X Международная студенческая научная конференция Студенческий научный форум - 2018

With increasing human population, urbanization and modernization, the shortage of food and energy as well as environmental impacts have become serious problems threatening the existence of humankind. The optimization of agricultural production could be a valid way to relieve these problems by producing more high-quality agricultural products with fewer resources and less impacts on the environment. Agricultural processing has been defined as an activity which is performed to maintain or improve the quality of an agricultural product or to change its form or characteristics. This includes drying, storage, sorting etc. An important step to optimize agricultural processing is to characterize, understand and predict it by analysis and modeling. Many scientists are dealing with this issue in our country and abroad [1, 2, 3, 4, 5]. These studies are different for their specific goals and the research methods. However, they all focus on introduction of modeling processes in agricultural production.

Of particular interest due to the subject of scientific investigation is the thesis paper for the degree of Master of Science by Congmu Zhang (Iowa State University)

Congmu Zhang collected data and developed a model which could be used for scale up design of full-scale storage systems for grain. In his work «Analysis and modeling of agricultural processes with regard to grain post-harvest handling and winemaking» he analyzes and assesses the efficiency of a closed circuit grain drying system named the DOROTHY cyclone moisture removal system. Results showed that the drying system in the fall trial was very efficient compared to common drying systems on the market and did not decrease germination. While in the winter trial, the efficiency of the drying system decreased by half compared to the fall trial but was still comparable to the common drying systems used in industry. Author has shown several specific applications of analyzing, assessing and modeling of agricultural processing to indicate, predict and optimize it [6].

The post-harvest grain processing includes series of operations during the course of which quantitative and qualitative losses can occur. The sequence of these operations and the conditions in which they take place can, furthermore, create physical and biochemical phenomena that will bring about an alteration of the grain at later stages in the post-harvest system. A late harvest, for example, can bring about losses from attacks by birds and other pests. Insufficient drying of grain can cause losses from the development of moulds and insects. Threshing can cause losses from broken grains and encourage the development of insects. Poor storage conditions can bring about losses caused by the combined action of moulds, insects, rodents and other pests [7]. In the paper«Study on prediction model of grain post-harvest loss», authors set up a variety of prediction models for the consumption of grain post-harvest loss [8].

Grain is finite and discrete material. Although flowing grain can behave like a continuum fluid at times, the discontinuous behavior exhibited by grain cannot be simulated solely with conventional continuum-based computer modeling such as finite-element or finite-difference methods. The discrete element method (DEM) is a proven numerical method that can model discrete particles like grain kernels by tracking the motion of individual particles. DEM has been used extensively in the field of rock mechanics. Its application is gaining popularity in grain postharvest operations, but it has not been applied widely. The author reviews existing applications of DEM in grain postharvest operations in the paper «Applications of Discrete Element Method in Modeling of Grain Postharvest Operations». Published literature that uses DEM to simulate postharvest processing is reviewed, as are applications in handling and processing of grain such as soybean, corn, wheat, rice, rapeseed, and the grain coproduct distillers dried grains with solubles (DDGS). Simulations of grain drying that involve particles in both free-flowing and confined-flow conditions are also included. Review of existing literature indicates that DEM is a promising approach in the study of the behavior of deformable soft particulates such as grain and coproducts and it could benefit from the development of improved particle models for these complex-shaped particles [9].

The described methods of modeling could be used not only for the specific practices mentioned in this work, but also could be used for all kinds of agricultural processing, therefore reducing the problems associated with food, energy and impacts on environment caused by the increase of human population.

With information on various types of modeling systematized and presented, practical experience studied, there appears an opportunity for us to create some improved model for post-harvest processing of farm plants and crops, grain in particular.

References

1. Xia fang Ying, Wenzhong Zheng, Yong He. Systematic analysis and optimization of grain post-harvest treatment model. Journal ofZhejiang University (Agricultureand Life Science Edition), 2005, (3): 337-340.

2. Yongsheng He, Yong He. Discussion on the evaluation method of grain post-harvest system.Proceedingsof the Chinese society ofAgriculture, 1991, 7 (1): 9-17.

3. Zhaoxian Zhang, Xiangen Hu, Yi xin Qian. A Practicable model f or predicting crop yield loss duo to weed competition.PlantProtection, 1997, 23 (2): 6-10.

4. Kaizheng Jin, Ren Jiang, Li Liu. A new method for prediction of weed density and crop yield loss. AnhuiAgricultural Science, 1999(5): 454-456.

5. Renyong Chi. Study on the regulationmodel of grain loss after birth. Scienceand Technology Bulletin, 1997 (3): 186-189.

6. Zhang, Congmu, "Analysis and modeling of agricultural processing with regard to grain post-harvest handling and winemaking" (2015). Graduate Theses and Dissertations. 14695. https://lib.dr.iastate.edu/etd/14695

7. http://www.fao.org/docrep/t0522e/T0522E04.htm

8. https://www.sciencedirect.com/science/article/pii/S1877050917325796#!

9. https://link.springer.com/article/10.1007/s12393-014-9090-y

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