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Статті в журналах з теми "RSM modelling"

Автор: Grafiati

Опубліковано: 25 січня 2025

Оновлено: 19 липня 2025

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Nwosu-Obieogu, Kenechi, Maureen A. Allen, Chukwunonso Nwogu, Bertrand Nwankwojike, Simeon Bright, and Christian Goodnews. "Modelling and optimization of luffa oil transesterification via acid activated waste marble catalyst." Journal of the Ghana Institution of Engineering (JGhIE) 24, no. 1 (2024): 58–71. http://dx.doi.org/10.56049/jghie.v24i1.140.

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This study assessed the performance of response surface methodology (RSM) and artificial neural network (ANN) in modelling the transesterification of luffa oil using acid activated waste marble catalyst. The waste marble was activated with 0.5 molar sulphuric acid at 600 oC for 4 hours and was characterized by SEM, FT-IR, XRD, XRF, and BET; the characterization proved that the catalyst was successfully activated. The experiments were conducted at a catalyst dosage (1-5 wt. %), temperature (40-80 oC), methanol-oil ratio (4:1-12:1), time (1-3 hours), and agitation speed of (100- 500 rpm) with output as biodiesel yield. ANN was assessed using three back-propagation (BP) procedures, each comprising five neurons (input layer), one (output layer) and ten (hidden layer). The Levenberg Marquardt technique offered the most accurate prediction for luffa oil transesterification. The models were developed based on experimental and algorithm simulations and designs. The models' performance was assessed using the R2 and MSE. Regarding R2 and MSE, the ANN model (R2=9.9921E-1, MSE=0.06311) has a superior predictive capacity in forecasting the process than the RSM (R2=0.9885, MSE=0.86). At a catalyst concentration (3wt %), time (2 hours), temperature (60 oC), agitation speed (100 rpm) and methanol-oil ratio (12:1), the experimental (92.57 %), RSM predicted (94.0487 %) and ANN predicted (91.1768 %) biodiesel yield showed an agreement between the experimental and predicted values. The findings via physicochemical analysis, FT-IR, and GC-MS confirm that the biodiesel was within ASTM specifications.

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Nair, Abhilash T., Abhipsa R. Makwana, and M. Mansoor Ahammed. "The use of response surface methodology for modelling and analysis of water and wastewater treatment processes: a review." Water Science and Technology 69, no. 3 (2013): 464–78. http://dx.doi.org/10.2166/wst.2013.733.

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In recent years, response surface methodology (RSM) has been used for modelling and optimising a variety of water and wastewater treatment processes. RSM is a collection of mathematical and statistical techniques for building models, evaluating the effects of several variables, and obtaining the values of process variables that produce desirable values of the response. This paper reviews the recent information on the use of RSM in different water and wastewater treatment processes. The theoretical principles and steps for its application are first described. The recent investigations on its application in coagulation–flocculation, adsorption, advanced oxidation processes, electro-chemical processes and disinfection are reviewed. The limitations of the methodology are highlighted. Attempts made to improve the RSM by combining it with other modelling techniques are also described.

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Irfan, Muhammad, Sharjeel Waqas, Ushtar Arshad, et al. "Response Surface Methodology and Artificial Neural Network Modelling of Membrane Rotating Biological Contactors for Wastewater Treatment." Materials 15, no. 5 (2022): 1932. http://dx.doi.org/10.3390/ma15051932.

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Membrane fouling is a major hindrance to widespread wastewater treatment applications. This study optimizes operating parameters in membrane rotating biological contactors (MRBC) for maximized membrane fouling through Response Surface Methodology (RSM) and an Artificial Neural Network (ANN). MRBC is an integrated system, embracing membrane filtration and conventional rotating biological contactor in one individual bioreactor. The filtration performance was optimized by exploiting the three parameters of disk rotational speed, membrane-to-disk gap, and organic loading rate. The results showed that both the RSM and ANN models were in good agreement with the experimental data and the modelled equation. The overall R2 value was 0.9982 for the proposed network using ANN, higher than the RSM value (0.9762). The RSM model demonstrated the optimum operating parameter values of a 44 rpm disk rotational speed, a 1.07 membrane-to-disk gap, and a 10.2 g COD/m2 d organic loading rate. The optimization of process parameters can eliminate unnecessary steps and automate steps in the process to save time, reduce errors and avoid duplicate work. This work demonstrates the effective use of statistical modeling to enhance MRBC system performance to obtain a sustainable and energy-efficient treatment process to prevent human health and the environment.

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Daramola, M. O., K. J. Keesman, and F. Spenkelink. "Process Modelling of Ultrafiltration Units: An RSM Approach." Journal of Applied Sciences 7, no. 23 (2007): 3687–95. http://dx.doi.org/10.3923/jas.2007.3687.3695.

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Narasimman, Pasupathi, K. Ravi, and Sandra Pinelas. "Modelling the Additive Functional Equations through RSM Matrices." JOURNAL OF ADVANCES IN MATHEMATICS 12, no. 10 (2016): 6714–19. http://dx.doi.org/10.24297/jam.v12i10.117.

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This paper suggests one possible method to model additive type of functional equations using eigenvalues and eigenvectors of matrices with suitable numerical examples. The authors have defined a new type of Row Sum Matrix(RSM) and have discussed its eigenvalues and eigenvectors in order to model functional equations. The famous additive cauchy functional equation and Logical functional equation have also been modelled using identity matrix and Logical matrix in this study.

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Eser, Aykut, Elmas Aşkar Ayyıldız, Mustafa Ayyıldız, and Fuat Kara. "Artificial Intelligence-Based Surface Roughness Estimation Modelling for Milling of AA6061 Alloy." Advances in Materials Science and Engineering 2021 (February 12, 2021): 1–10. http://dx.doi.org/10.1155/2021/5576600.

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This study introduces the improvement of mathematical and predictive models of surface roughness parameter (Ra) in milling AA6061 alloy using carbide cutting tools coated with CVD-TiCN in dry condition. An experimental model has been improved for estimating the surface roughness using artificial neural networks (ANN) and response surface methodology (RSM). For these models, cutting speed, depth of cut, and feed rate were evaluated as input parameters for experimental design. For the ANN modelling, the standard backpropagation algorithm was established to be the optimum selection for training the model. In the forming of the network construction, five different learning algorithms were used: the conjugate gradient backpropagation, Levenberg–Marquardt, scaled conjugate gradient, quasi-Newton backpropagation, and resilient backpropagation. The best consequent with single hidden layers for the surface roughness was obtained by 3-8-1 network structures. The statistical analysis was performed with RSM-based second-order mathematics model. The influences of the cutting parameters on surface roughness were defined by using analysis of variance (ANOVA). The ANOVA results show that the depth of cut is the most effective parameter on surface roughness. Prediction models developed using ANN and RSM were compared in terms of prediction accuracy R2, MEP, and RMSE. The data estimated from ANN and RSM were realized to be very close to the data acquired from experimental studies. The value R2 of RSM model was higher than the values of the ANN model which demonstrated the stability and sturdiness of the RSM method.

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Owolabi, Rasheed, Mohammed Usman, Adefunmilayo Anuoluwapo, and Onyekachi Oguamanam. "Modelling, Optimization and Green Metrics Evaluation of Bio-Catalytic Synthesis of Biodiesel." Tikrit Journal of Engineering Sciences 27, no. 3 (2020): 17–30. http://dx.doi.org/10.25130/tjes.27.3.03.

