Miller Magazine Issue: 121 January 2020

70 ARTICLE MILLER / JANUARY 2020 • Pack factor (varies with grain height) • Grain surface shape (varies with the operation: load- ing, emptying) An AI algorithm can correlate bulk density with the moisture content measurements, pack factor equations that compensate height, and determine the grains sur- face shape without user input. Thus, volume and grain mass could be calculated with improved accuracy. And without dust interruption! Predicting Grain Condition During grain storage, many factors influence the status of the grain which are difficult to know beforehand. For this reason, a new computational tool has been devel- oped to perform simulations on stored grain. The soft- ware is based on the Computational Fluid Dynamics ap- proach and Machine Learning methods and can evaluate sensor data, heat transfer effects that originate from the heat produced from grain respiration, the heating of the silo due to solar radiation as well as the cooling due to wind effects. Additionally, it implements equations pre- dicting the variations of grain moisture content and the two gases involved in grain respiration (combustion of a carbohydrate), O 2 and CO 2 . Furthermore, models evalu- ating grain deterioration are integrated into the software [2]. These models offer predictions of safe storage time based on losses in grain dry matter, the possibility of mold appearance and the reduction of germination ca- pacity (Figure 3). Grain Aeration To maintain grain quality, proper aeration is essential for most of the grain storage systems. During a typical aeration process, a fan forces cool air into the bottom of the silo. As the air moves upwards, it cools the grain and eventually exits from the aeration ducts at the top. An intelligent system considers a variety of factors to propose a tailored aeration plan (Figure 5) and make a prediction of the outcome of aeration including inlet air temperature, silo geometry, aeration fan characteristics, current and predicted climate conditions, and grain con- dition constantly measured by wireless sensors. Fumigations Utilizing the capabilities of advanced numerical mod- eling in fumigation applications and constant feedback from wireless sensors the phosphine concentration could then be determined for every location inside the storage volume and at any given time based on all the factors that occasionally affect the toxicity of the fumigant and prevent treatments to be successful [3]. Centaur Analyt- ics, Inc. have developed a unique system. • The degassing rate of metal phosphides like Mg 3 P 2 , AlP which depends on temperature and relative humidity values. • Weather conditions to evaluate accurately the stor- age interaction with its surroundings in terms of heat transfer, gas losses, and movement • Absorption of Phosphine gas by grain at differing rates depending on the commodity which can reduce the concentrations of fumigation doses to sublethal lev- els before grain has been disinfected. • Insect species and resistance • Even pot-fumigation-aeration and people safety can be measured in real time. Figure3: A user interface of a web platform that presents the grain conditions at every location in a silo, including forecast values (left). On the right the safe storage time (in days) is displayed based on several quality metrics, such as the dry matter loss, the appearance of visible molds, and the germination capacity. Figure 4: User interface of a web platform predic- ting the outcome of a phosphine fumigation.