes of agricultural products is exceeded due to the increasing agricultural activities. On the other hand, in the construction industry, cement production accounts for billions of tons of carbon dioxide emissions yearly. To address the said biowaste disposal problem, and environmental implications of the growing construction industry, this study evaluated the potential of utilizing coconut shell ash (CSA) and coconut shell granules (CSG) in concrete production as alternatives to cement and sand, being the conventional aggregates, respectively. Additionally, coconut coir (CC) was incorporated as fiber reinforcements in concrete. The experiment consisted of 15 mix designs using different proportions of CSA and CSG, ranging from 0% to 20%, and CC fiber reinforcements, ranging from 0% to 2%. The tests conducted on fresh concrete involved measuring its slump and unit weight, while the 28-day cured samples were tested for compressive and tensile strengths. Results showed that high concentrations of CSA, CSG and CC, when combined in the concrete mix, leads to poor workability; on the other hand, the modified mixes generally had lower unit weights than the conventional concrete. These are associated to the higher absorption rate, but lower density of the said agri-wastes than the conventional aggregates. In terms of the compressive and tensile strengths, all modified mixes produced lower strengths than the conventional concrete. Nonetheless, Response Surface Methodology (RSM) was utilised to model the relationship between the different independent variables considered in the study, namely CSA, CSG and CC contents, and their corresponding response to the compressive and tensile strengths. Based on the generated RSM Model, the optimum combination for obtaining the maximum strength consisted of 2% CC, resulting in a compressive and tensile strength of 23.046 MPa and 3.315 MPa, respectively. Overall, CSG-CSA coconut coir reinforced concrete is found to be a viable sustainable alternative for structures requiring low-strength, non-structural concrete, such as concrete slab patios and pathways.

Effect of seawater salinity, pH, and temperature on external corrosion behavior and microhardness of offshore oil and gas pipeline: RSM modelling and optimization

This research aims to investigate the effects of seawater parameters like salinity, pH, and temperature on the external corrosion behaviour and microhardness of offshore oil and gas carbon steel pipes. The immersion tests were performed for 28 days following ASTM G-1 standards, simulating controlled artificial marine environments with varying pH levels, salinities, and temperatures. Besides, Field emission scanning electron microscopy (FESEM) analysis is performed to study the corrosion morphology. Additionally, a Vickers microhardness tester was used for microhardness analysis. The results revealed that an increase in salinity from 33.18 to 61.10 ppt can reduce the corrosion rate by 28%. In contrast, variations in seawater pH have a significant effect on corrosion rate, with a pH decrease from 8.50 to 7 causing a 42.54% increase in corrosion rate. However, the temperature of seawater was found to be the most prominent parameter, resulting in a 76.13% increase in corrosion rate and a 10.99% reduction in the microhardness of offshore pipelines. Moreover, the response surface methodology (RSM) modelling is used to determine the optimal seawater parameters for carbon steel pipes. Furthermore, the desirability factor for these parameters was 0.999, and the experimental validation displays a good agreement with predicted model values, with around 4.65% error for corrosion rate and 1.36% error for microhardness.

Optimization of Flexural Performance of PETG Samples Produced by Fused Filament Fabrication with Response Surface Method.

Additive manufacturing (AM), particularly fused filament fabrication (FFF), has gained significant attention for its design flexibility and cost-effectiveness. This study focuses on optimizing FFF parameters that employ response surface methodology (RSM) to enhance the flexural performance of polyethylene terephthalate glycol (PETG) parts. Three essential parameters-layer height, print speed, and nozzle temperature-were varied, and their effects on flexural strength, flexural modulus, flexural toughness for ultimate strength, flexural toughness at 5% strain, and strain at ultimate strength were evaluated. Based on a Box-Behnken design, the experiments revealed significant effects of these parameters on the mechanical responses. The analysis of variance (ANOVA) indicates that layer height predominantly affects flexural modulus and toughness, while nozzle temperature significantly impacts flexural strength. The RSM models exhibited high accuracy, with R2 values exceeding 99%. Optimal parameter combinations yield remarkable improvements: flexural strength reached 39.55 MPa, flexural modulus peaked at 1344.60 MPa, flexural toughness for ultimate strength reached 218.22 J/mm3, flexural toughness at 5% strain reached 381.47 J/mm3, and strain at ultimate strength reached 3.50%. Validation experiments confirm the effectiveness of the optimization, with errors below 3.17%.