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The response surface methodology (RSM) was adopted in ths study to evaluate the influence, interplay and interaction of various process variables on the biodiesel yield using methanol and castor oil as feedstocks in the presence of bovine bones as bio-catalyst.Twenty experimental runs were designed using central composite design (CCD). RSM statistical model of second order was developed. Analysis of variance (ANOVA) tests were performed on the model to find the relative influence of the process variables. An optimum yield of 95.12% was obtained at 60 0C reaction temperature, 120 minutes reaction time, molar to oil ratio 6:1, catalyst concentration of 10 % w and a stirring rate of 900 rpm. The experimental conditions under which biodiesel was synthesized in this study was compared with those of previous studies .It can therefore be inferred that , the conditions herein is competing with prior established conditions. The biodiesel was found to possess fuel properties that fall within acceptable limits and green metrics estimated showed compliance of the process with the diictates of green and sustainable chemistry.

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Hudák, Peter, and Valéria Hrabovcová. "Mathematical Modelling and Parameter Determination of Reluctance Synchronous Motor with Squirrel Cage." Journal of Electrical Engineering 61, no. 6 (2010): 357–64. http://dx.doi.org/10.2478/v10187-010-0055-y.

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Mathematical Modelling and Parameter Determination of Reluctance Synchronous Motor with Squirrel Cage The paper provides an analysis of reluctance synchronous motor (RSM) with asymmetrical rotor cage. Its performances during its starting up is investigated. A mathematical model is created on the basis of detailed investigation of model parameters. The RSM starting up by switching it directly across the line was simulated and verified by measurements.

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Gadallah, Mohamed H. "Process re-modelling using RSM and the splitting approach." International Journal of Mechatronics and Manufacturing Systems 3, no. 1/2 (2010): 58. http://dx.doi.org/10.1504/ijmms.2010.029875.

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Dullah, Salinah, Hidayati Asrah, Movierawanna Bernard, and Jane Ann Jumi. "Preliminary Modelling of Hydrological Performance for Green Roof Drainage Layer using Response Surface Methodology." Journal of Advanced Research in Applied Sciences and Engineering Technology 55, no. 1 (2024): 105–24. https://doi.org/10.37934/araset.55.1.105124.

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Response Surface Methodology modelling is used in this study to investigate experimental result for waterproofing and drainage layers with slope ranging from 0% to 6% test beds with three types layers of drainage, rubber crumbs, oil palm shells and polyform. The aim of this study is to determine the accuracy and effectiveness of RSM through investigating the hydrological performance of green roofs. Nine sets of experimental data were used to analyse, and the input parameters include type of material drainage layer, slopes, and water absorption of the materials. The output variables were hydrograph and peak runoff, peak attenuation, and water retention for each material. It was proven that the mathematical equations developed by the RSM model can predict the output response, with ANOVA analysis being used to determine the level of significant effect of the input parameters on the green roof hydrological performance. RSM model's 2D contour plot and 3D surface plot expecting to revealed slope and water absorption. This modelling showed significant effect on peak runoff, peak attenuation, and water retention. It is proven that the RSM can be used to investigate various factors affecting green roof hydrological performance.

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Yaro, Aprael S., and Khalid H. Rashid. "Modelling and Optimization of Carbon Steel Corrosion in CO2 Containing Oilfield Produced Water in Presence of HAc." Iraqi Journal of Chemical and Petroleum Engineering 16, no. 2 (2015): 1–8. http://dx.doi.org/10.31699/ijcpe.2015.2.1.

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Previously, many empirical models have been used to predict corrosion rates under different CO2 corrosion parameters conditions. Most of these models did not predict the corrosion rate exactly, besides it determined effects of variables by holding some variables constant and changing the values of other variables to obtain the regression model. As a result the experiments will be large and cost too much. In this paper response surface methodology (RSM) was proposed to optimize the experiments and reduce the experimental running. The experiments studied effects of temperature (40 – 60 °C), pH (3-5), acetic acid (HAc) concentration (1000-3000 ppm) and rotation speed (1000-1500 rpm) on CO2 corrosion performance of the regression model calculated by RSM. The experiments were conducted in saturated solution of CO2 with 3.5 % NaCl solution. STATISTICA program version 10 was used for data analysis. In conclusion a quadratic model is proposed to predict the effect of mentioned variables in CO2 environment.

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Leone, Claudio, Silvio Genna, and Vincenzo Tagliaferri. "An integrated approach for the modelling of silicon carbide components laser milling process." International Journal of Advanced Manufacturing Technology 116, no. 7-8 (2021): 2335–57. http://dx.doi.org/10.1007/s00170-021-07516-2.

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AbstractThe paper deals with characterisation and modelling of laser milling process on silicon carbide hard ceramic. To this end, a Yb:YAG pulsed fiber laser was adopted to mill silicon carbide bars. Square pockets, 5×5 mm2 in plane dimension, were machined at the maximum nominal average power (30W), under different laser process parameters: pulse frequency, scan speed, hatching distance, repetitions and scanning strategy. After machining, the achieved depth and the roughness parameters were measured by way of digital microscopy and 3D surface profiling, respectively. In addition, the material removal rate was calculated as the ratio between the removed volume/process time. Analysis of variance (ANOVA) was adopted to assess the effect of the process parameters on the achieved depth, the material removal rate (MRR) and roughness parameters, while response surface methodology (RSM) and artificial neuronal networks (ANNs) were adopted to model the process behaviours. Results show that both RSM and ANNs fault in MRR and RSm roughness parameters modelling. Thus, an integrated approach was developed to overcome the issue; the approach is based on the use of the RSM model to obtain a hybrid Training dataset for the ANNs. The results show that the approach can allow improvement in model accuracy.

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Okewale, Akindele, Olusola Adesina, and Mustapha Oloko-Oba. "Comparative Study of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) on Optimization of Ethanol Production from Sawdust." International Journal of Engineering Research in Africa 30 (May 2017): 125–33. http://dx.doi.org/10.4028/www.scientific.net/jera.30.125.

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This work focused on optimization of production of ethanol from saw dust using two empirical methods, the ANN and the RSM. It further investigated the modeling and optimization efficiencies of RSM and ANN in separate hydrolysis and fermentation of sawdust for ethanol production. Box - Behnken Design (BBD) was used to generate 17 individual experiments which were carried out, RSM and Genetic Algorithm (GA) of ANN which were used to optimize the production which was then compared. The optimum concentrations of ethanol yield predicted were 56.968 wt. % and 57. 387263 wt. % for RSM and ANN models respectively. R2 value obtained for ANN model was 0.9989 while R2 value of 0.9046 was obtained for RSM model. The Root Mean Square Error (RMSE) value for ANN was found to be 0.143 while the RMSE value for RSM was 2.17. It showed that ANN had relatively higher predictive model ability and thus shows to be a better optimization tool for the ethanol from saw dust compared to RSM which also a good modelling tool.

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Kahsin, Maciej. "Numerical Modelling of Structures with Uncertainties." Polish Maritime Research 24, s1 (2017): 125–32. http://dx.doi.org/10.1515/pomr-2017-0030.

