Response Surface Methodology Model Research Articles

Sustainability assessment and mechanical characteristics of high strength concrete blended with marble dust powder and wheat straw ash as cementitious materials by using RSM modelling

Research into environmentally favorable alternatives to conventional concrete, such as reengineering techniques and the recycling of industrial and agricultural waste, has increased dramatically over the past decade. This research intended to examine the potential benefits of utilizing wheat straw ash (WSA) and marble dust powder (MDP) as supplementary cementitious materials (SCM) in high-strength concrete (HSC). The intention is to decrease carbon dioxide emissions and the amount of Portland cement (PC) required. Concrete formulations containing 0%, 15%, 20%, 30%, and 40% by weight of MDP and WSA as SCM, respectively, were evaluated by using response surface methodology (RSM) modelling for multi-objective optimization. This research assessed the environmental ramifications of employing WSA and MDP as SCM in HSC. Specifically, it examined the impacts on embodied carbon and eco-strength efficiency. Cubical specimens underwent a series of tests to attain the desirable compressive strength of approximately 60 MPa after a period of 28 days. The workability of green concrete decreased as the proportion of WSA and MDP used as SCM increased, according to the study. The optimum compressive strength, flexural strength, splitting tensile strength, and modulus of elasticity of HSC was recorded by 68 MPa, 5.98 MPa, 4.25 MPa, and 40 GPa, at 15% of SCM (composed of 10% WSA and 5% MDP) at 28 days respectively. Using ANOVA, response prediction models were validated and developed with a confidence level of 95%. In summary, the increased application of 20% WSA and 20% MDP as SCM in concrete leads to a 31.64% reduction in the carbon footprint of the material.

High throughput screening of promoted bimetallic catalysts supported on alumina for production of light olefins via Fischer-Tropsch synthesis with artificial neural network and response surface methodology

The production of light olefins via Fischer-Tropsch Synthesis (FTS) was investigated by both experimental and modeling studies. Experimental studies were conducted by employing promoted FeCo/α-Al2O3 (FeCo) bimetallic catalysts. The Response Surface Methodology (RSM) and Artificial Neural Network (ANN) methods were used in modeling. Specifically, effects of the types and concentration of promoters on the catalytic performances of α-Al2O3 supported iron-cobalt bimetallic catalysts are investigated by using Ce, Ni, La, Ho, Ga Cu, Mn, and Zn used as promoters. A total of 49 catalysts were prepared by co-impregnation method and tested at 310 °C and 1 bar in a high-speed catalyst performance test system (HT-CPA). The FTS performances of catalysts showed that both the types and amounts of the promoters can affect light olefin production. The light olefin (C2=–C3=) production of unpromoted FeCo catalyst was about 3.87 × 10−3 (mol C/g active metal. h). The best performances were obtained from FeCo catalysts promoted with Ho, Cu, and Zn. Presence of these promoters increased olefin production up to ∼ 84 %, 76 % and 70 %, respectively.Performance parameters (MSE and R2) of RSM and ANN models revealed that both methods can be used to support experimental studies and provide a procedure for optimizing catalyst formulations yielding the desired product selectivity in FTS processes. The RSM outcomes aligned with experimental findings, suggest that that Ho, Cu, and Zn can serve as suitable promoters for the light olefin production in FTS. ANN model attained higher performance parameters (R2 = 0.95 and MSE = 2E−7) than RSM model (R2 = 0.85 and MSE = 3.4E−7). This indicates that ANN model, which designed as one hidden layer including 7 neurons, can provide better predictions and a fitting capacity. Therefore, it may be used in further optimization and sensitivity analysis approaches. Alignment degree between the experimental and modeling results demonstrates that the proposed methodology can provide a reliable and efficient way to guide experiments to optimize catalyst formulations through identification of potential promoters and their amounts to be used.

