Modeling post-fumigation desorption of phosphine in bulk stored grain

Plumier, Benjamin M.; Schramm, Matthew W.; Ren, Yonglin; Maier, Dirk E.

doi:10.1016/j.jspr.2019.101548

Cited by 15 publications

(3 citation statements)

References 10 publications

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“…The same holds true in the case of the different commodities that are to be treated, as the interactions of decreased oxygen with specific characteristics are poorly understood. For instance, penetration of phosphine on empty containers may be faster than that with currants [28], which may lead to different expected desorption patterns [59].…”

Section: Future Trendsmentioning

confidence: 99%

Using Nitrogen for the Control of Stored Product Insects: One Single Application for Multiple Purposes

Athanassiou

Sakka

2022

Agrochemicals

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Nitrogen treatment can be used as an alternative method to chemical control. Most of the research on nitrogen treatment mainly investigated the optimum concentration of oxygen level as well as duration as a means of insect control. Other parameters such as temperature and different insect species have been extensively studied and recent research focus on the modelling of nitrogen concentration and the efficacy on commodity. In this paper, we briefly review the major parameters (temperature, oxygen level, relative humidity, exposure time) using nitrogen treatment against stored product insects. Exposure to different oxygen levels or different exposure times can remarkably change pest control mortality. Moreover, different insect species and life stages have differing susceptibility to nitrogen treatment. Finally, these studies are reviewed in this paper to illustrate that nitrogen treatment can be used as a part of an IPM strategy.

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Section: Future Trendsmentioning

confidence: 99%

Using Nitrogen for the Control of Stored Product Insects: One Single Application for Multiple Purposes

Athanassiou

Sakka

2022

Agrochemicals

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“…A novel three-dimensional finite element model coded in Fortran was developed and validated by Dr. Maier and one of his former Ph.D. students as part of his dissertation research [5]. The 3D finite element (FE) model was expanded by another Ph.D. student [6] and has been successfully used to investigate various scenarios of stored grain ecosystem management [7,8]. The 3D MLP-FE model has the capability to predict temperature and moisture content at specific locations within a stored grain mass, for example representing number of sensors and their distribution, during aerated and non-aerated periods as a function of grain conditions, silo sizes, and historic weather data.…”

Section: Introductionmentioning

confidence: 99%

Number of Sensors Needed to Achieve Reliable Aeration Cooling During the Fall Harvest and Monitoring of Grain Quality During the Non-Aerated Storage Period Based on Predicted Temperatures in a Grain Mass Using Cable-Based Sensors Versus Wireless Sensors a

Aby¹,

Maier²

2023

Preprint

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Monitoring the quality of stored bulk grain is generally done using temperature cables hung from silo roofs. Little investigation has been done into the effects of number of sensors and their placement in terms of reliability of the monitoring system with regard to making stored grain quality management decisions. A previously developed 3D finite element simulation model was verified and used to investigate these aspects. In the first study, a silo was loaded with about 228.6 Mg (9000 bushels) of maize and six temperature cables were placed in the grain mass. The maize was aerated continuously for a period of two weeks, and the cable sensor temperatures were compared to the predicted temperatures which were in close agreement with the observed readings. The standard error of prediction ranged from 2.0 to 3.7°C. In the second study, 15 and 30 sensors were placed at manufacturer recommended depths and horizontal locations in the grain mass of three silo sizes (i.e., 11x11, 14.6x14.6 and 14.6x18.3 m diameter by eave height). The average grain temperatures predicted by the 15 and 30 sensors over a one-year simulation period were compared to the average grain temperatures predicted for the entire grain mass (1968, 3052, and 3204 mesh nodes). The number of sensors needed to monitor stored grain temperatures reliably in the three silo sizes investigated heavily depended on whether the aeration control strategy achieved a sufficiently low temperature by the time the aeration fans were turned off and sealed ahead of the non-aerated storage period. Fifteen or 30 sensors were sufficient to monitor grain temperatures during the aeration cooling period but for the two larger silo sizes more than 30 sensors would be needed during the storage period. As silo size increased, and surface-to-volume ratio decreased, grain temperatures remained lower during the storage period. Results support the best management practice recommendation of leaving cooled grain cold and not warming it up in the spring ahead of storage into the summer.

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“…The 3D MLP (Maier, Lawrence, Plumier) finite element model has been previously used to investigate stored grain ecosystems in a variety of situations [8,[16][17][18]. It has the capacity to analyze the effects that changing sensor distributions have on aeration results by running multiple simulations with sensors distributed in ways consistent with the existing temperature cable technology and the randomized wireless sensor technology.…”

Section: Introductionmentioning

confidence: 99%

Effect of Temperature Sensor Numbers and Placement on Aeration Cooling of a Stored Grain Mass Using a 3D Finite Element Model

Plumier

Maier

2021

Agriculture

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Grain stored in silos in the United States of America is generally cooled with an aeration system to limit mold spoilage and insect infestation. Monitoring efficacy of aeration and real-time conditions of stored grain is generally done using temperature cables with fixed-spaced sensor locations that are hung from the roof of the silo. Numerous placement options exist in terms of the number of cables and their positions. However, little investigation has been done into the effects of cable placement on aeration system operation decisions and real-time monitoring of stored grain conditions. For a one-year period, the temperatures predicted by sensors in three recommended temperature cable configurations were evaluated for conditions in Ames, IA, USA. The average temperatures of each of the cable sensor configurations were lower than the average temperatures of the entire silo, with as much as an 11.4 °C difference. When sensor locations were used as inputs for aeration control, all cable sensor configurations predicted similar average temperatures. However, the temperature averages varied by as much as 3.6 °C depending on the temperature cable distribution chosen. Results demonstrated that temperature cables near the center or near the edges of the silos produce results that are not representative of the grain mass, resulting in less efficient aerations. Simulations were also conducted with randomized horizontal “wireless” sensor locations at fixed grain depths. The average temperatures were similar, but an increase in the number of sensors reduced variability between simulated storage years as the number of randomized sensors increased.

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Modeling post-fumigation desorption of phosphine in bulk stored grain

Cited by 15 publications

References 10 publications

Using Nitrogen for the Control of Stored Product Insects: One Single Application for Multiple Purposes

Using Nitrogen for the Control of Stored Product Insects: One Single Application for Multiple Purposes

Number of Sensors Needed to Achieve Reliable Aeration Cooling During the Fall Harvest and Monitoring of Grain Quality During the Non-Aerated Storage Period Based on Predicted Temperatures in a Grain Mass Using Cable-Based Sensors Versus Wireless Sensors a

Effect of Temperature Sensor Numbers and Placement on Aeration Cooling of a Stored Grain Mass Using a 3D Finite Element Model

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