Model-based predictive control of greenhouse climate for reducing energy and water consumption

Blasco, X.; Martínez, M.; Herrero, J. M.; Ramos, C.; Sanchis, Javier

doi:10.1016/j.compag.2006.12.001

Cited by 139 publications

(62 citation statements)

References 19 publications

(16 reference statements)

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“…Process models are usually developed as a part of model-based predictive control (MPC) algorithms. MPC are multivariable control algorithms which use prediction methods to estimate the future state of the system and an optimization cost function in order to calculate the optimum control signals taking into account the predicted future state [52–63]. ANN-based prediction methods as that proposed in this article can be used in a MPC algorithm as the prediction method.…”

Section: Discussionmentioning

confidence: 99%

Temperature and Relative Humidity Estimation and Prediction in the Tobacco Drying Process Using Artificial Neural Networks

Martínez-Martínez

Baladrón

Gómez-Gil

et al. 2012

Sensors

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This paper presents a system based on an Artificial Neural Network (ANN) for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless Sensor Network (WSN). A fitting ANN was used to estimate temperature and relative humidity in different locations inside the tobacco dryer and to predict them with different time horizons. An error under 2% can be achieved when estimating temperature as a function of temperature and relative humidity in other locations. Moreover, an error around 1.5 times lower than that obtained with an interpolation method can be achieved when predicting the temperature inside the tobacco mass as a function of its present and past values with time horizons over 150 minutes. These results show that the tobacco drying process can be improved taking into account the predicted future value of the monitored variables and the estimated actual value of other variables using a fitting ANN as proposed.

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Section: Discussionmentioning

confidence: 99%

Temperature and Relative Humidity Estimation and Prediction in the Tobacco Drying Process Using Artificial Neural Networks

Martínez-Martínez

Baladrón

Gómez-Gil

et al. 2012

Sensors

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“…Ambient temperature for cold days in Arak is considered to be 8°F equal to −13.33°C. Ground temperature is calculated based on outer temperature and it is considered 59°F in Arak . Heat loss and proper size for greenhouses are listed in Table and .…”

Section: Thermal Load Computation and Effect Of Greenhouse Parametersmentioning

confidence: 99%

Energy consumption in greenhouses and selection of an optimized heating system with minimum energy consumption

Mostafavi

Rezaei

2019

Heat Trans. Asian Res.

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In this paper, heat loss is precisely computed by a proposed code considering different climates. Estimating the costs of different central heating system, unit heaters were selected as the most feasible system. Finally, considering the heating capacity and unit heater's dimensions a computational fluid dynamics model was developed to find the optimized configuration of unit heaters in a typical greenhouse. Using this model, the required thermal load for a greenhouse based on the daily temperature of Arak city in 2017 was computed with a smart control system. It was found that savings in energy consumption were approximately 5447 m3 of natural gas each year.

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“…MPC with GAs facilitating the incorporation of energy and water consumption to adjust non-linear models parameters have been suggested. The combination of MPC and GAs permits the control of the greenhouse microclimate while achieving energy and water savings (Blasco et al, 2007). GAs in annealing form (AGAs) has also been applied for calibrating classical controllers such as PID, where the AGAs play a role in the parameter identification, demonstrating advantages over traditional GAs like premature convergence and low computing efficiency that are required to implemented these (Fan & Zuo-hua, 2006).…”

Section: Optimal Controlmentioning

confidence: 99%

Review. Advantages and disadvantages of control theories applied in greenhouse climate control systems

Duarte-Galvan

Torres-Pacheco

Guevara-González

et al. 2012

Span. j. agric. res.

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Today agriculture is changing in response to the requirements of modern society, where ensuring food supply through practices such as water conservation, reduction of agrochemicals and the required planted surface, which guarantees high quality crops are in demand. Greenhouses have proven to be a reliable solution to achieve these goals; however, a greenhouse as a means for protected agriculture has the potential to lead to serious problems. The most of these are related to the inside greenhouse climate conditions where controlling the temperature and relative humidity (RH) are the main objectives of engineering. Achieving appropriate climate conditions to ensure high yield and quality crops reducing energy consumption have been the objective of investigations for some time. Different schemes in control theories have been applied in this field to solve the aforementioned problems. Therefore, the objective of this paper is to present a review of different control techniques applied in protected agriculture to manage greenhouse climate conditions, presenting advantages and disadvantages of developed control platforms in order to suggest a design methodology according to results obtained from different investigations.Additional key words: controller; conventional control; optimal control; precision agriculture; protected agriculture. ResumenRevisión. Ventajas y desventajas de los sistemas de control climático aplicados en agricultura de precisión Hoy en día la agricultura está cambiando de acuerdo a las necesidades de la nueva sociedad. Las nuevas tendencias son asegurar la producción de alimentos a través de prácticas tales como ahorro de agua, reducción en el uso de agroquímicos y el espacio requerido para sembrar los cultivos mientras se garantiza la alta calidad de los cultivos. Los invernaderos han demostrado ser una solución viable para garantizar estos objetivos. Sin embargo, el uso de un invernadero conlleva serios problemas. Los más importantes están relacionados con las condiciones del microclima dentro del invernadero, donde el objetivo de la ingeniería es controlar la temperatura y humedad relativa (RH). Alcanzar las condiciones adecuadas del microclima para garantizar la alta productividad y calidad de los cultivos mientras se reducen los consumos de energía ha sido el objetivo de diversos investigadores a través del tiempo. Diversos esquemas de teoría de control han sido aplicados con el objetivo de resolver los problemas antes mencionados. Por lo tanto, el objetivo de este artículo es presentar una revisión de las diferentes técnicas de control aplicadas en agricultura de precisión para manejar las condiciones del microclima del invernadero, presentando las ventajas y desventajas de los sistemas desarrollados con la finalidad de proponer una metodología de diseño de acuerdo a los resultados obtenidos de las diferentes investigaciones.

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Model-based predictive control of greenhouse climate for reducing energy and water consumption

Cited by 139 publications

References 19 publications

Temperature and Relative Humidity Estimation and Prediction in the Tobacco Drying Process Using Artificial Neural Networks

Temperature and Relative Humidity Estimation and Prediction in the Tobacco Drying Process Using Artificial Neural Networks

Energy consumption in greenhouses and selection of an optimized heating system with minimum energy consumption

Review. Advantages and disadvantages of control theories applied in greenhouse climate control systems

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