Model-based predictive greenhouse parameter control of aquaponic system.

Area of Science:

• Agricultural Engineering
• Environmental Control Systems
• Aquaponics and Hydroponics

Background:

• Aquaponic systems require precise environmental control for optimal fish and plant health.
• Greenhouse environments allow for regulation of temperature, CO2, humidity, and light.
• Effective control strategies are crucial for maximizing aquaponic system productivity and sustainability.

Purpose of the Study:

• To develop and validate a novel control approach for aquaponic greenhouse systems.
• To maintain optimal greenhouse climate parameters (temperature, CO2 concentration, humidity).
• To enhance aquaponic system performance, profitability, and sustainability.

Main Methods:

• A two-layered control strategy combining Particle Swarm Optimization (PSO) for setpoint optimization and Constrained Discrete Model Predictive Control (CDMPC) for trajectory tracking.
• Comparison of PSO with Genetic Algorithms (GA) for setpoint optimization.
• Evaluation of CDMPC performance against a Proportional-Integral (PI) controller using Relative Average Deviation (RAD) and Mean Relative Deviation (MRD).

Main Results:

• The proposed CDMPC controller demonstrated superior performance compared to the PI controller, with significantly lower RAD and MRD values for temperature, CO2, and humidity.
• PSO and GA showed similar computational efficiency, with PSO's optimal values being adopted.
• The CDMPC controller exhibited robustness, efficient setpoint tracking, and effective disturbance rejection.

Conclusions:

• The developed two-layered control strategy is effective in maintaining optimal greenhouse climate parameters for aquaponics.
• This approach offers a promising technique for boosting aquaponic production, profitability, and sustainability.
• The controller's efficiency in environmental regulation can lead to improved yields and reduced labor needs.