The paper presents a mathematical model that characterizes the process of threshing and separation from the threshing machine with an axial flow of a thresher, taking into account the following input parameters: material flow, rotor speed, distance between rotor and counter rotor, mean density of processed material, feed speed, length of thresher and separating surface. Output parameters, such as the distribution function of separated seeds, distribution density function of separated seeds and distribution function of free seeds in the threshing space, as well as the distribution function of unthreshed seeds, together with the value of evacuation losses, were used to control the modeling process.

Starting from the influencing parameters of threshing and separation and implicit seed losses that occur within this process, this paper searched for and identified the optimal threshing regimes to minimize losses depending on the process parameters. The evacuation losses (p_ev) depend on threshing rotor speed (n) and implicit rotor peripheral speed (v_p), material feed speed (v_a), the space between the rotor and counter-rotor (δ), material feed flow (Q), material density (ρ), and the length of the threshing apparatus (L). As the parameters ρ and L are constant, the variation of losses in relation to each of the arguments was followed: v_p, Q, ρ, and v_a, respectively, for the minimization of losses by the variation of the loss function by two arguments each (represented graphically); the four arguments targeted being: v_p, v_a, ρ, and Q. Using these input parameters, it was possible to determine the optimal threshing regimes for the variation of losses in relation to the rotor peripheral speed, the feed flow, the space between the rotor and the counter-rotor, and the feed speed, so as to obtain a seed separation percentage (S_s) as close as possible to 100% (and implicitly the smallest threshing losses—towards zero) in relation to these parameters.

This paper presents theoretical and experimental research studying the influence of process parameters on the quality of biomass pellets. A validated mathematical model was developed, expressing the density of biomass pellets as determined by moisture content, compression pressure, process heat, the initial density of the material, pelleting speed and initial volume of the material. The experiments for determining the influence of these parameters on the compression of biomass into pellets and optimizing the process were conducted on a heated single pellet compression device, using fir sawdust as raw material. To describe and study the process, four input and control parameters were varied—raw material moisture, pelleting speed, maximum force applied and pelleting die temperature. From the experiments, it was noticed that overall, moisture and pressure have the most important effect on the compression process and pelleting speed, and heat applied also affected the process. Pellet density decreased when pelleting speed and material moisture increase and the density increased with a higher compression pressure and higher heat during the process.

In agrotechnical practice, it was found that between qualitative indices, which express the sowing precision achieved by precision planters determined in the laboratory, and those determined under operating conditions, that there are certain differences, which are sometimes quite significant. The decrease in the value of the quality indices was manifested by the increase of the number of mistakes, either of the number of double planting holes (with at least two seeds), or of the number of missing planting holes. Both cases are unfavorable for the agricultural producer, generating production losses. This paper discusses the influence of the degree of soil grinding on sowing precision in operating conditions, by determining the spectrum of the vibrations induced in the mechanical structure of row units of a precision planter in contact with the soil of three different plots for three working speeds: 4, 6, and 8 km·h⁻¹. Later, the vibrations were simulated under laboratory conditions, on the stand, by means of rubber hemispheres (with diameters between 30 and 100 mm, corresponding to soil fractions resulting from the determination of the degree of soil grinding) mounted on rubber bands, which actuated the seed meters, for testing under an accelerated regime, outside of the optimal agricultural periods (out of season: beginning mid-May until the end of March), in order to obtain the accuracy of the precision planters. It was found that the sowing precision determined in stationary conditions on the stand, and on a plot with an appropriate degree of seedbed preparation, decreased between 2.92% (at 4 km·h⁻¹) and 6.67% (at 8 km·h⁻¹). The main objective of the tests was to reduce labor costs, which was necessary for the staff involved for determining the qualitative indices of work in real field operating conditions, eliminating fuel consumption, while reducing the duration of testing dependent on meteorological factors (season, temperatures, and precipitation, etc.).

Heavy metals are naturally occurring elements, but their various applications have led to their wide circulation in the environment, raising concerns over their latent effects on the environment and human health. Their toxicity depends on numerous factors, including chemical species, concentration of heavy metal ions, environmental factors, etc. Experimental studies on the single or cumulative effects of heavy metals on plants are complex, time consuming and difficult to conduct. An alternative is mathematical modeling, which can include different factors into an integrated system and can predict plant and environmental behavior under multiple stressors. This paper presents a mathematical model that simulates the dependence of temperature, concentration of Zn in the soil and the subsequent bioaccumulation in lettuce (Lactuca sativa L.); respectively, the reaction of lettuce to Zn contamination. The main results consist of three mathematical models, based on systems of ordinary differential equations and checking their predictions with available experimental data. The models are applied to predict an optimal harvest time of lettuce with low concentration of Zn, in identifying the availability of the analyzed species to phytoremediation operations and the possibility of maneuvering certain control factors to reduce or increase the intensity of the bioaccumulation process.

The aim of the study was to identify new mathematical models and strategies that can characterize the behavior of pollutants accumulating in the soil over time, considering the special characteristics of these chemicals that cannot be degraded or destroyed easily. The paper proposes a statistical model for assessing the accumulation of Zn in the lettuce (Lactuca sativa L.), based on three indicators that characterize the development of lettuce plants over time. The experimental data can be used to obtain interpolated variations of the mass increase functions and to determine several functions that express the time dependence of heavy metal accumulation in the plant. The resulting interpolation functions have multiple applications, being useful in generating predictions for plant growth parameters when they are grown in contaminated environments, determining whether pollutant concentrations may be hazardous for human health, and may be used to verify and validate dynamic mathematical contamination models.