Green innovation refers to developing and implementing new technologies, practices, products, and processes that promote sustainability and reduce environmental impacts. This article postulates the conceptualisation and implementation of an intelligent decision support system (IDSS) tailored to the aerospace technology sector. The data were collected from open sources such as social media and analyzed using the natural language processing tool. The envisaged IDSS is a comprehensive and seamlessly integrated platform designed to undergird decision-making, problem-solving, and research initiatives within the aerospace industry. Catering to the sector’s engineers, technicians, and managerial cadres, it aims to unravel complex datasets, proffer incisive analyses, and furnish prudent advice and recommendations. Its multifaceted capabilities range from data search and optimisation to modelling and forecasting. With an emphasis on harmonious integration with extant aerospace systems, it strives to provide engineers and technicians with enriched data insights. Moreover, its design ethos is centred on user-friendliness, underscored by an intuitive graphical interface that expedites seamless access and utilisation. Ultimately, the envisioned IDSS will augment the aerospace industry’s analytical prowess and will serve as a potent instrument for effective decision-making.

Productivity of technological processes crucially depends on its continuity and absence of any malfunction. However, operating personnel should be trained for the cases of any emergencies and under conditions as close as it possible to real ones. That is why, companies require high quality training simulators. This paper is devoted to solve this problem, presenting a model of virtual training application (VTA).In this paper, taking into account the technical regulations, requirements to both workflow processes and training simulators, design of VTA is discussed. Moreover, by analysis of uranium in-situ leaching process, the application of this simulation technology is introduced in detail. Proposed model of VTA is designed to assist training operator personnel to understand and simulate the workflow of in-situ leaching process without acting on real system and without stopping workflow

The paper deals with simulation of in-situ uranium leaching technological process, collecting data for forecasting and leaching process control. It provides numerical simulation of uranium in-situ leaching (ISL) using Comsol Multiphysics software package application. Previous studies evaluated main hydrodynamic characteristics of wells and reservoirs, such as the coefficient of resistance and the saturation recovery; while this paper is concerned with determining the changes in process variables in the wells during operation.

The article proves the urgency of studying the models of the uranium mining process by the method of underground leaching, which will allow to evaluate the dynamics of the technological process. More and more attention is paid to the modeling of the process of underground leaching (ISL), with the aim of solving the problems of raising the effectiveness of the process under consideration. The problems of extraction of underground leaching are described, the current state and description of the technology, connection with management problems is very actual development of new systems using the simulation results of the ISL process in the control loop. The article considers that there is an optimal pressure on the pumping wells according to the chosen criterion. Models of searching for the solution of the required flow rate for a quench well providing the maximum productivity of the solution at the outlet are constructed. In the paper, the tasks of determining the initial hydrodynamic parameters of wells and seams after conducting special investigations of wells with the use of deep instruments and information measuring instruments are solved.

Producing high-quality camel milk powder requires precise control of vacuum sublimation drying parameters due to the product's sensitivity to thermal stress and oxidation. This study addresses the optimization of the drying process by integrating advanced machine learning (ML) techniques to predict optimal drying conditions. In this research, four ML algorithms were developed and evaluated: Logistic Regression, Random Forest, Decision Tree, and Extreme Gradient Boosting (XGBoost). Each algorithm was trained on detailed sensor data, including chamber temperatures, vacuum pressure, and milk thickness, with drying success defined by maintaining temperature deviations within ±5°C and completing drying cycles within 24 hours. The XGBoost model exhibited the best performance, achieving an accuracy of 99.3%, precision and recall of 96.4%, and the highest F1-score. Temperature parameters, particularly in specific chamber locations, emerged as critical predictors of successful drying outcomes. By enabling accurate forecasting and real-time parameter adjustments, this ML-driven approach significantly enhances drying efficiency, product quality, and sustainability, offering substantial economic and logistical benefits, particularly for remote regions in Kazakhstan where camel milk production is prominent.

This paper is devoted to designing, modeling, and analyzing a solar-powered street lighting system using artificial intelligence technologies to predict energy consumption. The system involves the integration of solar panels, batteries, and sensors to efficiently control the brightness of LED lamps depending on the illumination level and the presence of motion in the lighting area. The use of artificial neural networks (LSTM, GRU, Random Forest) provided accurate prediction of solar energy generation and the need to connect to the city power grid during periods of low solar activity. The developed model was implemented in MATLAB Simulink, considering actual weather data. The results showed that the system can operate autonomously for up to three days under unfavorable weather conditions, reducing power by 30%. The need for connection to the external network is no more than 10% in summer and up to 30% in winter. The prototype will be implemented in Kazakhstan to validate the model and further optimize its operation.