Comparative analysis of the response surface methodology (RSM) and artificial neural network (ANN) modelling for the removal of diclofenac potassium from synthesized pharmaceutical wastewater using a palm sheath fiber nano-filtration membrane and optimization

Measurement of performance and emission distinctiveness of Aegle marmelos seed cake pyrolysis oil/diesel/TBHQ opus powered in a DI diesel engine using ANN and RSM

Predicting the capability of carboxylated cellulose nanowhiskers for the remediation of copper from water using response surface methodology (RSM) and artificial neural network (ANN) models

Metal matrix composites (MMCs) have found many successful industrial applications in recent past as high-technology materials due to their properties. Wire electric discharge cutting (WEDC) process is considered to be one of the most suitable processes for machining MMCs. Lot of research work has been done on WEDC, but very few investigations have been done on WEDC of MMCs. This paper reports work on the analysis of material removal rate (MRR) and cutting width (kerf) during WEDC of 6061 Al MMC reinforced with silicon carbide particles (i.e. SiCp/6061 Al). Four WEDC parameters namely servo voltage (SV), pulse-on time (TON), pulse-off time (TOFF) and wire feed rate (WF) were chosen as machining process parameters. Artificial neural network (ANN) models and response surface methodology (RSM) models were developed to predict the MRR and kerf using Box-Behnken design (BBD) to generate the input/output database. It was observed that prediction of responses from both models closely agree with the experimental values. The ANN models and RSM models for WEDC of MMC were compared with each other on the basis of prediction accuracy which shows that ANN models are more accurate than RSM models for MRR and kerf because the values of percentage absolute errors are higher for RSM models than ANN models.

The application of additive manufacturing technologies for producing parts from polymer composite materials has gained significant attention due to the ability to create fully functional components that leverage the advantages of both polymer matrices and fiber reinforcements while maintaining the benefits of additive technology. Polymer composites are among the most advanced and widely used composite materials, offering high strength and stiffness with low mass and variable resistance to different media. This study aims to experimentally investigate the impact of selected process parameters, namely, wall thickness, raster angle, printing temperature, and build plate temperature, on the flexural properties of carbon fiber reinforced polyamide (CFrPA) fused deposition modeling (FDM) printed samples, as per ISO 178 standards. Additionally, regression and artificial neural network (ANN) models have been developed to predict these flexural properties. ANN models are developed for both normal and augmented inputs, with the architecture and hyperparameters optimized using random search technique. Response surface methodology (RSM), which is based on face centered composite design, is employed to analyze the effects of process parameters. The RSM results indicate that the raster angle and build plate temperature have the greatest impact on the flexural properties, resulting in an increase of 51% in the flexural modulus. The performance metrics of the optimized RSM and ANN models, characterized by low MSE, RMSE, MAE, and MAPE values and high R2 values, suggest that these models provide highly accurate and reliable predictions of flexural strength and modulus for the CFrPA material. The study revealed that ANN models with augmented inputs outperform both RSM models and ANN models with normal inputs in predicting these properties.

Hybrid optimisation studies on the microstructural properties and wear resistance of maraging steel 1.2709 parts produced by laser powder bed fusion

For the transesterification of biodiesel from Azolla oil, the safe and successful use of feed stocks is a very significant prerequisite. It is of high importance to determine the optimal reaction parameters to maximize the yield of low-cost biodiesel generated from Azolla oil. Ultrasonic energy was used in this work for the development of biodiesel from Azolla oil catalyzed by the KOH catalyst under different conditions. The effect on the transesterification of Azolla Oil to biodiesel of four reaction parameters, namely the methanol/Azolla oil molar ratio (A), KOH catalyst concentration (B), reaction time (C) and reaction temperature (D) were considered. In order to optimize the effects of reaction parameters for the transesterification of Azolla oil to biodiesel, response surface methodology (RSM) based on central composite rotatable design (CCRD) is applied. To obtain a good correlation between the input reaction parameters and the output response parameter (FAME yield) from Azolla oil to biodiesel, an artificial neural network (ANN) model with two feed-forward back-propagation neural-network architecture Multilayer Perceptron Network (MLP) and Radial Basis Function Network (RBFN) was developed. With the experimental information obtained from the RSM model, the built ANN models were trained and evaluated. Absolute Average Deviation (AAD), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and coefficient of determination were statistically compared with the predictive capacity of both RSM and ANN models (R2). The statistical analysis showed that the measured FAME yield from both the RSM and ANN models was able to predict the FAME yield, and the findings limited the ANN model to the much more reliable FAME yield prediction compared to the RSM model.

