“Utilizing the capabilities of fumigation modeling, the phosphine concentration could then be determined for every location inside the storage volume and at any given time, thus a prediction method for fumigation duration and success could be enabled. Additionally, as the CFD model correlates phosphine exposure with insect mortality, a methodology for planning precision fumigations can now be established.”
Dr. Efstathios Kaloudis
Phycist with PhD on Computational Fluid Dynamics
Centaur Analytics, Inc.
THE CHALLENGING NATURE OF
Phosphine (PH3) has been used for decades in various fields of pest control, and particularly for disinfestation of grains in bags or bulk. However, there are several factors that occasionally affect the toxicity of the fumigant and prevent treatments to be successful:
• leaky storage structures
• outdated monitoring procedures
• non-constant degassing rates
• unfavorable weather conditions
• sorption of phosphine by the grain
• poor correlation between PH3 concentration duration with insect mortality
Improper use leaves the treated commodity susceptible to insects, increasing the possibility of spoilage, but is also known to lead to tolerant strains among key stored product insects throughout the world.
PRECISION FUMIGATION METHOD
In view of the above, it is important to bolster the effectiveness of phosphine fumigation processes and ensure the ecosystem can continue to rely on this important fumigant. To achieve this, an in-depth knowledge and understanding of fumigant behavior is crucial. An efficient method for tackling this is through the combination of field experiments and computer simulation based on Computational Fluid Dynamics (CFD) models. CFD is a branch of fluid mechanics that uses numerical analysis and data structures to solve and analyze problems that involve fluid flows. Fast computers (typically on the cloud) are used to perform the calculations required to simulate the interaction of liquids and gases with surfaces defined by boundary conditions.
Utilizing the capabilities of fumigation modeling, the phosphine concentration could then be determined for every location inside the storage volume and at any given time, thus a prediction method for fumigation duration and success could be enabled. Additionally, as the CFD model correlates phosphine exposure with insect mortality, a methodology for planning precision fumigations can now be established.
To illustrate the capabilities of the model and the way it addresses all the challenges described above, a detailed description of the fumigation treatment inside 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.
The silo under consideration (Figure 1) was located in the area of Volos, Greece and the fumigation treatment took place 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 Analytics, 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 transmitted 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.
In order to evaluate accurately the storage interaction with its surroundings in terms of heat transfer, gas losses and movement, 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.
Phosphine is adsorbed by grain at differing rates depending on the commodity. Sorption can reduce the concentrations of fumigation doses to sublethal levels before grain has been disinfected. A model to predict fumigant losses due to sorption is implemented for the calculations which asserts that phosphine is absorbed into the grain and at the same time also degrades in air.
It is known that the effect of phosphine on the mortality of grain insects is due to both the level of the phosphine concentration and the time of exposure. An insect mortality indicator function is also included in the model with a dependency on the species and strain of insect (here set for the Rhyzopertha Dominica species)
The simulation model yielded, among other results, the development of phosphine concentration for the entire duration of the fumigation treatment (9 days). In Figure 5, the time evolution of phosphine at the 4 locations is presented. Specifically, sensor data are compared against model predictions. The best correlation occurs for A and B positions which are located on the silo side where the recirculation system was installed. Their maximum concentration is reached at the end of the 4th day, followed by a decrease due to diffusion, losses, and sorption by the stored product. Concerning, locations C and D, sensor data reveal lower concentration values as the model also predicts. A small discrepancy is observed at the time that the maximum value is reached. According to sensor data, phosphine concentration has an upward trend until the end of the 7th day, whereas the CFD model underestimates to the end of the 5th day. Minor fluctuations, with hourly timescales, occur due to natural convection currents which are the result of temperature differences imposed by the unsteadiness of ambient conditions. The currents create upward and downward air movements that transport phosphine along.
The overall performance of the CFD model is considered satisfactory ensuring the validity of the phosphine concentration predictions for the entire silo space as the ones presented in Figure 6. Particularly, Figure 6 shows the spatial distribution of phosphine at four time instances. The advantages of using a recirculation system can be clearly seen since on the second day, phosphine has reached every position inside the silo. Until the 6th day, higher concentration values are observed on the top regions of the silo, near the aluminum phosphide bags but as their degassification completes a more uniform phosphine distribution is reached (Figure 6, 8th day). A video showing the model predictions for the entire fumigation process could be found here: https://youtu.be/iISBS7eoWb8
A useful augmentation of the phosphine concentration profiles is the prediction of the insect extinction. Figure 7 shows the areas (red color) in which the Rhyzopertha Dominica species could not survive the fumigation process. As expected, areas near the Aluminum Phosphide bags and at the piping outlet are the first ones that reach lethal levels. According to the simulation, at the end of the 7th day, there are still some areas that insects could be still alive. A video showing the insect extinction predictions for the entire fumigation process could be found here: https://youtu.be/54uJ1ZJIkrk
Since the precision fumigation method is widely applicable on any type of commodity, storage or phosphine formulation, users from all over the world could experience significant benefits such as the minimization of failed fumigations, cost reduction of the pest control application, traceability and defensibility against quality claims from retailers.
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