The emissions from coal power plants have serious implication on the environment protection, and there is an increasing effort around the globe to control these emissions by the flue gas cleaning technologies. This research was carried out on the limestone forced oxidation (LSFO) flue gas desulfurization (FGD) system installed at the 2*660 MW supercritical coal-fired power plant. Nine input variables of the FGD system: pH, inlet sulfur dioxide (SO2), inlet temperature, inlet nitrogen oxide (NOx), inlet O2, oxidation air, absorber slurry density, inlet humidity, and inlet dust were used for the development of effective neural network process models for a comprehensive emission analysis constituting outlet SO2, outlet Hg, outlet NOx, and outlet dust emissions from the LSFO FGD system. Monte Carlo experiments were conducted on the artificial neural network process models to investigate the relationships between the input control variables and output variables. Accordingly, optimum operating ranges of all input control variables were recommended. Operating the LSFO FGD system under optimum conditions, nearly 35% and 24% reduction in SO2 emissions are possible at inlet SO2 values of 1500 mg/m3 and 1800 mg/m3, respectively, as compared to general operating conditions. Similarly, nearly 42% and 28% reduction in Hg emissions are possible at inlet SO2 values of 1500 mg/m3 and 1800 mg/m3, respectively, as compared to general operating conditions. The findings are useful for minimizing the emissions from coal power plants and the development of optimum operating strategies for the LSFO FGD system.

The surface coating is one of the novel approaches to enhance the performance and durability of the mechanical components by decreasing the wear and friction among two interacting bodies. In this study, tribological and mechanical properties of titanium nitride (TiN) coatings were investigated on the AISI 52100 bearing steel deposited by low-temperature physical vapor deposition system. Surface morphology and elemental composition of the TiN coating were analyzed by scanning electron microscope and energy-dispersive X-ray spectrum, respectively. Substrate surface roughness and coating thickness of TiN were varied for correlative analysis among adhesion, mechanical, and tribological properties. Scratch and tribo tests were performed for evaluating the adhesion and tribological properties, respectively. Samples having the substrate surface roughness (0.2 ± 0.05 µm) and the coating thickness of more than 2.83 µm presented relatively better adhesion, wear resistance, and lower coefficient of friction of the TiN coating.

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Friction and wear are very crucial aspects of the performance, service life, and the operational costs for a mechanical component or equipment. To reduce the friction and wear at the interface of the sliding or mating parts, different conventional binary coatings like TiN, CrN, TiC, etc., have been used in the last two decades. But ternary nitride coatings have replaced the binary coatings due to better tribo-mechanical properties. Now, ternary nitride coatings are being extensively used in several fields such as cutting tools, machinery parts, orthopedic implants, microelectronics, marine equipment, decorative purposes, automotive, aerospace industry, etc. Many researchers have developed and investigated the ternary nitride coatings for different applications. Nonetheless, there is a huge research potential in the development and optimization of the tribo-mechanical properties of the ternary nitride coatings. Therefore, tribo-mechanical studies of the ternary nitride coatings are needed for fostering the new industrial applications. This paper is focused to summarize and compare the tribo-mechanical properties of the ternary nitride coatings comprehensively and aims to explore the novel research directions in the development of the ternary nitride coatings.