In the era of the Internet of Things (IoT) and the Industrial Internet of Things (IIoT), elements along the supply chain can be connected to one another to offer tracking capabilities. The information obtained from an always connected and working supply chain is then incorporated into the simulations of the virtual world (digital twin). This allows for an instantaneous simulation of the environment at any point in time and better, more optimized, and quicker decisions are made based on the results. This translates into more performance and a stronger competitive advantage. This paper will examine the main concepts surrounding the supply chain from the perspective of digitalization. In this paper, we will take a closer look at the main concepts related to the supply chain in light of digitalization.

The exploitation of fossil fuels has fueled the modern world’s development since the industrial revolution. Other energy sources, such as wood, charcoal, and animal power, were displaced by these fuels, which were relatively easy to obtain, had low cost of production, and were easily transportable. The possibility of these fossil reserves being depleted in the medium term, combined with an increase in environmental awareness and the reality of environmental degradation, has changed the situation, reactivating the search for alternative fuels. Biofuels such as bioethanol, biomethanol, and biodiesel are among the alternative fuels gaining popularity due to their environmental benefits. This research investigates the behaviour of a diesel engine that runs on biodiesel (a fuel made from new and unrefined algae oil), ethanol (an essential raw nanomaterial that is readily available in India), and nanometal additives.

The research work presents the results of testing using an internal combustion engine ignition/compression using diesel and LPG mixtures without preheating. The energy performance of regulated brake emissions and changes in fuel consumption for a compression ignition engine is investigated in this study. It is assured that the engine's operation is not harmed as a result of the installation of this mix. The engine produces torque and power when it is working according to the design parameters. In tests with these combinations, results with a thermal efficiency of 10% were obtained, which was higher than the 5% obtained in diesel tests. It is used in compression ignition engines to offer a fuel source for the generation of electrical energy.

Nanoparticles are an emerging concept for increasing fuel properties. The purpose of this research work is to determine the effect of magnesium oxide nanoparticles on the performance and emission characteristics of diesel engines that run on a spirulina microalgae biodiesel blend (B20) as a fuel. The ultrasonication was used to disperse MgO nanoparticles in B20 fuel at various concentrations (25, 50, 75, and 100 ppm). The significant findings indicated that B20+100 blends reduced specific fuel consumption by 20.1% and had a 5.09% higher brake thermal efficiency than B20. B20+100 blends reduced CO, hydrocarbon, and smoke emissions by a maximum of 32.02%, 30.03%, and 26.07%, respectively, compared to B20.

The copper found in Earth’s soil ranks fourth in abundance among structural metals. Copper alloys are composed of copper and other elements. Most commonly, these alloys are used in aerospace, medical, and energy applications, but they are also used in many other areas. The amount of the stabilizing agents and the temperature determine which phase copper alloys exist in, including α, α + β, and β. The temperature in the cutting zone is one of the most important factors to control when machining copper alloys. Copper alloys have low thermal conductivity and high heat capacity, meaning that they have low heat conduction from the cutting zone, which leads to the built-up heat in the cutting edge. As the workpiece and cutting tool moves at different speeds, the temperature is strongly affected by the cutting speed. The physical and chemical properties of tool wear progression have been used in several studies and research projects to model tool life and metal removal as a result of this fact. The focus of this article is on establishing a model connecting cutting parameters and measured responses in terms of tool life, using the design of experiments and metamodeling to establish a model that can be used to predict tool life from milling experiments. In order to secure reliable machining operations, these models were designed for customer recommendation and cutting data optimization. The study focused on copper alloys 6Al-4V, the most common being α + β alloy. In conclusion, the two models developed in this study are able to predict the tool life based on the cutting speed and radial width of cuts. As long as certain parameters are met, the models will ensure the highest possible metal removal rate.