Tailored transition metal-doped TiO2@Fe3O4 nanohybrids for efficient photocatalytic dye removal: Optimization via neural networks and response surface approaches

The study focused on predicting the degradation of Remazol Turquoise Blue (RTB) dye using a photocatalyst comprised of transition metal-doped TiO2@Fe3O4. Out of the transition metal ions considered, namely Cu, Cr, Co, and Ni, Cr emerges as the most effective dopant for TiO2@Fe3O4 nanohybrid photocatalysts. This optimal performance was achieved with a catalyst loading of 50 mg and a dye concentration of 10 ppm within a 1-h reaction time, resulting in a photocatalytic efficiency of 96.70%. The optimization strategy employed a combination of Response Surface Methodology (RSM) and Artificial Neural Networks (ANN). To conduct experiments systematically, a Box-Behnken Design (BBD) rooted in RSM principles was utilized to create experimental setups for the photocatalytic degradation of the dye with the doped photocatalyst. The variables such as reaction time, catalyst dosage and dye concentration were designed to optimize the RTB dye degradation. The Regression coefficient (R2) for the developed RSM and ANN models were found to be 0.9299 and 0.9925 respectively, and revealed that the ANN has higher prediction capability and accuracy than other model. The outcomes underlined that the dosage of the catalyst prominently emerged as the principal determinant governing the efficacy of the photocatalytic activity. Remarkably, the Cr doped TiO2@Fe3O4 photocatalyst maintained its activity through six degradation cycles, with efficiencies of 99.7%, 99.4%, 99.1%, 88.8%, 87.3%, and 86.3%, respectively.

Enhanced removing of cyanobacterium by NZVI coupled with H2O2: Influencing factors and removal mechanisms

AbstractAs advanced oxidation processes (AOPs) is considered to be a highly effective approach for degrading organic pollutants, the simultaneous coagulation and oxidation process by the Fenton‐like reaction of nanoscale zero‐valent iron (NZVI) and hydrogen peroxide (H2O2) is investigated to eliminate the harmful cyanobacterium Microcystis aeruginosa in this study, and the process conditions are optimized using the central composite design of response surface methodology (RSM); in addition, the removal efficiency of M. aeruginosa (in terms of chlorophyll a, Chl a) and the verifications of the antioxidant abilities, as well as extracellular organic matters (EOM) and intracellular organic matters (IOM) are investigated under the optimized conditions. Results indicate that H2O2 concentration is the key factor affecting the Chl a removal efficiency, and the maximum Chl a removal reaches 98.10% under the optimized conditions: NZVI concentration 62.82 mg L−1, H2O2 concentration 54.2 mmol L−1, pH 4.38 and rotating speed 67 rpm. The high correlation coefficient (R2 > 0.80) of analysis of variance (ANOVA) demonstrates the RSM model is extremely significant and suitable for experimental results. Moreover, the total organic carbon (TOC) and fluorescent substances (soluble cyanobacteria metabolic byproducts, aromatic proteins II, humic and fulvic acid‐like compounds) for both EOM and IOM are enhanced removal. It is speculated the removal mechanisms of the Fenton‐like process of NZVI/H2O2 for cyanobacterium belongs to the combined actions of the oxidation of Fe(II)/H2O2 and the coagulation of Fe(III), which destroy the defense system and result in the removal of M. aeruginosa.

Optimizing recombinant antibody fragment production: A comparison of artificial intelligence and statistical modeling.

Maximizing the recombinant protein yield necessitates optimizing the production medium. This can be done using a variety of methods, including the conventional "one-factor-at-a-time" approach and more recent statistical and mathematical methods such as artificial neural network (ANN), genetic algorithm, etc. Every approach has advantages and disadvantages of its own, yet even when a technique has flaws, it is nevertheless used to get the best results. Here, one categorical variable and four numerical parameters, including post-induction time, inducer concentration, post-induction temperature, and pre-induction cell density, were optimized using the 232 experimental assays of the central composite design. The direct and indirect effects of factors on the yield of anti-epithelial cell adhesion molecule extracellular domain fragment antibody were examined using statistical methods. The analysis of variance results indicate that the response surface methodology (RSM) model is effective in predicting the amount of produced single-chain fragment variable (p-value = 0.0001 and R2 = 0.905). For ANN modeling, the evaluation using normalized root mean square error (NRMSE) and R2 values shows a good fit (R2 = 0.942) and accurate predictions (NRMSE = 0.145). The analysis of error parameters and R2 of a dataset, which contained 30 data points randomly selected from the complete dataset, showed that the ANN model had a higher R2 value (0.968) compared to the RSM model (0.932). Furthermore, the ANN model demonstrated stronger predictive ability with a lower NRMSE (0.048vs. 0.064). Induction at the cell density of 0.7 and an isopropyl β-D-1-thiogalactopyranoside concentration of 0.6mM for 32 h at 30°C in BW25113 was the ideal culture condition leading to the protein yield of 259.51mg/L. Under the optimum conditions, the output values predicted by the ANN model (259.83mg/L) were more in line with the experimental data (259.51mg/L) than the RSM (276.13mg/L) expected value. This outcome demonstrated that the ANN model outperforms the RSM in terms of prediction accuracy.