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Abstract The nature of environmental interactions, as well as large dimensions and complex structure of marine offshore objects, make designing, building and operation of these objects a great challenge. This is the reason why a vast majority of investment cases of this type include structural analysis, performed using scaled laboratory models and complemented by extended computer simulations. The present paper focuses on FEM modelling of the offshore wind turbine supporting structure. Then problem is studied using the modal analysis, sensitivity analysis, as well as the design of experiment (DOE) and response surface model (RSM) methods. The results of modal analysis based simulations were used for assessing the quality of the FEM model against the data measured during the experimental modal analysis of the scaled laboratory model for different support conditions. The sensitivity analysis, in turn, has provided opportunities for assessing the effect of individual FEM model parameters on the dynamic response of the examined supporting structure. The DOE and RSM methods allowed to determine the effect of model parameter changes on the supporting structure response.

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Kupolusi, Kupolusi, Joseph A, Adedeji Adedeji, and Shalom Odunayo. "Modelling of Aerial Biomass and Its Physicochemical Properties Using Artificial Intelligence and Response Surface Methodology." Scholars Journal of Physics, Mathematics and Statistics 12, no. 03 (2025): 33–42. https://doi.org/10.36347/sjpms.2025.v12i03.001.

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Aerial biomass and its associated physicochemical properties are pivotal components in understanding ecosystem dynamics, with far-reaching implications for environmental and agricultural management. This study delves into the dynamics of aerial biomass and its correlation with physicochemical properties, utilizing a dataset derived from research on Spartina alterniflora in the Cape Fear Estuary of North Carolina. Machine learning (ML) models, including Response Surface Methodology (RSM), the Quadratic Model, Artificial Neural Networks (ANN), and the Adaptive Neuro-Fuzzy Inference System (ANFIS), are employed to evaluate the relationships between these properties and aerial biomass. The RSM model outperformed other models with a remarkable Mean Squared Error (MSE) of 0.0579 and a high R-squared value of 0.9518, emphasizing its efficiency in estimating biomass. The quadratic model follows closely, with an MSE of 0.1778 and an R-squared value of 74.61%, providing valuable insights into biomass variation. Furthermore, Particle Swarm Optimization (PSO) is applied to optimize the models. The results highlight RSM-PSO as the most efficient technique, with a PSO value of 0.9872 and an ML R-squared of 0.9518, underscoring the robustness of the RSM model when combined with PSO for predicting aerial biomass. The findings emphasize the significance of pH and potassium content in biomass estimation and recommend the RSM model, particularly when coupled with PSO, for efficient biomass prediction. These insights have critical implications for environmental and agricultural management and may serve as a valuable tool for ecosystem optimization.

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Rezk, Hegazy, A. G. Olabi, Mohammad Ali Abdelkareem, Hussein M. Maghrabie, and Enas Taha Sayed. "Fuzzy Modelling and Optimization of Yeast-MFC for Simultaneous Wastewater Treatment and Electrical Energy Production." Sustainability 15, no. 3 (2023): 1878. http://dx.doi.org/10.3390/su15031878.

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Microbial fuel cells convert the chemical energy conserved in organic matter in wastewater directly to electrical energy through living microorganisms. These devices are environmentally friendly thanks to their ability to simultaneously produce electrical energy and wastewater treatment. The output power of the yeast microbial fuel cell (YMFC) depends mainly on glucose concentration and glucose/yeast ratio. Thus, the paper aims to boost the power of YMFC by identifying the best values of glucose concentration and glucose/yeast ratio. The suggested approach comprises fuzzy modelling and optimization. Fuzzy is used to build the model based on the measured data. In the optimization stage, the marine predators’ algorithm (MPA) is applied to identify the best glucose concentration values and glucose/yeast ratio corresponding to the maximum output power of YMFC. The results revealed the superiority of the combination of fuzzy and MPA compared with the response surface methodology (RSM) approach. Regarding the modelling accuracy, the coefficient of determination increased by 13.32% and 8.37%, respectively, for without methylene blue and with methylene blue compared with RSM. The integration between fuzzy and MPA succeeded in maximizing the output power from YMFC. Without MB, the power density increased by 25% and 29.3%, respectively, compared with measured data and RSM. In addition, with MB, the power density increased by 22.4% and 26%, compared with measured data and RSM.

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Hassan Nejad, H., S. Shafiei Zadeh, and S. Alam. "Modelling of platinum extraction by Aliquat 336 utilising RSM technique." Canadian Metallurgical Quarterly 52, no. 4 (2013): 342–47. http://dx.doi.org/10.1179/1879139513y.0000000067.

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Grbatinić, Ivan, Nemanja Rajković, and Nebojša Milošević. "Computational RSM modelling of dentate nucleus neuron 2D image surface." Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 6, no. 1 (2016): 43–50. http://dx.doi.org/10.1080/21681163.2016.1160798.

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Nalbur, Berrak Erol, Arzu Teksoy, Seval Kutlu Akal Solmaz, and Hilal Safiye Azak. "Modelling Dimethoate Removal by Fenton-Like Process Using Response Surface Methodology." Proceedings 2, no. 11 (2018): 649. http://dx.doi.org/10.3390/proceedings2110649.

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The (RSM) is a useful method for optimizing analytical methods and it has been applied to evaluate independent variables in FPs. In this study, the removal of dimethoate (DMT) which is a commonly used pesticide and has a toxic effect on the environment, was evaluated in terms of oxidation and mineralization efficiency using response surface methodology (RSM) in the Fenton-like process (FLP). The obtained optimum conditions for DMT oxidation and mineralization using the FLP included DMT/Fe+3/H2O2 ratio of 0.018 mM/0.03 mM/0.15 mM and reaction time of 65 min. DMT oxidation efficiency was 78% and mineralization efficiency was 18%. The initial DMT concentration was the most significant variable affecting both the oxidation and mineralization efficiency of DMT.

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Fathy, Ahmed, Hegazy Rezk, Dalia Yousri, Abdullah G. Alharbi, Sulaiman Alshammari, and Yahia B. Hassan. "Maximizing Bio-Hydrogen Production from an Innovative Microbial Electrolysis Cell Using Artificial Intelligence." Sustainability 15, no. 4 (2023): 3730. http://dx.doi.org/10.3390/su15043730.

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In this research work, the best operating conditions of microbial electrolysis cells (MECs) were identified using artificial intelligence and modern optimization. MECs are innovative materials that can be used for simultaneous wastewater treatment and bio-hydrogen production. The main objective is the maximization of bio-hydrogen production during the wastewater treatment process by MECs. The suggested strategy contains two main stages: modelling and optimal parameter identification. Firstly, using adaptive neuro-Fuzzy inference system (ANFIS) modelling, an accurate model of the MES was created. Secondly, the optimal parameters of the operating conditions were determined using the jellyfish optimizer (JO). Three operating variables were studied: incubation temperature (°C), initial potential of hydrogen (pH), and influent chemical oxygen demand (COD) concentration (%). Using some measured data points, the ANFIS model was built for simulating the output of MFC considering the operating parameters. Afterward, a jellyfish optimizer was applied to determine the optimal temperature, initial pH, and influent COD concentration values. To demonstrate the accuracy of the proposed strategy, a comparison with previous approaches was conducted. For the modelling stage, compared with the response surface methodology (RSM), the coefficient of determination increased from 0.8953 using RSM to 0.963 using ANFIS, by around 7.56%. In addition, the RMSE decreased from 0.1924 (using RSM) to 0.0302 using ANFIS, whereas for the optimal parameter identification stage, the optimal values were 30.2 °C, 6.53, and 59.98 (%), respectively, for the incubation temperature, the initial potential of hydrogen (pH), and the influent COD concentration. Under this condition, the maximum rate of the hydrogen production is 1.252 m3H2/m3d. Therefore, the proposed strategy successfully increased the hydrogen production from 1.1747 m3H2/m3d to 1.253 m3H2/m3d by around 6.7% compared to RSM.