Remediation of cationic dye from wastewater using a new environmentally friendly adsorbent: A response surface methodology and artificial neural network modeling study

Remediation of cationic dye from wastewater using a new environmentally friendly adsorbent: A response surface methodology and artificial neural network modeling study

Low-cost optimization of industrial textile landfill sludge re-dewatering using ferrous sulfate and blast furnace slag

This study was based on an industrial sludge landfill with a scale of 1 million cubic meters, which had been filled for more than 10 years. It focused on the secondary dewatering of industrial textile landfill sludge (LS) with a total organic carbon (TOC) content greater than 50% and a volatile suspended solids to suspended solids (VSS/SS) ratio of 0.59. A response surface methodology (RSM) model was established using the coagulant ferrous sulfate (FeSO4) and conditioning agents such as hydrated magnesium oxide (MgO), blast furnace slag (BFS), and calcium oxide (CaO). By solving the RSM equations for the respective indicators, the optimal dosages of FeSO4, MgO, and BFS were determined to be 90 mg/g of dry sludge (DS), and for CaO 174.85 mg/g DS. Further examinations of the dewatering performance, apparent properties, extracellular polymeric substances (EPS) components, rheological characteristics, moisture distribution, and pollutant content variation led to the development of a green waste-based dewatering agent composed of FeSO4 and BFS. In small-scale diaphragm plate and frame filter press tests, the optimal water content (WC) was 69.11%. In the final production-scale experiments, it was 65.72%, with the actual application cost being only 13.07 $/ton DS. Additionally, when FeSO4 and BFS were used together, the combined action of Fe and Si could significantly reduce the biotoxicity of heavy metals (HMs), cut down 75.2% of the LS's TOC, and effectively reduced the leaching of organic substances from the leachate, which was beneficial for subsequent disposal. In conclusion, the combined use of FeSO4 and BFS for the secondary dewatering of industrial textile LS was economically efficient, effective in dewatering, and had significant harm reduction effects, making it a worthwhile for waste treatment.

Insights into water-lubricated transport of heavy and extra-heavy oils: Application of CFD, RSM, and metaheuristic optimized machine learning models

With diminishing light crude oil reserves, the focus shifts to heavy and extra-heavy crude oil, posing challenges with high viscosity impeding flow. Water-lubricated technology addresses this issue in oil transmission lines. This study introduces a novel method integrating response surface methodology (RSM), computational fluid dynamics (CFD), and optimized machine learning (ML) models to analyze pipeline pressure gradients (PG) in oil–water two-phase flows downstream of T-junctions. The present study uses the D-optimal technique for simulation design to optimize CFD computational demands efficiently. This study breaks new ground by proposing a framework that leverages support vector machines (SVMs). The proposed framework incorporates metaheuristic optimization algorithms (genetic algorithm (GA) and particle swarm optimization (PSO)) to achieve superior PG prediction accuracy. The optimized ML models outperformed RSM models for predicting PG. Results indicated that oil-to-water viscosity ratio and oil inlet velocity significantly affect PG, followed by water inlet velocity and surface tension between phases. In contrast, the oil-to-water density ratio, oil entry angle at the T-junction, and wall contact angle have minimal impact. Furthermore, statistical metrics and visual comparison tools identified the PSO-optimized SVM model based on linear kernel function as the most effective (MAPE = 13.2 % and R = 0.9949). The hybrid methodology presented in this research holds significant promise for optimizing heavy oil transfer efficiency in applications involving water-lubricated technologies.

