The problem of global warming and the emission of greenhouse gases is already directly affecting the world’s energy. In the future, the impact of CO2 emissions on the world economy will constantly grow. In this paper, we review the available literature sources on the benefits of using algae cultivation for CO2 capture to decrease CO2 emission. CO2 emission accounts for about 77% of all greenhouse gases, and the calculation of greenhouse gas emissions is 56% of all CO2 imports. As a result of the study of various types of algae, it was concluded that Chlorella sp. is the best at capturing CO2. Various methods of cultivating microalgae were also considered and it was found that vertical tubular bioreactors are emerging. Moreover, for energy purposes, thermochemical methods for processing algae that absorb CO2 from flue gases were considered. Of all five types of thermochemical processes for producing synthesis gas, the most preferred method is the method of supercritical gasification of algae. In addition, attention is paid to the drying and flocculation of biofuels. Several different experiments were also reviewed on the use of flue gases through the cultivation of algae biomass. Based on this literature review, it can be concluded that microalgae are a third generation biofuel. With the absorption of greenhouse gases, the growth of microalgae cultures is accelerated. When a large mass of microalgae appears, it can be used for energy purposes. In the results, we present a plan for further studies of microalgae cultivation, a thermodynamic analysis of gasification and pyrolysis, and a comparison of the results with other biofuels and other algae cultures.

This paper deals with the design of a CPU cooling system using a novel numerical modelling approach based on automatic calculation in a commercial software. A research object is an aluminium CPU heat sink with a thermal design power of 50 W with a new fin design. A numerical model of the cooling process has been developed, and the heat sink efficiency has been investigated. The main goal of optimization was to get the minimum temperature of the CPU processor at the minimum mass of the heat sink. The comparative analysis of the results that obtained via three methods (screening, adaptive multiple-objective, multi-objective genetic algorithm) was performed. This analysis showed that screening was the least time-consuming method, but it did not provide the required solution. Adaptive multiple-objective and multi-objective genetic algorithm solutions show similar results but significantly differ in time. It was established that the adaptive multiple-objective method is the best method for the heat sink optimization task. At the determined optimal design parameter, the CPU temperature is in the range 304–307[Formula: see text]K, while the mass was 81–87[Formula: see text]g. In comparison, the heat sink mass before optimization of the design parameters was 93[Formula: see text]g at the CPU temperature of 309–311[Formula: see text]K.