scholarly journals Performance Optimization of Groundnut Shelling Using Response Surface Methodology

2021 ◽  
Vol 24 (1) ◽  
pp. 1-7
Author(s):  
Aliyu Idris Muhammad ◽  
Moshud Isiaka ◽  
Muhammed Lawal Attanda ◽  
Sarafadeen Kolawole Shittu ◽  
Ibrahim Lawan ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Moisture Content ◽  
Response Surface ◽  
Performance Optimization ◽  
Feed Rate ◽  
Performance Indices ◽  
Cleaning Efficiency ◽  
Output Capacity ◽  
Order Polynomial ◽  
Input Variables

AbstractThis study evaluated the influence of drum speed, moisture content, and feed rate on the performance indices of groundnut sheller using Ex-Dakar groundnut variety. Response surface methodology was used to study the influence of input variables and optimize the processing conditions. The developed second-order polynomial model adequately described the performance responses, including output capacity, shelling and cleaning efficiencies, and kernel damage. The input variables indicated significant influences on performance responses. The optimized processing variables for the responses were drum speed of 210 rpm, moisture content of 8%, and feed rate of 350 kg∙h−1. The optimum responses obtained were output capacity of 302.52 kg∙h−1, shelling efficiency of 97.61%, cleaning efficiency of 53.16%, and kernel damage of 4.04%. These performance responses were validated experimentally and were close to the observed results.

scholarly journals Performance Evaluation of a Rice (Oryza sativa) Thresher

2021 ◽  
Vol 6 (3) ◽  
Author(s):  
Sarafadeen K Shittu ◽  
Nnaemeka M Ezeh
Keyword(s):  
Moisture Content ◽  
Feed Rate ◽  
Teaching And Learning ◽  
Mechanical Damage ◽  
Performance Indices ◽  
Cleaning Efficiency ◽  
Rice Varieties ◽  
Mean Values ◽  
Output Capacity ◽  
Randomized Design

A paddy thresher supplied to the Department of Agricultural and Environmental Engineering, Bayero University, Kano Nigeria as part of its takeoff equipment for teaching and learning was evaluated to determine its performance indices in terms of threshing efficiency, cleaning efficiency, output capacity, mechanical damage and scatter loss. The variables used were two levels of moisture content (20% and 14%), two levels of speed (500 rpm and 700 rpm) and feed rate at two levels (100 kg/hr and 150 kg/hr). The factorial experiments were run in a complete randomized design in three replications. Two local Rice varieties Sipi (variety 1) and Jemila (variety2) were used. Analysis of Variance (ANOVA) and LSD were used to assess the effects of the independent variables on the performance indices of the paddy thresher. The mean values for cleaning efficiency, mechanical damage, scatter loss and output capacity ranges from 59.28 – 87.82%, 0.02 - 0.23%, 0.02 - 0.05% and 25.26 - 58.82 kg/h respectively. ANOVA results showed that moisture content, feed rate and speed significantly affected cleaning efficiency, mechanical damage and the output capacity at 5% probability for the two paddy varieties. Keywords— Efficiency, output capacity, performance, rice, threshing machine.

scholarly journals Optimization of Operational Parameters of an Improved Maize Sheller Using Response Surface Methodology

2021 ◽  
Vol 2 (2) ◽  
pp. 413-424
Author(s):  
Adewale SEDARA ◽  
Emmanuel ODEDİRAN
Keyword(s):  
Response Surface Methodology ◽  
Statistical Analysis ◽  
Moisture Content ◽  
Response Surface ◽  
Feed Rate ◽  
Factorial Experiment ◽  
Throughput Capacity ◽  
Operational Parameters ◽  
Commercial Farms ◽  
Machine Speed

The research was carried out to optimize parameters for evaluating an improved motorize maize sheller. Statistical analysis was performed using response surface methodology (RSM) with 3 by 3 factorial experiment with 3 replicates. The three parameters are speed (850 rpm, 950 rpm and 1100 rpm), moisture content (12, 15, and 17%) and feed rate (120 kg h-1, 130 kg h-1 and 140 kg h-1) used to illustrate the ability of the machine to shell maize (throughput capacity, shelling rate and machine efficiency). Results obtained showed that for optimum throughput capacity of 630.97 kg h-1; shelling rate 485.34 kg h-1 and machine efficiency 93.86% of the machine; is maximum for 129.6 kg h-1 feed rate and moisture content 16.49% and machine speed of 1026.9 rpm. The machine can be used on commercial farms with these operational results.

scholarly journals Optimization of Operational Parameters of an Improved Maize Sheller Using Response Surface Methodology