Response surface methodology of subcritical water-reactive extraction of gingerol to shogaol ginger dregs through ultraviolet light pretreatment

The increase of shogaol production through reactive extraction of subcritical water depends on the stages of the pretreatment process in destroying the cellular cells of ginger pulp. Pretreatment using ultraviolet B (UVB) is predicted to be able to damage cellular cell walls and outer cell membranes (plasmalemma). In addition, to initiate the degradation of gingerols to shogaols, the reaction conversion and selectivity of shogaols are increased. This study aimed to examine the optimum process conditions for the subcritical water-reactive extraction of gingerol to shogaol from the ginger pulp by utilizing UVB pretreatment: reactive extraction temperature (110 – 130oC), ratio solvent/feed (22:1 – 32:1 b/b), and time (10 – 30 minutes). The extract of shogaol in solvent was separated from the raffinate with a separator funnel and analysed using HPLC-MS. Interpretation of experimental data to optimize the reactive extraction process used the Response Surface Methodology (RSM). The study results show that at 120°C, solvent:inlet ratio 27:1 for 20 minutes through ultraviolet B-light pretreatment for 10 minutes, the yield of shogaol increased from 177 µg/L (without UV) to 18646 µg/L. Moreover, the RSM model was very good at predicting shogaol acquisition responses with a determination coefficient of R2 to be 0.8554. The optimum condition for reactive extraction through UV-B light pretreatment was reached at a temperature of 121.46°C and an inlet solvent ratio of 27.96 at 18.36 minutes, with a shogaol concentration of 18567.1 µg /L.

Bio-hydrogen production from crude glycerol: Optimisation through response surface methodology and artificial neural network approach

Crude glycerol (CG) is the main by-product of biodiesel production from the transesterification of vegetable or animal oils. The CG's high energy content makes it an effective carbon source for bio-hydrogen production. However, the purity of CG derived from the transesterification process may vary due to impurities such as methanol, soap, salt, etc., which influence the hydrogen production yield. The present study investigated the impacts of initial glycerol, methanol, and soap concentration of CG on the hydrogen production by dark fermentation, and the effect of various factors was evaluated and optimised with the response surface methodology (RSM) and artificial neural network (ANN) approach to maximise the yield of hydrogen production. Both the results of these approaches showed good fitting. The correlation coefficient between actual and predicted values from RSM and ANN models is closer to ∼1, and the lower error shows the applicability of these models. A maximum of 17.67 mmol/L of hydrogen production was obtained via RSM with optimum methanol, soap and glycerol concentrations of 2.75 g/L, 6.07 g/L, and 20.45 g/L, respectively. Besides, the 3-D response surface graphs illustrated the suppressive impact of increased initial concentration of methanol and soap, along with higher initial concentrations of glycerol, on hydrogen production. Further, the outcome of the controlled experiment shows that the presence of impurities (methanol and soap) in glycerol reduces the cumulative hydrogen production by 5 %–17 % compared to the control for over five days.

Optimizing reverse osmosis desalination from brackish waters: Predictive approach employing response surface methodology and artificial neural network models

A mathematical model frame has been developed to optimize the performance of reverse osmosis (RO) desalination in brackish waters with medium to high salt concentrations using response surface methodology (RSM) and artificial neural network (ANN), specifically catered towards efficiently operating RO desalination systems. The RSM model aims at predicting RO performance with second-order polynomials based on 15 experimental datasets. Inputs for RSM model include feed TDS concentration, feed water flow rate, and concentration-to-desalination ratio, while the outputs consider rejection, membrane flux, performance index, and specific energy consumption. The Genetic Algorithm optimized back-propagation Neural Network was employed to update network weights and biases, leading to an optimal ANN network structure of 3–10-4. A ratio of 70 % training, 15 % validation, and 15 % test data was chosen to establish an ANN model that predicts the performance of RO desalination. The ANN model demonstrated significantly higher prediction accuracy than the RSM model, with impressive correlation coefficients of 0.9936 for the performance index and 0.9932 for SEC prediction, and low RMSE of 0.85 for rejection. To maximize the performance index and minimize specific energy consumption, a two-step optimization method was further set up by applying the RSM model to initially determine optimal process conditions followed by the ANN model to enhance the accuracy of performance forecasts. Optimal process conditions were proposed for specific feed TDS scenarios, which involved setting a feed TDS concentration limit to meet drinking water standards.

