A Comparative Application of Latin Hypercube Design and Box-Behnken Design Methods in Extracting Sesameoil

2021 ◽  
Vol 43 (2) ◽  
pp. 135-135
Author(s):  
Aman Elmi Tufa Aman Elmi Tufa ◽  
Youmin Hu Youmin Hu ◽  
Shuai Huang Shuai Huang ◽  
Wenwen Jin Wenwen Jin ◽  
Fengcheng Li Fengcheng Li
Keyword(s):  
Extraction Methods ◽  
Oil Extraction ◽  
Design Methods ◽  
Oil Yield ◽  
Process Conditions ◽  
Latin Hypercube Design ◽  
Latin Hypercube ◽  
Box Behnken Design ◽  
Design Options ◽  
The Given

In the past decades, most researchers focus on process optimization and extraction methods to improve oil extraction from oilseeds. However, no information available on comparative analysis of different design methods to improve the process. The objective of this study was to evaluate the applicability of Latin hypercube design (LHD) and Box-Behnken Design (BBD) in oil extraction. Experimental oil yield, analysis of variance (ANOVA) of the model, and practical observation were used to compare the methods. The result shows both methods can supply adequate data for experiments. The range of oil yield is 26 – 41% for BBD and 31 – 41% for LHD. Analytically, the ANOVA result indicates that the model constructed of the LHD experiment has a better prediction of observed oil yield at a regression coefficient (R2) of 0.98 and Root Mean Square Error (RMSE) of 0.4 while BBD has R20.87 and RMSE 1.4. From the experiment result, BBD is more suit to design, efficient, and easier to extract oil. LHD has better design options, more flexible but less efficient in the experiment. For the given process conditions, theresult comparison empirically analyzed suggests both methods can be applied for oil extraction.

scholarly journals Effect of process conditions on Sandbox seed oil yield by mechanical expression: A response surface approach

2020 ◽  
Vol 45 (3) ◽  
pp. 70-81
Author(s):  
Onwe Nwabueze ◽  
Bamgboye Isaac
Keyword(s):  
Moisture Content ◽  
Response Surface ◽  
Oil Extraction ◽  
Oil Yield ◽  
Process Conditions ◽  
Process Variables ◽  
Roasting Temperature ◽  
Oil Expression ◽  
Expression Time ◽  
Roasting Time

Cost of solvent oil extraction methods has made mechanical oil expression a desirable alternative. The effect of process variables on mechanical oil expression from sandbox seed was studied. The experimental design used for the study was a 52 Central Composite Rotatable Design of Response Surface Methodology. Experimental factors considered were: moisture content, roasting temperature, roasting time, expression pressure and expression time. Results obtained were analyzed at a0.05. The oil yield from the sandbox seed ranged from 16.38-38.68%, and was increased at processing variable ranges of (4.0-8.0%) moisture content, (80.0-90.0°C) roasting temperature, (5.0-15.0%) roasting time, (15.0-20.0 MPa) expression pressure and (6.0-8.0 min) extraction time. The maximum oil yield of 38.68% was obtained at the processing conditions of 6% moisture content, 85 °C roasting temperature, 15 min roasting time, expression pressure of 20 MPa and 8 min pressing time. Model equation relating the process variables to oil yield was developed. Coefficient of determination (R2) relating the process was 0.8908. The result showed that moisture content, roasting time, expression pressure and expression time had a significant influence on the sandbox oil yield. The results obtained in this study can serve for process and equipment designs for oil extraction from sandbox and other oilseeds and nuts.

scholarly journals Sampling Design Method of Fast Optimal Latin Hypercube

2019 ◽  
Vol 37 (4) ◽  
pp. 714-723 ◽  
Author(s):  
Pengcheng Ye ◽  
Guang Pan ◽  
Shan Gao
Keyword(s):  
Large Scale ◽  
Sampling Design ◽  
Design Method ◽  
Design Methods ◽  
Small Scale ◽  
Optimal Sampling ◽  
Latin Hypercube Design ◽  
Latin Hypercube ◽  
Computation Efficiency ◽  
Optimal Sampling Design

In engineering design optimization, the optimal sampling design method is usually used to solve large-scale and complex system problems. A sampling design (FOLHD) method of fast optimal Latin hypercube is proposed in order to overcome the time-consuming and poor efficiency of the traditional optimal sampling design methods. FOLHD algorithm is based on the inspiration that a near optimal large-scale Latin hypercube design can be established by a small-scale initial sample generated by using Successive Local Enumeration method and Translational Propagation algorithm. Moreover, a sampling resizing strategy is presented to generate samples with arbitrary size and owing good space-filling and projective properties. Comparing with the several existing sampling design methods, FOLHD is much more efficient in terms of the computation efficiency and sampling properties.

