Optimization of an algae ball mill grinder using artificial neural network

College

Gokongwei College of Engineering

Department/Unit

Mechanical Engineering

Document Type

Conference Proceeding

Source Title

IEEE Region 10 Annual International Conference, Proceedings/TENCON

First Page

3752

Last Page

3756

Publication Date

2-8-2017

Abstract

Effects of the various ball mill operational grinding parameters for extracting microalgae were evaluated. This paper presents the use of MATLAB artificial neural network (ANN) for optimizing and improving the micro algae ball mill grinding process configuration set-up particularly on Nannochloropsis sp. The input parameters that was gathered, used and analyse are critical speed, duration, ball material, ball diameter, jar diameter, load percentage and ball-algae ratio. The researcher determines the amount of protein in the sample by using the Bradford Protein Assay Analysis. A total of 42 datasets was used to predict the optimize combination of the dataset. The authors used the MATLAB Programming and trained the neural network. MATLAB is used as an optimization tool to determine the best ball mill grinding configuration for the prototype set-up. © 2016 IEEE.

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Digitial Object Identifier (DOI)

10.1109/TENCON.2016.7848762

Disciplines

Mechanical Engineering

Keywords

Microalgae; Nannochloropsis; Neural networks (Computer science)

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