Modeling and optimization of triclosan biodegradation by the newly isolated Bacillus sp. DL4: kinetics and pathway speculation.

Triclosan is a widely used antibacterial agent and disinfectant, and its overuse endangered ecological safety and human health. Therefore, reducing residual TCS concentrations in the environment is an urgent issue. Bacillus sp. DL4, an aerobic bacterium with TCS biodegradability, was isolated from pharmaceutical wastewater samples. Response surface methodology (RSM) and artificial neural network (ANN) were carried out to optimize and verify the different condition variables, and the optimal growth conditions of strain DL4 were obtained (35°C, initial pH 7.31, and 5% v/v). After 48h of cultivation under the optimal conditions, the removal efficiency of strain DL4 on TCS was 95.89 ± 0.68%, which was consistent with the predicted values from RSM and ANN models. In addition, higher R2 value and lower MSE and ADD values indicated that the ANN model had a stronger predictive capability than the RSM model. Whole genome sequencing results showed that many functional genes were annotated in metabolic pathways related to TCS degradation (e.g., amino acid metabolism, xenobiotics biodegradation and metabolism, carbohydrate metabolism). Main intermediate metabolites were identified during the biodegradation process by liquid chromatography-mass spectrometry (LC-MS), and a possible pathway was hypothesized based on the metabolites. Overall, this study provides a theoretical foundation for the characterization and mechanism of TCS biodegradation in the environment by Bacillus sp. DL4.

Effects on quality characteristics of ultrasound-treated gilaburu juice using ANFIS and RSM modeling with machine learning algorithm

Gilaburu (Viburnum opulus L.) is a red-colored fruit with a sour taste that grows in Anatolia. It is rich in various antioxidant and bioactive compounds. In this study, bioactive compounds and ultrasound parameters of ultrasound-treated gilaburu water were optimized by response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS). As a result of RSM optimization, the independent ultrasound parameters were determined as an ultrasound duration of 10.7 min and an ultrasound amplitude of 53.3, respectively. The R2 values of the RSM modeling level were 99.93%, 98.54%, and 99.80%, respectively, and the R2 values of the ANFIS modeling level were 99.99%, 98.89%, and 99.87%, respectively. Some quality parameters of gilaburu juice were compared between ultrasound-treated gilaburu juice (UT-GJ), thermal pasteurized gilaburu juice (TP-GJ), and control group (C-GJ). The quality parameters include bioactive compounds, phenolic compounds, minerals, and sensory evaluation. Bioactive compounds in the samples increased after ultrasound application compared to C-GJ and TP-GJ samples. The content of 15 different phenolic compounds was determined in Gilaburu juice samples, and the phenolic compound of UT-GJ samples increased compared to TP-GJ and C-GJ samples, except for gentisic acid. Ultrasound treatment applied to gilaburu juice enabled its bioactive compounds to hold more in the juice.

Lipase-catalyzed solvent-free synthesis of monoglycerides from biodiesel-derived crude glycerol: Optimized using response surface methodology

The growth of the biodiesel industry has resulted in significant quantity of crude glycerol. It is necessary to explore the synthesis of high-value-added products from crude glycerol. In this study, the enzymatic synthesis of monoglycerides under solvent-free conditions, employing crude glycerol as the primary feedstock, had been investigated. The analysis showed that the highest yield of monoglycerides was obtained after 12 h, and Novozym 435 showed the highest monoglyceride yield of 18.41 % among the three lipases tested, followed by Lipozyme TL IM and Lipozyme RM IM. Monoglycerides were synthesized from biodiesel-derived crude glycerol using Novozym 435 as the catalyst under solvent-free conditions at different parameters, which were catalyst concentration, substrate molar ratio, and temperature. The yield of monoglycerides was examined in single-factor experiments. Response surface methodology (RSM) was subsequently employed to optimize the synthesis conditions based on the single-factor experimental results. The optimal conditions were at an enzyme concentration of 12.7 wt% and a molar ratio of crude glycerol:oil of 5.7:1 at a reaction temperature of 65.2 °C. The experimental yield of monoglycerides under the optimal conditions was 28.93 %, which is close to the value predicted from the RSM model (29.02 %).

