摘要
Modern aquaponic systems can be highly successful,but they require intensive monitoring,control and management.Consequently,the Automation Pyramid(AP)with its layers of Supervisory Control and Data Acquisition(SCADA),Enterprise Resource Planning(ERP)and Manufacturing Execution System(MES)is applied for process control.With cloudbased IoT-based Predictive Analytics at the fore marsh,it is worth finding out if IoTwill make these technologies obsolete,or they can work together to gain more beneficial results.In this paper,we will discuss the enhancement of SCADA,ERP and MES with IoT in aquaponics and likewise how IoT-based Predictive Analytics can help to get more out of it.An example use case of an aquaponics project with five demonstration sites in different geographical locations will be presented to show the benefits of IoT on example Predictive Analytics services.Innovative is the collection of data from the five demonstration sites over IoT to make the models of fish,tomatoes,technical components such as filters used for remote monitoring,predictive remote maintenance and economical optimization of the individual plants robust.Robustness of the various models,fish and crop growth models,models for econometric optimization were evaluated using Monte Carlo Simulations revealing as expected the superiority of the IoT-based models.Our analysis suggest that the models are generally tolerant to the temperature coefficient variations of up to 15%and the econometric models tolerated a variation of for example feed ration size for fish of up to 4%and by the energy optimization models a tolerance of up to 14%by variations of solar radiation could be noticed.Furthermore,from the analysis made,it can be concluded that MES has several capabilities which cannot be replaced by IoT such as responsiveness to trigger changes on anomalies.It act as proxy when there is no case for sensors and reliably ensure correct execution in the aquaponics plants.IoT systems can produce unprecedented improvements in many areas but need MES to leverage their true potential and benefits.
基金
The research leading to these results has received funding from the European Union’s Seventh Framework Programme FP7-ENV-2013-WATER-INNO-DEMO under Grant agreement N°619137.
