Response Surface Grey Relational Analysis On The Manufacturing of High Grade Biomedical Ti-13Zr-13Nb

Optimization of the manufacturing conditions with more than one performance characteristics have been a thing of concern, especially for Response Surface Method (RSM) optimization. Hence, this study addressed this challenge by reanalyzing a data presented in a previous study using grey relational analysis (GRA) and regression analysis. Central Composite Design (CCD) of RSM with high and low values of manufacturing conditions; voltage (50, 70) V, current (8, 16) A, pulse ON time (6, 10) μs, and pulse OFF time (7, 11) μs. The manufacturing conditions for optimal biomedical Ti-13Zr-13Nb alloy were obtained to be 50V voltage, 8A current, 6 μs pulse ON time, and 11 μs pulse OFF time. It was also revealed that the mathematical model was very efficient because the modeled GRG was in consonant with the experimental one. In addition, it was also established that current was the most significant manufacturing condition with a contribution of 47.27%. Voltage, factors interactions and residual error were insignificant on the GRG value of the titanium alloy. In conclusion, it can be deduced that the a small value of voltage within the considered settings could be used to manufacture better grade Ti-13Zr-13Nb alloy and also the small value of residual error showed the high manufacturability of the material.

Optimization of Turning Parameter of Composite Materials Using Response Surface Method in Minitab

This paper represents the optimization method utilized in machining process for figuring out the most advantageous manner design. Typically, the technique layout parameters in machining procedures are noticeably few turning parameters inclusive of reducing velocity, feed and depth. The optimization of speed, feed depth of cut is very tough because of several other elements associated with processing situations and form complexities like surface Roughness, material removal rate (MRR) that are based Parameters. On this task a new fabric glass fibre composite is introduced through which could lessen costing of manufacturing and time and additionally it will boom the technique of productiveness. Composite substances have strength, stiffness, light weight, which gives the large scope to engineering and technology. The proposed research work targets to analyze turning parameters of composite material. The machining parameters are very important in manufacturing industries. The present research work is optimized surface roughness of composite material specifically in turning procedure with the aid of changing parameter including intensity of reduce, slicing velocity and feed price and additionally expect the mechanical houses of composite material. The RSM optimization is important because it evaluates the effects of multiple factors and their interactions on one or more responsive variables. It is observed that the material removal rate increases and surface roughness decreases as per the increase of Spindle speed and feed rate.

Development of Antioxidant and Nutritious Lentil (Lens culinaris) Flour Using Controlled Optimized Germination as a Bioprocess

Germination is an efficient and natural strategy that allows the modification of the nutritional value and the nutraceutical properties of seeds, enabling one to tailor the process according to its final use. This study aimed at optimization of germination conditions to produce novel lentil flours with improved nutritional and functional features. Response Surface Methodology (RSM) was applied to model the effect of temperature (15–27 °C) and time (1–5 days) on different nutritional and quality parameters of lentil flours including proximate composition, content and profile of fatty acids, content of phytic acid, ascorbic acid and γ-aminobutyric acid (GABA), content and profile of phenolic compounds, antioxidant activity, expected glycemic index (GI) and color during germination. As shown by RSM polynomial models, sprouting promoted the reduction of phytic acid content and enhanced the levels of ascorbic acid, GABA, insoluble phenolic compounds, antioxidant activity and expected GI, and modified the color of the resultant lentil flours. RSM optimization of germination temperature and time using desirability function revealed that the optimal process conditions to maximize the nutritional, bioactive and quality properties of sprouted lentil flours were 21 °C for 3.5 days.

Synthesis of Magnetic Catalyst Derived from Oil Palm Empty Fruit Bunch for Esterification of Oleic Acid: An Optimization Study

Biomass, renewable, abundantly available and a good source of energy. The conversion of biomass waste into valuable products has received wide attention. In this study, an empty fruit bunch (oil palm EFB) supported magnetic acid catalyst for esterification reaction was successfully prepared via the one-step impregnation process. The new magnetic catalyst achieved a higher surface area of 188.87 m2/g with a total acidity of 2.4 mmol/g and identified iron oxide as g-Fe2O3. The magnetization value of 24.97 emu/g demonstrated that the superparamagnetic catalyst could be easily recovered and separated after the reaction using an external magnet. The catalytic performance of oil palm EFB supported magnetic acid catalyst was examined by esterification of oleic acid. Esterification process parameters were optimized via Response Surface Methodology (RSM) optimization tool with Box-Behnken design (BBD). The following optimum parameters were determined: an amount of 9 wt% catalyst, molar ratio of methanol to oleic acid of 12:1, reaction time of 2 h and reaction temperature of 60 °C with a maximum conversion of 94.91% was achieved. The catalyst can be recycled up to five cycles with minimal loss in its activity. The oil palm waste-based magnetic acid catalyst indicates its potential replacement to the existing solid catalysts that are economical and environmentally friendly for the esterification process in biofuel applications. Copyright © 2021 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