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Yıkmış, Seydi, Filiz Aksu, Sema Sandıkçı Altunatmaz, and Başak Gökçe Çöl. "Ultrasound Processing of Vinegar: Modelling the Impact on Bioactives and Other Quality Factors." Foods 10, no. 8 (2021): 1703. http://dx.doi.org/10.3390/foods10081703.

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In recent years, non-thermal technology has been used for the enrichment of ultrasound bioactive components. For this purpose, it was applied to tomato vinegar and modeled with response surface methodology (RSM) and artificial neural network (ANN). At the end of the RSM, cupric reducing antioxidant capacity (68.64%), 1,1-diphenyl-2-picrylhydrazyl (62.47%), total flavonoid content (2.44 mg CE/mL), total phenolic content (12.22 mg GAE/mL), total ascorbic acid content (2.53 mg/100 mL) and total lycopene (5.44 μg/mL) were determined. The ANN model has higher prediction accuracy than RSM. The microstructure, microbiological properties, sensory analysis, ACE (angiotensin-converting–enzyme) inhibitor and antidiabetic effects of the ultrasound-treated tomato vinegar (UTV) (8.9 min and 74.5 amplitude), traditional tomato vinegar (TTV) and pasteurized tomato vinegar (PTV) samples were then evaluated. UTV was generally appreciated by the panelists. It was determined that the microbiological properties were affected by the ultrasound treatment. UTV was found to have more effective ACE inhibitor and antidiabetic properties than other vinegar samples. As a result, the bioactive components of tomato vinegar were enriched with ultrasound treatment and positive effects on health were determined.

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Mangesh Dadarao Shende. "RSM and ANN Modeling Techniques to Forecast How Various Parameters will Affect the Improvement of Electronics Cooling using Radial Heat Sink." Advances in Nonlinear Variational Inequalities 27, no. 2 (2024): 650–62. http://dx.doi.org/10.52783/anvi.v27.1352.

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This research provides the mathematical modeling for temperature difference for natural convection using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) based modelling. Length of fin (L), height of fin (H), number of fins (N) and the heat input (Q) for the Radial heat sink are the parameters selected under natural convection heat transfer. Looking at the pattern of the data, feed forward back propagation type neural network is chosen. The RSM mathematical model of temperature difference is used to compare the performance of the created ANN models. ANN Simulations proved to be successful in terms of agreement with actual values of experimentation. ANN simulations perform accurate to validate the experimental results and the results obtained from the RSM for the output under natural convection. The optimum values for the dimensional parameters namely length of fin, height of the fin, number of fins are obtained by RSM method. Also the optimum operating parameter that is heat input for minimum temperature difference is obtained.

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Doraisamy, Amrishraj, Thiyagarajan Senthilvelan, and S. Sampath Kumar. "Empirical Modelling and Optimization of Sliding Wear Behaviour of Copper-Graphite Composites Using RSM." Applied Mechanics and Materials 592-594 (July 2014): 260–68. http://dx.doi.org/10.4028/www.scientific.net/amm.592-594.260.

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Copper-Graphite composites have been prepared through powder metallurgy (P/M) method. 0 3 and 6 wt % of graphite particles have been added as the reinforcement to the Copper powder which forms the matrix and composites have been prepared. Microstructure of the newly prepared composites was analyzed using Scanning Electron Microscopy (SEM). Wear test was done using Pin-on-disc tribometer according to G99 standards. A linear regression Mathematical model has been developed using RSM to predict the sliding wear behavior of the composites. Optimization has been done using RSM and also based on ANOVA to find the significant parameters affecting the sliding wear.

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Yamin, Jehad A. A. "Response Surface Modelling of Diesel Engine Emissions under Variable Stroke Length and Constant Compression Ratio." Modern Applied Science 12, no. 10 (2018): 36. http://dx.doi.org/10.5539/mas.v12n10p36.

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A theoretical investigation using RSM statistical technique on the relative change of emissions of a four-stroke, direct injection, water-cooled, 4-stroke, diesel engine with variable stroke length was carried out.&nbsp; The performance parameters were studied over wide range of speeds (1000 - 3000 RPM at an increment of 500 RPM) and stroke lengths (130 mm to 210mm at an increment of 20mm). The compression ratio was kept constant by adjusting the piston bowl volume. It was found within the range of stroke length studied, that larger stroke lengths are favorable for lower NOx and specific CO2 emissions. This is due to the lower availability of Oxygen. As for specific PM and BSN, the shorter the stroke length the lower the levels. This is attributed to improved engine charging efficiency, hence, better availability of oxygen.

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Deng, Zhao Hui, Lin Lin Wan, Xiao Hong Zhang, and Sheng Chao Li. "Modelling the Processing Parameters of Rotary Curved Surface Grinding Using RSM." Advanced Materials Research 338 (September 2011): 130–35. http://dx.doi.org/10.4028/www.scientific.net/amr.338.130.

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The NC grinding of Si3N4 ceramics rotary curved surface workpieces was carried on the CNC jig grinders, combining the nomal tracing grinding and the arc envelope forming grinding. According to the grinding track, a grinding surface scallop height model was built. The model analysis indicated that workpiece curvature, wheel radius and feed rate affected the surface roughness. A prediction model based on the response surface methodology (RSM) was built to study the influences of these processing parameters on the surface roughness, using the Box-Behnken design to design the grinding experiments. It was shown that the surface roughness of rotary curved surface workpieces grinding which increased with increasing workpiece curvature could be reduced by chosen larger radius wheel and lower feed rate. The confidence and practicality of the prediction model is high in experimental conditions. The process parameters can be selected to improve the quality of the surface based on the model of the paper.

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Kundu, R. R., and B. N. Lahiri. "Study and statistical modelling of Green Sand Mould properties using RSM." International Journal of Materials and Product Technology 31, no. 2/3/4 (2008): 143. http://dx.doi.org/10.1504/ijmpt.2008.018016.

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Vates, Umesh Kumar, Nand Jee Kanu, Eva Gupta, Gyanendra Kumar Singh, Naveen Anand Daniel, and Bhupendra Prakash Sharma. "Optimization of FDM 3D Printing Process Parameters on ABS based Bone Hammer using RSM Technique." IOP Conference Series: Materials Science and Engineering 1206, no. 1 (2021): 012001. http://dx.doi.org/10.1088/1757-899x/1206/1/012001.

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Abstract Rapid prototyping (RP) uses a cycle where a real model is made by explicitly adding material as thin cross-sectional layers. Fused deposition modelling (FDM) 3D printer is being use for synthesis of ABS based bone hammer. Response surface methodology (RSM) based L27 design of experiment were adopted to perform the experiment using four influencing parameters such as layer thickness, infill percentage, orientation and nozzle temperature for the three responses deflection, hardness and weight. Response surface methodology was used for modelling and optimization of considered process parameters. In present investigation, it is evident that bone hammer fabrication process parameters have been optimized on data such as bone hammer weight 19.8091g, hardness 104.5921 BHN, and force of 15 degree deflection 36.0681 N has been produced with RSM prediction with influence of process parameters such as layer thickness 0.250 mm, infill percentage 63.3333, orientation 60 degree, nozzle temperature 240°C.