scholarly journals Green Solvent Improving Mechanical Disruption of Palm Kernel Oil Extraction

2020 ◽  
Author(s):  
Eko K. Sitepu ◽  
Andy Chandra ◽  
Emma F. Zaidar ◽  
Annur Vika ◽  
Firman Sebayang ◽  
...  
Keyword(s):  
Ethyl Acetate ◽  
Palm Kernel ◽  
Extraction Process ◽  
Extraction Methods ◽  
Oil Extraction ◽  
Extraction Time ◽  
Oil Yield ◽  
Green Solvents ◽  
Palm Kernel Oil ◽  
Kernel Oil

Abstract Even though the mechanical extraction process offers a simple and environmentally friendly process, the recovery of oil is relatively low. Thermal pre-treating the oilseed increases the oil yield but produces unwanted oil colour. A new method which combines grinding and extraction using green solvents was developed to extract palm kernel oil. The performance of six different green solvents such as water, ethanol, isopropyl alcohol, dimethyl carbonate, ethyl acetate, and d-limonene in extraction palm kernel oil was determined using a controllable blender extractor (CBE), new extraction equipment modified from a household blender appliance. Further, ethyl acetate, which produced the maximum oil yield, was used to study the effect of the operating parameters of the CBE. The oil yield of 34.2 ± 0.02% was obtained in the extraction condition of the ratio of palm kernel to ethyl acetate of 1:7, rotational speed of 5000 rpm and 10 minutes extraction time. Compared to other green extraction methods, the CBE-intensified palm kernel oil extraction could save >70% energy consumption. In terms of extraction time, the CBE-intensified could extract palm kernel oil faster than existing extraction methods.

scholarly journals Optimization of Mechanical Oil Expression from Sandbox (Hura crepitans Linn.) Seeds

2021 ◽  
Vol 2 (2) ◽  
pp. 434-449
Author(s):  
David ONWE ◽  
Adeleke Isaac BAMGBOYE
Keyword(s):  
Moisture Content ◽  
Extraction Process ◽  
Oil Extraction ◽  
Oil Yield ◽  
Process Conditions ◽  
Statistical Tool ◽  
Study Results ◽  
Roasting Temperature ◽  
Expression Time ◽  
Roasting Time

Optimization of process variables has become very vital in oil extraction processes to obtain maximum oil yield from oilseeds and nuts. This work focussed on the optimization of process oil extraction process from sandbox seed by mechanical expression. Effects of moisture content, roasting temperature, roasting time, expression pressure and expression time on oil yield from sandbox seed was studied using a 5×5 Central Composite Rotatable Design of Response Surface Methodology experimental design. Results obtained were subjected to Analysis of Variance (ANOVA) and SPSS statistical tool at (p = 0.05). Optimum conditions predicted were validated by experiments. All the processing factors were significant at (p = 0.05) for the sandbox oil yield except roasting temperature. The experimental results and predicted values showed low deviation (0.01-0.62). Oil yields obtained from the sandbox seed at varying process conditions varied from 16.38-38.68%. The maximum oil yield of 38.68% was obtained when the sandbox seed was subjected to process conditions of 6% moisture content, 85°C roasting temperature, 15 min roasting time, expression pressure of 20 MPa and 8 min pressing time. Mathematical equations to predict sandbox seed oil yield at varying process conditions were developed with an R2 (0.8908). The optimum extractable oil yield of 38.95% was predicted for sandbox seed at processing conditions of 7.03% moisture content, 97.72°C roasting temperature, 11.32 min roasting time, 15.11 MPa expression pressure and 8.57 min expression time. The study results provide data for designs of process and equipment for oil extraction from sandbox and other oilseeds.

scholarly journals Identification of VOCs in essential oils extracted using ultrasound- and microwave-assisted methods from sweet cherry flower

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Huimin Zhang ◽  
Hongguang Yan ◽  
Quan Li ◽  
Hui Lin ◽  
Xiaopeng Wen
Keyword(s):  
Essential Oil ◽  
Sweet Cherry ◽  
Dimethyl Sulfide ◽  
Solid Phase ◽  
Oil Yield ◽  
Gas Chromatography Mass Spectrometry ◽  
Process Conditions ◽  
Important Indicator ◽  
Microwave Assisted ◽  
Essential Oil Yield

