Optimal control of prawn aquaculture water quality index using artificial neural networks

The water quality index (wqi) of the artificial habitat for prawn aquaculture is monitored and controlled by an artificial neural network. The states of the five critical parameters needed for an optimal aquaculture environment are monitored and tuned to go to their corresponding optimal values, all...

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Main Authors: Gustilo, R. C., Dadios, E.
Format: Conference Proceeding
Language:English
Subjects:
Aquaculture
artificial neural network
Artificial neural networks
control simulations
Indexes
Monitoring
optimization
Real time systems
Temperature measurement
Temperature sensors
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Dadios, E.
description The water quality index (wqi) of the artificial habitat for prawn aquaculture is monitored and controlled by an artificial neural network. The states of the five critical parameters needed for an optimal aquaculture environment are monitored and tuned to go to their corresponding optimal values, allowing the water quality index to go and stay to its optimal state. This optimal setting will improve the artificial habitat for aquaculture systems. Results show that the five critical parameters for artificial habitat can be monitored properly and set to their optimal states. The system is designed using parameters for tiger prawns but is capable to be adjusted for any species raised in aquaculture farming. The system can be used for real time aquaculture environment control.
doi_str_mv 10.1109/ICCIS.2011.6070339
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subjects Aquaculture
artificial neural network
Artificial neural networks
control simulations
Indexes
Monitoring
optimization
Real time systems
Temperature measurement
Temperature sensors
title Optimal control of prawn aquaculture water quality index using artificial neural networks
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