Response Surface Methodology Model Research Articles

Optimization of composite-modified asphalt ratio and performance evaluation of rich bottom layer mixes

This study focused on developing a high-toughness composite-modified asphalt (CMA), to enhance the performance of rich bottom layer mixes and its crack resistance. The content of crumb rubber (CR), epoxy resin (ER), and polyaryl polymethylene isocyanate (PMDI) were chosen as design parameters. Response surface methodology (RSM) was employed to develop models for creep strength (S), fatigue performance parameter (Nf), yield energy (YE), and Brookfield viscosity (BV) of CMA. Then, these models were used to optimize the content of modifiers. Additionally, rich bottom layer mixes were designed using SBS-modified asphalt (SMA) and the newly developed CMA as binders, and their performances were evaluated. The results demonstrated the accuracy and predictive ability of the RSM models. The composite-modified asphalt mixes (CMAM) showed superior road performance and crack resistance compared to SBS-modified asphalt mixes (SMAM). Importantly, CMAM aligned effectively with the functional requirements of the rich bottom layer. This research contributes novel materials and methods for enhancing pavement crack resistance, providing valuable insights for improving the durability and longevity of pavements.

Activated Carbon as an Adsorbent for CO2 Capture: Adsorption, Kinetics, and RSM Modeling.

The main objective of this research is to investigate the adsorption isotherms and the optimization of the carbon dioxide (CO2) adsorption process on activated carbon (AC). The AC has been characterized by X-ray diffraction, scanning electron microscopy, and nitrogen (N2) adsorption-desorption. The CO2 isotherms measured under three adsorption temperatures (318, 308, and 298 K) were investigated by the Langmuir and linear driving force model. It is found that the Langmuir model could well predict the adsorption behavior of AC with a correlation factor of about 0.99. The kinetic model shows that the amount of CO2 increases at higher pressures. The response surface methodology (RSM) was applied to investigate the effects of process variables and their interaction on the response to reach the optimal conditions. Based on the analysis of variance, pressure and temperature are the main factors influencing the CO2 adsorption capacity. The effective parameters obtained through this model are found to have a value of p < 0.05. In addition, a semiempirical correlation was developed under the optimal operating conditions which are 25 °C and 9 bar. The results indicated that RSM models provide an effective method for evaluating CO2 adsorption. These results point that AC is a potential adsorbent for CO2 capture.

Enhanced anti-impact resistance of polyurethane foam composites with multi-phase shear thickening fluids containing various carbon nanofillers

Polyurethane (PU) foams are commonly used as impact-absorbing materials in various applications. However, the anti-impact performance of PU foams can be improved by incorporating non-Newtonian based smart fluids in their strudture. This study enhanced a open-cell based PU foam with multi-phase shear thickening fluids (M-STFs) containing three types of carbon nanofillers (carbon nanotubes, carbon nanofibers, and their mixture). M-STFs were prepared by adding 0.5, 1.0, 1.5, and 2.0 wt% of carbon nanofillers to the STF (55 wt% SiO2) and subsequently impregnating them into the structure of PU foams. Various configurations of PU foam composites were designed and evaluated for their impact resistance performance using a low-velocity drop tower system. The rheological behavior of fabricated suspensions were characterized using a rotational two plate-based system. The chemical structure and morphology of M-STF-treated PU foams were characterized by FTIR spectroscopy and SEM, respectively. The peak deceleration values in terms of G were measured after subjecting the designed samples to impacts at three different heights of 0.1, 0.2, and 0.3 m. The results indicated that the addition of nanofillers enhaced the initial and peak viscosity of the STF, and carbon nanotube had the most significant effect. The PUF-STF/CNT(1.5) sample exhibited approximately 62.7% to 73.0% reduction at different heights in peak deceleration compared to the clean PU foam sample. Moreover, to investigate the impact of filler content, drop height, and type of fillers on the peak deceleration of M-STF-treated PU foams, a response surface methodology analysis was utilized and the model provided insight into the optimal conditions for minimizing peak deceleration. This study highlights the potential of using M-STFs to enhance the anti-impact performance of PU foams.

Unraveling Biotic and Abiotic Factors Shaping Sugarcane Straw Polyphenolic Richness: A Gateway to Artificial Intelligence-Driven Crop Management.

