A knowledge-and-data-driven modeling approach for simulating plant growth and the dynamics of CO 2 /O 2 concentrations in a closed system of plants and humans by integrating mechanistic and empirical models - Université de Montpellier
Article Dans Une Revue Computers and Electronics in Agriculture Année : 2018

A knowledge-and-data-driven modeling approach for simulating plant growth and the dynamics of CO 2 /O 2 concentrations in a closed system of plants and humans by integrating mechanistic and empirical models

Résumé

Modeling and the prediction of material flows (plant production, CO2/O2 concentrations, H2O) is an important but challenging task in the design and control of closed ecological life support systems (CELSS). The aim of this study was to develop a novel knowledge-and-data-driven modeling (KDDM) approach for simultaneously simulating plant production and CO2/O2 concentrations in a closed system of plants and humans by integrating mechanistic and empirical models. The KDDM approach consists of a ‘knowledge-driven (KD)’ sub-model and a ‘data-driven (DD)’ sub-model. The KD sub-model describes hourly and up to daily plant photosynthesis, respiration and assimilation partitioning using the components of GreenLab and TomSim models. The DD sub-model describes the dynamics of CO2 production and O2 consumption by the crew member using a piecewise linear model. The two sub-models were integrated with a mass balance model for CO2/O2 concentrations in a closed system. The KDDM was applied with a two-person, 30-day integrated CELSS test. This model provides accurate computation of both the dry weights of different plant compartments and CO2/O2 concentrations. The model also quantifies the underlying material flows among the crew members, plants and environment. This approach provides a computational basis for lifetime optimization of cabin design and experimental setup of CELSS (e.g., environmental control, planting schedule). With extension, this methodology can be applied to a half-closed system such as a glasshouse.

Dates et versions

hal-02170559 , version 1 (02-07-2019)

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Xing-Rong Fan, Xiujuan X. Wang, Mengzhen Kang, Jing Hua, Shuangsheng Guo, et al.. A knowledge-and-data-driven modeling approach for simulating plant growth and the dynamics of CO 2 /O 2 concentrations in a closed system of plants and humans by integrating mechanistic and empirical models. Computers and Electronics in Agriculture, 2018, 148, pp.280-290. ⟨10.1016/j.compag.2018.03.006⟩. ⟨hal-02170559⟩
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