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Reddy Paturi, Uma Maheshwera, Konidhala Nandan, Nudurupati Achintya Vamshi, Omkar Sunil Sahasra Bhojane, Goturi Sheshank Reddy, and N. S. Reddy. "Multi-objective parametric modelling during minimum quantity lubrication machining of Incoloy 800H." Journal of Physics: Conference Series 2837, no. 1 (2024): 012064. http://dx.doi.org/10.1088/1742-6596/2837/1/012064.

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Abstract This study utilizes multi-objective optimization to minimize surface roughness and maximize material removal rate (MRR) during minimum quantity lubrication (MQL) turning of Incoloy 800H under different cutting conditions. The correlations among process conditions are examined using response surface methodology (RSM) and the desirability function. The obtained optimal values for the cutting parameters are as follows: 125 m/min cutting speed, 0.1 mm/rev feed rate, and 0.05 mm depth of cut. The predicted surface roughness and MRR were 0.585 µm and 4377.932 mm3/min, respectively. A correlation coefficient of 0.9006 for surface roughness and 0.9979 for MRR indicates a significant degree of agreement between experimental data and model predictions. The results show that the application of RSM can help in identifying optimal cutting conditions and minimize the need for extensive experimental trials in machining.

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Reddy Paturi, Uma Maheshwera, Omkar Sunil Sahasra Bhojane, Goturi Sheshank Reddy, Nudurupati Achintya Vamshi, Konidhala Nandan, and N. S. Reddy. "Multi-objective parametric modelling during wire-cut electric discharge machining of Incoloy 800H." Journal of Physics: Conference Series 2837, no. 1 (2024): 012077. http://dx.doi.org/10.1088/1742-6596/2837/1/012077.

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Abstract Incoloy 800H exhibits exceptional resistance to corrosion in aqueous environments and demonstrates a strong resistance to chloride stress-corrosion cracking. In this study, a multi-objective parametric optimization technique is used to model and optimize the process inputs and the response parameters during wire electrical discharge machining (WEDM) of Incoloy 800H. Statistical techniques, response surface methodology (RSM) and analysis of variance (ANOVA) were employed for this purpose. The correlation coefficient is used to determine the accuracy of statistical model predictions based on experimental data. The results of statistical analysis demonstrate that the model predictions and experimental observations are in high agreement, with correlation coefficients of 0.9224 for surface roughness and 0.9968 for material removal rate (MRR). Thus, RSM helps in choosing the best input parameters, minimizing experimental trials, and enhancing process efficiency.

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Jamil Hosni, Nor Ain Binti, Mohd Amri Bin Lajis, and Muhammad Ridzuan Bin Idris. "Modelling and optimization of Chromium Powder Mixed EDM Parameter Effect Over the Surface Characteristics by Response Surface Methodology Approach." International Journal of Engineering Materials and Manufacture 3, no. 2 (2018): 78–86. http://dx.doi.org/10.26776/ijemm.03.02.2018.02.

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In this paper, an optimization of chromium powder mixed parameters effect, i.e. discharge current, pulse on time and Cr powder concentration of AISI D2 steels in Powder Mixed EDM (PMEDM) has been made. RSM has been employed to plan and analyzed the experiment. Central composite design (CCD) was chosen as the RSM design that is useful for investigating the quadratic effects. The version 8.0 of the Design Expert software was used to develop the experimental plan for RSM. A mathematical model in the form of the multiple regression equation for second order response surface with the best fittings was developed. The results identify that discharge current and pulse on time the most important parameters effect to minimize recast layer. With the topmost desirability solution, the suggested optimum parameter of discharge current is 20.12 A, pulse-on time 50.14 µs and 3.96 g/L powder concentration to minimize recast layer.

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Ghorbani, Mehdi, Aghil Ghorbani, Mansoor Omidi, and Seyed Mohammad Hashemi. "Response Surface Modelling of Noradrenaline Production in Hairy Root Culture of Purslane (Portulaca oleracea L.)." Turkish Journal of Agriculture - Food Science and Technology 3, no. 6 (2015): 349. http://dx.doi.org/10.24925/turjaf.v3i6.349-443.320.

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Common purslane (Portulaca oleracea L.) is an annual plant as one of the natural sources for noradrenaline hormone. In this research, hairy root culture of purslane was established by using Agrobacterium rhizogenes strain ATCC 15834. In the following, Box-Behnken model of response surface methodology (RSM) was employed to optimize B5 medium for the growth of P. oleracea L. hairy root line. According to the results, modelling and optimization conditions, including sucrose, CaCl2.H2O, H2PO4 and NO3-/NH4+ concentrations on maximum dry weight (0.155 g) and noradrenaline content (0.36 mg.g-1 DW) was predicted. These optimal conditions predicted by RSM were confirmed the enhancement of noradrenaline production as an application potential for production by hairy root cultures.

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Nwose, Sebastian Azowenu, Francis Odikpo Edoziuno, and Sylvester O. Osuji. "Statistical analysis and Response Surface Modelling of the compressive strength inhibition of crude oil in concrete test cubes." Algerian Journal of Engineering and Technology 4 (April 16, 2021): 99–107. https://doi.org/10.5281/zenodo.4696030.

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The use of crude oil contaminated fine aggregates in the production of concrete significantly affect the properties of such concrete, especially the compressive strength. In the present investigation, response surface methodology (RSM) of the Design-Expert software version 11.1.0.1 was used for the statistical analysis and predictive modelling of the compressive strength of concrete cubes made from crude oil contaminated fine aggregates at 7, 14, 28, and 56 days curing periods. The fine aggregates were mixed with varying concentrations of crude oil contamination (ranging from 0% to 5% by weight of the fine aggregates, at 1% interval). Concrete test cubes were produced for compressive strength determination and prediction phase of the modelling. A steady reduction in the compressive strength of the concrete cubes was recorded as the crude oil content increases, due to the inhibitive and surface shielding influence of crude oil molecules on the fine aggregates, thereby hindering physical bond formation between the cement paste and the aggregates. Statistical analysis of the output/response was carried out; a correlation coefficient of 0.9923 was obtained. The result of the modelling has shown that the use of RSM is adequate in the prediction of the compressive strength inhibition of crude oil in concrete made from crude oil-contaminated sand. 

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Shu, Dun Tao, Yan Ling He, Qing Yi Wang, and Wang Li. "Optimization and Modelling of Denitrification and Methanogenic Activity in Start up of Mixotrophic Anammox Reactor." Advanced Materials Research 1073-1076 (December 2014): 127–35. http://dx.doi.org/10.4028/www.scientific.net/amr.1073-1076.127.