Sugarcane straw (Saccharum officinarum) is a valuable coproduct renowned for its abundant polyphenolic content. However, extracting these polyphenols for natural ingredients faces challenges due to their inherent variability, influenced by biotic stress factors and plant characteristics. We explored the impact of five crucial factors on sugarcane straw polyphenolic diversity: (i) production area (Guariba, Valparaíso), (ii) borer insect (Diatraea saccharalis) infestation, (iii) plant age (first to seventh harvest), (iv) harvest season, and (v) plant variety. Response surface methodology (RSM) and artificial neural networks (ANN) were used to optimize polyphenol extraction conditions. A second-order polynomial model guided us to predict ideal sugarcane straw harvesting conditions for polyphenol-rich extracts. The analysis identified CU0618-variety straw, harvested in Guariba during the dry season (October 2020), at the seventh harvest stage, with 13.81% borer insect infection, as the prime source for high hydroxybenzoic acid (1010 µg/g), hydroxycinnamic acid (3119 µg/g), and flavone (573 µg/g) content and consequently high antioxidant capacity. The ANN model surpasses the RSM model, demonstrating superior predictive capabilities with higher coefficients of determination and reduced mean absolute deviations for each polyphenol class. This underscores the potential of artificial neural networks in forecasting and enhancing polyphenol extraction conditions, setting the stage for AI-driven advancements in crop management.

Supercritical CO2 for efficient extraction of high-quality starfish (Asterias rubens) oil

The growing demand for healthy marine lipids and the unmatched supply demand required diversification of their sources. Characterization of starfish oil has shown that it is a new possible source of marine lipids. However, optimized extraction conditions are needed to extract high quality oil. Hence, this study aimed to extract starfish oil using supercritical carbon dioxide (ScCO2) and optimize the extraction process using response surface methodology (RSM). Temperature, pressure, and ethanol flow rate were the most influential parameters for oil extraction. The RSM model predicted 19.40% and 25.40% for extraction yield and phospholipids content respectively at 40 °C, 350 bar, and 2.61 mL/min of ethanol flow rate. The result of validation extraction showed close agreement with the model. Moreover, the oils were characterized by fatty acid composition, peroxide value, free fatty acid, and tocopherol contents. The current study showed that high-quality lipids could be extracted from underutilized species using ScCO2 extraction.

Removal of Dibenzothiophene from Liquid Fuels by Photocatalytic Desulfurization over Zn‐Terephthalate Complex: A Process Modelling and Optimization by Response Surface Methodology

AbstractThe Zn‐terephthalate complex was synthesized by hydrothermal method at 180 °C and applied as a novel macroporous photocatalyst in the desulfurization of dibenzothiophene (DBT) from a fuel model to produce DBT sulfone. The physical‐chemical properties of the photocatalyst were characterized by Fourier Transform Infrared (TIR), Raman spectroscopy, powder X‐ray diffraction (XRD), field‐emission scanning electron microscopy (FESEM), photoluminescence spectroscopy and UV‐Vis diffuse reflectance spectroscopy (UV‐Vis DRS). The characterization results indicated the formation of mono‐nuclear paddlewheel metal‐organic framework (MOF) as Zn [COO]2 frameworks. The photocatalytic experiments were designed by the Box‐Behnken method of response surface methodology (RSM) and the photocatalytic process was modelled and optimized. Three independent process variables i. e. concentration of dibenzothiophene, the amount of photocatalyst and light irradiation time were considered in the experimental design. The determination coefficient (R2) of the model is 0.97. The optimum removal of DBT (50.4 %) resulted in a fuel model containing 50 ppm DBT, desulfurized by 0.060 g of Zn‐MOF photocatalyst under visible light for 180 min. The predicted response (DBT removal%) by the RSM model was 50.68 %. According to the Pareto analysis, the order of importance of independent factors on the response was as follows: Irradiation time&gt;m(photocatalyst)&gt;C(DBT).

Optimization and modeling of vacuum impregnation of pineapple rings and comparison with osmotic dehydration.