Optimization of ultrasound-assisted extraction of phenolics from Cordia dichotoma leaves using response surface methodology

In this study, ultrasound-assisted extraction (UAE) was employed to extract phenolic compounds from Cordia dichotoma (C. Dichotoma) leaves. The extraction process was optimized using response surface methodology (RSM). The C. Dichotoma tree is found in tropical and subtropical areas and has medicinal properties. The obtained extracts were subjected to phytochemical screening using standard tests, antioxidant potential, and total phenolic content (TPC) of C. Dichotoma leaf extract by 2,2-diphenylpicrylhydrazyl assay and Folin-Ciocalteu assay respectively. The impact of process parameters like different solvents, solute-to-solvent ratio, ultrasonic power, and sonication time on TPC and antioxidant activity were studied. UAE offers a more rapid and efficient extraction process when compared to conventional extraction techniques such as Soxhlet extraction due to effective mass transfer. RSM models identified that ultrasonic power of 200 W and 30:1 (mL/g) solvent-to-solute ratio, using methanol, and a sonication time of 15 min are the optimal conditions for extraction of phenolics. FTIR analysis of methanolic extract confirmed the presence of phenolic functional groups. The compounds obtained from C. Dichotoma leaf extracts were identified by GCMS. UAE proves to be a promising technique for obtaining bioactive compounds with antioxidant potential from Cordia dichotoma leaves which can be potential applications in pharmaceutical, nutraceuticals, and functional food industries.

Evaluation of hydrogen production via steam reforming and partial oxidation of dimethyl ether using response surface methodology and artificial neural network

This study aims to develop two models for thermodynamic data on hydrogen generation from the combined processes of dimethyl ether steam reforming and partial oxidation, applying artificial neural networks (ANN) and response surface methodology (RSM). Three factors are recognized as important determinants for the hydrogen and carbon monoxide mole fractions. The RSM used the quadratic model to formulate two correlations for the outcomes. The ANN modeling used two algorithms, namely multilayer perceptron (MLP) and radial basis function (RBF). The optimum configuration for the MLP, employing the Levenberg–Marquardt (trainlm) algorithm, consisted of three hidden layers with 15, 10, and 5 neurons, respectively. The ideal RBF configuration contained a total of 80 neurons. The optimum configuration of ANN achieved the best mean squared error (MSE) performance of 3.95e−05 for the hydrogen mole fraction and 4.88e−05 for the carbon monoxide mole fraction after nine epochs. Each of the ANN and RSM models produced accurate predictions of the actual data. The prediction performance of the ANN model was 0.9994, which is higher than the RSM model's 0.9771. The optimal condition was obtained at O/C of 0.4, S/C of 2.5, and temperature of 250 °C to achieve the highest H2 production with the lowest CO emission.

Synthesis, characterization of FeNi3@SiO2@CuS for enhance solar photocatalytic degradation of atrazine herbicides: Application of RSM

This study was focused on the response surface methodology modeling (RSM) for the removal of Atrazine residue (ATZ) using nanocomposite. The FeNi3@SiO2@CuS nanocomposite was fabricated by co-precipitation and sol-gel methodologies, and their properties were studied by XRD, FTIR, VSM, and SEM techniques. The synthesis core-shell composite was used to degrade atrazine herbicide (ATZ) from synthetic wastewater. The efficiency of FeNi3@SiO2@CuS was calculated as a function of different input parameters (pH, ATZ concentration, catalyst concentration, H2O2 concentration, and reaction time by using a central composite design (CCD) adapted from response surface methodology. The CCD created a matrix of 60 runs of the experiment to look into how the five input elements interacted with one another. A reduced quadratic model was created using the findings and demonstrated good predictability of outcomes consistent with the research findings. Variance analysis (ANOVA) indicated that the chosen model was highly significant (p < 0.0001), and the coefficients of determination R2, adjusted R2, and predicted R2 were 0.9587, 0.9513, and 0.9380, respectively, confirming that the second-order regression model had been satisfactorily adjusted to obtained data. Furthermore, the findings provide validation that the degradation rate of ATZ adheres to a pseudo-first-order kinetic model, exhibiting an impressive R2 value surpassing 0.98. Through the use of a numerical optimization technique, a 95.24 % ATZ elimination was represented by the desirability of 81.12 %. This study suggests that sunlight, catalyst, and H2O2 together have a significant influence on ATZ degradation. Additionally, RSM was an appropriate process for optimizing the factors related to the elimination of ATZ by a solar-induced photocatalytic process.

