Identification of simple mass balance models for plant growth - Towards food production on manned space missions Heather Maclean * Denis Dochain *,⋆ Geoff Waters ** Mike Dixon ** Laury Chaerle *** Dominique Van Der Straeten *** * CESAME, Universit´e catholique de Louvain, 4-6 avenue G. Lemaˆıtre 1348 Louvain-la-Neuve, Belgium ** Controlled Environment Systems Research Facility, School of Environmental Sciences, University of Guelph, 50 Stone Road East, Guelph, Ontario, N1G 2W1, Canada *** Unit Plant Hormone Signaling and Bio-Imaging, Department of Physiology, Ghent University, Ledeganckstraat 35, 9000 Gent, Belgium Abstract: This paper presents a simple mass balance model for plant growth. This work is a first step in the development of a model intended to enable the prediction and control of a plant production chamber for MELiSSA, a regenerative life support system project developed by the ESA. Photosynthesis and respiration were selected as key reactions for biomass production. Considering these reactions, the model was developed using a mass balance approach. Reaction kinetics were chosen based on plant physiology and standard biochemical reaction knowledge. The identification and validation of yield and kinetic parameters were performed using data from lettuce and beet experiments in a closed plant chamber. The model adequately predicts lettuce growth, and predicts beet biomass and carbon dioxide flux well after an initial acclimation phase. The oxygen prediction could be improved and should be the subject of further study. Keywords: Plant growth modelling, identification 1. INTRODUCTION 1.1 State of the Art Plant growth models have been developed for the pur- pose of increasing our knowledge of plants, improving agricultural practices, as a tool in landscaping, and for the purpose of optimization and control. Modelling ef- forts have taken several different approaches depending on the intended use of the model. Many plant growth models are empirical, and apply fitted functions without considering the biological mechanisms underlying plant growth. These models have the benefit of simplicity, but are not mechanistic and therefore cannot be applied to a variety of species or over a wide range of conditions [1]. In contrast, complex metabolic models give a more com- plete description of reactions taking place within the cells, and are useful tools for studying plant development [2][3]. However, because of the large complexity, these models are typically over-parameterized and unidentifiable, making them unsuitable for prediction and control purposes. Process based models and functional-structural models attempt to bridge this gap. These models consider at least some plant processes and interactions between the plant and the environment. Process based models typically ⋆ Honorary Research Director FNRS, Belgium. e-mail : de- nis.dochain@uclouvain.be, fax : +3210472180 refer to those models that do not take plant morphol- ogy into account [4][5], while functional structural models generally include an empirical view of plant architecture [6][7]. These models work well under certain environmental conditions, but they are usually developed for plant growth under field conditions, and therefore neglect the effect of some important environmental variables (for example carbon dioxide and oxygen concentration). More mecha- nistic models, based on the reaction kinetics of the most important processes, should be applicable over a wider range of condtions. 1.2 The MELiSSA Project The MELiSSA (Micro-Ecological Life Support System Al- ternative) project aims to develop technology for a future regenerative life support system for long term manned space missions. Developed by the European Space Agency, the concept is to use microorganisms and plants to re- generate the atmosphere, recycle water, and to produce food for the crew on such missions. An important part of the MELiSSA loop is the growth of higher plants in a controlled greenhouse environment for the production of food and oxygen from ’waste’ carbon dioxide. A model of plant growth is required for the prediction and eventual control of this compartment. The model must be appli- cable at normal operating conditions, as well as during failure and stress conditions. The main control objective will be to provide a certain desired ’flow’ of biomass from 11th International Symposium on Computer Applications in Biotechnology Leuven, Belgium, July 7-9, 2010 978-3-902661-70-8/10/$20.00 © 2010 IFAC 335 10.3182/20100707-3-BE-2012.0028