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In the present study, the SDA (specific denitrification activity) and SMA (specific methanogenic activity) in Start up of mixotrophic anammox reactor was optimized by applying the response surface method (RSM). The purpose of this work was to find the optimal combination of C/N ratio, influent ammonium (NH4+-N) and volatile suspended solid (VSS) with respect to minmum the SDA and SMA. Based on the RSM results, the quadratic model developed for the responses indicated that optimal conditions were C/N ratio of 0.5, influent NH4+-N content of 200mg L-1, and VSS content of 59.31g L-1. Under this conditions, the SDA and SMA were minimize and found to be 0.05 mmol N2 (g VSS d)-1, 0.017 mmol N2 (g VSS d)-1, respectively

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Kristianto, Y., W. Wignyanto, B. D. Argo, and I. Santoso. "Antioxidant increase by response surface optimization and Bayesian neural network modelling of pumpkin (Cucurbita moschata Duch) freezing." Food Research 5, no. 3 (2021): 73–82. http://dx.doi.org/10.26656/fr.2017.5(3).598.

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Pumpkin antioxidants have been found to benefit diabetics. This current study was attempted to optimize slow freezing treatment for a pumpkin to obtain maximum antioxidant gain using response surface methodology (RSM) and Bayesian regularized neural network (BRANN) approaches. A central composite design was used to generate the freezing experiment and to examine response change as a function of temperature and freezing time. Feedforward neural networks with a 2-15-1 structure were developed and trained using the Bayesian regularization algorithm. The results showed that the freezing data were well fitted to quadratic models generating R2 for total phenolic compounds (TPC), flavonoid of 0.850 and 0.857 respectively. The RSM optimized freezing of -20oC for 9 hrs were well confirmed to produce an increase in TPC and flavonoid by 54.44% and 60.4% respectively. The BRANN performances were found to be similar to that of RSM. While overfitting was mitigated during the supervised training, the BRANN model served excellent predictive and confirmatory tool for the optimization. In conclusion, slow freezing at -20oC for 9 hrs significantly increases TPC and flavonoid of pumpkin. This novel process may be adopted to provide healthier pumpkins food products for targeted consumers.

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Sentyabov, A. V., D. V. Platonov, A. V. Minakov, and A. S. Lobasov. "Numerical study of the vortex breakdown and vortex reconnection in the flow path of high-pressure water turbine." Journal of Physics: Conference Series 2088, no. 1 (2021): 012040. http://dx.doi.org/10.1088/1742-6596/2088/1/012040.

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Abstract The paper presents a study of the instability of the precessing vortex core in the model of the draft tube of a hydraulic turbine. The study was carried out using numerical modeling using various approaches: URANS, RSM, LES. The best agreement with the experimental data was shown by the RSM and LES methods with the modelling of the runner rotation by the sliding mesh method. In the regime under consideration, the precessing vortex rope is subject to instability, which leads to reconnection of its turns and the formation of an isolated vortex ring. Reconnection of the vortex core leads to aperiodic and intense pressure fluctuations recorded on the diffuser wall.

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Vitas, Jasmina. "ANN and RSM modelling of antioxidant characteristics of kombucha fermented milk beverages with peppermint." Mljekarstvo 68, no. 2 (2018): 116–25. http://dx.doi.org/10.15567/mljekarstvo.2018.0205.

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Antioxidant activity to stable DPPH radical (AADPPH) and unstable hydroxyl radicals (AA.OH) and nutraceuticals (monounsaturated fatty acids (MUFAs), polyunsaturated fatty acids (PUFAs) and ascorbic acid) content of kombucha fermented milks with peppermint (KFM-P) were modelled and optimised. Beverages were produced by the addition of 10 % of kombucha peppermint inoculum to the milk containing 0.8, 1.6 and 2.8 % milk fat at 37, 40 and 43 °C. Response surface methodology (RSM) indicated opposite response surfaces for AADPPH and AA.OH PUFAs and ascorbic acid, as most significant and influential factors, were included in graphical optimization and gave the working region for obtaining products of highest antioxidant quality: lower temperatures and milk fat up to 1.8 %; higher temperatures and milk fat of maximum 1.6 %. ANN modelling of antioxidant characteristics of kombucha fermented milk beverages with peppermint was, as expected, more accurate than RSM.

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Anantha Padmanabha, E., P. Shashivardhan Reddy, B. Narender, S. Muralikrishnan, and V. K. Dadhwal. "Photogrammetric processing of hexagon stereo data for change detection studies." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-8 (November 27, 2014): 151–57. http://dx.doi.org/10.5194/isprsannals-ii-8-151-2014.

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Hexagon satellite data acquired as a part of USA Corona program has been declassified and is accessible to general public. This image data was acquired in high resolution much before the launch of civilian satellites. However the non availability of interior and exterior orientation parameters is the main bottle neck in photogrammetric processing of this data. In the present study, an attempt was made to orient and adjust Hexagon stereo pair through Rigorous Sensor Model (RSM) and Rational Function Models (RFM). The study area is part of Western Ghats in India. For rigorous sensor modelling an arbitrary camera file is generated based on the information available in the literature and few assumptions. A terrain dependent RFM was generated for the stereo data using Cartosat-1 reference data. The model accuracy achieved for both RSM and RFM was better than one pixel. DEM and orthoimage were generated with a spacing of 50 m and Ground Sampling Distance (GSD) of 6 m to carry out the change detection with a special emphasis on water bodies with reference to recent Cartosat-1 data. About 72 new water bodies covering an area of 2300 hectares (23 sq. km) were identified in Cartosat-1 orthoimage that were not present in Hexagon data. The image data from various Corona programs like Hexagon provide a rich source of information for temporal studies. However photogrammetric processing of the data is a bit tedious due to lack of information about internal sensor geometry.

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Jenarthanan, M. P., A. Ajay Subramanian, and R. Jeyapaul. "Comparative analysis of surface roughness prediction using DOE and ANN techniques during endmilling of glass fibre reinforced polymer (GFRP) composites." Pigment & Resin Technology 45, no. 2 (2016): 126–39. http://dx.doi.org/10.1108/prt-03-2015-0026.

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Purpose – This paper aims to study the comparison between a response surface methodology (RSM) and artificial neural network (ANN) in the modelling and prediction of surface roughness during endmilling of glass-fibre-reinforced polymer composites. Design/methodology/approach – Aiming to achieve this goal, several milling experiments were performed with polycrystalline diamond inserts at different machining parameters, namely, feed rate, cutting speed, depth of cut and fibre orientation angle. Mathematical model is created using central composite face-centred second-order in RSM and the adequacy of the model was verified using analysis of variance. ANN model is created using the back propagation algorithm. Findings – With regard to the machining test, it was observed that feed rate is the dominant parameter that affects the surface roughness, followed by the fibre orientation. The comparison results show that models provide accurate prediction of surface roughness in which ANN performs better than RSM. Originality/value – The data predicted from ANN are very nearer to experimental results compared to RSM; therefore, this ANN model can be used to determine the surface roughness for various fibre-reinforced polymer composites and also for various machining parameters.

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Hossain, Md Shahriar Jahan, and Nafis Ahmad. "Surface roughness prediction modelling for commercial dies using ANFIS, ANN and RSM." International Journal of Industrial and Systems Engineering 16, no. 2 (2014): 156. http://dx.doi.org/10.1504/ijise.2014.058834.

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Reddy, P. Venkateshwar, B. Veerabhadra Reddy, and P. Janaki Ramulu. "Mathematical modelling for prediction of tube hydroforming process using RSM and ANN." International Journal of Industrial and Systems Engineering 35, no. 1 (2020): 13. http://dx.doi.org/10.1504/ijise.2020.106848.