A novel univariate interpolation and bivariate regression hybrid method application to biodegradation of bisphenol A diglycidyl ether using laccases from Geobacillus stearothermophilus and Geobacillus thermoparafinivorans strains.

Bisphenol A diglycidyl ether (BADGE), a derivative of the well-known endocrine disruptor Bisphenol A (BPA), is a potential threat to long-term environmental health due to its prevalence as a micropollutant. This study addresses the previously unexplored area of BADGE toxicity and removal. We investigated, for the first time, the biodegradation potential of laccase isolated from Geobacillus thermophilic bacteria against BADGE. The laccase-mediated degradation process was optimized using a combination of response surface methodology (RSM) and machine learning models. Degradation of BADGE was analyzed by various techniques, including UV-Vis spectrophotometry, high-performance liquid chromatography (HPLC), Fourier transform infrared (FTIR) spectroscopy, and gas chromatography-mass spectrometry (GC-MS). Laccase from Geobacillus stearothermophilus strain MB600 achieved a degradation rate of 93.28% within 30 min, while laccase from Geobacillus thermoparafinivorans strain MB606 reached 94% degradation within 90 min. RSM analysis predicted the optimal degradation conditions to be 60 min reaction time, 80°C temperature, and pH 4.5. Furthermore, CB-Dock simulations revealed good binding interactions between laccase enzymes and BADGE, with an initial binding mode selected for a cavity size of 263 and a Vina score of -5.5, which confirmed the observed biodegradation potential of laccase. These findings highlight the biocatalytic potential of laccases derived from thermophilic Geobacillus strains, notably MB600, for enzymatic decontamination of BADGE-contaminated environments.

Prediction, optimization, and validation of the combustion effects of diisopropyl ether-gasoline blends: A combined application of artificial neural network and response surface methodology

This research study mainly focuses on identifying the significant factors to be considered to discover the accuracy and reliability of the predictive models. The experimental results were employed to develop three different models: an artificial neural network (ANN), a response surface methodology (RSM), and a hybrid model. Brake thermal efficiency, specific fuel consumption, and regulated emissions were predicted using ANN, and inputs such as fuel blend concentration, CR, and engine speed were optimized using the RSM and hybrid models. The accuracy and reliability of the model results were validated with the least mean square error, mean absolute percentage error, and a higher signal-to-noise ratio. The higher R2 between 0.99426 and 0.9998 was observed by ANN whereas R2 by RSM and the hybrid model were relatively less. Similarly, the mean square error of ANN was relatively less compared to RSM and hybrid. However, the mean absolute percentage error observed in the validation test results for the optimized input parameters discovered by RSM, was less than 5 % for all the responses and higher in the hybrid model. Thus, the authors concluded that the ANN's predictive ability was much higher and RSM is the best suited for optimizing the engine parameters compared to the hybrid model.

Impact of Thermosonication Treatment on Parsley Juice: Particle Swarm Algorithm (PSO), Multiple Linear Regression (MLR), and Response Surface Methodology (RSM).

Thermosonication (TS), also known as ultrasonic-assisted heat treatment, is gaining attention in liquid product processing due to its ability to improve quality parameters and can serve as an alternative to thermal treatments. The parsley juice (TS-PJ) was subjected to thermosonication treatment (frequency: 26 kHz; power: 200 W; amplitude 60, 70, 80, 90, and 100%; temperature: 40, 45, 50, 55, and 60 °C; time: 4, 6, 8, 10, and 12 min) and was compared with untreated control parsley juice (C-PJ) and pasteurized treated (P-PJ) (85 °C/2 min) parsley juice samples. The objectives of the research work were to determine the effect of thermosonication on the quality attributes such as total chlorophyll and ascorbic acid of parsley juice using particle swarm algorithm (PSO), multiple linear regression (MLR), and response surface methodology (RSM). Thermosonication enhanced the bioactive compounds of parsley juice. The results showed that 15 phenolic compounds were detected in the samples. There was a significant (p < 0.05) increase in gallic acid contents in ultrasound-treated TS-PJ. There was no significant difference in total chlorophyll and ascorbic acid content between C-PJ and TS-PJ samples. Na and K from macro minerals and Fe and Zn from micro minerals were high in PJ samples. While K contents were increased, P contents were lower in the TS-PJ sample. RSM modeling provided superior prediction compared to MLR. PSO, on the other hand, made good predictions intuitively. Thermosonication enriched parsley juice's bioactive components and had positive health effects.

