1. Introduction
Greenhouse production plays an important role in the development of modern agriculture, especially in densely-populated areas with tight land, such as eastern China. The environmental conditions in greenhouses are essential for crop growth, pest/disease prevention, energy saving, etc. For most active or passive greenhouses, appropriate environmental parameters for crop growth are assessed based on the analysis of environmental factors as lumped parameters [
1]. Meanwhile, for large/medium-sized greenhouses, the environmental parameters at the crop growing area are not equal to the values at the sensors’ location. For more accuracy, parameters such as temperature, ventilation rate and
concentration, should be considered based on a precise micro-climate model considering the spatial distribution inference.
Improvements in computing facilities together with theoretical and experimental studies increased our understanding of the biophysical process in a greenhouse system. At present, computational fluid dynamics (CFD) has been widely applied for greenhouse climate simulation [
2,
3,
4,
5,
6]. Ould Khaous et al. [
3] applied CFD to greenhouse ventilation efficiency evaluation. Results showed that air velocity at plant level varied from 0.1 to 0.5
according to opening configurations and compartment positions, whereas air temperature differences varied from 2 to 6
C. Similarly, Lee and Short [
2] used CFD to evaluate natural ventilation rates and airflow distributions in a multi-span greenhouse; Santolini et al. [
5] studied the effect of shading screens on airflow patterns within the greenhouse through CFD simulations. To relieve computational burden of CFD simulation, alternative models based on an artificial neural network (ANN) [
7] or support vector machine (SVM) [
8] are also reported for greenhouse system analysis. For these data-driven models, the modeling error issue could not be ignored.
High resolution modeling may facilitate precise regulation. To search optimal parameters of greenhouse environment, researchers employed manual CFD simulations for evaluation of candidate parameters. Tong et al. [
9] used CFD method for span dimension selection of Chinese solar greenhouses. Three groups of span configurations (10 m, 12 m and 14 m) were investigated using CFD simulations and their characteristics including solar heat gains, heat losses and temperature distributions were analyzed. Wang et al. [
10] used CFD method for thermal performance improvement of solar greenhouses. Three solar greenhouses with different north walls were simulated and analyzed; to achieve the best north wall thickness of the greenhouse, Zhang et al. [
11] carried out 18 case studies based on CFD simulation. An evaluation model using weighted entropy and fuzzy optimization scheme was then employed for decision making; to optimize the vent dimension and position in greenhouse design, He et al. [
12] investigated all effects of different back wall vent configurations by two groups of CFD models.
By contrast with solution searching through manual simulations, an interactive optimization scheme using a hybrid simulation–optimization method has superior performances in terms of computation efficiency, solution accuracy, and design convenience. In the field of building enclosure optimal design, commercial middleware such as GenOpt [
13] and OpenFOAM [
14] have been used in various scenarios. Asadi et al. [
15] combined building energy simulation program with GenOpt to regulate energy usage and thermal comfort of a residential building. Futrell et al. [
16] used similar hybrid method to search optimal solutions of daylighting and thermal performance of a campus building. Liu and Chen [
17] combined CFD simulation with genetic algorithm (GA) through OpenFOAM for optimization of indoor environmental parameters.
Compared with residential buildings, the optimization task of greenhouse environment is still challenging. Firstly, there are multiple optimization goals within greenhouse systems, such as crop quality, economic benefits, energy usage, etc.; secondly, many environmental variables contribute to the goals of optimization, for instance, air temperature, illumination intensity, wind speed, soil fertility, etc.; most differently, the optimization goals of the greenhouse environment are not constant, but changing with space and time. In this study, we design an interactive optimization scheme for environmental performance regulation in greenhouse system. To facilitate the combination of existing CFD simulation and proper optimization algorithms, an efficient interactive module is realized, which links CFD and Matlab software through data exchange mechanism. On this basis, the paper aims to understand how the spatial distribution of environmental factors influence crop growth as well as to present a multi-objective optimization case study based on the hybrid computational fluid dynamics—evolutionary algorithm (CFD-EA) method. A summer’s 720 Venlo type commercial greenhouse located in east China is chosen as the case scenario. The temperature fields and airflow patterns at essential sections are validated by field experiments. Three indexes, i.e., proper temperature distribution, proper concentration, and electrical energy consumption are chosen as optimization objectives. Simulation study provides precise set points at one selected period for feedback regulation within the greenhouse system. Results will show the feasibility and high resolution of the hybrid optimization method.