The vacuum impregnation (VI) process parameters (vacuum pressure=20-60kPa; VI temperature=35-55°C; concentration of the sucrose solution=40-60°Brix; and vacuum process time=8-24min) for pineapple rings were optimized based on the moisture content (MC), water loss (WL), solids gain (SG), yellowness index (YI), and total soluble solids (TSS) content of pineapple rings using response surface methodology (RSM). A relationship was developed between the process and response variables using RSM and artificial neural network (ANN) techniques. The effectiveness of VI was evaluated by comparing it with the osmotic dehydration (OD) technique. The optimum condition was found to be 31.782kPa vacuum pressure, 50.441°C solution temperature, and 60°Brix sucrose concentration for 20.068min to attain maximum TSS, YI, SG, and WL, and minimum MC of pineapple rings. The R2 values of RSM models for all variables varied between 0.70 and 0.91, whereas mean square error values varied between 0.76 and 71.58 and for ANN models varied between 0.87-0.93 and 0.53-193.78, respectively. Scanning electron micrographs (SEM) revealed that parenchymal cell rupture was less in VI than in OD. The VI pineapple rings exhibited more pores and high SG, as compared to OD, due to the pressure impregnation. Spectroscopic analysis affirmed that the stretching vibrations of intermolecular and intramolecular interactions were significant in VI as against OD. The VI reduced the drying time by 35% compared to OD, with the highest overall acceptability score and lower microbial load during storage. PRACTICAL APPLICATION: Pineapple is a perishable fruit, which necessitates processing for extended shelf life. This study highlights the potential of the vacuum impregnation process as a promising alternative to conventional preservation methods such as osmotic dehydration for pineapples.

Application of Response Surface Methodology (RSM) to Study Transesterification of Palm Oil in the Presence of Zeolite-A as Catalyst

In this research, the application of response surface methodology with central composite design (RSM-CCD) to optimizetransesterification of palm oil in the presence of zeolite-A as catalyst was investigated. Zeolite-A was synthesized from rice husk silica (RHS) and food-grade aluminium foil using hydrothermal method and then characterized using XRD and SEM. The synthesized zeolite was then applied to an optimized transesterification reaction using response RSM with three factorial levels, for three variables including methanol to oil ratio, catalyst load, and reaction time. The experimental results indicate that the yield of 99% was achieved at optimum conditions of methanol to oil volume ratio of 6, catalyst load of 9.6%, and reaction time of 4.3 hours. The results of experiments and predicted results based on the RSM model are in agreement as shown by the p-value less than 0.05 at a confidence level of 95%.

A comparative and comprehensive study on robust silver-immobilized chitosan/carboxy methyl chitosan macromolecules for elimination of dyes

This study explores the potential of modified chitosan (CH) resulting from carboxy methyl (CM) group substitution in the chitosan skeleton, producing carboxy methyl chitosan (CMCH). Silver (Ag) was loaded to chitosan and modified chitosan (carboxy methyl substituted chitosan) supported on cellulose filter paper (FP) to tailor organic–inorganic macromolecule composites “Ag-Chi/CFP and Ag-CMCH/CFP” further applied as substantial composite for the reductive removal of methyl orange (MO) and rhodamine B (RB) dyes from wastewater. The synthesized catalyst was characterized via FTIR, 1H NMR, XRD, and SEM analysis. The degree of substitution was also measured, which was approximately 0.85. The reductive elimination efficiency of Ag-CMCH/CFP and Ag-Chi/CFP for MO dye was 95.31% and 88.85 %. In comparison, the elimination of RB was 98.28 and 94.04 %, respectively, in 16 min time, 0.6 mM Ag concentration, 40 mg/L, dye concentration and 0.3 M NaBH4 concentration. The synthesized catalysts were recycled three times. The degradation of MO and RB dyes using Ag-CMCH/CFP and Ag-Chi/CFP catalysts can be described by the first-order kinetic model. The determination coefficients (R2) and rate constants (k) values for MO degradation were found to be 0.9934 and 0.1736 s−1 for Ag-CMCH/CFP, and 0.9242 and 0.1001 s−1 for Ag-Chi/CFP, respectively. Similarly, for RB degradation, the R2 values were 0.9866 for Ag-CMCH/CFP and 0.9803 for Ag-Chi/CFP, with corresponding k values of 0.219 s−1 and 0.1548 s−1, respectively. The degradation process was optimized using response surface methodology (RSM) to determine the optimal conditions for maximum dye removal efficiency. The RSM models were statistically significant, as validated by ANOVA and correlation coefficient values. The F values of MO and RB models are significant as they have 2450.15 and 21999.37, respectively. The P value for both was <0.0001.

