Bibliometric approach to “applications of response surface methodology (RSM) publications” and its review as the most widely used optimization tool

An overview of the effect of stirrer design on the mechanical properties of Aluminium Alloy Matrix Composites fabricated by stir casting

Efficient system optimization techniques through subspace decomposition and response surface refinement

Innovation processes in services remain under-researched, but recently large-scale surveys have been conducted which allow for a more systematic appraisal of the level and scope of innovation in services. To date, much of the literature on innovation in services focuses on the adoption and use of information and communication technologies (ICTs). Innovation in services, and services innovation, should, however, be understood in broader terms, and this understanding should extend to non-technological innovation. This paper presents evidence from a recent large-scale survey of innovation amongst German commercial service firms. It shows that services are much more active with respect to innovation than is widely thought. They are innovative in terms of being producers of service and process innovations. From our broader perspective, we examine the pattern of innovation as it relates to the standardisation-particularisation of service products, across a range of service sectors and across firms of various sizes. The production of bespoke or customised services shaped by client inputs has long been considered a defining characteristic of many services, and one that has affected their innovation potential. The present analysis reveals a pattern of diversity in behaviour, which reflects the diversity amongst service firms, and demonstrates the need for more subtle and differentiated analyses of services and services innovation.

This study examined the modelling and optimisation of the electrocoagulation-flocculation (ECF) recovery of aquaculture effluent (AQE) using aluminium electrodes. The response surface methodology (RSM), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) were used for the modelling, while the optimisation tools were the numerical RSM and genetic algorithm (GA). Furthermore, the kinetics of the ECF process was studied to provide insight into the mechanism governing the ECF of AQE. The experimental design was performed using the central composite design (CCD) of the RSM. The ANFIS modelling was accomplished via the Grid Partition (GP) of the data set, while the ANN used the multi-layer perceptron (MLP) based feed-forward system. Statistically, the prediction accuracy of the models followed the order: ANFIS (R2: 0.9990), ANN (R2: 0.9807), and RSM (R2: 0.9790). The process optimisation gave optimal turbidity (TD) removal efficiencies of 98.98, 97.81, and 96.01% for ANFIS-GA, ANN-GA, and RSM optimisation techniques, respectively. The ANFIS-GA gave the best optimization result at optimum conditions of pH 4, current intensity (3 A), electrolysis time (7.2min), settling time (23min), and temperature (43.8°C). In the kinetics study, the experimental data was analysed using pseudo-first-order (0.8787), pseudo-second-order (0.9395), and Elovich (R2: 0.9979) kinetic models; the Elovich model gave the best correlation with the experimental data showing that the process is governed by electrostatic interaction mechanism. This study effectively demonstrated that ECF recovery of AQE can effectively be modelled using RSM, ANN, and ANFIS and be optimised using RSM, ANN-GA, and ANFIS-GA techniques, and the order of performance is ANFIS > ANN > RSM and ANFIS-GA > ANN-GA > RSM, respectively.

VeritasDGC. Summary We present a new approach to the simultaneous pre-stack inversion of PP and, optionally, PS angle gathers for the estimation of P-impedance, S-impedance and density. Our algorithm is based on three assumptions. The first is that the linearized approximation for reflectivity holds. The second is that PP and PS reflectivity as a function of angle can be given by the Aki-Richards equations (Aki and Richards, 2002). The third is that there is a linear relationship between the logarithm of P-impedance and both S-impedance and density. Given these three assumptions, we show how a final estimate of Pimpedance, S-impedance and density can be found by perturbing an initial P-impedance model. After a description of the algorithm, we then apply our method to both model and real data sets.

This paper reviews the current state of conductive adhesive technology. Most work to date has been carried out with isotropically-conductive adhesives which conduct electricity in any direction. In this review, particular attention has been paid to recently-developed anisotropically-conductive adhesives which are electrically conductive along one axis only. Patents filed in this area have been surveyed and the key points relating to the technology employed are summarized. A survey of the market was carried out and is presented. Adhesive processing techniques were studied and reliability issues relating to adhesives in general and to conductive adhesives in particular investigated. Voids in the adhesive leading to reduced adhesion and stress concentration were seen to be an area of concern while the effect of moisture leading to increased joint resistance and reduced strength was concluded to be the key limiting factor in the long-term reliability of conductive adhesives.