Evaluation and optimization of ASM1 parameters using large-scale WWTP monitoring data from a subtropical climate region in Brazil

This study aimed at providing a set of optimal kinetic and stoichiometric parameters of ASM1 representative of wastewater from a subtropical climate region in Brazil. ASM1 was applied on STOAT program, and the model parameters were evaluated and optimized with sensitivity analysis and Response Surface Methodology (RSM) to reach minimum prediction errors of effluent TSS, COD, and NH3. Six sensitive parameters were identified: YH, YA, μA, KNH, bA, and kOA. Predictions of RSM regression models were strongly correlated to the STOAT predictions. YH mainly affected TSS and COD, and the other parameters affected NH3. ASM1 calibration with estimated optimal values of sensitive parameters resulted in approximately null prediction errors for modeling state variables. NH3 presented similar results in the ASM1 validation; meanwhile, TSS and COD presented high errors related to the increase in YH due to the RSM optimization. The optimal parameters, mainly YA, μA, KNH, bA, and kOA, constitute references for other studies on ASM1 modeling using wastewater data from a subtropical climate region. YH optimal value should be evaluated as well as the effect of sludge wastage methods and the simulation periods.

Response Surface Methodology (RSM) Optimization of the Batch Process in a Rectangular Passive Greenhouse Dryer

The prototyping of dryer design and performance by application of the trial-and-error technique in one-factor-at-a-time (OFAT) testing is completely arbitrary, expensive and time consuming. Reducing product development lead-time and cost while concurrently improving customer satisfaction for a good manufacturer enhance rapid response to market demand which is a highly effective way of improving returns on investment. In this study a numerical model for the digital prototyping of the rectangular passive greenhouse dryer design and the optimization of the batch process in the solar dryer was developed. An interactive, user-friendly computer package ANSYS 14.0 was used to develop an empirical model. The package was used among others, for the response surface methodology (RSM) optimization to specify the dryer parameters that maximize the dryer mean temperature. The factorial experiments in a central composite design (CCD) revealed that only the inlet vent dimensions influence the mean temperature within the greenhouse dryer. The parametric analysis for robust design yielded the inlet vent height of 0.27m and inlet vent width of 0.45m as the optimum design variables that maximize the mean temperature of the drying air as 320.48K (47.30 °C). The numerical approach established facilitated the prototyping and optimization of the batch process in the passive greenhouse dryer.The prototyping of dryer design and performance by application of the trial-and-error technique in one-factor-at-a-time (OFAT) testing is completely arbitrary, expensive and time consuming. Reducing product development lead-time and cost while concurrently improving customer satisfaction for a good manufacturer enhance rapid response to market demand which is a highly effective way of improving returns on investment. In this study a numerical model for the digital prototyping of a rectangular passive greenhouse dryer design and the optimization of the batch process in the solar dryer was developed. Multiple regression was used as the data-analytic system for the factorial experiment to develop an empirical model, predict the response variable and then test hypothesis in an interactive, user-friendly computer package ANSYS 14.0. The package was further used for the response surface methodology (RSM) optimization to specify the dryer parameters that maximize the dryer mean temperature. The factorial experiments in a central composite design (CCD) revealed that only the inlet vent dimensions influence the mean temperature within the dryer. Appraisal of the model through the coefficient of determination ( =0.99973) showed that the model can account for 99.973% variability observed in the dryer mean temperature consequently, the suitability of RSM for the analysis of the dryer variables. The parametric analysis for robust design yielded the inlet vent height of 0.27m and inlet vent width of 0.45m as the optimum design variables that maximize the mean temperature of the drying air as 320.48K (47.30°C). The numerical approach established facilitated the prototyping and optimization of the batch process in the passive dryer.