Ecofriendly process optimization of natural dye extraction from clove (Syzygium aromaticum L.) using response surface methodology and artificial neural network modeling

Process optimization through statistical and machine learning techniques has been used as the recent growing interest for the researchers. Current study has been undertaken to optimize the dyeing behavior of clove (Syzygium aromaticum L.) for cotton under microwave treatment. Response surface methodology and artificial neural network modeling with simplest structure was performed to observe the significance of selected color parameters. The results reveal that if extract and fabric is simulated for optimal MW time then high yield is obtained. For shade development, dyeing of irradiated cotton at 70 °C for 50 min with irradiated extract of 7 pH, using 3 g/100 mL salt has given color fast shades. ISO standard reveal that using selected dyeing conditions with carefully chosen chemical and bio-mordants has possessed good to excellent ratings. Microwave treatment should be used to get effective yield of colorant from clove for cotton dyeing. Additionally the utilization of ecofriendly mordants to make dyeing process greener should be included. It is recommended that artificial neural network model has better predicative capacity for process optimization as compared to response surface methodology.

Process optimization and quality prediction of laser aided additive manufacturing SS 420 based on RSM and WOA-Bi-LSTM

Laser aided additive manufacturing (LAAM) is a promising method for surface modification, repair, and direct fabrication 3D components. Nevertheless, the high cost and time-consuming nature of trial-and-error based process development hinder the progress of this technology. Consequently, machine learning has garnered substantial attention from both the academia and industrial sectors due to its ability to expedite process study for both research and industrial applications. This paper presents a study on the LAAM of 420 stainless steel on the surface of Q235 steel using the combined Response Surface Methodology (RSM) and Whale Optimization Algorithm-Bidirectional Long Short-Term Memory (WOA-Bi-LSTM) model. The results show that the process parameters can be effectively optimised to improve the prediction accuracy using the multi-objective whale optimisation algorithm for the fitted equations obtained in response surface analysis. At the same time, with the support of a large number of data, the WOA-Bi-LSTM model can accurately predict the quality of the LAAMed material, which is better than the response surface analysis method.

Response surface optimization and support vector regression modeling of microwave-assisted essential oil extraction from cumin seeds

The current research involved creating models using Response Surface Methodology (RSM) and Support Vector Regression (SVR) to forecast the amount of extractable essential oil that can be obtained from powdered cumin seeds. Influence of microwave power (140–280–420–560–700 W), amount of water (500–600–700–800–900 ml), duration of distillation (30–45–60–75–90 min) and soak time (15–30–45–60–75 min) on essential oil yield were investigated. Microwave Assisted Extraction (MAE) allowed higher recoveries compared to conventional Soxhlet extraction, without altering the chemical components of the extract. A five-level four FCC experimental design was developed using Minitab (15.1.20.0). A total of 31 runs were performed in microwave-assisted extraction apparatus. Experimental data obtained was then used for developing RSM and SVR models for the prediction of the yield of essential oil. The optimum conditions for maximum yield of cumin oil were given by RSM. Maximum yield of 3.4 ml (0.017 ml/g) was found at 140 W of microwave power, 500 ml of water, 90 min duration of distillation, and 15 min of soak time. In this work, epsilon SVR with RBF kernel was used. The grid search (depth-first search) methodology was applied for tuning the values of epsilon, gamma, and cost using the LIBSVM module on the MATLAB interface. The statistical parameters namely, average absolute relative error (AARE), coefficient of determination (R2), standard deviation (SD), and root mean square error (RMSE) were selected as the performance parameters. The developed SVR model was compared with the RSM model. The AARE values of 2.27% and 1.29%, R2 values of 0.86 and 0.99, SD values of 1.73 and 0.29, and RMSE values of 0.0284 and 0.0132 were obtained for RSM and SVR models respectively. It is found that SVR is more accurate and better tool for modeling of MAE process.