Structural reliability theory stems from the nature of randomness, fuzziness, characteristic and some other uncertainties in the process of engineering structural design, construction and employment. With the rapid development of science technology and industry, many departments have realized the importance of structural reliability problem and its potential economic benefits. Solving the problem of structural reliability has quickly become an important issue in the field of academic research. Because of the complexity of the structure and the harsh working environment which lead to a complex structural reliability problem, the traditional quality analysis method can’t explain the failure problems in practical engineering. How to solve the problem of large-scale complex structural reliability, improve the accuracy and efficiency of reliability analysis method, and further obtain great economic benefits, have become one of the most important exploration areas for the enterprises and scholars at home and abroad. The response surface method, which can replace the implicit limit state function by small amount of computation arises at this time. Through a series of deterministic response surface method, it uses polynomial function to approximate implicit limit state function. By reasonably selecting sites and iteration strategy, it ensures that the polynomial function on the failure probability can converge to the failure probability of the implicit limit state function. Response surface method, with high efficiency of clarity and precision, strong operability, combined with finite element advantage, is a reliability method widely used at present. In the process of the implementation of the response surface method, it uses three key steps of selecting the response surface function forms, obtaining sample points by the experiment design and using the regression fitting model, which has direct impact on the degree of the response surface method approximating limit state function and determines the performance of the response surface method. This is a problem that the response surface method must solve. It is the paper’s original intention to conduct the research work based on the response surface method of structural reliability optimization, improving the above three steps and the efficiency of engineering structure .The paper aims at the implicit limit state function problem, studying the structural reliability analysis of the weighted response surface method, combining the advantage of obtaining better sites by vector projection method and increases weighted coefficient by weighted response surface method. The improved weighted response surface method based on vector projection sampling is proposed. It uses the vector of the gradient projection method to get new design point and sample points, giving the actual limit state function of sample points more weights to construct the quadratic response surface function, updating the iterative response surface function, and solves the problem of the structural reliability of the implicit limit state function. Example analysis shows the characteristics of the proposed method, a steady design point can be found, and the calculation of stability has been improved considerably. The classical response surface method is optimized and reduces the defects that the calculation results are seriously affected by the interpolation coefficient. To some extent, it improves the calculation accuracy and can get relatively better results. Improved weighted response surface method based on vector projection sampling combines the advantages of obtaining test sample points by vector projection and weighted regression method, which is effective, feasible and can operate directly. It extends the application field of the response surface method to some degree and gets better approximate fitting limit state function at design point. It has better stability and robustness and provides new approach and ideas to solve the implicit limit state function.

Decolourization of bromocresol green dye solution by acid functionalized rice husk: Artificial intelligence modeling, GA optimization, and adsorption studies

In this article, dynamics and stability of milling operations with cylindrical end mills are investigated. A unified–mechanics–based model, which allows for both regenerative effects and loss–of–contact effects, is presented for study of partial–immersion, high–immersion and slotting operations. Reduced–order models that can be used for certain milling operations such as full–immersion operations and finishing cuts are also presented. On the basis of these models, the loss of stability of periodic motions of the workpiece–tool system is assessed by using Poincare sections and the numerical predictions of stable and unstable motions are found to correlate well with the corresponding experimental observations. Bifurcations experienced by periodic motions of the workpiece–tool system with respect to quasi–static variation of parameters such as axial depth of cut are examined and discussed. For partial–immersion operations, consideration of both time–delay effects and loss–of–contact effects is shown to have a significant influence on the structure of the stability boundaries in the space of spindle speed and axial depth of cut. The sensitivity of system dynamics to multiple–regenerative effects, mode–coupling effects and feed rate is also discussed.

The Fenton oxidation is rarely used industrially due to its high operating cost, large chemical consumption, excessive sludge production, and operability only within a narrow pH range. Therefore, there is a need to evaluate the Fenton oxidation to maximize its ability to degrade high-strength dye wastewater at reduced operating cost. Optimization tools are among the most commonly used tool to maximize the degradation of pollutants. The current study aims at evaluating the applicability of response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS) to optimize the degradation of Remazol brilliant blue through the Fenton oxidation. The effects of four operating parameters including dye concentration, retention time, and mass ratios of Dye:Fe2+ and H2O2:Fe2+ were evaluated by applying RSM. According to the RSM results, color and chemical oxygen demand (COD) removal of 99.9% and 84%, respectively, were obtained at 120 min at the COD value of 795 mg/L, mass ratios of Dye:Fe2+ = 16, H2O2:Fe2+ = 15 and pH = 3. ANFIS was also used to evaluate the most influential operating parameters on the COD removal based on the RSM results. The ANFIS results showed that the mass ratio of H2O2:Fe2+ had the most significant contribution to the COD removal. High R2 values (≥90%) indicated that the predictions of RSM and ANFIS models for COD removal were acceptable. In conclusion, this study demonstrated that RSM and ANFIS were able to determine the most significant operating parameters and optimum ratios of pollutant:oxidant:catalyst, which reduced the operating cost directly.

