Modelling and optimizing performance parameters in the wire-electro discharge machining of Al5083/B4C composite by multi-objective response surface methodology

2020 ◽  
Vol 42 (6) ◽  
Author(s):  
Ram Singh ◽  
Syed Abou Iltaf Hussain ◽  
Aruntapan Dash ◽  
Ram Naresh Rai
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Objective Response ◽  
Performance Parameters ◽  
Multi Objective ◽  
Electro Discharge Machining ◽  
The Wire

scholarly journals Exploring the Exhaust Emission and Efficiency of Algal Biodiesel Powered Compression Ignition Engine: Application of Box–Behnken and Desirability Based Multi-Objective Response Surface Methodology

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5968
Author(s):  
Prabhakar Sharma ◽  
Ajay Chhillar ◽  
Zafar Said ◽  
Saim Memon
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Fossil Fuels ◽  
Objective Response ◽  
Interaction Model ◽  
Operating Conditions ◽  
Exhaust Emission ◽  
Operating Parameters ◽  
Compression Ignition Engine ◽  
Multi Objective

Sustainable Development Goals were established by the United Nations General Assembly to ensure that everyone has access to clean, affordable, and sustainable energy. Third-generation biodiesel derived from algae sources can be a feasible option in tackling climate change caused by fossil fuels as it has no impact on the human food supply chain. In this paper, the combustion and emission characteristics of Azolla Pinnata oil biodiesel-diesel blends are investigated. The multi-objective response surface methodology (MORSM) with Box–Behnken design is employed to decrease the number of trials to conserve finite resources in terms of human labor, time, and cost. MORSM was used in this study to investigate the interaction, model prediction, and optimization of the operating parameters of algae biodiesel-powered diesel engines to obtain the best performance with the least emission. For engine output prediction, a prognostic model is developed. Engine operating parameters are optimized using the desirability technique, with the best efficiency and lowest emission as the criteria. The results show Theil’s uncertainty for the model’s predictive capability (Theil’s U2) to be between 0.0449 and 0.1804. The Nash–Sutcliffe efficiency is validated to be excellent between 0.965 and 0.9988, whilst the mean absolute percentage deviation is less than 4.4%. The optimized engine operating conditions achieved are 81.2% of engine load, 17.5 of compression ratio, and 10% of biodiesel blending ratio. The proposed MORSM-based technique’s dependability and robustness validate the experimental methods.

Optimization of Aqueous Two Phase Extraction of Proteins from Litopenaeus Vannamei Waste by Response Surface Methodology Coupled Multi-Objective Genetic Algorithm

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
P. Saravana Pandian ◽  
S. Sindhanai Selvan ◽  
A. Subathira ◽  
S. Saravanan
Keyword(s):  
Genetic Algorithm ◽  
Molecular Weight ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Litopenaeus Vannamei ◽  
Value Added ◽  
Phase System ◽  
Two Phase ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

Abstract Waste generated from industrial processing of seafood is an enormous source of commercially valuable proteins. One among the underutilized seafood waste is shrimp waste, which primarily consists of head and carapace. Litopenaeus vannamei (L. vannamei) is the widely cultivated shrimp in Asia and contributes to 90 % of aggregate shrimp production in the world. This work was focused on extraction as well as purification of value-added proteins from L. vannamei waste in a single step aqueous two phase system (ATPS). Polyethylene glycol (PEG) and trisodium citrate system were chosen for the ATPS owing to their adequate partitioning and less toxic nature. Response surface methodology (RSM) was implemented for the optimization of independent process variables such as PEG molecular weight (2000 to 6000), pH (6 to 8) and temperature (25 to 45 °C). The results obtained from RSM were further validated using a Multi-objective genetic algorithm (MGA). At the optimized condition of PEG molecular weight 2000, pH 8 and temperature 35 °C, maximum partition coefficient and protein yield were found to be 2.79 and 92.37 %, respectively. Thus, L. vannamei waste was proved to be rich in proteins, which could be processed industrially through cost-effective non-polluting ATPS extraction, and RSM coupled MGA could be a potential tool for such process optimization.

Multi-objective Optimization of UV Spot Curing Technique of Slider - Suspension Attachment Process Using Response Surface Methodology Approach

2021 ◽  
Author(s):  
Santi Pumkrachang
Keyword(s):  
Response Surface Methodology ◽  
Shear Strength ◽  
Response Surface ◽  
Process Parameters ◽  
Epoxy Adhesive ◽  
Quadratic Model ◽  
Uv Curable ◽  
Multi Objective ◽  
Validation Experiment ◽  
Cure Time

The ultraviolet (UV) curing of slider-suspension attachment is going to change from a manual to an automated process. As a result, the bonding parameters of adhesive between slider and suspension needs to be optimized. This paper aims to study two output responses of the UV curable epoxy adhesive i.e., shear strength force and pitch static attitude (PSA) of the joint between slider and suspension in a head gimbal assembly (HGA). Four process parameters were investigated using response surface methodology (RSM) based on face-centered central composite design (FCCD). The RSM was applied to establish a mathematical model to correlate the significance of process parameters and the responses. Then the based multi-objective was applied to determine a quadratic model and obtained the output maximization at 224 g of shear strength force and PSA value close to the target at 1.8 degrees. The input process parameters were optimized at 0.7 s of UV bottom cure time, 120 °C of UV dual side temperature, 5.0 s of UV dual side cure time, and 230 μm of adhesive dot size. The validation experiment showed a prediction response error of less than 7% of the actual value.

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