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Danso-Boateng, Eric, Melissa Fitzsimmons, Andrew B. Ross, and Ted Mariner. "Response Surface Modelling of Methylene Blue Adsorption onto Seaweed, Coconut Shell and Oak Wood Hydrochars." Water 15, no. 5 (2023): 977. http://dx.doi.org/10.3390/w15050977.

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Adsorption of methylene blue (MB) dye from an aqueous solution onto hydrochars produced from brown seaweed (Fucus Serratus) (FS-HC), coconut shell (CS-HC), and oak wood (Oak-HC) at different temperatures (200–250 °C) was investigated in a batch system. Response surface modelling (RSM) was used to investigate the effect of initial MB concentration (50–300 mg/L), contact time (0–240 min), and solution pH (2–12) on the adsorption process. RSM was also used to model and optimise these parameters for efficient adsorption. Kinetic and isotherms studies were carried out to study the adsorption mechanism onto the hydrochars. It was found that the best adsorbent from the RSM model was FS-HC200, and the optimal conditions for greater MB dye uptake were lower initial MB concentration (50 mg/L), pH 6 and contact time of 84 min; removing >99% of MB. Langmuir and Redlich–Peterson isotherm models fitted the adsorption of MB onto hydrochars prepared at 200 and 250 °C. Freundlich and Redlich–Peterson isotherms were suitable for hydrochars produced at 220 °C. FS-HCs have the highest maximum adsorption capacity of MB of about (8.60–28.57) mg/g calculated from the Langmuir isotherm. The adsorption process for all the hydrochars followed a pseudo-second-order model (R2 = 0.96–1.00), and film diffusion and intraparticle diffusion were the rate-determining steps. Therefore, this work identifies cheap adsorbents from biowaste that are effective for the removal of cationic pollutants from wastewater.

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Pachiyappan, Jayakaran, Gnanasundaram Nirmala, Selvaraju Sivamani, et al. "Biogenic Synthesis, Characterization, and Photocatalytic Evaluation of Pristine and Graphene-Loaded Zn50Mg50O Nanocomposites for Organic Dyes Removal." Nanomaterials 12, no. 16 (2022): 2809. http://dx.doi.org/10.3390/nano12162809.

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Algal biomass synthesised nanocomposites have a higher surface area and reusability advantages. This study aimed to synthesise and characterise ZnMgO and silica-supported graphene with ZnMgO (G-ZnMgO) nanocomposites from Kappaphycusalvarezii and evaluate their potential in the application of photocatalysis to remove Rhodamine-B (RhB) and methylene blue (MB) dyes from their aqueous medium by maximising the percentage removal using response surface methodology (RSM) modelling. Nanocomposites were synthesised and characterised by biogenic and instrumental (Powder X-ray diffraction (P-XRD), electron microscopic analysis (SEM and TEM), Fourier transform infrared spectroscopy (FTIR), Energy dispersive analysis of X-rays (EDAX). and UV-visible diffuse reflectance spectroscopy (UV-DRS)) methods, respectively; modelling predicted the optimal conditions to be photocatalyst dosage and contact time of 1 g/L and 90 min, respectively, to obtain maximum MB dye removal of 80% using G-ZnMgO. The results showed the best fit between experimental and RSM predicted values. Thus, the obtained results conclude that the algal biomass synthesised nanocomposites were found to be one of the potential photocatalysts for the removal of RhB and MB dyes from their aqueous solution.

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Barışçı, Sibel, and Ozge Turkay. "Optimization and modelling using the response surface methodology (RSM) for ciprofloxacin removal by electrocoagulation." Water Science and Technology 73, no. 7 (2015): 1673–79. http://dx.doi.org/10.2166/wst.2015.649.

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In this study, response surface methodology (RSM) was used to investigate the effects of different operating conditions on the removal of ciprofloxacin (CIP) by the electrocoagulation (EC) with pure iron electrodes. Box-Behnken design was used for the optimization of the EC process and to evaluate the effects and interactions of process variables such as applied current density, process time, initial CIP concentration and pH on the removal of CIP by the EC process. The optimum conditions for maximum CIP removal (86.6%) were found as pH = 4; Co = 5 mg.L1−; Id = 4.325 mA.cm2−; tprocess = 10 min. The model adequacy and the validity of the optimization step were confirmed with additional experiments which were performed under the proposed optimum conditions. The predicted CIP removal as 86.6% was achieved at each experiment by using the optimum conditions. These results specify that the RSM is a useful tool for optimizing the operational conditions for CIP removal by the EC process.

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Panneerselvam, K., and Kasirajan Lenin. "Parameters optimization in FSW of polypropylene based on RSM." Multidiscipline Modeling in Materials and Structures 11, no. 1 (2015): 32–42. http://dx.doi.org/10.1108/mmms-07-2013-0048.

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Purpose – The purpose of this paper is to weld polypropylene (PP) material by friction stir welding (FSW) process. The input process parameters considered were: tool pin profile, feed rate and tool rotational speed and the process output characteristics were tensile strength, Shore-D hardness, Rockwell hardness, Izod strength, Charpy strength and nugget area. Design/methodology/approach – Optimization of process parameters were carried out based on response surface methodology (RSM) and significant parameters were obtained by performing analysis of variance (ANOVA). Findings – The optimized results were the threaded pin profile for feed of 60 mm/min and tool rotational speed of 1,500 rpm. A confirmation test was carried out to verify the optimized results. Originality/value – In this paper, the process parameters were optimized based on RSM. This is newly adopted optimization techniques in the FSW process of PP materials and also it gives better results.

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Maalihan, Reymark D. "Modelling the Toughness of Nanostructured Polyhedral Oligomeric Silsesquioxane Composites Fabricated by Stereolithography 3D Printing: A Response Surface Methodology and Artificial Neural Network Approach." Materials Science Forum 1053 (February 17, 2022): 41–46. http://dx.doi.org/10.4028/p-6s4jp4.

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In this study, stereolithography (SLA) 3D printing was used to prepare toughened composites by facile blending of chemically compatible nanoscale polyhedral oligomeric silsesquioxanes (POSS) to commercial photoreactive resin. Due to the complex nature of 3D printing, the mechanical performance of the final parts cannot be simply determined or even estimated until they are manufactured and tested. Thus, response surface methodology (RSM) and artificial neural network (ANN) were used to build regression models for determining the toughness of fabricated composites as function of toughener (POSS) amount and printing conditions (layer thickness and annealing temperature). The influence of the mentioned process parameters on toughness were investigated through a 17-run three-factor three-level Box-Behnken RSM design (BBD). The same experimental design was also used to acquire a data set for ANN. Finally, both the modeling methodologies were compared by coefficient of determination (R2) and residual distribution values. Results reveal that ANN possesses a better data fitting and predictive power as compared to RSM.

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Fourati, Mariam, Slim Smaoui, Karim Ennouri, et al. "Multiresponse Optimization of Pomegranate Peel Extraction by Statistical versus Artificial Intelligence: Predictive Approach for Foodborne Bacterial Pathogen Inactivation." Evidence-Based Complementary and Alternative Medicine 2019 (October 13, 2019): 1–18. http://dx.doi.org/10.1155/2019/1542615.