Biodiesel is found an effective alternative of petroleum diesel fuel as the properties of biodiesel are matching with diesel. However, biodiesel production is a time and energy consuming process which also increases the final cost of the product. Hence, to reduce the process time and energy consumption it becomes necessary to optimize the production process which can be done by conducting number of experiments. For reducing the total number of experiment for optimization researchers are using optimization tool. These tools give the optimal parameters with small number of experiments. In this scenario response surface methodology (RSM) gaining the publicity among all optimization tools. RSM not only reduces the number of experiments but also give the trends in which different parameters affect the final products. In the present research, orange peel oil is converted into orange peel oil methyl ester, and the process is optimized with the help of RSM. After optimization, it is found that the yield of OPOME is increased by more than 2% by using RSM.

Bioprocess development for umqombothi (a South African traditional beer) as with other traditional beer products can be complex. As a result, beverage bioprocess development is shifting towards new systematic protocols of experimentation. Traditional optimization methods such as response surface methodology (RSM) require further comparison with a relevant machine learning system. Artificial neural network (ANN) is an effective non-linear multivariate tool in bioprocessing, with enormous generalization, prediction, and validation capabilities. ANN bioprocess development and optimization of umqombothi were done using RSM and ANN. The optimum condition values were 1.1 h, 29.3 °C, and 25.9 h for cooking time, fermentation temperature, and fermentation time, respectively. RSM was an effective tool for the optimization of umqombothi’s bioprocessing parameters shown by the coefficient of determination (R2) closer to 1. RSM significant parameters: alcohol content, total soluble solids (TSS), and pH had R2 values of 0.94, 0.93, and 0.99 respectively while the constructed ANN significant parameters: alcohol content, TSS, and viscosity had R2 values of 0.96, 0.96, and 0.92 respectively. The correlation between experimental and predicted values suggested that both RSM and ANN were suitable bioprocess development and optimization tools.

Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: Case study of fermentative production of scleroglucan

The release of dye containing effluent is a great threat to the world today. The purpose of this study is to optimize the removal of Reactive Blue 19 (RB19) dye from aqueous solutions using advanced oxidation process (AOP). Magnesium oxide nanoparticles (MgO NPs) and hydrogen peroxide (H2O2) were used as the catalyst and oxidizer, respectively. Central composite design (CCD) based on response surface methodology (RSM) was applied for optimization of the AOP process. The effects of pH (3–7), molar H2O2/MgO NPs ratio (1–3), initial concentration of RB19 (20–80 mg/L), and contact time (30–90 min) were investigated on the oxidation process. The CCD was applied to determine the interactive effects of the process parameters and their optimum conditions. One-way analysis of variance (ANOVA) was applied for statistical data analysis. A quadratic model was generated by the CCD to represent the AOP on RB19 degradation. The experimental values obtained for percentage RB19 decolorization were found to be very close to the predicted response values. Based on the design, optimum conditions of pH 3, contact time of 60 min, RB19 concentration of 80 mg/L and H2O2/MgO NPs molar ratio of 3 were obtained which resulted in 93.77% RB19 removal. High value for the coefficient of determination, R2 (0.912) and adjusted R2 (0.805) showed that the removal of RB19 dye using AOP can be described by the RSM. The ANOVA results showed that the quadratic model developed from the RSM was statistically significant for RB19 decolorization. From the study, it could be concluded that the RSM can be a useful tool for optimization and moderation of the process parameters to maximize RB19 dye removal from aqueous solutions and the advanced H2O2/MgO NPs oxidation process.