Superior decomposition of xenobiotic RB5 dye using three-dimensional electrochemical treatment: Response surface methodology modelling, artificial intelligence, and machine learning-based optimisation approaches

The highly efficient electrochemical treatment technology for dye-polluted wastewater is one of hot research topics in industrial wastewater treatment. This study reported a three-dimensional electrochemical treatment process integrating graphite intercalation compound (GIC) adsorption, direct anodic oxidation, and ∙OH oxidation for decolourising Reactive Black 5 (RB5) from aqueous solutions. The electrochemical process was optimised using the novel progressive central composite design–response surface methodology (CCD–NPRSM), hybrid artificial neural network–extreme gradient boosting (hybrid ANN–XGBoost), and classification and regression trees (CART). CCD–NPRSM and hybrid ANN–XGBoost were employed to minimise errors in evaluating the electrochemical process involving three manipulated operational parameters: current density, electrolysis (treatment) time, and initial dye concentration. The optimised decolourisation efficiencies were 99.30%, 96.63%, and 99.14% for CCD–NPRSM, hybrid ANN–XGBoost, and CART, respectively, compared to the 98.46% RB5 removal rate observed experimentally under optimum conditions: approximately 20 mA/cm2 of current density, 20 min of electrolysis time, and 65 mg/L of RB5. The optimised mineralisation efficiencies ranged between 89% and 92% for different models based on total organic carbon (TOC). Experimental studies confirmed that the predictive efficiency of optimised models ranked in the descending order of hybrid ANN–XGBoost, CCD–NPRSM, and CART. Model validation using analysis of variance (ANOVA) revealed that hybrid ANN–XGBoost had a mean square error (MSE) and a coefficient of determination (R2) of approximately 0.014 and 0.998 respectively, for the RB5 removal efficiency, outperforming CCD–NPRSM with MSE and R2 of 0.518 and 0.998, respectively. Overall, the hybrid ANN–XGBoost approach is the most feasible technique for assessing the electrochemical treatment efficiency in RB5 dye wastewater decolourisation.

Ultrasound-assisted extraction of polyphenols from lotus rhizome epidermis by alcohol/salt-based aqueous two-phase system: Optimization, extraction mechanism and antioxidant activities

In this study, ultrasonic-assisted (UA) alcohol/salt-based aqueous two-phase system (ATPS) method was constructed to extract lotus rhizome epidermis (LRE) polyphenols. The extraction conditions were optimized as salt concentration 26.75 %, ethanol concentration 25.45 %, ultrasonic power 487 W and liquid–solid ratio 35.33 mL/g by comparing response surface methodology (RSM) and artificial neural network (ANN) models. Then, l-dopa (2.35 ± 0.036 mg/g dw), gallocatechin (1.66 ± 0.0035 mg/g dw) and epigallocatechin (1.37 ± 0.0035 mg/g dw) were determined as major polyphenols in LRE by using UA-ATPS method. Moreover, study showed that ultrasound, van der Waals force, hydrogen bond and salting out could accelerate the mass transfer and extraction of polyphenols in LRE cells. The high-pressure cavity and collapse effect of ultrasound could also accelerate the extraction of polyphenols. In vitro antioxidant experiments showed that LRE polyphenols have good antioxidant ability. In sum, this study developed a green and efficient extraction method to enhance the profitability of LRE in food and medicine industries.

Adsorption and antibacterial studies of a novel hydrogel adsorbent based on ternary eco-polymers doped with sulfonated graphene oxide developed from upcycled plastic waste

