Mathematical modeling and multivariate analysis applied earliest soybean harvest associated drying and storage conditions and influences on physicochemical grain quality

Anticipating the harvest period of soybean crops can impact on the post-harvest processes. This study aimed to evaluate early soybean harvest associated drying and storage conditions on the physicochemical soybean quality using of mathematical modeling and multivariate analysis. The soybeans were harvested with a moisture content of 18 and 23% (d.b.) and subjected to drying in a continuous dryer at 80, 100, and 120 °C. The drying kinetics and volumetric shrinkage modeling were evaluated. Posteriorly, the soybean was stored at different packages and temperatures for 8 months to evaluate the physicochemical properties. After standardizing the variables, the data were submitted to cluster analysis. For this, we use Euclidean distance and Ward's hierarchical method. Then defining the groups, we constructed a graph containing the dispersion of the values of the variables and their respective Pearson correlations for each group. The mathematical models proved suitable to describe the drying kinetics. Besides, the effective diffusivity obtained was 4.9 × 10–10 m2 s−1 promoting a volumetric shrinkage of the grains and influencing the reduction of physicochemical quality. It was observed that soybean harvested at 23% moisture, dried at 80 °C, and stored at a temperature below 23 °C maintained its oil content (25.89%), crude protein (35.69%), and lipid acidity (5.54 mL). In addition, it is to note that these correlations' magnitude was substantially more remarkable for the treatments allocated to the G2 group. Furthermore, the electrical conductivity was negatively correlated with all the physicochemical variables evaluated. Besides this, the correlation between crude protein and oil yield was positive and of high magnitude, regardless of the group formed. In conclusion, the early harvest of soybeans reduced losses in the field and increased the grain flow on the storage units. The low-temperature drying and the use of packaging technology close to environmental temperatures conserved the grain quality.

Effect of Drying Method and Hydrothermal Treatment on Physicomechanical Properties of Parboiled Rice

Rice grain was obtained from milled paddy, in which the husk and bran are removed. During the milling process, rice varieties having inherent poor milling quality or those have been processed under unfavorable conditions, break and so their head rice yield (HRY) decrease. Parboiling is one of the hydrothermal postharvest treatment before drying and milling processes. After parboiling, the grains become stronger and breakage of rice is very much reduced during milling, leading to often a remarkable increase in the HRY. In this study the paddy was soaked in hot water (70 °C) for 1 h and drained, then steaming was done at four periods (0, 5, 10 and 15 minutes) at atmospheric pressure. Drying process was carried out using two driers techniques, solar and continuous, at three inlet air temperature to the chamber dryer of 35, 40 and 45○C. Results revealed that the effects of drying air temperature and steaming time showed a significant effect on HRY and broken kernels for Hashemi rough rice cultivar (p<0.05) in continuous dryer. In both drying techniques the higher HRY performance was appeared at 35○C drying air temperature and 10 min steaming time. It was observed that dried kernels in continuous dryer were stronger (more bending strength) than those dried in the solar dryer and had a higher rate of HRY during subsequent processing operations. It was proved that increasing the steaming time to a safe level caused a reduction in broken kernels which was associated with higher HRY.

Energy-Qualitative and Sustainable Impacts on Differents Soy Grain Drying Technologies

The objective of this current paper is to evaluate, in real production scale, the management of soybean batches in the storage unit of harvested grains that are submitted to drying processes with different technologies, such an evaluation can contribute to minimizing energy and qualitative losses, and to ensuring the grain quality and sustainability of the postharvest system. The experiment was realized in full-scale production and the treatments utilized were lots moist soybean crop (SUL), RR dry soybean (SSLRR), RR2 dry soybean (SSLRR2), dried soybean in continuous dryer (SSS1) (11.0%), dried soybean silo-dryer (SSS2) (12.5%), dried soybean in silo aerator (SSS3) (14.0%). Energy losses and grain quality as a function of drying management ranged from 2.5 to 16.4% in energy, from 0.23 to 3.26% in crude protein and 0.15 to 3.05% in oil&mdash;the maximum yield of wet soybeans harvested from the crop (SUL) at 17% (w.b.). Considering the annual Brazilian soybean production, energy losses reach up to 162,282.50 m&sup3; of firewood, approximately 2,116,963,470 kg of crude protein and 810,616,800 liters of crude oil. This would ensure lower losses and higher grain quality, including better yield of protein and crude oil, specifically reducing energy impacts by increasing the efficiency of the drying system. The current study concluded that the SSS1 drying system reduces energy-environmental impacts by 80.23%, reduces crude protein losses by 94.73%, and crude oil by 95.08%.

End-Point Prediction of Granule Moisture in a ConsiGmaTM-25 Segmented Fluid Bed Dryer

Continuously operated pharmaceutical manufacturing lines often consist of a wet granulation unit operation, followed by a (semi-) continuous dryer. The operating conditions of the dryer are crucial for obtaining a desired final granule moisture. Commercially available dryers lack of a thorough online measurement of granule moisture during the drying process. However, this information could improve the operation of the equipment considerably, yielding a granule moisture close to the desired value (e.g., by drying time and process parameter adjustments in real-time). The paper at hand proposes a process model, which can be parameterized from a very limited number of experiments and then be used as a so-called soft sensor for predicting granule moisture. It utilizes available process measurements for the estimation of the granule moisture. The development of the model as well as parameter identification and validation experiments are provided. The proposed model paves the way for the application of sophisticated observer concepts. Possible future activities on that topic are outlined in the paper.