Miller Magazine Issue 108 / December 2018

79 MILLER / DECEMBER 2018 ARTICLE above, a detailed description of the fumigation treatment in- side a cylindrical silo is presented, presenting correlations of numerical (CFD) analysis with wireless gas sensor readings based on a rich sample of phosphine distribution during the entire duration of treatment. Numerical results are employed to provide a map of insect mortality rates, thus binding the analysis with the end objective of pest treatment. Storage structure The silo under consideration (Figure 1) was located in the area of Volos, Greece and the fumigation treatment took pla- ce during December 2017. The steel silo diameter was D=15 [m] and its height was H=12 [m]. A recirculation system was installed and used during the process. Stored grain (whole wheat) temperature was 12 [oC]. A coefficient accounting for gas leaks (customizable for each storage structure) wass considered in the calculations. Measurement of phosphine concentration Data collection of phosphine concentration inside the silo was made with sensor devices provided by Centaur Analy- tics, Inc. The devices are based on electrochemical sensors thus providing high accuracy, and are equipped with wireless connectivity with the ability to transmit data frequently (e.g. every 2 hours) from inside stored grain. The data were trans- mitted in real time to Centaur’s cloud platform, from which they were downloaded and further processed. Figure 1 shows the position of the 4 sensors inside the silo, whereas Figure 2 shows how one of the sensors is installed inside the silo. Fumigation parameters – PH3 degassing rate Phosphine gas was generated using Aluminum Phosphide bags. Approximately 10 gr of AlP per tonne of stored product was used, which is equivalent to 2.53 gr of phosphine gas per m3. The degassing evolution of phosphine depends on temperature and humidity values presented in Figure 3. Weather conditions In order to evaluate accurately the storage interaction with its surroundings in terms of heat transfer, gas losses and mo- vement, the computational model integrates weather data for the specific location and time period. The time series of ambient temperature, wind velocity, and solar radiation used as inputs are presented in Figure 4. Sorption effects Phosphine is adsorbed by grain at differing rates depending on the commodity. Sorption can reduce the concentrations Figure 1: The three-dimensional model of the cylindrical silo considered in this work Figure 2: Installation of sensor B inside the silo Figure 3: Degassing evolution of phosphine gas from AlP bags during fumigation (data provided by Detia Degesch) Figure 4: Time variation of ambient conditions: solar radiation, temperature and wind velocity