Influence of Self-Compaction on the Airflow Resistance of Aerated Wheat Bulks (Triticum aestivum L., cv. ‘Pionier’)

Abstract

Aeration is a key post-harvest grain processing operation that forces air through the pore volume of the grain bulk to establish favorable conditions to maintain grain quality and improve its storability. However, during storage, grain bulk experiences self-compaction due to its dead weight, which alters the bulk properties and impedes the uniform flow of air during aeration. Thus, this study focused on investigating the effect of self-compaction on the pressure drop ΔP of wheat bulk (Triticum aestivum L., cv. ‘Pionier’, X = 0.123 kg·kg−1 d.b.) accommodated in a laboratory-scale bin (Vb = 0.62 m3) at a coherent set of airflow velocities va. Pressure drop ΔP was measured at bulk depths Hb of 1.0, 2.0, 3.0 and 3.4 m and storage times t of 1, 65, 164 and 236 h. For the semi-empirical characterization of the relationship between ΔP and va, the model of Matthies and Petersen was used, which was proficient in describing the experimental data with decent accuracy (R2 = 0.990, RMSE = 68.67 Pa, MAPE = 12.50%). A tailored product factor k was employed for the specific grain bulk conditions. Results revealed a reduction of in-situ pore volume ε from 0.413 to 0.391 at bulk depths Hb of 1.0 to 3.4 m after 1 h storage time t and from 0.391 to 0.370 after 236 h storage time t, respectively. A disproportional increase of the pressure drop ΔP with bulk depth Hb and storage time t was observed, which was ascribed to the irreversible spatio-temporal behavior of self-compaction. The variation of pore volume ε was modeled and facilitated the development of a generalized model for predicting the relationship between ΔP and va. The relative importance of modeling parameters was evaluated by a sensitivity analysis. In conclusion, self-compaction has proven to have a significant effect on airflow resistance, therefore it should be considered in the analysis and modeling of cooling, aeration and low-temperature drying of in-store grain bulks.

Sustainable Nonfarm Approaches to Achieve Zero Hunger and Its Unveiled Reality

“…They observed that the pressure drop or air flow resistance increased up to 24.76% for storage period of 236 h at the bulk depth of 3.40 m and air velocity of 0.10 ms −1 . 40 Therefore, self-compaction should be considered while designing aerated storage systems. A smart aeration control system can also be effectively used in the storage system.…”

“…Ramaj et al studied the effect of self-compaction on the resistance to air flow of wheat bulk. They observed that the pressure drop or air flow resistance increased up to 24.76% for storage period of 236 h at the bulk depth of 3.40 m and air velocity of 0.10 ms –1 . Therefore, self-compaction should be considered while designing aerated storage systems.…”

Numerical Study of Low-Temperature Ventilation Drying in a Wheat Grain Silo Considering Non-Uniform Porosity Distribution

“…The non-uniform porosity distribution formula of wheat grain piles in silos in this study is as follows [19], with the variation range of non-uniform porosity with a depth of 0.37-0.42 being obtained via Equation (10), and the average value of 0.40 being taken as the uniform porosity:…”

“…However, the heat and moisture transfer inside the grain pile during the drying process is complex [18]. For this reason, many drying models have been used to study the drying process for wheat grain piles [19]. There are more than 100 different thin-layer drying models based on semi-theoretical models and the empirical models used in grain piles under different drying conditions [20].…”

Special Issue on the Engineering of Smart Agriculture

“…In addition, the resource use of deep learning models for weed detection in smart agriculture [5] and the impact of microbial preparations on yields and soil nutrients [6] are discussed. In addition, it examines the energy consumption and environmental impact of lettuce production [7] and the resistance to airflow in aerated wheat masses [8]. Finally, it investigates real-time plant classification using deep learning techniques [9] and the design of a controller for pH regulation of liquid fertilizers [10].…”