AbstractThe floral fragrance of plants is an important indicator in their evaluation. The aroma of sweet cherry flowers is mainly derived from their essential oil. In this study, based on the results of a single-factor experiment, a Box–Behnken design was adopted for ultrasound- and microwave-assisted extraction of essential oil from sweet cherry flowers of the Brooks cultivar. With the objective of extracting the maximum essential oil yield (w/w), the optimal extraction process conditions were a liquid–solid ratio of 52 mL g−1, an extraction time of 27 min, and a microwave power of 435 W. The essential oil yield was 1.23%, which was close to the theoretical prediction. The volatile organic compounds (VOCs) of the sweet cherry flowers of four cultivars (Brooks, Black Pearl, Tieton and Summit) were identified via headspace solid phase microextraction (SPME) and gas chromatography–mass spectrometry (GC–MS). The results showed that a total of 155 VOCs were identified and classified in the essential oil from sweet cherry flowers of four cultivars, 65 of which were shared among the cultivars. The highest contents of VOCs were aldehydes, alcohols, ketones and esters. Ethanol, linalool, lilac alcohol, acetaldehyde, (E)-2-hexenal, benzaldehyde and dimethyl sulfide were the major volatiles, which were mainly responsible for the characteristic aroma of sweet cherry flowers. It was concluded that the VOCs of sweet cherry flowers were qualitatively similar; however, relative content differences were observed in the four cultivars. This study provides a theoretical basis for the metabolism and regulation of the VOCs of sweet cherry flowers.

A Novel Hybrid Method Based on Three-Stage Extraction and DEA-CCR Models for Selecting the Optimal Conditions for Citronella Oil Extraction

2021 ◽  
Author(s):  
Thaithat Sudsuansee ◽  
Narong Wichapa ◽  
Amin Lawong ◽  
Nuanchai Khotsaeng
Keyword(s):  
Energy Consumption ◽  
Flow Rate ◽  
Energy Cost ◽  
Oil Extraction ◽  
Steam Flow ◽  
Oil Yield ◽  
Steam Flow Rate ◽  
Citronella Oil ◽  
Extraction Model ◽  
Ccr Model

In citronella oil extraction process by steam distillation, inefficient use of steam is the main cause of excessive energy consumption that affects energy cost and oil yield. This research is aimed to reduce the energy cost and increase the oil yield by studying the steam used in the process. The proposed method is the three-stage extraction model combined with the Data Envelopment Analysis developed by Charnes, Cooper and Rhodes (DEA-CCR model). Although the three-stage extraction model has been widely used, there is no research integrate this model with DEA-CCR model. It is well known that DEA-CCR model is an effective tool to evaluate efficiency of decision making units/alternatives. The advantages of this research were presented as the calculation of the optimum distillation conditions, including the steam flow rate and the distillation time, were achieved as discussed in this article. The study was comprised of 3 parts. Firstly, the three-stage extraction model for citronella oil was formulated. Secondly, the results of the proposed model were calculated under different conditions, classified by steam flow rates from 5,000 to 60,000 cm3/min for the distillation period of 15–180 min. Finally, the DEA-CCR model was utilized to evaluate and rank alternatives. The results expressed that the best condition for producing citronella oil was at the steam flow rate of 40,000 cm3/min and the distillation time of 60 min. The optimal energy cost and percentage of oil yield were equal to 0.440 kWh/mL and 0.7%, respectively. When comparing to the experimental results, the percentage error of optimal energy cost and oil yield were slightly different, with a value of 0.98% and 0.85%, respectively. Moreover, the energy consumption was also reduced by 34.6% compared to the traditional operating conditions.

scholarly journals Adaptive Response Surface Method Using Inherited Latin Hypercube Design Points

2003 ◽  
Vol 125 (2) ◽  
pp. 210-220 ◽  
Author(s):  
G. Gary Wang
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Adaptive Response ◽  
High Dimensional ◽  
Latin Hypercube Design ◽  
Latin Hypercube ◽  
Design Problems ◽  
Surface Method ◽  
Adaptive Response Surface Method ◽  
Design Experiments

This paper addresses the difficulty of the previously developed Adaptive Response Surface Method (ARSM) for high-dimensional design problems. ARSM was developed to search for the global design optimum for computation-intensive design problems. This method utilizes Central Composite Design (CCD), which results in an exponentially increasing number of required design experiments. In addition, ARSM generates a complete new set of CCD points in a gradually reduced design space. These two factors greatly undermine the efficiency of ARSM. In this work, Latin Hypercube Design (LHD) is utilized to generate saturated design experiments. Because of the use of LHD, historical design experiments can be inherited in later iterations. As a result, ARSM only requires a limited number of design experiments even for high-dimensional design problems. The improved ARSM is tested using a group of standard test problems and then applied to an engineering design problem. In both testing and design application, significant improvement in the efficiency of ARSM is realized. The improved ARSM demonstrates strong potential to be a practical global optimization tool for computation-intensive design problems. Inheriting LHD points, as a general sampling strategy, can be integrated into other approximation-based design optimization methodologies.

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