Impact of extrusion processing on bioactive compound enriched plant-based extrudates: A comprehensive study and optimization using RSM and ANN-GA

The present study aimed to investigate the influence of extrusion process parameters, specifically screw speed (300 to 500 rpm) and temperature (130 to 150 °C) on the stability of phytosterols and the physical, functional, and structural characteristics of extrudates made from composite blends containing pea protein isolate (PPI). Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA) were employed to optimize the process parameters.Extrudates produced at lower temperatures exhibited higher phytosterol retention compared to those produced at higher temperatures. X-ray diffraction analysis showed that incorporating PPI significantly increased crystallinity from 22% to 33%. Fourier Transform Infrared analysis revealed a reduction in the Amide II stretching of the secondary structure in pea protein isolate following extrusion. Scanning electron microscopy images demonstrated that the addition of PPI and functional oil altered the structure of the extrudates.Statistical comparison of the process parameters using higher R2 values, lower root mean square as well as mean absolute percentage error indicated that the ANN model outperformed the RSM model in predicting all responses. Overall, incorporating PPI and phytosterols into corn-based extrudates improved their nutritional quality. These findings contribute to meeting the demand for fortified foods.

Employing RSM and ANN-based applications for modelling enhanced bacterial cellulose production from pineapple peel waste using Komagateibacter saccharivorans APPK1

Bacterial cellulose (BC) is a bio-polymer with variety of applications in diverse fields such as biomedical, cosmetics, environment and food. Owing to its superior crystallinity, purity and other unique properties BC outshines plant cellulose in several aspects. The current study focuses on a cost-effective method for BC production from pineapple waste using Komagateibacter saccharivorans APPK1. The research includes a systematic optimisation approach that includes screening of various components in the medium. The variables under investigation in this study were ammonium sulphate, ethanol volume and incubation days. These variables were selected and evaluated using the Plackett-Burman experimental design model. Additionally, the engineering of process parameters using a process-driven approach i.e., response surface methodology (RSM) and a data-driven approach i.e., artificial neural network (ANN) were employed. BC production was increased up to 43 % with a maximum yield of 47.5 g/100 mL using RSM. However, on comparing RSM and ANN models for enhanced BC production based on error functions such as R2 value, mean absolute percentage error, root mean square error, and mean absolute deviation values, the ANN model emerged as the superior one. As this is the first report to the authors’ best knowledge on model development using a hybrid approach of RSM and ANN for enhanced BC production using Komagateibacter saccharivorans APPK1, this could lead to more in-depth and large-scale production of BC.

Establishment of temperature‐strength response prediction model of supramolecular gel temporary plugging agent by response surface method analysis

AbstractIn this study, response surface methodology (RSM) was applied to optimize the preparation of a supramolecular gel temporary plugging agent. According to single‐factor experiments, results showed that the gelling temperature was preset at 100°C, and the gelling strength reached the maximum. Three‐factor RSM model prediction was carried out to explore the determination coefficient (R2) of the gelling temperature and strength and their degree of influence, and the predicted results were verified by experiments. No difference was found between the experimental results and the predicted results, indicating that this model could be used to optimize the preparation of DMF supramolecular gels. DSC, FT‐IR, XRD, and SEM were used to investigate the formation mechanism of the optimized supramolecular gel temporary plugging agent (CD‐Da), and its compatibility, stability, rheology, and plugging performance were evaluated. Experimental results showed that CD‐Da has the characteristics of good injectability in low‐permeability rock cores, strong plugging capacity, self‐breaking, and less damage to the formation. Therefore, this study not only provides a stable and efficient supramolecular gel plugging agent for tight oil and gas reservoirs, but also establishes a prediction model between each component and supramolecular gel, providing a new optimization method for the preparation of supramolecular gel.