The rest of the paper is organized as the following.
Section 2 provides the details of the proposed optimization method.
Section 3 depicts the construction of the CFD model for the real greenhouse. Accuracy validation with field experiment is presented in
Section 4.
Section 5 provides the optimization procedure and results. Some concluding remarks and future works are given in
Section 6.
2. Method
Generally, the greenhouse climate management can be resolved into two dynamic processes: slow crop growth dynamics and relatively fast greenhouse climate dynamics [
1,
18]. For fast climate dynamics, the set-points of the greenhouse environmental variables such as air temperature, humidity and
concentration, should be decided in advance by the grower or computer systems. Once these set-points are decided, the desired climate in the greenhouse can be achieved by proportional–integral (PI) or other classic feed-back control methods. However, these set-points are usually time-varying and spatially distributed. In recent years, many optimal control schemes have been reported for such greenhouse climate control problems [
18,
19,
20]. The environmental parameters’ spatial feature is seldom mentioned, which is important for crop growth and energy saving in large and medium-sized greenhouse.
Figure 1 describes the basic block diagram of the climate control procedure and the highlighted optimization part is the focus of this study.
2.1. Interactive Optimization Scheme
Considering the spatial influences of environmental parameters, CFD simulation is the most accurate way of modeling. Taking advantage of the current high-speed development of computing technology, hybrid simulation–optimization has been widely used in the field of building enclosure optimal design and operation. Combined with previous reported interactive optimization methods [
17,
21,
22], we propose a set of greenhouse environment optimization solutions.
Using a validated CFD model, a global optimization scheme was combined for greenhouse environmental parameters optimization. At each iteration of optimization, distributed indoor micro-climate model was calculated by CFD (Airpak3.0.16 with Fluent engine), and the results including temperature field,
concentration, and energy consumption were format converted and transmitted to the optimization scheme through a middle module; in order to adapt to the features of the greenhouse environment optimization, we developed a middleware (C++ program) instead of directly using commercial softwares [
21].
The spatial distributions of multiple environmental parameters are non-linearity, discontinuity and with high uncertainty. EAs working with a population of stochastic solutions can be used to find multiple Pareto-optimal solutions in one single simulation run. For this study, the non-dominant genetic algorithm with elite strategy (NSGA-II) [
23] was chosen, which can find much better spread of solutions and better convergence near the true Pareto-optimal front compared to its previous version. This algorithm is realized in the Matlab environment. After finding out the Pareto frontier of main environmental parameters, the corresponding control variables were updated and exchanged back to CFD for next simulation. The interactive optimization loop continues until the stop criteria is reached.
Figure 2 describes the basic flow chart of the proposed interactive scheme.
In the scheme, the C++ program is applied for data exchange between CFD and optimization algorithms. For each iteration of optimization algorithm, the data exchange module works as the follows.
Start |
Read control variables from Matlab; |
Delete old model file of Airpak (); |
Create new model file () according to template file’s format |
() and update the control variables; |
Call Airpak to run the CFD simulation; |
WHEN the CFD simulation meets the stop criteria |
Save the results and transfers them from Airpak to Matlab by files; |
End |
2.2. Multiple Objectives and Control Variables
For greenhouse production, the optimal indoor environmental parameters have characteristics of time-varying and spatial heterogeneity, especially for the uneven planting of crops. The indoor environmental parameters that this article focuses on include air temperature, concentration and corresponding energy consumption.
