regression modeling

The study emphasises the key role of nuclear power in ensuring energy security and reducing carbon dioxide emissions, which is in line with global sustainable development requirements. Given its importance for the transition to clean energy, a detailed assessment of environmental, technical, and socio-economic dynamics in the nuclear industry is required. The purpose of the work is to comprehensively analyse and forecast the key indicators of Rosenergoatom Concern OJSC in order to gain insights into the future of the company and the industry.Multivariate analysis, regression modelling, BFGS algorithm and Excel tools were used to identify key trends and influencing factors. The results obtained show positive dynamics in the analysed areas, although some dependencies in the model need to be revised. Nevertheless, the developed methodology is an effective basis for assessing sustainable development, including for other energy companies.The paper develops an approach to the systematization and analysis of indicators of sustainable development of the nuclear industry using integral indicators. It is assumed that the methodology is also applicable for assessing the efficiency of other energy companies. A list of indicators characterizing environmental, technical and socio-economic efficiency has been compiled. In addition, the forecast values of key indicators for the near future have been determined, which demonstrates the practical applicability of the approach.The scientific novelty lies in the combination of multivariate analysis with optimisation techniques, such as BFGS and Solution Finder in Excel, to assess and predict the sustainable development of the nuclear industry.

In this study a technique has been addressed in order to model and optimize AWJM process. The required data for modeling and optimization purposes has been achieved using orthogonal array Taguchi (OA-Taguchi), D-optimal techniques and their combination based on design of experiments (DOE) approach. Water pressure, abrasive flow rate, machining speed, and machining gap are the process variables considered in this study. To evaluate the process, surface roughness (SR) has been taken into account as the process characteristic. Regression modeling using which has been turned into a widely used method has been employed to establish a relationship between process input variables and output characteristic. Analysis of variance (ANOVA) has been employed to evaluate the adequacy of the proposed models among which the most fitted and proper ones selected as the authentic representative of the process and considered as the process objective function to be optimized. Next, to optimize the objective function in order to get the desired characteristic (SR), the proposed model has been embedded into simulated annealing (SA) algorithm. Based on the computational results (less than 4% error), the proposed procedure is quite effective in modeling and optimization of the process.

This work presents an experimental investigation of the influence of the six important machining parameters (tool nose radius, tool rake angle, feed rate, cutting speed, cutting environment (dry, wet and cooled) and depth of cut) on surface roughness & material removal rate in the machining unidirectional glass fiber reinforced plastics (UD-GFRP) composite using carbide (K10) cutting tool during turning operation. Orthogonal L18 array in Taguchi method was employed to carry out the experimental work. ANOVA is performed for significant parameter and later Regression model is developed for the significant parameters. Validation (confirmatory) results indicate that the model is suitable for surface roughness & material removal rate during the study