This paper describes the simulation, exergy analysis and comparison of two commonly applied liquefaction of technologies natural gas, namely: propane precooled mixed refrigerant process (C3MR) and dual mixed refrigerant process (DMR) alongside two modifications of each employing end flash systems. The C3MR and DMR process schemes were simulated using the commercial software to mathematically model chemical processes. These schemes were then analysed using energy and exergy calculations to determine their performances. The exergy efficiency for the C3MR processes without end flash system, with simple end flash system and extended end flash system were evaluated as 29%, 31%, and 33%, respectively, while the exergy efficiency for the DMR processes without end flash system, with simple end flash system, and extended end flash system were evaluated as 26%, 25.5%, and 30%, respectively. The results achieved show that the extended end flash system versions of the schemes are most efficient. Furthermore, the exergy analysis depicted that the major equipment that must be enhanced in order to improve the cycle exergy efficiencies are the compressors, heat exchangers, and coolers.

The research was focused on the selection of the best conditions for the lactic acid production. As the organic source diluted waste whey was used. Two facultative anaerobic bacteria strains were examined: Lactobacillus rhamnosus and Lactococcus lactis. The neeed of anaerobic conditions as well as mineral supplementation of cultivationwere investigated. It turned out that the oxidationwas not the key parameter, but cultivationmediumneeded a supplementation for higher process efficiency. Finally, Lactobacillus rhamnosus strain was selected, for which LA production was app. 45% higher than for Lc. lactis. On the other hand, Lactobacillus rhamnosus was active at higher lactose concentration, thus waste whey needed to be less diluted. Additionally, high values of product/substrate yield coefficient make the process very efficient.

This study proposes a new integrated analytical-field design method for multi-disc magnetorheological (MR) clutches. This method includes two stages, an analytical stage (composed of 36 algebraic formulas) and a field stage based on the finite element method (FEM). The design procedure is presented systematically, step-by-step, and the results of the consecutive steps of the design calculations are depicted graphically against the background of the entire considered clutch. The essential advantage of the integrated method with this two-stage structure is the relatively high accuracy of the first analytical stage of the design procedure and the rapid convergence of the second field stage employing the FEM. The essence of the new method is the introduction of a yoke factor kY (the concept of which is based on the theory of induction machines) that determines the ratio of the total magnetomotive force required to magnetise the entire magnetic circuit of the clutch to the magnetomotive force required to magnetise the movement region. The final value, the yoke factor kY is determined using loop calculations. The simplicity of the developed design method predisposes its use in optimisation calculations. The proposed method can also be adapted to other MR devices analysed in shear mode.

Robots that can comprehend and navigate their surroundings independently on their own are considered intelligent mobile robots (MR). Using a sophisticated set of controllers, artificial intelligence (AI), deep learning (DL), machine learning (ML), sensors, and computation for navigation, MR's can understand and navigate around their environments without even being connected to a cabled source of power. Mobility and intelligence are fundamental drivers of autonomous robots that are intended for their planned operations. They are becoming popular in a variety of fields, including business, industry, healthcare, education, government, agriculture, military operations, and even domestic settings, to optimize everyday activities. We describe different controllers, including proportional integral derivative (PID) controllers, model predictive controllers (MPCs), fuzzy logic controllers (FLCs), and reinforcement learning controllers used in robotics science. The main objective of this article is to demonstrate a comprehensive idea and basic working principle of controllers utilized by mobile robots (MR) for navigation. This work thoroughly investigates several available books and literature to provide a better understanding of the navigation strategies taken by MR. Future research trends and possible challenges to optimizing the MR navigation system are also discussed.