Machine learning and response surface methods for predicting the compressive strength of high-strength concrete have not been adequately compared. Therefore, this research aimed to predict the compressive strength of high-strength concrete (HSC) using different methods. To achieve this purpose, neuro-fuzzy inference systems (ANFISs), artificial neural networks (ANNs), and response surface methodology (RSM) were used as ensemble methods. Using an ANN and ANFIS, high-strength concrete (HSC) output was modeled and optimized as a function of five independent variables. The RSM was designed with three input variables: cement, and fine and coarse aggregate. To facilitate data entry into Design Expert, the RSM model was divided into six groups, with p-values of responses 1 to 6 of 0.027, 0.010, 0.003, 0.023, 0.002, and 0.026. The following metrics were used to evaluate model compressive strength projection: R, R2, and MSE for ANN and ANFIS modeling; R2, Adj. R2, and Pred. R2 for RSM modeling. Based on the data, it can be concluded that the ANN model (R = 0.999, R2 = 0.998, and MSE = 0.417), RSM model (R = 0.981 and R2 = 0.963), and ANFIS model (R = 0.962, R2 = 0.926, and MSE = 0.655) have a good chance of accurately predicting the compressive strength of high-strength concrete (HSC). Furthermore, there is a strong correlation between the ANN, RSM, and ANFIS models and the experimental data. Nevertheless, the artificial neural network model demonstrates exceptional accuracy. The sensitivity analysis of the ANN model shows that cement and fine aggregate have the most significant effect on predicting compressive strength (45.29% and 35.87%, respectively), while superplasticizer has the least effect (0.227%). RSME values for cement and fine aggregate in the ANFIS model were 0.313 and 0.453 during the test process and 0.733 and 0.563 during the training process. Thus, it was found that both ANN and RSM models presented better results with higher accuracy and can be used for predicting the compressive strength of construction materials.

Metal-organic framework MIL-100(Fe) for dye removal in aqueous solutions: Prediction by artificial neural network and response surface methodology modeling

This research investigated and optimized the separation of carbon dioxide (CO2) from natural gas in an adsorption column filled with grafted-beam nanofiber adsorbent. The main purpose of using ANN and RSM models in this manuscript is to compare these two methods in predicting the CO2 adsorption capacity. In other words, it was made to find a suitable model that has the highest agreement with the experimental data. Also, the other purpose of using the RSM model is to detect the optimized empirical conditions. Moreover, two common ANN models are applied in this work, including a multilayer perceptron (MLP) and radial basis function (RBF). The novelties of this work are explained as follows: (1) detecting the optimized synthesis factors of NF-PAN/PUGMA sorbent which possess the highest CO2 adsorption capacity with the help of response surface methodology (RSM), (2) studying the simultaneous interaction of synthesis parameters on the CO2 adsorption capacity with the help of both RSM and artificial neural networks (ANNs), (3) testing two types of ANN models including multilayer perceptron (MLP) and radial basis function (RBF) to predict the effect of monomer volume percentage and irradiation dose on the CO2 adsorption capacity. Indeed, finding the best model (ANN or RSM) can help engineers in practical applications predict CO2 adsorption capacity using NF-PAN/PUGMA under different conditions without incurring high-priced chemical materials, electricity, or human resources. The validation results were examined using correlation coefficients (R2) of RSM, RBF, and MLP models. The correlation coefficients for the RSM, RBF, and MLP models were 0.9910, 0.9949, and 0.9968, respectively. Additionally, the average absolute relative deviation (AARD) values for the RBF and MLP models were 0.00046512 and 0.00045511, respectively, indicating that the MLP model is better than the RBF model. To identify the optimal network structure, the trial-and-error method was conducted for MLP and RBF models. The number of neurons was found at 12 and 45 for MLP and RBF, respectively. The optimized effective parameters were obtained using RSM: 25.80% GMA, 66.45% amine, and an irradiation intensity of 28kGy.

BackgroundSalsola laricifolia is a typical C3–C4 typical desert plant, belonging to the family Amaranthaceae. An efficient single-cell system is crucial to study the gene function of this plant. In this study, we optimized the experimental conditions by using Box-Behnken experimental design and Response Surface Methodology (RSM)-Artificial Neural Network (ANN) model based on the previous studies.ResultsAmong the 17 experiment groups designed by Box-Behnken experimental design, the maximum yield (1.566 × 106/100 mg) and the maximum number of viable cells (1.367 × 106/100 mg) were obtained in group 12, and the maximum viability (90.81%) was obtained in group 5. Based on these results, both the RSM and ANN models were employed for evaluating the impact of experimental factors. By RSM model, cellulase R-10 content was the most influential factor on protoplast yield, followed by macerozyme R-10 content and mannitol concentration. For protoplast viability, the macerozyme R-10 content had the highest influence, followed by cellulase R-10 content and mannitol concentration. The RSM model performed better than the ANN model in predicting yield and viability. However, the ANN model showed significant improvement in predicting the number of viable cells. After comprehensive evaluation of the protoplast yield, the viability and number of viable cells, the optimal results was predicted by ANN yield model and tested. The amount of protoplast yield was 1.550 × 106/100 mg, with viability of 90.65% and the number of viable cells of 1.405 × 106/100 mg. The corresponding conditions were 1.98% cellulase R-10, 1.00% macerozyme R-10, and 0.50 mol L−1 mannitol. Using the obtained protoplasts, the reference genes (18SrRNA, β-actin and EF1-α) were screened for expression, and transformed with PEG-mediated pBI121-SaNADP-ME2-GFP plasmid vector. There was no significant difference in the expression of β-actin and EF1-α before and after treatment, suggesting that they can be used as internal reference genes in protoplast experiments. And SaNADP-ME2 localized in chloroplasts.ConclusionThe current study validated and evaluated the effectiveness and results of RSM and ANN in optimizing the conditions for protoplast preparation using S. laricifolia as materials. These two methods can be used independently of experimental materials, making them suitable for isolating protoplasts from other plant materials. The selection of the number of viable cells as an evaluation index for protoplast experiments is based on its ability to consider both protoplast yield and viability. The findings of this study provide an efficient single-cell system for future genetic experiments in S. laricifolia and can serve as a reference method for preparing protoplasts from other materials.