2.2.1. Objectives
The suitability of environmental temperature is a primary assessment criterion for crop growth. In a greenhouse, solar radiation and other external factors affect indoor temperature dramatically. How to reduce external disturbance and improve the indoor temperature distribution is an important issue in large greenhouse systems. For greenhouse production, the ideal temperature field is a function of time and crop growth. In this paper, we refer to the crop growth model based on temperature management technology [
24,
25], and choose one period in summer for the later case study.
Another important factor affecting crop growth is indoor
concentration. The optimum concentration in the greenhouse depends on several factors: crop photosynthetic rate,
loss rate, indoor temperature, and
cost. A benchmark curve of
concentration in one day was referred [
24], and one segment was chosen for the later case study. Although relative humidity also has a great influence on crop growth, it was not controlled in this study because its value is usually greater than 80% in summer of east China, and the spatial difference can be ignored.
Three assessment indexes for the multi-objectives optimization were set, which were temperature distribution index,
distribution index, and energy consumption index. It should be noted that the essential economic cost for greenhouse crop production is heating energy. Considering the climate characteristics of eastern China in summer (air temperature is up to 40
C), we only chose the economic costs of electricity and
injection representing the main energy consumption in this study. The three indexes are formulated below,
where
and
represent values at
/
sampling points in interested crop areas.
and
are idea values of temperature and
concentration in crop areas.
is the total number of sampling points in crop areas and
is the number of fans.
and
are the overall volumetric flow rate of supply air and
(
) respectively.
is the pressure rise through the supply fan (Pa) and
is the fan efficiency.
and
are prices of electricity and
at local market. The parameters
and
are mass fraction and density of injected
.
is the duration time of the regulation.
2.2.2. Control Variables
To achieve these three objectives, three control variables (setting points) were set in the optimization scheme including fans’ speeds, injection rate of
, and heat load of solar radiation. According to previous research [
18,
19], these variables have obvious effects on indoor micro-climate. Since the optimization is simulation based, the heat load of solar radiation is assumed to be changed by the shading coefficient, and the practical implementation of these control variables are beyond the scope of the article.
2.3. Online–Offline Strategy
To derive the multiple environmental parameters’ steady fields of the greenhouse, the full CFD procedure may be very time consuming. This further makes the computational cost of interactive optimization very expensive.
For efficient computation, an online–offline scheme was employed. In the offline phase, the full CFD simulations were executed to calculate the original greenhouse model with precise solutions. In the online phase, on the basis of ensuring convergence, the mesh was simplified and the converge conditions are relaxed to acceptable extents. The purpose of this setting was to relieve the calculation burden and improve the optimization efficiency.
6. Conclusions
Considering the environmental factors’ spatial influences in greenhouses, this paper presents a CFD-EA optimization scheme that combines CFD simulations with multi-objective evolutionary algorithms. The NSGA-II is stochastic in nature and able to extract the inter features between environmental parameters and energy costs, providing information on optimal control variables and performances of greenhouse systems with high spatial resolution. A field greenhouse located in Jiangsu Province, east China, is used for the CFD model construction and validation. A simulated crop growing area (180 ) in the greenhouse in a summer noon scenario is chosen for the optimization case study. The used multiple objectives include: indoor temperature field, distribution and energy costs. The heat load of roofs, the speeds of ventilation fans, and the simulated emission are involved as control variables. As a result, 25 pairs of control variables from a total of 250 chromosomes were identified belonging to the optimum set point basis. Using this method, we can adjust optimal environmental variables with high spatial resolution to find the balance of energy saving and environment suitability. A detailed analysis may be provided that helps find the potential of the crop yield and energy conservation.
In the future works, a supercomputer with high computing speeds will be hired to solve the time-consuming problem; the interactive optimization scheme will be applied for optimal design of size, materials, and layout of greenhouse models.