(587f) Model Predictive Control of Green-Powered Zero Waste Urban Plant Factories for Sustainable Food Production

The United Nations has predicted that by 2050, more than two-thirds of the nine billion world population will live in the cities, relatively far from open-field farms. Securing the supply chain of fresh fruits and vegetables in this new scenario with concentrated population and water-energy demand will be an overwhelming challenge that current greenhouse technologies cannot address economically and sustainably. Controlled environment agriculture (CEA) technology in the form of agri-cubes and plant factories can resolve this critical challenge by the year-round production of fresh vegetables in urban areas if adequately designed, automated, and operated [1-4]. The remarkable potential of the CEA approach to enhance decentralized agricultural production enables a sustainable solution for energy supply, waste and water treatment, and recycling of nutrients through a shared system between plant factories and residential/commercial buildings. Furthermore, the integrated design of the residential-CEA-water-energy systems can minimize the demand for external water-energy supplies and reduce the carbon footprint by utilizing renewable energy (solar, wind, and biomass) and reclaimed water (rainwater, wastewater, and green waste) resources.

The proposed process system in this work contains six subsystems that are closely connected: (1) the renewable energy generation system, (2) the plant factory, (3) the residential building, (4) the energy storage and distribution system, (5) the carbon cycle – biowaste to biogas and nutrients, and (6) the wastewater treatment system. Operating such an integrated process system is a critical task. The objective is to maximize the efficiency and profits of the networked system as a whole subject to time-varying external disturbances, e.g., weather conditions, energy and food product demand, and availability of renewable energy and water resources. The main challenge is the complexity originating from the individual units and the strong interactions between them (due to the material and energy integration), leading to a networked structure (system of systems) [5], where model predictive control (MPC) appears to be a promising approach to address the problems with real-time decision-making due to its relative conceptual simplicity, flexibility, robustness, and ability to efficiently handle complex multivariable systems with the path and terminal constraints [6, 7].

We first develop a mathematical model to describe the proposed integrated residential-CEA-water-energy system dynamics. The integrated system can be considered as a network of combined lumped parameter systems (LPSs), which can be described by ordinary differential equations (ODEs), and distributed parameter systems (DPSs), which are described by PDEs. For such a system of systems, the MPC’s solvability becomes more crucial because its underlying dynamic optimization problem must be solved in the presence of PDE constraints. To bypass this issue, we develop a model order reduction framework to construct low-dimensional reduced-order models (ROMs) in the form of ODEs, which can approximate the spatiotemporal dynamics of the governing PDEs. The resulting discrete ROMs are then combined with the governing ODEs of the LPSs and used as the basis for a combined MPC and moving horizon estimation (MHE) design, where MHE estimates the unmeasurable states and unknown parameters required by MPC using the measured system outputs. Finally, the closed-loop performance of the proposed control architecture is evaluated subject to the accuracy of the ROM. A supervisory structure is employed to monitor the controller performance and revise the ROMs as needed during the process operation to preserve the ROM accuracy.

References:

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[2] T. Kozai, “Towards sustainable plant factories with artificial lighting (PFALs) for achieving SDGs,” Int. J. Agric. Biol. Eng., 12(5), 28–37, 2019.

[3] L. Graamans, A. van den Dobbelsteen, E. Meinen, and C. Stanghellini, “Plant factories; crop transpiration and energy balance,” Agric. Syst., 153, 138–147, 2017.

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[5] P. Daoutidis, M. Zachar, and J.S. Jogwar, “Sustainability and process control: A survey and perspective,” Journal of Process Control, 44, 184-206, 2016.

[6] Q. Mayne, “Model predictive control: Recent developments and future promise,” Automatica, 50(12), 2967-2986, 2014.

[7] J.B. Rawlings, D.Q. Mayne, and M.M. Diehl, “Model Predictive Control: Theory, Computation, and Design,” 2nd Edition, Nob Hill Publishing, 2019.