A novel ternary blended polymer composed of cost-effective and readily available polymers was synthesized using poly (vinyl alcohol) (PVA), iota carrageenan (IC), and poly (vinyl pyrrolidone) (PVP). Sulfonated graphene oxide (SGO), prepared from recycled drinking water bottles, was utilized as a doping agent. Varying amounts (1–3 wt%) were combined into the polymer matrix. The produced hydrogel film was examined as a potential adsorbent hydrogel film for the removal of methylene blue (MB) and Gentamicin sulfate (GMS) antibiotic from an aqueous solution. The experimental results demonstrate that the presence of SGO significantly increased the adsorption efficiency of PVA/IC/PVP hydrogel film. The antimicrobial tests revealed that the PVA/IC/PVP-3% SGO hydrogel film exhibited the most potent activity against all the tested pathogenic bacteria. However, the adsorption results for MB and GMS showed that the addition of 3 wt% SGO resulted in a removal percentage that was a two fold increase in the removal percentage compared with the undoped PVA/IC/PVP hydrogel film. Furthermore, the response surface methodology (RSM) model was utilized to examine and optimize several operating parameters, including time, pH of the solution, and initial pollutant concentration. The adsorption kinetics were better characterized by the pseudo-second-order kinetics model. The composite film containing 3 wt% SGO had a maximum adsorption capacity of 606 mg g−1 for MB and 654 mg g−1 for GMS, respectively. The generated nanocomposite hydrogel film demonstrated promising potential for application in water purification systems.

Optimization of biogas production from thermal-alkali pre-treated sludge using response surface methodology and random forest regressor model

BackgroundUnderstanding the intricate relationships between input variables and methane potential is crucial for optimizing anaerobic digestion process after thermal-alkali pre-treatment. This study aimed to optimize low-temperature thermal alkali pre-treatment, focusing on dosage based on the total solids (TS) content of raw sludge. A comparative analysis was conducted to optimize input parameters related to biogas generation potential as the primary response. MethodsBiomethane potential data were collected following the pre-treatment of sludge within the temperature range of 40–70 °C, using various doses of NaOH, KOH, Mg(OH)2, and Ca(OH)2 ranging from 40 mg alkali/g TS to 120 mg alkali/g TS. The optimization of biogas and methane production was conducted through Response Surface Methodology (RSM) and the Random Forest Regressor (RFR) model considering dosage and temperature as input variables. Additionally, sensitivity analysis was performed using the permutation feature importance function to gain insights into the influence of alkali types on biogas and methane production. FindingsThe results obtained from the RFR model showcased a notably high accuracy in predicting optimal solutions. NaOH addition was the most effective pre-treatment with an optimal dose of 65 mg/g TS and at a temperature of 56 °C. NaOH pre-treatment increased methane yield to 414.40 mL/g VS, compared to 117.08 mL/g VS for the untreated sludge. The sensitivity analysis results indicated that the temperature had a more significant effect on biogas potential than the alkali dosage.

An appraisal of statistical and probabilistic models in highway pavements

Accurate performance prediction is crucial for safe and efficient travel on highway pavements. Within pavement engineering, statistical models play a pivotal role in understanding pavement behavior and durability. This comprehensive study critically evaluates a spectrum of statistical models utilized in pavement engineering, encompassing mechanistic-empirical, Weibull distribution, Markov chain, regression, Bayesian networks, Monte Carlo simulation, artificial neural networks, support vector machines, random forest, decision tree, fuzzy logic, time series analysis, stochastic differential equations, copula, hidden semi-Markov, generalized linear, survival analysis, response surface methodology and extreme value theory models. The assessment meticulously examines equations, parameters, data prerequisites, advantages, limitations, and applicability of each model. Detailed discussions delve into the significance of equations and parameters, evaluating model performance in predicting pavement distress, performance assessment, design optimization, and life-cycle cost analysis. Key findings emphasize the critical aspects of accurate input parameters, calibration, validation, data availability, and model complexity. Strengths, limitations, and applicability across various pavement types, materials, and climate conditions are meticulously highlighted for each model. Recommendations are outlined to enhance the effectiveness of statistical models in pavement engineering. These suggestions encompass further research and development, standardized data collection, calibration and validation protocols, model integration, decision-making frameworks, collaborative efforts, and ongoing model evaluation. Implementing these recommendations is anticipated to enhance prediction accuracy and enable informed decision-making throughout highway pavement design, construction, maintenance, and management. This study is anticipated to serve as a valuable resource, providing guidance and insights for researchers, practitioners, and stakeholders engaged in asphalt engineering, facilitating the effective utilization of statistical models in real-world pavement projects.

