scholarly journals COMBINING ARTIFICIAL NEURAL NETWORK- GENETIC ALGORITHM AND RESPONSE SURFACE METHOD TO PREDICT WASTE GENERATION AND OPTIMIZE COST OF SOLID WASTE COLLECTION AND TRANSPORTATION PROCESS IN LANGKAWI ISLAND, MALAYSIA

2014 ◽  
Vol 33 (2) ◽  
pp. 118-140 ◽  
Author(s):  
Elmira Shamshiry ◽  
Mazlin Mokhtar ◽  
Abdul-Mumin Abdulai ◽  
Ibrahim Komoo ◽  
Nadzri Yahaya
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Solid Waste ◽  
Response Surface ◽  
Waste Generation ◽  
Waste Collection ◽  
Solid Waste Collection ◽  
Surface Method ◽  
Transportation Process

Optimization and Prediction of Machining Responses Using Response Surface Methodology and Adaptive Neural Network by Wire Electric Discharge Machining of Alloy-X

2021 ◽  
Vol 1026 ◽  
pp. 28-38
Author(s):  
I. Vishal Manoj ◽  
S. Narendranath ◽  
Alokesh Pramanik
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Response Surface ◽  
Electric Discharge ◽  
Cutting Speed ◽  
Electric Discharge Machining ◽  
Surface Method ◽  
Wire Electric Discharge Machining ◽  
Pulse On Time

Wire electric discharge machining non-contact machining process based on spark erosion technique. It can machine difficult-to-cut materials with excellent precision. In this paper Alloy-X, a nickel-based superalloy was machined at different machining parameters. Input parameters like pulse on time, pulse off time, servo voltage and wire feed were employed for the machining. Response parameters like cutting speed and surface roughness were analyzed from the L25 orthogonal experiments. It was noted that the pulse on time and servo voltage were the most influential parameters. Both cutting speed and surface roughness increased on increase in pulse on time and decrease in servo voltage. Grey relation analysis was performed to get the optimal parametric setting. Response surface method and artificial neural network predictors were used in the prediction of cutting speed and surface roughness. It was found that among the two predictors artificial neural network was accurate than response surface method.

Batch growth of Kluyveromyces lactis cells from deproteinized whey: Response surface methodology versus Artificial neural network—Genetic algorithm approach

2016 ◽  
Vol 109 ◽  
pp. 305-311 ◽  
Author(s):  
Fábio Coelho Sampaio ◽  
Tamara Lorena da Conceição Saraiva ◽  
Gabriel Dumont de Lima e Silva ◽  
Janaína Teles de Faria ◽  
Cristiano Grijó Pitangui ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Kluyveromyces Lactis ◽  
Genetic Algorithm Approach ◽  
Artificial Neural ◽  
Batch Growth

Closure to “Waste Generation Models for Solid Waste Collection”

1976 ◽  
Vol 102 (2) ◽  
pp. 490-490
Author(s):  
Donald Grossman ◽  
James F. Hudson ◽  
David H. Marks
Keyword(s):  
Solid Waste ◽  
Waste Generation ◽  
Waste Collection ◽  
Solid Waste Collection
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