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Pomegranate (Punica granatum L.) peel is a potential source of polyphenols known for their activity against foodborne pathogen bacteria. In this study, the effects of pomegranate peel extraction time (10–60 min), agitation speed (120–180 rpm), and solvent/solid ratio (10–30) on phytochemical content and antibacterial activity were determined. Response surface methodology (RSM) and artificial neural network (ANN) methods were used, respectively, for multiresponse optimization and predictive modelling. Compared with the original conditions, the total phenolic content (TPC), the total flavonoid content (TFC), and the total anthocyanin content (TAC) increased by 56.22, 63.47, and 64.6%, respectively. Defined by minimal inhibitory concentration (MIC), the maximum of antibacterial activity was higher than that from preoptimized conditions. With an extraction time of 11 min, an agitation speed 125 rpm, and a solvent/solid ratio of 12, anti-S. aureus activity remarkably decreased from 1.56 to 0.171 mg/mL. Model comparisons through the coefficient of determination (R2) and mean square error (MSE) showed that ANN models were better than the RSM model in predicting the photochemical content and antibacterial activity. To explore the mode of action of the pomegranate peel extract (PPE) at optimal conditions against S. aureus and S. enterica, Chapman and Xylose Lysine Deoxycholate broth media were artificially contaminated at 104 CFU/mL. By using statistical approach, linear (ANOVA), and general (ANCOVA) models, PPE was demonstrated to control the two dominant foodborne pathogens by suppressing bacterial growth.

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Soomro, Shakeel Ahmed, Kunjie Chen, and Sohail Ahmed Soomro. "Mathematical Modelling and Optimisation of Low-Temperature Drying on Quality Aspects of Rough Rice." Journal of Food Quality 2020 (January 25, 2020): 1–10. http://dx.doi.org/10.1155/2020/6501257.

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Rice when harvested normally has a high moisture content of 20–25% which requires immediate drying, reducing its mass loss and preventing it to spoil. This situation is more crucial with the areas under humid tropical conditions, where moisture and temperature mainly play an important role in deteriorating the quality of rough rice. Keeping the importance of quality attributes of rough rice, the study was carried out to assess the effects of low-temperature drying and suggest an optimum condition. Response surface methodology (RSM) with a central composite design was employed to study the effects of variables, i.e., temperature (X1), time (X2), and air velocity (X3) on responses, i.e., head rice yield (HRY), hardness, lightness, and cooking time. The experimental data were fitted to the quadratic model, studying the relationship between independent and dependent variables. The results revealed that the HRY, hardness, lightness, and cooking time increased with increasing variables, whereas for HRY, it particularly increased and then decreased. It was observed that temperature had more influence on the quality of rough rice followed by time and velocity. Results for analysis of variance revealed that the quality aspects of rough rice were significantly (p<0.05) affected by temperature and time, whereas for velocity, it only significantly affected hardness. The optimal drying conditions predicted by RSM for variables were 25°C, 600 min, and 1 m·s−1, and the optimal predicted HRY, hardness, lightness, and cooking time were 73.93%, 38.28 N, 71.40, and 27.58 min respectively. Acceptable values of R2, Adj R2, and nonsignificance of lack of fit demonstrated that the model applied was adequate and can be used for optimization. The study concluded that the RSM with a central composite design was successfully used to study the dependence of quality aspects of rough rice at low temperature and can be utilized by the rice processing industries.

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Lin, Weiyun, Liang Jing, and Baiyu Zhang. "Micellar-Enhanced Ultrafiltration to Remove Nickel Ions: A Response Surface Method and Artificial Neural Network Optimization." Water 12, no. 5 (2020): 1269. http://dx.doi.org/10.3390/w12051269.

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Nickel ions from aqueous solutions were removed by micellar-enhanced ultrafiltration (MEUF), using the surfactant sodium dodecyl sulfate (SDS) as a chelating agent. Process variables and indicators were modeled and optimized by a response surface methodology (RSM), using the Box–Behnken design (BBD). The generated quadratic models described the relationship between a performance indicator (nickel rejection rate or permeate flux) and process variables (pressure, nickel concentration, SDS concentration, and molecular weight cut-off (MWCO)). The analysis of variance (ANOVA) showed that both models are statistically significant. To remove 1 mM of nickel ions, the optimal condition for maximum nickel removal and flux were: pressure = 30 psi, CSDS = 10.05 mM, and MWCO = 10 kDa, resulting in a rejection rate of 98.16% and a flux of 119.20 L/h∙m2. Experimental verification indicates that the RSM model could adequately describe the performance indicators within the examined ranges of the process variables. An artificial neural network (ANN) modelling followed to predict the MEUF performance and validate the RSM results. The obtained ANN models showed good fitness to the experimental data.

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Yeo, J. S., S. Koting, C. C. Onn, and K. H. Mo. "Optimisation of mix design of concrete paving block using response surface methodology." Journal of Physics: Conference Series 2521, no. 1 (2023): 012012. http://dx.doi.org/10.1088/1742-6596/2521/1/012012.

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Abstract This study investigated the optimal water/binder (w/b) and aggregate/binder (a/b) ratios in producing a concrete paving block. The w/b and a/b ratios in the concrete paving block were optimised using the response surface methodology (RSM), considering the performances of the ultrasonic pulse velocity (UPV), flexural, and compressive strengths. Regression modelling was conducted to represent the relationships between the UPV and compressive strength and the compressive and flexural strengths. Generally, the UPV, flexural, and compressive strengths increased with the increment of w/b ratio and reduction of a/b ratio. The RSM suggested optimal ratios of 0.35 for w/b and 3.50 for a/b, that the paving block could exhibit UPV, flexural, and compressive strengths of 4.11 km/s, 4.13 MPa, and 33.2 MPa, respectively. The predicted values from the RSM varied less than 6% compared to the experimental values. The polynomial regression model could effectively represent the relationship between the UPV and the compressive strength and the relationship between the compressive and flexural strengths of the concrete paving block.

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O.U.Osazuwa;, E.I. Ehiawaguan G.J. Obiuwevbi and A.N. Amenaghawon. "Bio-Inspired Hydroxyapatite-Chitosan Composite for Enhanced Indigo Blue Dye Removal: Kinetic and Adsorption Optimization Study." Journal of Energy Technology and Environment 6, no. 4 (2024): 15–28. https://doi.org/10.5281/zenodo.14523041.

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<em>The presence of dye molecules in water has a number of negative effects on aquatic and terrestrial animals. This study aimed to synthesize and characterize a composite of hydroxyapatite and chitosan to adsorb Indigo Blue (IB) dye from water, and to optimize its use for the adsorption process using the Response Surface Methodology (RSM) modelling tool. The combination of hydroxyapatite obtained from goat bone and chitosan obtained from periwinkle shell was carried out to enhance the mechanical strength and removal efficiency of the adsorbent. The formulated adsorbent was characterized using BET surface area, SEM-EDX, FTIR, XRD, and XRF techniques. Response surface methodology (RSM) using Box-Behnken Design (BBD) was employed to develop a series of batch-wise adsorption experiments. Findings revealed the R<sup>2</sup> value of the RSM optimization tool to be 0.9854. The optimized conditions used for the adsorption experiments were 180 min, 8.6, 150 mg/L, and 1 g for the adsorption time, pH, dye concentration, and adsorbent dosage, respectively, resulting in an adsorption efficiency of 80.92 %.</em>

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