Optimization of an algae ball mill grinder using artificial neural network

Arvin H. Fernando, Archie B. Maglaya, Aristotle Tulagan Ubando

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationProceedings of the 2016 IEEE Region 10 Conference, TENCON 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781509025961
Publication statusPublished - 2017 Feb 8
Event2016 IEEE Region 10 Conference, TENCON 2016 - Singapore, Singapore
Duration: 2016 Nov 222016 Nov 25

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450


Other2016 IEEE Region 10 Conference, TENCON 2016

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering


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