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Intelligent control and energy optimization in controlled environment agriculture via nonlinear model predictive control of semi-closed greenhouse

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  • Chen, Wei-Han
  • Mattson, Neil S.
  • You, Fengqi

Abstract

Greenhouse climate is a highly complex system that contains nonlinearity and dependencies between each system state. This paper proposes a novel nonlinear model predictive control (NMPC) framework for greenhouse climate control to minimize the total control cost mainly coming from energy use. A nonlinear dynamic model of the greenhouse climate, including temperature, humidity, CO2 level, and light intensity, is first constructed based on developed mass balances and energy transport phenomena. Real-world greenhouse climate data and outdoor weather data are gathered to systematically identify the system parameters for the nonlinear greenhouse climate model. The nonlinear dynamic model is then integrated into the proposed NMPC framework which iteratively solves a nonlinear programming problem to obtain the optimal control inputs of fan airflow rate, pad cooling air velocity, heating pipe flow rate, CO2 injection rate, fogging rate, supplemental light intensity, and shade curtain coverage. The stability and feasibility issues of the proposed NMPC framework on a semi-closed greenhouse are explicitly discussed in this work. Case studies on controlling a greenhouse located in Cornell University campus are simulated to demonstrate the performance of the proposed NMPC framework. The results show the NMPC framework could efficiently minimize total control cost and constraint violation. Humidity, CO2 level, and light intensity can be controlled with nearly no violation on the predetermined constraints over different seasons and climate conditions. As for temperature, it is almost always maintained within the acceptable region in winter and spring. On extreme days of summer, there are some temperature violations due to the limited cooling capacity.

Suggested Citation

  • Chen, Wei-Han & Mattson, Neil S. & You, Fengqi, 2022. "Intelligent control and energy optimization in controlled environment agriculture via nonlinear model predictive control of semi-closed greenhouse," Applied Energy, Elsevier, vol. 320(C).
    • Handle: RePEc:eee:appene:v:320:y:2022:i:c:s0306261922006845
    DOI: 10.1016/j.apenergy.2022.119334
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    References listed on IDEAS

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    1. Lin, Dong & Zhang, Lijun & Xia, Xiaohua, 2021. "Model predictive control of a Venlo-type greenhouse system considering electrical energy, water and carbon dioxide consumption," Applied Energy, Elsevier, vol. 298(C).
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    Cited by:

    1. Hu, Guoqing & You, Fengqi, 2023. "An AI framework integrating physics-informed neural network with predictive control for energy-efficient food production in the built environment," Applied Energy, Elsevier, vol. 348(C).
    2. Blaud, Pierre Clement & Haurant, Pierrick & Chevrel, Philippe & Claveau, Fabien & Mouraud, Anthony, 2023. "Multi-flow optimization of a greenhouse system: A hierarchical control approach," Applied Energy, Elsevier, vol. 351(C).
    3. Mahmood, Farhat & Govindan, Rajesh & Bermak, Amine & Yang, David & Al-Ansari, Tareq, 2023. "Data-driven robust model predictive control for greenhouse temperature control and energy utilisation assessment," Applied Energy, Elsevier, vol. 343(C).
    4. Xiao, Tianqi & You, Fengqi, 2023. "Building thermal modeling and model predictive control with physically consistent deep learning for decarbonization and energy optimization," Applied Energy, Elsevier, vol. 342(C).
    5. Chen, Wei-Han & You, Fengqi, 2022. "Sustainable building climate control with renewable energy sources using nonlinear model predictive control," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).

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