Application of response surface methodology and artificial neural network modeling to assess non-thermal plasma efficiency in simultaneous removal of BTEX from waste gases: Effect of operating parameters and prediction performance

In this experimental investigation, Artificial Neural Network (ANN) and Response Surface Methodology (RSM) model structures were constructed to predict and optimize the performance and exhaust emissions of a diesel engine operating on a blend of diesel fuel and waste oil biodiesel. The test engine was operated with 0%, 50%, and 100% biodiesel content under varying injection pressures and loads. The RSM model was used to derive regression equations from the experimental results. The correlation coefficient (R2) for all responses of the constructed model ranged from 0.9785 to 0.9997. By applying the developed model, the brake thermal efficiency (BTE) response was optimized to its maximum value, while all other responses were minimized. All responses were predicted using an ANN model with R > 0.99 and a maximum mean absolute error (MAAE) of 1.723%. RSM-based optimization analysis was applied to the design of experiments (DOE). At an injection pressure of 180 bar, an engine torque of 3.846 Nm, and a 100 percent biodiesel ratio, optimal diesel engine performance characteristics, the lowest exhaust emissions, and the lowest specific fuel consumption values were achieved. In addition, the RSM approach performed satisfactorily, with a desirability value of 0.750. The RSM regression equations were assessed using the Actor Critic with Kronecker-Factored Trust Region-Differential Evolution (ACKTR-DE) and Harris Hawks Optimization (HHO) algorithms. The outcomes derived from the ACKTR-DE and HHO algorithms corroborated the results obtained from the RSM. Furthermore, verification experiments were conducted to confirm the optimal results, thus demonstrating that the combined use of RSM, ANN, and advanced algorithms offers a robust and accurate framework for optimizing biodiesel engine performance.Graphical

In this work, total solids reduction process was numerically modeled with response surface methodology (RSM) and Artificial neural network (ANN) models. The experimental data was used for training these models. Amplitude of the ultrasonic waves, time of ultrasonication and total solids present in the sludge are input to the model. These factors are varied to five levels and by conducting design of experiments, the actual values were measured. The response surface methodology was used to determine the relation between the factors and total reduction in solids. To overcome the flaws in the response surface methodology, an artificial neural network model is developed and the results of the ANN models are compared with RSM models and experimentally measured values.

Evaluation of pistachio shells as solid wastes to produce activated carbon for CO2 capture: Isotherm, response surface methodology (RSM) and artificial neural network (ANN) modeling

The competent and efficient utilization of feedstocks is highly essential in the transesterification of biodiesel from sardine fish oil. The identification of optimal reaction parameters is of high importance to maximize the yield of biodiesel produced from sardine fish oil at low cost. Application of ultrasonic energy-assisted biodiesel production from sardine fish oil catalyzed by KOH catalyst has been studied under different conditions. Response surface methodology (RSM) based on central composite rotatable design (CCRD) was employed to optimize the three important process parameters: methanol/oil molar ratio (X1), KOH catalyst concentration (X2), and reaction time (X3) for transesterification of sardine fish oil using ultrasonic energy. Artificial neural network (ANN) models with two feed-forward back-propagation neural network architecture, multilayer perceptron networks and radial basis function networks have been developed to obtain a good correlation between the input variables responsible for the input reaction parameters and the output parameter yield of fatty acid methyl ester (FAME) from sardine fish oil to biodiesel. The developed ANN models were trained and tested with the experimental data obtained from the RSM–CCRD method. The developed ANN models’ performances were compared with experimental data and were statistically compared by the coefficient of determination (R2), root-mean-square error, and mean absolute error. From the statistical analysis, it was found that the estimated yield of FAME from both RSM and ANN models was able to predict the FAME yield, and the results showed that the ANN model is much more accurate in the prediction of FAME yield as compared to the RSM model.