Optimization of pollutants removal from anaerobically digested dairy wastewater by electro-oxidation process: a response surface methodology modeling and validation

AbstractThe release of anaerobically digested dairy wastewater (ANDDW) without a treatment can lead to severe environmental pollution, prompting the exploration of effective and sustainable treatment methods. Amidst various wastewater treatment approaches, the electro-oxidation (EO) process is considered as a promising, clean, and adaptable solution. In this study, the major operational parameters viz. current density, electrolyte concentration, treatment time, and mixing speed of an EO comprising Ti/PbO2 anode and stainless-steel cathode, were optimized using response surface methodology (RSM) for efficient removal of chemical oxygen demand (COD), ammonia nitrogen (NH3-N), total phosphorus (TP), orthophosphate (OP), total nitrogen (TN), and total Kjeldahl nitrogen (TKN) from ANDDW. Optimal conditions were identified as a current density of 90 mA cm−2, 0.08% electrolyte concentration, 180 min treatment time, and 400 rpm mixing speed. Under the optimum conditions, the COD, NH3-N, TP, OP, TN, and TKN removal efficiencies were 78.36, 63.93, 87.41, 92.39, 67.01, and 81.42%, respectively. Furthermore, the reaction rate followed the first-order kinetic model for the pollutants removal with correlation coefficients (R2) close to 1. The findings highlight the potential of using the EO process to treat high pollutant-laden ANDDW and encourage further studies to confirm the corresponding outcomes on a pilot scale. Graphical abstract

Comparative analysis of conventional optimization techniques with Artificial Neural Network (ANN) and Response Surface Methodology (RSM) models for extracting oil from Chrysophyllum albidum (C. albidum) seed for biodiesel production

Comparative analysis of conventional optimization techniques with Artificial Neural Network (ANN) and Response Surface Methodology (RSM) models for extracting oil from Chrysophyllum albidum (C. albidum) seed for biodiesel production

Multi-objective optimization and performance assessment of response surface methodology (RSM), artificial neural network (ANN) and adaptive neuro-fuzzy interfence system (ANFIS) for estimation of fouling in phosphoric acid/steam heat exchanger

Fouling is a common occurrence in industrial heat exchangers, leading to a decrease of the thermal efficiency. This research addresses the challenge of applying various machine learning algorithms including Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to effectively model the fouling resistance in a heat exchanger in phosphoric acid concentration loop. Subsequently, a multi-objective optimization approach was employed to minimize the fouling resistance. Confirmatory experiments are then conducted in the phosphoric acid concentration plant using optimized variables.The findings of this study indicate that the three models accurately align with one year of operational data, achieving a remarkably high coefficient of correlation (R). Among the models utilized, RSM demonstrates the highest level of prediction accuracy, with an R of 0.9998, accompanied by the lowest mean square error (MSE) of 5.9388 10−13 and root mean squared error (RMSE) of 7.7064 10−7. The RSM optimization process identifies the optimal conditions for variables such as time, acid inlet and outlet temperature, steam temperature, acid density, and volume flow rate, which are determined to be 114.805 h; 72.214 °C; 80.407 °C; 116.784 °C; 1642.47 kg/m3; and 2308.1 m3/h, respectively. The predicted fouling resistance demonstrates a strong correlation with the actual data, with a negligible percentage difference of 2.19 %, further validating the accuracy and reliability of the RSM model.

Investigation of gold adsorption by ironbark biochar using response surface methodology and artificial neural network modelling

Due to the importance and economic value of gold in various applications, recovering gold from waste streams like tailings and industrial wastewater is essential. Biochar with a high surface area and porosity is considered a potential low-cost adsorbent for gold reclaiming from aqueous media. Therefore, this study aims to develop a high-quality biochar with excellent physicochemical properties to efficiently remove gold from aqueous media. To accomplish this, biochar was obtained from pyrolysis of ironbark (IB) biomass at 500 °C which has a surface area of 493.79 m2/g. Subsequently, the biochar was investigated for adsorption of gold from aqueous media, which exhibited a maximum adsorption capacity of 858 mg/g. Isotherm results showed that the adsorption of gold by biochar followed the Langmuir model, indicating monolayer adsorption. Further, response surface methodology (RSM) and an artificial neural network (ANN) combined with the Salp Swarm Algorithm (SSA) were used to analyze the generated results (ANN-SSA). The RSM prediction model fit was adequate (R2 = 0.99). However, comparing the statistical data revealed that ANN-SSA outperformed RSM in predicting experimental results. Overall, this study suggested that biochar derived from IB biomass could be used as a potential adsorbent to recover gold from an aqueous solution. This article presents a unique and innovative study on the utilization of ironbark biochar for the purpose of gold adsorption.