The International Conference ModTech2016 held between 15-18 of June 2016 at the Ramada Hotel in Iaşi has been a representative scientific event for producers of technology, universities, research institutions and industrial entities where significant national and international scientific achievements have been presented and discussed in the fields of manufacturing engineering, composite materials and technology, characterization, modeling and simulation of mechanical processes, robotics and integrated computer-aided manufacturing, technology transfer, micro and nanotechnologies, maritime engineering and navigation.Organized by the Professional Association of Modern Manufacturing Technology with “Lucian Blaga” University of Sibiu, “Alecu Russo” State University of Bălţi, Republic of Moldova, Silesian University of Technology from Gliwice, Poland, Maritime University of Constanţa-Romania, “Vasile Alecsandri” University of Bacau-Romania and Dunarea de Jos” University of Galaţi-Romania acting as co-organizers, this scientific event has been a driver for stimulating scientific creation locally, regionally and internationally through the publication of presented papers in an ISI- abstracted volume (IOP Conference Series: Materials Science and Engineering) in Great Britain. In ISI journals, having an impact factor from Great Britain (International Journal of Materials and Product Technology), India (Indian Journal of Engineering and Materials Sciences) and in a journal abstracted in international databases (International Journal of Modern Manufacturing Technology), 131 participants from different national and international universities presented papers in plenary, section sessions and in poster presentations. Authors and co-authors of abstracts have been from 25 countries: USA, Sweden, Poland, Japan, Germany, Serbia, South Korea, Romania, Turkey, Republic of Moldova, Spain, China, India, the Netherlands, Uzbekistan, England, Ukraine, Iraq, Slovakia, Slovenia, Croatia, Bosnia-Hertzegovina, Vietnam, Bulgaria and Finland.The opening ceremony took place in the presence of the Mayor of Iaşi, Mihai Chirica, and the Rector of “Gheorghe Asachi” Technical university of Iaşi, Professor Dan Cascaval Ph.D. Moreover, three Octavian Pruteanu excellence awards have been given to Professor Marcel Van de Voorde, DELFT University in the Netherlands, Professor Claudiu Vasile Kifor, Vice Rector in charge of research at “Lucian Blaga” University of Sibiu and to Dr. Marek Placzek from the Silesian University of Gliwice, Poland for their contribution to the promotion and development of ModTech International Conference.10 papers of high scientific value were presented in the three plenary sessions by Professor Elsayed A. Orady University of Michigan - Dearborn, USA; Professor Esteban Broitman Linkoping University, Sweden; Professor Wojciech Sitek Silesian University of Technology, Gliwice, Poland; Prof. Toshihiro Ishikawa Tokyo University of Science, Yamaguchi, Japan; Prof. Rainer Gadow University of Stuttgart, Germany; Professor Olivera Milosevic Institute of Technical Sciences of the Serbian Academy, Serbia; Professor Makio Naito JWRI, Osaka University, Japan; Professor Hong- Seok Park University of Ulsan, South Korea,; Professor Radu Comaneci “Gheorghe Asachi” Technical University of Iasi, Romania; Professor Burak Ozkal, ITU, Metallurgy and Materials Engineering Department, Turkey.The beginning of each session included presentations given by the following invited speakers: Dr. Sinan Sezek, Ataturk University, Erzurum, Turkey; Prof. Mihaita Horodinca, "Gheorghe Asachi" Technical University of Iasi, Romania; Assoc. Prof. Ferhat Bingol, Ataturk University, Erzurum, Turkey; Lect.Dr. Marek Placzek, Silesian University of Technology, Gliwice, Poland; Phys. Mircea-Petru Ursu, University of Oradea, Romania; Prof. Claudiu Vasile Kifor, "Lucian Blaga" University of Sibiu, Romania; Prof. Pavel Topala, "Alecu Russo" State University of Balti, Republic of Moldova and Prof. Remus Zagan, Constanta Maritime University, Constanta, Romania.In the closing session, Best Paper Awards were given for papers presented in 7 sections and poster presentations. A special award for involvement in conference promotion was awarded to Dr. Milos Vasic, the Institute of Material Testing, Belgrade, Serbia.The main publications of ModTech2016 International Conference are as follows: Indian Journal of Engineering & Materials Sciences (IJEMS); International Journal of Materials and Product Technology; International Journal of Modern Manufacturing Technologies (IJMMT) and IOP Conference Series: Materials Science and Engineering, IOP Publishing Ltd., Bristol, United Kingdom.Special thanks to Conference Sponsors: Iasi City Hall, Society of Powder Technology Japan, Cotnari Iasi and Redesign Solutions Iasi.