Overall efficiency increment in a pin-fin microchannel heat sink using response surface methodology and Pareto optimization

In modern technology, anticipating the optimal thermal performance in microchannel heat sinks (MCHSs) is a vital response to overheating challenges in high-performance electronic devices. The Response Surface Methodology (RSM) application presents an innovative way of modeling such equipment's behavior. In the current study, an MCHS with pin fins was chosen to examine the Nusselt number (Nu) and pressure drop (ΔP). Using two RSM models and multi-objective optimization, the study tried to discern the optimal values of the responses in the MCHS. The RSM models examined three key parameters: length, installation angle, and the spacing between pin fins. The interactive impacts of independent parameters and their consequent effect on the responses were also explored. The RSM models attained exceptional predictive accuracy, with the determination coefficient values of 0.9946 and 0.9986 for Nu and ΔP, respectively. The working fluid applied in the present work was water with an inlet temperature of 288 K and a Reynolds number of 300. Besides, the constant heat flux of 100 W/cm2 was exposed to the bottom wall of the MCHS. In pursuit of an optimal MCHS design that maximizes Nu while minimizing ΔP, the study employed the relative efficiency index (η) parameter. Considering heat transfer and ΔP in the pin-fin MCHS, the optimal design showed an impressive improvement of about 64.5 % compared to the fin-free heat sink. Moreover, the study provided Pareto optimal points for Nu, ΔP, and η, adding depth to understanding the complex trade-offs within the investigated MCHS.

Screening of Metal Oxides and Hydroxides for Arsenic Removal from Water Using Molecular Dynamics Simulations.

Arsenic in groundwater is a harmful and hazardous substance that must be removed to protect human health and safety. Adsorption, particularly using metal oxides, is a cost-effective way to treat contaminated water. These metal oxides must be selected systematically to identify the best material and optimal operating conditions for the removal of arsenic from water. Experimental research has been the primary emphasis of prior work, which is time-consuming and costly. The previous simulation studies have been limited to specific adsorbents such as iron oxides. It is necessary to study other metal oxides to determine which ones are the most effective at removing arsenic from water. In this work, a molecular simulation computational framework using molecular dynamics and Monte Carlo simulations was developed to investigate the adsorption of arsenic using various potential metal oxides. The molecular structures have been optimized and proceeded with sorption calculations to observe the adsorption capabilities of metal oxides. In this study, 15 selected metal oxides were screened at a pressure of 100 kPa and a temperature of 298 K for As(V) in the form of HAsO4 at pH 7. Based on adsorption capacity calculations for selected metal oxides/hydroxides, aluminum hydroxide (Al(OH)3), ferric hydroxide (FeOOH), lanthanum hydroxide La(OH)3, and stannic oxide (SnO2) were the most effective adsorbents with adsorption capacities of 197, 73.6, 151, and 42.7 mg/g, respectively, suggesting that metal hydroxides are more effective in treating arsenic-contaminated water than metal oxides. The computational results were comparable with previously published literature with a percentage error of 1%. Additionally, SnO2, which is rather unconventional to be used in this application, demonstrates potential for arsenic removal and could be further explored. The effects of pH from 1 to 13, temperature from 281.15 to 331.15 K, and pressure from 100 to 350 kPa were studied. Results revealed that adsorption capacity decreased for the high-temperature applications while experiencing an increase in pressure-promoted adsorption. Furthermore, response surface methodology (RSM) has been employed to develop a regression model to describe the effect of operating variables on the adsorption capacity of screened adsorbents for arsenic removal. The RSM models utilizing CCD (central composite design) were developed for Al(OH)3, La(OH)3, and FeOOH, having R2 values 0.92, 0.67, and 0.95, respectively, suggesting that the models developed were correct.

Research on a Multi-Objective Optimization Design for the Durability of High-Performance Fiber-Reinforced Concrete Based on a Hybrid Algorithm