Unlocking groundwater desalination potential for agriculture with fertilizer drawn forward osmosis: prediction and performance optimization via RSM and ANN.

The agricultural sector uses 70% of the world's freshwater. As clean water is extracted, groundwater quality decreases, making it difficult to grow crops. Brackish water desalination is a promising solution for agricultural areas, but the cost is a barrier to adoption. This study investigated the performance of the fertilizer drawn forward osmosis (FDFO) process for brackish water desalination using response surface methodology (RSM) and artificial neural network (ANN) approaches. The RSM model was used to identify the optimal operating conditions, and the ANN model was used to predict the water flux (Jw) and reverse solute flux (Js). Both models achieved high accuracy, with RSM excelling in predicting Js (R2 = 0.9614) and ANN performing better for Jw (R2 = 0.9801). Draw solution (DS) concentration emerged as the most critical factor for both models, having a relative importance of 100% for two outputs. The optimal operating conditions identified by RSM were a DS concentration of 22 mol L-1, and identical feed solution (FS) and DS velocities of 8.1 cm s-1. This configuration yielded a high Jw of 4.386 LMH and a low Js of 0.392 gMH. Furthermore, the study evaluated the applicability of FDFO for real brackish groundwater. The results confirm FDFO's potential as a viable technology for water recovery in agriculture. The standalone FO system proves to be less energy-intensive than other desalination technologies. However, FO exhibits a low recovery rate, which may necessitate further dilution for fertigation purposes.

RSM and ANN Comparative Modelling with a Granulation Treatment in Mixed Waters

AbstractA Box‐Behnken design was used for the analysis using a gray wolf optimizer (GWO)‐coupled artificial neural network (ANN) model and response surface methodology (RSM) to analyze the effect of three operating parameters (volumetric exchange ratio [VER], aeration rate [AR], and cycle time [CT]) manipulated during an aerobic granular sludge process (AGS) sequencing batch reactor on modeling the removal of chemical oxygen demand (COD) in mixed wastewater. The most efficient architecture for COD showed the highest efficiency for modeling the AGS. The RSM model and plot results indicate that the CT and AR were the most influential on COD removal efficiency. When compared with models with statistical indices, GWO‐ANN demonstrated higher performance compared to RSM.

Multi-objective optimization for microbial electrolysis cell-assisted anaerobic digestion of swine manure

As a new technology, microbial electrolysis cell-assisted anaerobic digestion (MEC-AD) has been applied to the utilization of swine manure. Methane production and total energy efficiency are both important indicators in MEC-AD; thus, it is necessary to simultaneously optimize methane production and total energy efficiency. In this study, the Box–Behnken design (BBD) was used as the experimental design, the response surface methodology (RSM) and back propagation neural network (BP-NN) methods were used to construct multi-objective models, and the non-dominated sorting genetic algorithm II (NSGA II) was used for multi-objective optimization. The BP-NN model was superior to the RSM model in terms of goodness of fit. Additionally, the relative error between the predicted and experimental values of the Pareto front was determined to be <3%. The maximum methane production and total energy efficiency were 333.97 mL/g total solid and 61.38%, respectively. Therefore, multi-objective optimization of MEC-AD systems may be accomplished using BBD-(BP-NN)-NSGA II.

