Optimization of process parameters and catalytic pyrolysis of Cascabela thevetia seeds over low-cost catalysts towards renewable fuel production

2020 ◽  
Vol 93 (5) ◽  
pp. 2033-2043
Author(s):  
Ranjeet Kumar Mishra ◽  
Abhishek Muraraka ◽  
Kaustubha Mohanty
Keyword(s):  
Process Parameters ◽  
Catalytic Pyrolysis ◽  
Low Cost ◽  
Fuel Production ◽  
Renewable Fuel ◽  
Optimization Of Process Parameters

Research on the Properties of Milling and the Process Parameters Optimization for Aluminum-Alloy Thin-Walled Parts

2010 ◽  
Vol 44-47 ◽  
pp. 2842-2846
Author(s):  
Xiao Hui Jiang ◽  
Bei Zhi Li ◽  
Jian Guo Yang ◽  
He Long Wu
Keyword(s):  
Aluminum Alloy ◽  
Process Parameters ◽  
Low Cost ◽  
Parameters Optimization ◽  
Milling Parameters ◽  
Milling Simulation ◽  
Optimization Of Process Parameters ◽  
Thin Walled ◽  
Parts Manufacturing

In this paper, with the milling processing of aluminum-alloy thin-walled parts as the research object, using software AdvantEdge, a milling simulation model is developed to study milling parameters affect on the cutting force, heat and catenation. It is found that by adjusting the ratio of milling parameters, the effects of cutting forces and heat can turn to the favorable direction of workpiece. In addition, we combine numerical simulation with experiments to explore the law of optimization of process parameters. It is discovered that the method of improving the milling speed and reducing the cutting depth properly can ensure the milling efficiency and the quality of the workpiece, providing a scientific insight for achieving high-quality, low-cost and efficient thin-walled parts manufacturing.

Computational Intelligence in Optimization of Process Parameters in Turning Metals and Composites – A Review

2015 ◽  
Vol 766-767 ◽  
pp. 914-920
Author(s):  
V. Sivaraman ◽  
S. Prakash
Keyword(s):  
Process Parameters ◽  
Computational Intelligence ◽  
Low Cost ◽  
Manufacturing Industries ◽  
Machining Process ◽  
Turning Process ◽  
Machining Processes ◽  
Swarm Optimization ◽  
The Past ◽  
Optimization Of Process Parameters

In the modern competitive scenario in manufacturing industries, producing products with low cost, less time and good quality are the ultimate goal of any manufacturer. To achieve the goal, several optimization tools are developed to optimize the process parameters of the machining process. Turning is one of the machining processes that cannot be avoided in any manufacturing industries. In this review, optimization of process parameters in turning process by computational intelligence (CI) paradigms for the past ten years is studied. Optimization by CI paradigms such as Fuzzy System (FS), Evolutionary Computation techniques Genetic Algorithm (GA), Swarm Intelligence including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Neural Networks (ANN) etc., is considered. In turning process, surface roughness, tool wear, production time and cost are optimized.

scholarly journals Optimization of Performance Parameters in Drilling Process for Minimizing the Burr Formation

2018 ◽  
Vol 7 (2) ◽  
pp. 127-131
Author(s):  
Jatinder Singh ◽  
Kulvinder Garg
Keyword(s):  
Surface Roughness ◽  
Process Parameters ◽  
Low Cost ◽  
Environmental Parameters ◽  
Burr Formation ◽  
Drilling Process ◽  
Burr Height ◽  
Optimization Of Process Parameters ◽  
Product Acceptability ◽  
The Cost

Presently a-days precision manufacturing has picked up its significance in all assembling industries. The best product dimensions at low cost with minimum time become a measure of concern. The drilling process imparts more than 30% of all the metal removing operations done on a job or assembly. The burr is a plastically deformed material, generated during drilling is unnecessary output and reduces the product acceptability, often lowers the surface quality of the product requires deburring which increases the cost of product. Burr is caused due to improper machining, tooling and environmental parameters. Total elimination of burrs during drilling process is a difficult task but using proper process parameters it can be minimized. In the present experimental study, the optimization of process parameters for minimization of burr formation in drilling process has been carried out for Al6082 plate. The tool type (coated/uncoated), spindle speed, feed rate, and drill diameters were used as the process parameters. Taguchi’s L18 orthogonal has been applied for DOE and drilling of Al6082 plates has been performed using ply-board as a backup support and without using any backup support on CNC drilling machine. The burr height and surface roughness were analyzed and optimized using S/N ratio and ANOVA and the optimum combinations for burr height and surface roughness has been found from S/N plots. The most influencing factors for burr height and surface roughness have been found from ANOVA tables.

Optimization of Process Parameters for Production of Pectinase using Bacillus Subtilis MF447840.1

2019 ◽  
Vol 13 (1) ◽  
pp. 69-73 ◽  
Author(s):  
Ram Balak Mahto ◽  
Mukesh Yadav ◽  
Soumya Sasmal ◽  
Biswnath Bhunia
Keyword(s):  
Bacillus Subtilis ◽  
Process Parameters ◽  
Volume Ratio ◽  
Optimum Process ◽  
Agitation Rate ◽  
Fermentation Condition ◽  
Optimization Of Process Parameters ◽  
Food And Beverage ◽  
Pectinase Production ◽  
Sterile Product

Background: Pectinase enzyme has immense industrial prospects in the food and beverage industries. </P><P> Objective: In our investigation, we find out the optimum process parameters suitable for better pectinase generation by Bacillus subtilis MF447840.1 using submerged fermentation. </P><P> Method: 2% (OD600 nm = 0.2) of pure Bacillus subtilis MF447840.1 bacterial culture was inoculated in sterile product production media. The production media components used for this study were 1 g/l of pectin, 2 g/l of (NH4)2SO4, 1 g/l of NaCl, 0.25 g/l of K2HPO4, 0.25 g/l of KH2PO4 and 1 g/l of MgSO4 for pectinase generation. We reviewed all recent patents on pectinase production and utilization. The various process parameters were observed by changing one variable time method. </P><P> Results: The optimum fermentation condition of different parameters was noticed to be 5% inoculums, 25% volume ratio, temperature (37°C), pH (7.4) and agitation rate (120 rpm) following 4 days incubation. </P><P> Conclusion: Maximum pectinase generation was noticed as 345 ± 12.35 U following 4 days incubation.

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