Prediction of Moisture Content of Chlorella vulgaris Microalgae Using Hybrid Evolutionary Computing and Neural Network Variants for Biofuel Production

Heinrick L. Aquino, Ronnie S. Concepcion, Andres Philip Mayol, Argel A. Bandala, Alvin Culaba, Joel Cuello, Elmer P. Dadios, Aristotle T. Ubando, Jayne Lois G. San Juan

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

Abstract

Moisture content is an imperative indicator of biofuel lipid content in microalgae. This paper developed a reliable, computationally cost-effective combination of artificial neurons and an optimization tool for moisture content concentration prediction using computational intelligence. A total of 83 data of microalgae var. Chlorella vulgaris moisture content parameter factors were utilized. Using feed-forward, recurrent, and deep neural networks as prediction models, their MSE and R2 values were analyzed. Genetic programming GPTIPSv2, a multigene symbolic regression genetic programming (MSRGP) tool, was used to create objective functions of the ANNs. This convergence function was the main element in developing a genetic algorithm (GA)-optimized recurrent neural network model considered to suggest the optimal quantity of neurons in each of the hidden layers in neural network architecture. The feed-forward artificial neural network with 22 neurons in its layer was recommended using the Levenberg-Marquardt training tool. The MSE (5.27e-6) and R2 (0.9999) results of this model surpassed the other neural networks models. Hence, it implies that the developed optimized Levenberg-Marquardt-based feed-forward neural network is an effective moisture content predictor as it provided highly accurate and sensitive results at a low cost.

Original languageEnglish (US)
Title of host publication2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665401678
DOIs
StatePublished - 2021
Externally publishedYes
Event2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2021 - Manila, Philippines
Duration: Nov 28 2021Nov 30 2021

Publication series

Name2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2021

Conference

Conference2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2021
Country/TerritoryPhilippines
CityManila
Period11/28/2111/30/21

Keywords

  • genetic algorithm
  • machine learning
  • microalgae
  • microwave drying
  • neural network

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Human-Computer Interaction
  • Information Systems
  • Information Systems and Management
  • Environmental Science (miscellaneous)
  • Control and Optimization

Fingerprint

Dive into the research topics of 'Prediction of Moisture Content of Chlorella vulgaris Microalgae Using Hybrid Evolutionary Computing and Neural Network Variants for Biofuel Production'. Together they form a unique fingerprint.

Cite this