2021 ◽  
Vol 2 (2) ◽  
pp. 413-424
Author(s):  
Adewale SEDARA ◽  
Emmanuel ODEDİRAN
Keyword(s):  
Response Surface Methodology ◽  
Statistical Analysis ◽  
Moisture Content ◽  
Response Surface ◽  
Feed Rate ◽  
Factorial Experiment ◽  
Throughput Capacity ◽  
Operational Parameters ◽  
Commercial Farms ◽  
Machine Speed

The research was carried out to optimize parameters for evaluating an improved motorize maize sheller. Statistical analysis was performed using response surface methodology (RSM) with 3 by 3 factorial experiment with 3 replicates. The three parameters are speed (850 rpm, 950 rpm and 1100 rpm), moisture content (12, 15, and 17%) and feed rate (120 kg h-1, 130 kg h-1 and 140 kg h-1) used to illustrate the ability of the machine to shell maize (throughput capacity, shelling rate and machine efficiency). Results obtained showed that for optimum throughput capacity of 630.97 kg h-1; shelling rate 485.34 kg h-1 and machine efficiency 93.86% of the machine; is maximum for 129.6 kg h-1 feed rate and moisture content 16.49% and machine speed of 1026.9 rpm. The machine can be used on commercial farms with these operational results.

Demulsification of a Water-in-crude Oil Emulsion with a Corn Oil BasedDemulsifier using the Response Surface Methodology: Modelling and Optimization

2021 ◽  
Vol 14 ◽  
Author(s):  
Abed Saad ◽  
Nour Abdurahman ◽  
Rosli Mohd Yunus
Keyword(s):  
Response Surface Methodology ◽  
Crude Oil ◽  
Water Content ◽  
Response Surface ◽  
Separation Efficiency ◽  
Corn Oil ◽  
Oil Emulsion ◽  
Optimal Values ◽  
Input Variables ◽  
Oil Emulsions

: In this study, the Sany-glass test was used to evaluate the performance of a new surfactant prepared from corn oil as a demulsifier for crude oil emulsions. Central composite design (CCD), based on the response surface methodology (RSM), was used to investigate the effect of four variables, including demulsifier dosage, water content, temperature, and pH, on the efficiency of water removal from the emulsion. As well, analysis of variance was applied to examine the precision of the CCD mathematical model. The results indicate that demulsifier dose and emulsion pH are two significant parameters determining demulsification. The maximum separation efficiency of 96% was attained at an alkaline pH and with 3500 ppm demulsifier. According to the RSM analysis, the optimal values for the input variables are 40% water content, 3500 ppm demulsifier, 60 °C, and pH 8.

scholarly journals Modeling and Sensitivity Analysis of the Forward Osmosis Process to Predict Membrane Flux Using a Novel Combination of Neural Network and Response Surface Methodology Techniques

Membranes ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 70
Author(s):  
Jasir Jawad ◽  
Alaa H. Hawari ◽  
Syed Javaid Zaidi
Keyword(s):  
Response Surface Methodology ◽  
Experimental Design ◽  
Response Surface ◽  
Forward Osmosis ◽  
Feed Solution ◽  
Operating Conditions ◽  
Ann Model ◽  
Membrane Flux ◽  
Input Variables ◽  
Rsm Model

The forward osmosis (FO) process is an emerging technology that has been considered as an alternative to desalination due to its low energy consumption and less severe reversible fouling. Artificial neural networks (ANNs) and response surface methodology (RSM) have become popular for the modeling and optimization of membrane processes. RSM requires the data on a specific experimental design whereas ANN does not. In this work, a combined ANN-RSM approach is presented to predict and optimize the membrane flux for the FO process. The ANN model, developed based on an experimental study, is used to predict the membrane flux for the experimental design in order to create the RSM model for optimization. A Box–Behnken design (BBD) is used to develop a response surface design where the ANN model evaluates the responses. The input variables were osmotic pressure difference, feed solution (FS) velocity, draw solution (DS) velocity, FS temperature, and DS temperature. The R2 obtained for the developed ANN and RSM model are 0.98036 and 0.9408, respectively. The weights of the ANN model and the response surface plots were used to optimize and study the influence of the operating conditions on the membrane flux.

