A. Anastasiou

Energy saving in greenhouses using temperature integration: a simulation survey

Abstract A method is presented for controlling greenhouse air temperature achieving significant energy savings by use of temperature integration. The method is implemented in a commercial greenhouse control system using intelligent tools available in most modern control and management systems. Results compare the proposed technique with standard growers’ temperature control practice based on simulation of a model greenhouse. Tests against two types of weather show how the energy savings relate to weather spectral characteristics and crop tolerance bounds. The available tools allow the user to define time windows where the temperature setpoint can be either ‘strictly user specified’ or ‘model derived’ or ‘float within constraints for energy savings’. The method was developed for immediate application so the grower can define his policy in a simple way, without any requirements for a priori or predicted weather information. Results presented prove the viability of the method and its potential for energy savings.

AN INTELLIGENT NONINTERACTING TECHNIQUE FOR CLIMATE CONTROL OF GREENHOUSES

Abstract A new approach to system linearization and decoupling is presented for climate control of greenhouses and more specifically for the operation of heating/cooling and moisturizing. High-level programming, which provides an easy way to building models, is a feature of most research but also field control systems. The method is applicable to any air-conditioning system and is expected to gain wide acceptance in modern SCADA systems with extended computational capabilities.

An Adaptive Optimizer for Process Control

Abstract An optimization based methodology for control of irrigation and nutrient supply is developed using common measurements of greenhouse climate. The method allows onsite on-line identification of plant water needs and as an added benefit provides information for the creation of crop transpiration models.

New Ways on Supervisory Control: A Virtual Greenhouse: To Train, To Control and to Manage

Abstract Due to demands for higher levels of automation, the use of hierarchical and distributed control systems that integrate estimation, prediction, adaptation, learning and fault tolerance, is becoming a reality. A scada system, including its own special PLC with a class of generic control functions and signal processing math library, was built to control and manage complex process operations properly. It also includes an embedded fuzzy inference system for high level decision making, coordination and management of hierarchical systems. Added some special built-in functions for event driven control loop activation, it has been tested for applications in greenhouses. With recent additions for IP connectivity, operations modelling, a callable optimiser and remote camera support it has become a virtual laboratory system for training but also for remote management. This paper explores the potentialities of a new approach to the general virtual laboratory concept.

An Open System for the Management and Control of Greenhouses

Abstract The development of a modern Control and Management system for greenhouses used recent advances in software design and development tools to provide a “no programming needed OPEN system”. This system, MACQU, provides a vehicle through which all research achievements can be easily implemented in the field. To effectively integrate expert system applications in a greenhouse management system, an environment was built that supports all the interfaces between AI applications and the GMS. This environment has a native fuzzy KBS system and a number of procedural control functions that can effectively interact. The development of an open KBS, in the form of tasks and subtasks, provides an elegant way of rapid program development. New rules can be added at any given time and new control scenaria may be implemented by adding new tasks. Fuzzy decisions and fuzzy controllers at the supervisory level, provide adaptive reference generators, which is a real key element in optimal greenhouse control

Experiments with a Process Control Optimizer

Abstract An on-line process optimization tool was built based on a modified search algorithm with accelerated learning. The system is performance driven, conducts real experiments on the site and uses the modified descent method to maximize performance. A mathematical model of the process is not necessary, when the performance can be measured by simulation or experiment. The generalized tool was tested for the optimization of a timing program, using a linear percetron structure, tocontrol a fog system. Testing with other processes, such as control loop adaptation, is underway. An alternative approach has started to use dynamic clustering of the input space. Fuzzy separations replace the “crisp” dictionary grid for smoother operation. The system was prepared for field operation and tested by simulation and in the field. It has shown that it can handle interrupted experiments without loss of information and converges to a global optimum in reasonable time, depending on weather variability. The method is applicable to any system whose performance depends on a number of adjustable parameters.

Management and Control for Quality in Greenhouses

Abstract The development of an open KBS, in the form of tasks and subtasks, provides an elegant way of rapid program development. New rules can be added at any given time and new control scenaria may be implemented by adding new tasks. The approach discusse provides unlimited tasks sequences, based on a well designed set of subtasks and effectively provides a vehicle for immediate exploitation of new research findings or grower’s experience. Fuzzy decisions and fuzzy controllers at the supervisory level, provide adaptive reference generators, which is a real key element in optimal greenhouse control. At the low level the adaptive reference generators make possible the realization of optimality criteria in real time, based on maximizing the “gain” when possible, and minimizing the “expenditure” based on a sequential estimate of plant’s reserves. Specific research targets are the reduction of water and chemicals consumption in both soil and soil-less cultivation, as well as quantification of the effects of nutrients concentration and transpiration rate to product quality.

A Comparison of Optimal Greenhouse Heating Setpoint Generation Algorithms for Energy Conservation

Abstract In this paper, a comparison of two heating set-point generation algorithms is performed, using well-known greenhouse models and data for a variety of weather conditions. Their main difference isthat the first method uses meteorological weather forecasts to decide on the most appropriate heating setpoints for energy conservation, while the second method uses past information in order to achieve the desired average for any user-defined period of interest and thus, no advanced weather information is required. Simulation resultsshow that for short integration periods (e.g. 24 hours) the two algorithms have similar performance regarding energy consumption while the first method provides slightly better energy conservation, provided that weather forecasts as well as the greenhouse energy consumption model used by this method are exact. Under the same assumptions, for longer integration periods (e.g. 2 or 3 days), the second method is always comparable to the first. In thecase of uncertainty in the forecasts or in the consumption model, the second method appears to be more advantageous and effective, regarding energy conservation and quality of climate.

Field Experiments with an Intelligent Leaf Sensor

Abstract This paper describes an innovative sensing method which has been developed to replace leaf sensors in plant propagation systems. Such chambers present critical situations for control because of the high humidity levels to be maintained, which makes direct sensing methods unsuitable. The method of humidity application involves psychrometrie processes that create large time delays and renders feedback only control inoperable. The new method implicitly relates the transfer phenomena between the plants’ microspace and the environmental macrospace, through the selection of control parameters (net weights). An adaptive system guides the selection of control parameters on-line, conducts real experiments on the site and uses a modified descent method to maximise performance. The performance surface was studied and a modification of the searching algorithm has improved the learning rate significantly.