To achieve durable high-performance fiber-reinforced concrete that meets economic requirements, this paper introduces a hybrid intelligent framework based on the Latin hypercube experimental design, response surface methodology (RSM), and the NSGA-III algorithm for optimizing the mix design of high-performance fiber-reinforced concrete. The developed framework allows for the prediction of concrete performance and obtains a series of Pareto optimal solutions through multi-objective optimization, ultimately identifying the best mix proportion. The decision variables in this optimization are the proportions of various materials in the concrete mix, with concrete’s frost resistance, chloride ion permeability resistance, and cost as the objectives. The feasibility of this framework was subsequently validated. The results indicate the following: (1) The RSM model exhibits a high level of predictive accuracy, with coefficient of determination (R-squared) values of 0.9657 for concrete frost resistance and 0.9803 for chloride ion permeability resistance. The RSM model can be employed to construct the fitness function for the optimization algorithm, enhancing the efficiency of multi-objective optimization. (2) The NSGA-III algorithm effectively balances durability and cost considerations to determine the optimal mix proportion for the concrete. After multi-objective optimization, the chloride ion permeability resistance and frost resistance of the high-performance fiber-reinforced concrete improved by 38.1% and 6.45%, respectively, compared to the experimental averages, while the cost decreased by 2.53%. The multi-objective optimization method proposed in this paper can be applied to mix design for practical engineering projects, improving the efficiency of concrete mix design.

Effect of sodium dodecyl sulfate on preferential degradation of naphthalene by thermally activated persulfate: Implications for soil flushing solution reuse

Various flushing solutions that contain hydrophobic organic compounds and surfactants are generated during surfactant-enhanced soil flushing processes. How to effectively remove the target pollutants while minimizing the degradation of surfactants for reuse of the flushing solutions has become a major problem. A series of batch experiments were conducted to investigate the preferential degradation mechanisms of naphthalene by sodium dodecyl sulfate (SDS) as well as the effect of SDS concentration on the degradation efficiency using a competitive kinetic method. In addition, the effects of persulfate (PS) dosage, temperature, and pH on the degradation efficiency of naphthalene were also studied, and a prediction model based on response surface methodology (RSM) was built to optimize the preferential degradation of naphthalene. One-dimensional column flushing experiments were performed to evaluate the feasibility of reusing soil flushing solutions at the predicted PS dosage. Results show that: (i) SDS inhibited naphthalene degradation owing to competitive oxidation and micellar protection, with a decreasing degradation rate constant as SDS concentration increased. However, naphthalene was still degraded by reactive oxygen species (ROS) at a higher rate than SDS, indicating the preferential degradation of naphthalene. When the concentration of SDS increased from 5 to 10 times of critical micelle concentration (CMC; 8.2 mM), the ratio for rate constants of naphthalene to SDS (kNAP/kSDS) decreased significantly from 31.61 to 3.38, illustrating the obvious effect of SDS concentration on preferential degradation of naphthalene; (ii) The formation of single-chambered vesicles with a higher thermodynamic stability was observed at 10 CMC, compared to the spherical micelles formed at 5 CMC. The accessibility of naphthalene to ROS was greatly reduced at 10 CMC, resulting in a significant reduction in kNAP/kSDS value. Moreover, the method in which ROS attacked naphthalene in surfactant micelles varied with SDS concentration, which also impacted the preferential degradation of naphthalene; (iii) Under the optimal PS dosage predicted by the RSM model, a good reusability of SDS was observed in the soil flushing solutions after thermally activated PS oxidation, with a significant reduction on the toxic risk of soil after flushing.

Artificial neural network in the prediction of surface roughness: A comparative study

Surface roughness is a key parameter to consider in the machining of aluminum alloy. It is rendered as one of the important determinants of the performance of mechanical instruments or components. Owing to its excellent mechanical properties, and ease of machinability, Aluminum 6061 (Al6061) is rendered a popular choice in many industries. Achieving a desired surface finish is crucial for the performance and longevity of machined components. This study aimed to compare the predictive performance of the artificial neural network (ANN) model versus the response surface methodology (RSM) in the prediction of surface roughness in the turning process of Al6061. ANN performed better than RSM in the prediction of surface roughness (A20 index 0.93 and 0.86 for ANN and RSM models respectively). MAPE and sMAPE were also found to be lower in the ANN model compared with the RSM model (8.06 versus 9.69, and 0.039 versus 0.047 respectively) indicating that the ANN model had a better predictive performance compared with the RSM model. Both ANN and RSM models showed that cutting speed and feed rate were the most important determinants of surface roughness in the turning process of Al6061 in other words to achieve a smoother surface during the turning process of Al6061 high cutting speed and low feed rate should be used. The findings of this study reflect the potential utility of ANN in the prediction and subsequently optimizing cutting parameters to achieve a smoother surface.