The final model expresses enthalpy E as a function of
temperature (T) and relative humidity (RH) or
absolute humidity (w), subject to both management functions in
terms of temperature and humidity
variations. This physical model can be represented in a
Cartesian axis by using the horizontal line as
temperature, T, and the vertical line as relative humidity
𝐸 = 𝐸 𝑇, 𝑅𝐻, 𝑤 (1)
Explicitly,
(2)
where C1, C2, and C3 are the parameters for sensible and latent
heat. The first case, where the initial and
final information corresponds for temperature and relative
humidity, it is needed to calculated the absolute
humidity w used in equation 2. Later on, the changes in
temperature are calculated with equally
distributed five stages and w variable to reach the optimal
point. The second case, it is also needed to
calculate the absolute humidity w, and necessarily the
temperature can be obtained strake forward from
equation 2, i.e.
𝑇 = (𝐸 − 𝐶2𝑤)/(𝐶1 + 𝐶3𝑤) (3)
Introduction
Results
Conclusions
The main objective of crop production is to maximize
profits,
thought high quantity and quality of products, by obtaining
best
climatic conditions at minimum possible cost. The
environment
management with control strategies is necessary to achieve
crop
quality and predictability in mild-wilder climates. The
objective
of this paper is to describe the practical and trajectories
considering simultaneously temperature and humidity for
rustic
constructions in Santa Rosa Sinaloa, Mexico, and the Venlo
type
greenhouse of Berlin, Germany, in tomato crops. Physically,
the
link between temperature and humidity is widely known with
nonlinear relationships derived from the ideal and real gas
theory,
however, for adequate conditions, the management seems to be
almost regular and linear
Methodology
Mears, D. R., W. J. Roberts, G. A. Taylor (1975). Controlling
moisture levels in trough
culture tomato and cucumber production. Trans. ASAE 18(1):
145-148, 151.
Montero J. I., P. Muñoz, A. Antón, and N. Iglesias
(2005).Computational fluid dynamics
modeling of night-time energy fluxes in unheated greenhouses.
Acta Horticulturae
691(1): 403-409.
Reichrath, S., and T. W. Davies (2002). Using CFD to model the
internal climate of
greenhouses: Past, present, and future. Agronomie 22(1):
3-19.
Rojano, A. A., Salazar, M. R., Schmidt, W., Huber C., López,
C.I, Ojeda B.W. 2011.
Temperature and Humidity as Physical Limiting Factors for
Controlled Agriculture.
Proceedings of the International Symposium on High Technology
for Greenhouse
Systems. Quebec Canada, June 2009. Acta Horticulturae 893, April
2011. (1): 503-507
References
Simulation of regular trajectories to reach the comfort zone in
agriculture. A. Rojano1, R. Salazar1, U. Schmidt2, J. Flores1,
T. Espinosa1., F. Rojano3, I. López 1. 1Universidad Autónoma
Chapingo. Km 38.5 Carr. México-Texcoco MÉXICO.
(E-mail: [email protected]; [email protected];
[email protected]) 2Horticulture Faculty, Humboldt
University. Berlin GERMANY.
2University of Arizona, Tucson, AZ, USA.
Paper C0518
4
9,60
Venlo greenhouse
0
3,20 m
4,80
Figure 1. Left. Venlo type greenhouse at Institute for
Horticultural
Science. Right. Transversal section of a single cabinet.
Case Temperature
(oC)
Relative
Humidity(%)
Absolute
Humidity (gr/m3)
A 19 100 16.37
B 34 30 11.36
C 23 45 9.30
D 23 85 17.57
E 18 85 13.11
F 28 85 23.29
G(optim) 23 70 14.40
Stage Temperature(oC) Absolute Humidity(g/m3)
0 19 34 23 23 18 28 16.37 11.36 9.30 17.57 13.11 23.29
1 19.81 31.81 23.01 23.02 19.01 27.03 15.99 11.98 10.34 16.95
13.38 21.53
2 20.61 29.60 23.01 23.01 20.01 26.04 15.61 12.61 11.37 16.33
13.65 19.77
3 21.41 27.40 23.01 23.01 21.01 25.04 15.23 13.23 12.40 15.71
13.93 18.00
4 22.21 25.20 23.01 23.01 22.01 24.03 14.85 13.85 13.44 15.09
14.20 16.24
5 23.01 23.01 23.01 23.01 23.01 23.01 14.47 14.47 14.47 14.47
14.47 14.47
Case A B C D E F A B C D E F
Action hea coo ---- ---- hea coo con hum hum con hum con
Table 1. Information of initial data corresponding to the six
representative points selected.
In this paper is analyzed six different conditions surrounding
the comfort
zone as well as they are calculated the amount of either water
to be
provided or extracted per cubic meter, and the temperature
modification
by heating or cooling the system. The Table 2 in the last two
rows
illustrate the selected six conditions with their respective
actions to carry
out. Besides the attractive of the simplified quantitative
calculations, it is
still important to explore not only more general conditions
with
computational fluid dynamics, but also to answer in how to
implement the
suggested actions, experimentally.
Table 2. Constant water management with condensation(con) and
humidification(hum), also variable temperature for heating(hea)
and
cooling (coo) procedures.
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