International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

The main sources of green energy (GE) as the cleanest form of energy or types of renewable energy sources (RES) are: wind, water, sun and earth. And solar energy (SE), which originates from the Sun, is one of the cleanest and most efficient renewable energy sources (RES). While the world, especially in the most developed countries of the world, has advanced far in the application of various forms of green energy (GE), in Georgia this field is in its infancy and it is not possible to predict when the first major positive developments in this field will be made. The paper presents a trend analysis and examples of the use of solar energy (SE) in Georgia currently and with a forecast until 2030. According to the data on the plans of the state authorities (Ministry of Economy and Ministry of Energy of Georgia), the development and use of solar energy (SE) with a plan of 250 MW for 2025 will increase according to the forecast for 2030 to 810 MW, with a CAGR value of 26,51 [%] and to 1330 [MW] in 2040, with a CAGR index of 11,79 [%] for that period. (or app. 5.32 times today’s value). The paper also presents several examples of the use of solar energy (SE) in Georgia in various areas of application.

This study focuses on Laser-Induced Plasma (LIP) diagnostics based on high-resolution spectroscopic analysis to improve the reliability of Laser-Induced Breakdown Spectroscopy (LIBS) for elemental characterization. The inherently non-uniform and temporally unstable nature of laser-induced plasmas remains one of the major challenges affecting the accuracy of quantitative LIBS results. In this work, we combined deterministic and stochastic modeling approaches to describe plasma evolution, with a particular emphasis on ionization–recombination dynamics. Plasma parameters such as electron temperature (Tₑ) and electron density (nₑ) were derived using Stark broadening and Boltzmann plot methods, while the effects of temporal fluctuations were evaluated using stochastic differential equations (SDE) solved by the Euler–Maruyama algorithm. Experimental validation was performed with StellarNet Nd:YAG-based LIBS systems on a variety of metallic samples. The results demonstrate that incorporating stochastic fluctuations into traditional deterministic models significantly improves plasma parameter estimation. This integrated methodology strengthens the diagnostic capability of LIBS, reduces uncertainty in quantitative analysis, and provides a robust framework for applying high-resolution spectroscopic techniques to the study of complex materials.