In the Indian agricultural sector, millions of diesel-driven pump-sets were used for irrigation purposes. These engines produce carcinogenic diesel particulates, toxic nitrogen oxides (NOx), and carbon monoxide (CO) emissions which threaten the livelihood of large population of farmers in India. The present study investigates the use of n-propanol, a less-explored high carbon bio-alcohol that can be produced by sustainable pathways from industrial and crop wastes that has an attractive opportunity for powering stationary diesel engines meant for irrigation and rural electrification. This study evaluates the use of n-propanol addition in fossil diesel by up to 30% by vol. and concurrently reports the effects of exhaust gas recirculation (EGR) on emissions of an agricultural DI diesel engine. Three blends PR10, PR20, and PR30 were prepared by mixing 10, 20, and 30% by vol. of n-propanol with fossil diesel. Results when compared to baseline diesel case indicated that smoke density reduced with increasing n-propanol fraction in the blends. PR10, PR20, and PR30 reduced smoke density by 13.33, 33.33, and 60%, respectively. NOx emissions increased with increasing n-propanol fraction in the blends. Later, three EGR rates (10, 20, and 30%) were employed. At any particular EGR rate, smoke density remained lower with increasing n-propanol content in the blends under increasing EGR rates. NOx reduced gradually with EGR. At 30% EGR, the blends PR10, PR20, and PR30 reduced NOx emissions by 43.04, 37.98, and 34.86%, respectively when compared to baseline diesel. CO emissions remained low but hydrocarbon (HC) emissions were high for n-propanol/diesel blends under EGR. Study confirmed that n-propanol could be used by up to 30% by vol. with diesel and the blends delivered lower soot density, NOx, and CO emissions under EGR.
In this paper, the concepts of neutrosophic complex graph, complete neutrosophic complex graph, strong neutrosophic complex graph, balanced neutrosophic complex graph and strictly balanced neutrosophic complex graph are introduced. Some of the interesting properties and related examples are established.
Smart machine-machine (M2M) interactions, such as those enabled by the Internet of Things (IoT), have enabled people and machines to communicate and make decisions together. Furthermore, these systems have become increasingly important in the commercial and industrial sectors over the previous two decades. The Industrial Internet of Things (IIoT) is a smart system comprising engineering equipment which can connect to one another to improve manufacturing operations. This task would become more complicated if the amount of energy used by the IIoT ecosystems, as well as the amount of network traffic they generate, increased dramatically. Consequently, decision-making processes during communication are essential for autonomous interaction in critical IoT infrastructure. Smart factories employ communication technology to track and gather information in real-time to enhance the output, effectiveness, and predictability while lowering the overall cost of vital operations. In this context, Industry 4.0 not only limits to addresses the issues of integrating technologies, but it also focuses on data collection, dissemination, utilization, and organization and also improves the delivery of the solution or services quicker with more sustainability. This study intends to create an NF-based communication system for IIoT platforms to leverage those benefits. The proposed model includes smart decision-making procedures to deal with communication issues. Compared with the many methods already in use, the suggested mechanism's functional viability in the automated system is found to be optimal. Outcomes from simulations reveal that the suggested method has improved the accuracy and communication reliability of the IIoT platforms in comparison with the previous methods. Aside from these, the suggested model keeps the throughput of the local automation unit at 96.03% and the throughput of the production hall at 95.58% on average while maintaining the lowest average PLR of about 26.48% across different data rates.
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