“A new computational tool has been developed to perform simulations on stored grain. The software can evaluate 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 predicting the variations of grain moisture content. Furthermore, models evaluating grain deterioration are integrated into the software. These models offer predictions of safe storage time based on losses in grain dry matter, the possibility of mould appearance and the reduction of germination capacity.”
Dr. Stathis Kaloudis - Senior Engineer, Computational Fluid Dynamics, Centaur Analytics, Inc.
Meeting food demand of an increasing population remains a major global concern. Reducing postharvest losses presents a sustainable solution to increasing food availability. Cereal grains are the main staple food in most of the developing nations and account for the maximum postharvest losses on a calorific basis among all agricultural commodities [Kumar and Kalita, 2017]. As much as 50%–60% of cereal grains can be lost during the storage stage. New technologies based on smart wireless sensors, data analytics, and advanced modeling software can now address this challenge and ensure reduced postharvest losses.
The ability to store durable agricultural products depends on many interrelated factors, which are:
• physical (temperature, humidity, storage structure)
• chemical (carbon dioxide, oxygen)
• biological (grain type, microorganisms, insects, mites, rodents, birds)
The interaction of the above factors as they affect grain in a storage structure should be thoroughly evaluated to form the overall perspective of grain storage management, assure crop quality and prevent losses.
It is important to monitor grain temperature and keep stored grain cool and dry by regular aeration. High moisture and warm temperatures in grain allow for the rapid growth of insects, fungi and the possible production of mycotoxins. Grain deterioration is also related to respiration of the grain itself and of the accompanying microorganisms. The evolution of carbon dioxide, water and heat is associated with this respiration or deterioration [Kaleta and Gornicki, 2013].
Grain aeration is the most common procedure to regulate grain temperature (using ambient or refrigerated air) and maintain grain quality and control of moisture migration. Parameters affecting the planning of the aeration process include, among others, aeration time, energy consumption, grain deterioration, target grain temperature and moisture content, ambient weather conditions, etc.
An accurate, long-term forecast of the two crucial factors (temperature, moisture content) in the entire storage space would give the advantage to the silo manager to plan the most efficient and cost-effective grain-handling strategy. Additionally, the maximum allowable/safe storage time (how long the grain may be kept with stable grain quality during storage) would need to be estimated precisely.
REASONS FOR FAILURE
Traditional methods of grain monitoring systems employ sensors that are hardwired into the storage structure or by probing the grain with an electronic sensor device. These methods are inefficient since most of the time not all the parameters of interest are being recorded and the cost for supplementary sensors and installation is substantially high. Furthermore, the use of models predicting grain condition is quite limited, on top of the fact that such models are quite simplified and have limited accuracy. Subsequently, early detections of spoilage are often not possible thus aeration planning is based on insufficient information.
NEW TRENDS IN GRAIN STORAGE TECHNOLOGY
Recent advancements in grain storage technology enabled the development of wireless sensors (such as the ones developed by Centaur Analytics, www.centaur.ag) able to measure accurately all the parameters that affect grain quality (e.g. temperature, relative humidity, O2 and CO2). They also transmit safely their data to the cloud in real time, offering worldwide accessibility. Their advantages also include the ability to move with the grain, in case of grain transit, offering enhanced traceability.
Complementary with the hardware, further improvements have been accomplished in mathematical modeling of methods for grain drying/aeration, leading to automated expert systems that become valuable tools for stored grain management. Cognitive data analytics and advanced mathematical models can predict the condition of the grain in storage weeks or even months ahead, based on the interaction of all the relevant factors including weather data. Signs of spoilage can be traced much earlier than before, allowing accurate predictions of safe storage time.
PREDICTING GRAIN CONDITION
During grain storage, there are many factors that influence the status of the grain which are difficult to know beforehand.
These parameters include among others the variations of the grain respiration rate and the transfer of heat between the storage structure and the ambient environment. For this reason, a new computational tool has been developed to perform simulations on stored grain. The software is based on the Computational Fluid Dynamics approach and can evaluate 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 predicting the variations of grain moisture content and the two gases involved in grain respiration (combustion of a carbohydrate), O2 and CO2. Furthermore, models evaluating grain deterioration are integrated into the software. These models offer predictions of safe storage time based on losses in grain dry matter, the possibility of mould appearance and the reduction of germination capacity.
A typical scenario is that of grain silos, which are subject to weather changes. In the present example, a cylindrical steel grain silo (shown in Figure 1), partially filled with wheat, is exposed to weather conditions (Figure 2) for a 6-month period, summer to winter.
Simulations predict the temperature and moisture content patterns of the entire silo storage. As Figure 3 reveals, after 3 months of storage, grain temperature and moisture content will not be uniform, but their values will vary both vertically and horizontally in the structure.
More specifically, in Figures 4 and 5, grain temperature and moisture content predictions for 3 positions (probe points of Figure 1) are presented for the entire 6-months period respectively. Concerning temperature, the two side (near silo walls) positions have similar behavior with the top center position for the first 70 days of storage. Afterwards, they start to diverge and exhibit different patterns. This kind of information is crucial to the silo managers since the number of the sensors and consequently, the coverage of the structure is often limited.
These new predictive capabilities, with the simultaneous use of wireless sensors, offer an in-depth knowledge of the grain temperature and moisture content distribution throughout the storage period, bringing unprecedented benefits to the users:
• prevention of crop spoilage
• improved grain quality
• lower operating costs and reduced aeration fan running times
• supply chain transparency – defensibility against quality claims from retailers
• higher grain market value
Kaleta A. and Gornicki, K., Criteria of Determination of Safe Grain Storage Time – A Review, Advances in Agrophysical Research, Ch. 12, 2013
Kumar D. and Kalita P., Reducing Postharvest Losses during Storage of Grain Crops to Strengthen Food Security in Developing Countries, Foods 2017, 6(1), 8.