Optimizing Uniaxial Oil Extraction of Bulk Rapeseeds; Spectrophotometric and Chemical Analyses of the Extracted Oil under Pretreatment Temperatures and Heating Intervals

Optimizing the operating factors in edible oil extraction requires a statistical technique such as a response surface methodology for evaluating their effects on the responses. The examined input factors in this study were the diameter of pressing vessel, VD (60, 80, and 100 mm), temperature, TPR (40, 60, and 80 °C), and heating time, HTM (30, 60 and 90 min). The combination of these factors generated 17 experimental runs where the mass of oil, oil yield, oil extraction efficiency, and deformation energy were calculated. Based on the response surface regression analysis, the combination of the optimized factors was VD: 100 (+1) mm; TPR: 80 °C (+1) and HTM: 60 (0) min); VD: 60 (−1) mm; TPR: 80 °C (+1) and HTM: 75 (+0.5) min and VD: 100 (+1) mm; TPR: 80 °C (+1) and HTM: 90 (+1). The absorbance and transmittance values significantly (p < 0.05) correlated with the wavelength and temperature, but they did not correlate significantly (p > 0.05) with heating time. The peroxide value did not correlate significantly with temperature, however, it correlated significantly with heating time. Neither the acid value nor the free fatty acid value correlated with both temperature and heating time. The findings of the present study are part of our continuing research on oilseeds’ processing optimization parameters.

Response Surface Regression with LTS and MM-Estimator to Overcome Outliers on Red Roselle Flowers

The surface response method is similar to the regression analysis method which uses procedures or ways of estimating the response function regression model based on the Ordinary Least Square (OLS) method. Unfortunately, using the quadratic method has no drawbacks because it is easily sensitive to assumption deviations due to outlier cases. One of the solutions to the outlier problem is using robust regression. The method of parameters in the regression is very diverse, but the methods used in this study are the Least Trimmed Square (LTS) and MM-estimator methods because both methods have a high breakdown point of nearly 50%. The variables studied were the response variable consisting of red roselle plant height (Y1) and red roselle flower weight (Y2). While the independent variables were soil moisture factor (X1) and NPK fertilizer application factor (X2). The purpose of this study is to estimate the response surface regression parameters. using the LTS and MM-estimator methods on data that contains outliers. The resulting model in data analysis shows the same result that the best model is using the LTS estimation method. The modeling result of plant height obtained an R-Square value of 98,27% with an error is 1,243. Meanwhile, for the red rosella plant flower weight model, the R-Square value was 97,31% with an error is 0.6632.

Modeling and Recipe Optimization of Anti-Glare Process Using Sandblasting for Electronic Display Glass

Recently, anti-glare (AG) surface treatment technology has been considered as a standard process to enhance the visibility of electronic display devices. For AG, the hydrofluoric acid (HF)-based chemical etch method is the most common approach for the current display glass industry. However, in order to overcome the environmental and durability degradation problems of the HF-based chemical etch method, this paper proposes an eco-friendly physical surface treatment technology using the sandblasting method. Based on the preliminary analysis results using the central composite design (CCD) method-based response surface modeling methodology (RSM), additional experiments and analyses were performed for process modeling and optimal process recipe generation. To characterize the sandblasting process, the mean value of haze was considered as the process output, and the pressure of the nozzle, the distance of the nozzle from the surface of glass, the glass feed rate, and the grit size of the abrasives were considered as process inputs. Based on the process model using the statistical response surface regression method and machine learning-based approaches, the proposed method can generate optimized process recipes for various haze targets of 10%, 20%, and 30%, with an average haze difference of 0.84%, 0.02%, and 0.86%, and maximum deviations of 1.26%, 1.14%, and 1.4%, respectively. Through the successful completion of this work, it is expected that the proposed surface treatment method can be applied to various products including mobile phones, tablet PCs, and windshields of vehicles.

Utilization of Unwanted Weed, Paragis Leaves (Eleusine indica Linn.) in Cookie Production

Paragis is a common grass which is abundant and can be seen everywhere but is regarded as having no economic value. To add value to this grass, the study generally aimed to formulate cookies with powdered paragis leaves and mashed bananas; and specifically, it aimed to evaluate the sensory quality of the product. A 3 x 3 factorial design was used, with three levels for both powdered paragis leaves (0, 5, 10 % w/w) and mashed bananas (0, 15, 20 % w/w). Sensory evaluation was done to determine the product’s acceptability in terms of color, taste, aroma, texture, and flavor using a sensory panel. Acceptability ratings were subjected to response surface regression analysis using STATISTICA software. Results revealed that the combination of powdered paragis leaves and mashed bananas showed a significant effect on the color, aroma, texture, taste, flavor, and general acceptability of the product. The optimum combination was 8.8 % and 1.3 to 1.8% of mashed bananas and powdered paragis leaves, respectively, based on the volume of flour. It can be concluded that paragis leaves could be utilized in cookie production, providing potential value to this unwanted commodity using the optimum combination.

Effect of Machining Parameter and Multi-Response Optimization during End-Milling in GFRP-VARTM Composites using RSM Based Response Surface Regression Method

This paper is going to find out the correlation between cutting parameters and responses for GFRP composite material during end milling using the WC-CO tool. An experimental plan, based on Response Surface Methodology (RSM) techniques according to the Response Surface Regression (RSR), made up of Vaccum-Assisted Resin Transfer molding using solid carbide end mills. The objective of this paper is to investigate the machining parameters on glass fiber reinforcement polymer during the end milling process of a WC-Co cutting tool grade.

Study on the tensile modulus of seamless fabric and tight compression finite element modeling

Tight compression garments are able to exert pressure on the surface of the human body, helping to relieve muscle fatigue and accelerate recovery. In order to study the effect of the tensile property on fabric pressure on the human body, by response surface methodology, in this paper 13 seamless knitting fabrics were designed with various knitting parameters, including the linear density of bare elastic yarn (33.3, 55.6 and 77.8 dtex), yarn feed tension (0.015, 0.030 and 0.045 cN/dtex) and fabric structure (1 × 1 mock rib, cross-float and plain stitch). Through tensile testing, the tensile moduli of 13 fabrics were measured and the effect of knitting parameters was analyzed. In addition, a finite element method was used to simulate the tight pressure on the human thigh of fabrics with different tensile moduli with ANSYS workbench 19.0. Furthermore, an actual pressure experiment was designed to prove the accuracy and validation of the simulation. The result showed that the effect of yarn feed tension on the elasticity modulus was the least significant, while linear density and structure had a great influence. A quadratic response surface regression model of the elasticity modulus was created, which could calculate a bare elastic yarn knitting fabric by using its parameters. It was proved that the finite element model was able to predict pressure accurately. Through the pressure numerical simulation of three fabric samples with different tensile moduli (0.111, 0.253 and 0.523 MPa), it was indicated that for fabric with a low tensile modulus, its tight compression merely changed as elongation increased; however, for fabric with a high tensile modulus, tight compression was promoted as elongation increased.