Optimization of Manufacturing Process of Traditional Algerian Jben Cheese Using Response Surface Methodology (RSM)

2018 ◽  
Vol 8 (1) ◽  
pp. 31-42
Author(s):  
M. Amimour ◽  
T. Idoui ◽  
A. Cheriguene
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Manufacturing Process ◽  
Milk Powder ◽  
Statistical Design ◽  
Polynomial Equation ◽  
Enzymatic Extract ◽  
Traditional Cheese ◽  
Order Polynomial ◽  
Predicted Values

The Aim of this study was to develop an optimized method for manufacturing process of traditional Algerian Jben cheese, using response surface methodology (RSM). In order to develop the objective method of making this traditional cheese, several factors have been studied and a Plackett-Burman statistical design was applied. The effects of the four screened factors (enrichment with milk powder, 10 - 20 g/l; pH of milk, 5.75 - 6.75, enzymatic extract dose, 0.5 - 1.5 ml and coagulation temperature 40 - 60 °C) on the response were investigated, using a Box-Behnken statistical design. Multiple regression analysis was used so that experimental data fits to a second-order polynomial equation. This multiple analysis showed that the model explains about 90.73% of the variation. Based on statistical results, it can be noticed that enrichment with milk powder and pH of milk (Ë‚0.0001***) were highly significant factor influincing cheese yield. The optimal production parame-ters that maximized cheese product (20 g/l enrichment with milk powder, 5.75 pH of milk, 1.29 ml enzymatic extract dose and 60°C coagulation temperature) and the maximal predicted cheese yield (52.68 % ) were found out through response surface methodology. Under these conditions, a verification experiment was carried out and cheese yield was found to be 49.46 %. The overall percentage of agreement for the experimental results (more than 93 % validity) with the predicted values indicates the validation of the statistical model and the success of the optimization process.

Multi-Performance Optimization in Friction Stir Welding of Aluminum Alloy Using Response Surface Methodology

2018 ◽  
pp. 240-263
Author(s):  
Rajat Gupta ◽  
Kamal Kumar ◽  
Neeraj Sharma
Keyword(s):  
Response Surface Methodology ◽  
Aluminum Alloy ◽  
Friction Stir Welding ◽  
Response Surface ◽  
Process Parameters ◽  
Performance Optimization ◽  
Desirability Function ◽  
High Carbon Steel ◽  
Friction Stir ◽  
Tool Shoulder

This chapter presents the friction stir welding (FSW) of aluminum alloy AA-5083-O using vertical milling machine. In present FSW experimentation, effects of different process parameter namely tool rotation speed, welding speed, tool geometry, and tool shoulder diameter have been determined on welding quality of two pieces of AA-5083-O using response surface methodology (RSM). The optimal sets of process parameters have been determined for weld quality characteristics namely tensile strength (UTS) and percentage elongation (%EL). In present experimentations, a specially designed tool made of high carbon steel with different shoulder diameters (15mm, 17.5mm, and 20 mm) having constant pin length (6 mm) were used for FSW of two pieces of aluminum alloy. The ANOVA and pooled ANOVA were used to study the effect of FSW parameters on UTS and %EL. Multi response optimization has been carried out using desirability function in conjunction with RSM to obtain the optimal setting of process parameters for higher UTS and lower %EL.

scholarly journals Pelletization of Sunflower Seed Husks: Evaluating and Optimizing Energy Consumption and Physical Properties by Response Surface Methodology (RSM)

Processes ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 591 ◽  
Author(s):  
Xuyang Cui ◽  
Junhong Yang ◽  
Xinyu Shi ◽  
Wanning Lei ◽  
Tao Huang ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Energy Consumption ◽  
Moisture Content ◽  
Physical Properties ◽  
Response Surface ◽  
Sunflower Seed ◽  
Biomass Energy ◽  
Raw Material ◽  
The Novel ◽  
Seed Husk

Pelletization is a significant approach for the efficient utilization of biomass energy. Sunflower seed husk is a common solid waste in the process of oil production. The novelty of this study was to determine the parameters during production of a novel pellet made from sunflower seed husk. The energy consumption (W) and physical properties (bulk density (BD) and mechanical durability (DU)) of the novel pellet were evaluated and optimized at the laboratory by using a pelletizer and response surface methodology (RSM) under a controlled moisture content (4%–14%), compression pressure (100–200 MPa), and die temperature (70–170 °C). The results show that the variables of temperature, pressure, and moisture content of raw material are positively correlated with BD and DU. Increasing the temperature and moisture content of raw materials can effectively reduce W, while increasing the pressure has an adverse effect on W. The optimum conditions of temperature (150 °C), pressure (180 MPa), and moisture content (12%) led to a BD of 1117.44 kg/m3, DU of 98.8%, and W of 25.3 kJ/kg in the lab. Overall, although the nitrogen content was slightly high, the novel manufactured pellets had excellent performance based on ISO 17225 (International Organization for Standardization of 17225, Geneva, Switzerland, 2016). Thus, sunflower seed husk could be considered as a potential feedstock for biomass pelletization.

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