نشریه ماشین های کشاورزی
سال چهاردهم شماره 1 (پیاپی 31، بهار 1403)

The main objective of this paper is to develop a seven-link dynamic model of the operator’s body while working with a motorized backpack sprayer. This model includes the coordinates of the sprayer relative to the body, the rotational inertia of the sprayer, the muscle moments acting on the joints, and a kinematic coupling that keeps the body balanced between the two legs. The constraint functions were determined and the non-linear differential equations of motion were derived using Lagrangian equations. The results show that undesirable fluctuations in the ankle force are noticeable at the beginning and end of a swing phase. Therefore, injuries to the ankle joint are more likely due to vibrations. The effects of engine speed and sprayer mass on the hip and ankle joint forces were then investigated. It is found that the engine speed and sprayer mass have significant effects on the hip and ankle forces and can be used as effective control parameters. The results of the analysis also show that increasing the engine speed increases the frequency of the hip joint force. However, no significant effects on the frequency of the ankle joint force are observed. The results of this study may provide researchers with insight into estimating the allowable working hours with the motorized backpack sprayers, prosthesis design, and load calculations of hip implants in the future.

In the quest for enhanced anaerobic digestion (AD) performance and stability, iron-based additives as micro-nutrients and drinking water treatment sludge (DWTS) emerge as key players. This study investigates the kinetics of methane production during AD of dairy manure, incorporating varying concentrations of Fe and Fe3O4 (10, 20, and 30 mg L-1) and DWTS (6, 12, and 18 mg L-1). Leveraging an extensive library of non-linear regression (NLR) models, 26 candidates were scrutinized and eight emerged as robust predictors for the entire methane production process. The Michaelis-Menten model stood out as the superior choice, unraveling the kinetics of dairy manure AD with the specified additives. Fascinatingly, the findings revealed that different levels of DWTS showcased the highest methane production, while Fe3O420 and Fe3O430 recorded the lowest levels. Notably, DWTS6 demonstrated approximately 34% and 42% higher methane production compared to Fe20 and Fe3O430, respectively, establishing it as the most effective treatment. Additionally, DWTS12 exhibited the highest rate of methane production, reaching an impressive 147.6 cc on the 6th day. Emphasizing the practical implications, this research underscores the applicability of the proposed model for analyzing other parameters and optimizing AD performance. By delving into the potential of iron-based additives and DWTS, this study opens doors to revolutionizing methane production from dairy manure and advancing sustainable waste management practices.

This research aims to optimize the mixing process in gas-lift anaerobic digesters of municipal sewage sludge since mixing and maintaining uniform contact between methanogenic bacteria and nutrients is essential. Wastewater municipal sludge sampling was performed at the Ahvaz West treatment plant (Chonibeh, Iran) during the summer of 2022. A Computational Fluid Dynamics (CFD) model was implemented to simulate, optimize, and confirm the simulation process using ANSYS Fluent software 19.0. The velocity of the inlet-gas into the digester was determined and a draft tube and a conical hanging baffle were added to the digester design. Different inlet-gas velocities were investigated to optimize the mixing in the digester. Furthermore, turbulence kinetic energy and other evaluation indexes related to the sludge particles such as their velocity, velocity gradient, and eddy viscosity were studied. The optimal inlet-gas velocity was determined to be 0.3 ms-1. The simulation results were validated using the Particle Image Velocimetry (PIV) method and the correlation between CFD and PIV contours was statistically sufficient (98.8% at the bottom corner of the digester’s wall). The results showed that the model used for simulating, optimizing, and verifying the simulation process is valid. It can be recommended for gas-lift anaerobic digesters with the following specifications: cylindrical tank with a height-to-diameter ratio of 1.5, draft tube-to-digester diameter ratio of 0.2, draft tube-to-fluid height ratio of 0.75, the conical hanging baffle distance from the fluid level equal to 0.125 of the fluid height, and its outer diameter-to-digester diameter of 2/3.

Machinery traffic is associated with the application of stress onto the soil surface and is the main reason for agricultural soil compaction. Currently, probes are used for studying the stress propagation in soil and measuring soil stress. However, because of the physical presence of a probe, the measured stress may differ from the actual stress, i.e. the stress induced in the soil under machinery traffic in the absence of a probe. Hence, we need to model the soil-stress probe interaction to study the difference in stress caused by the probe under varying loading geometries, loading time, depth, and soil properties to find correction factors for probe-measured stress. This study aims to simulate the soil-stress probe interaction under a moving rigid wheel using finite element method (FEM) to investigate the agreement between the simulated with-probe stress and the experimental measurements and to compare the resulting ratio of with/without probe stress with previous studies. The soil was modeled as an elastic-perfectly plastic material whose properties were calibrated with the simulation of cone penetration and wheel sinkage into the soil. The results showed an average 28% overestimation of FEM-simulated probe stress as compared to the experimental stress measured under the wheel loadings of 600 and 1,200 N. The average simulated ratio of with/without probe stress was found to be 1.22 for the two tests which is significantly smaller than that of plate sinkage loading (1.9). The simulation of wheel speed on soil stress showed a minor increase in stress. The stress over-estimation ratio (i.e. the ratio of with/without probe stress) noticeably increased with depth but increased slightly with speed for depths below 0.2 m.

Due to the numerous variables that may influence the soil-machine interaction systems, predicting the mechanical response of soil interacting with off-road traction equipment is challenging. In this study, deep neural networks (DNNs) are chosen as a potential solution for explaining the varying soil sinkage rates because of their ability to model complex, multivariate, and dynamic systems. Plate sinkage tests were carried out using a Bevameter in a fixed-type soil bin with a 24 m length, 2 m width, and 1 m depth. Experimental tests were conducted at three sinkage rates for two plate sizes, with a soil water content of 10%. The provided empirical data on the soil pressure-sinkage relationship served as the basis for an algorithm capable of discerning the soil-machine interaction. From the iterative process, it was determined that a DNN, specifically a feed-forward back-propagation DNN with three hidden layers, is the optimal choice. The optimized DNN architecture is structured as 3-8-15-10-1, as determined by the Grey Wolf Optimization algorithm. While the Bekker equation had traditionally been employed as a widely accepted method for predicting soil pressure-sinkage behavior, it typically disregarded the influence of sinkage velocity of the soil. However, the findings revealed the significant impact of sinkage velocity on the parameters governing the soil deformation response. The trained DNN successfully incorporated the sinkage velocity into its structure and provided accurate results with an MSE value of 0.0871.

Today, almost half of the total human food, especially in Asia, is directly supplied from grains, and nearly 70% of the cultivated area of the world, which is one billion hectares, is used for growing grains. Therefore, non-destructive methods must be found and developed to increase seed quality in agriculture and industry. Cold plasma is a novel and efficient method that can be used in the agricultural and food sectors for the inactivation of surface microorganisms and the excitation of seeds. This review presents a summary of the effectiveness of cold plasma treatment on the characteristics of four important cereal plants: wheat, rice, corn, and barley. The focus is on the effects of this treatment on seed germination, surface property changes, water uptake of seeds, growth parameters of root, shoot, and seedling length, biomass parameters, and metabolic activities. By examining the research conducted by the researchers, it can be seen that the cereal seeds treated with cold plasma had better germination power, water absorption, shoot length, growth efficiency, shoot and root weight, and metabolic activity. This review can provide insight into the promising trends in utilizing plasma as a method to decrease the prevalence of harmful plant diseases transmitted through seeds and reduce the dormancy of hard seeds.