Injectable chitosan-polyvinylpyrrolidone composite thermosensitive hydrogels with sustained submucosal lifting for endoscopic submucosal dissection

To develop a submucosal injection material with sustained submucosal lifting for endoscopic submucosal dissection (ESD), this study designed and prepared a novel composite thermosensitive hydrogel system with high pH chitosan-polyvinylpyrrolidone-β-glycerophosphate (HpHCS-PVP-GP). HpHCS improved the injectability of the hydrogels and retained the rapid gelation ability at low concentrations. The modification of PVP significantly improved the stability of low-temperature hydrogel precursor solutions and the integrity of hydrogels formed at 37 °C through hydrogen bonds between PVP and HpHCS. A mathematical model was established using response surface methodology (RSM) to evaluate the synergistic effect of HpHCS, GP, and PVP concentrations on gelation time. This RSM model and submucosal lifting evaluation using in vitro pig esophageal models were used to determine the optimal formula of HpHCS-PVP-GP hydrogels. Although the higher PVP concentration (5 % (w/v)) prolonged gelation time, it improved hydrogel mechanical strength, resulting in better submucosal lifting performance. The experiments of Bama mini pigs showed that the heights of the cushions elevated by the HpHCS-5%PVP-GP hydrogel remained about 80 % 1 h after injection. Repeated injections were avoided, and the hydrogel had no cytotoxicity after electric cutting. Therefore, the HpHCS-PVP-GP thermosensitive hydrogel might be a promising submucosal injection material for ESD.

Optimization of friction stir process parameters for enhanced mechanical properties in surface-alloyed ZK60 magnesium with Tin (Sn): an RSM-ANN hybrid approach

ABSTRACT In the present work, the surface of the ZK60 Mg alloy was alloyed with tin (Sn), and the FSP process parameters have been optimized for the better mechanical properties by employing response surface methodology (RSM). Furthermore, RSM was combined with artificial neural networks (ANNs) to evaluate and compare the predictive capacity of both the models. FSP process parameters, namely, tool rotational speed (S), feed rate (F), number of passes (N), and weight percentage of Sn (W) were selected as influential parameters for optimization. The optimum conditions that were predicted by the RSM model to maximize the ultimate tensile strength (UTS) and % elongation (%EL) were a tool rotational speed of 2000 rpm, a feed rate of 0.39 mm/sec, 3 number of passes, and 8 wt.% of Sn which yields the maximum tensile strength of 217 MPa and the maximum %El of 26%.

Degradation of cefixime by photocatalysis via Ba-doped BiFeO3 nanomaterial using RSM analysis under LED light source.

In the current work, Response Surface Methodology (RSM)-a statistical method-is used to optimize procedures like photocatalysis with the least amount of laboratory testing. However, to determine the most effective model for achieving the maximum rate of removal efficiency, the Response Surface Methodology was employed. The Ba-doped BiFeO3 photocatalyst was synthesized by the co-precipitation method, and its intrinsic properties were investigated by utilizing a range of spectroscopic techniques, such as FESEM, EDX, XRD, FTIR, and UV-vis. Herein, four independent factors such as, pH, contact time, pollutant concentration, and catalyst dosage were chosen. The results revealed that under acidic conditions with a contact duration of 2min, a moderate catalyst dosage, and higher pollutant concentration, a degradation rate of 89.8% was achieved. The regression coefficient (R2) and probability value (P) were determined to be 0.99551 and 0.0301, respectively, therefore confirming the excellent fit of the RSM model. Furthermore, this research investigated the potential photocatalytic degradation mechanisms of cefixime, demonstrating that the removal efficiency of cefixime is greatly influenced by the functional parameters.

Calibration of contact parameters of sandy soil for planting tiger nut based on non-linear tools

A methodology combining physical experiments with simulation was employed to acquire contact parameters of sandy soil precisely for planting tiger nuts in the desert area of Xinjiang. The stacking angle under different parameter combinations was applied as a response value. Through the Plackett–Burman test, several factors that have a significant influence were determined. The steepest ascent test was conducted to establish the finest scope of values for these parameters. The stacking angle was considered the response variable, and non-linear tools were used to optimize these parameters for simulation. The findings showed that applying response surface methodology (RSM) resulted in a relative error of 1.24%. In the case of BP-GA, the relative error compared to the physical test value was 0.34%, while for BP, it was 2.18%. After optimization using Wavelet Neural Network (WNN), the relative error was reduced to only 0.15%. Results suggest that WNN outperforms the RSM model, and the sandy soil model and parameters generated using WNN can be effectively utilized for discrete element simulation research.