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The scope of this study was to determine the concentration and composition of atmospheric particulate matter of aerodynamic diameter < 10 μm (PM₁₀) in the vicinity of coal-fired Ropar thermal power plant near Chandigarh, India. Two sampling sites, one inside the thermal plant and the other, outside the thermal plant were chosen. The elemental analysis was done using Proton Induced X-ray Emission (PIXE) technique. The elements detected at both the sites were common i.e. Si, S, Cl, K, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu and Zn however, their concentration vary at both the sampling sites. Also, Principal Component (PC) and Enrichment Factor (EF) analysis were done in order to identify the contributing elemental sources towards the particulate matter. Contributing sources to the elements were found not only the emission from the coal-fired thermal power plant but also from other activities like vehicular emissions, household cooking and natural soil dust etc.

With the rapid advancement of Digital Twins (DTs) technologies, they have emerged as viable solutions for enhancing the control and optimization of thermal power combustion processes. This paper presents a pioneering approach that leverages DTs to revolutionize the combustion process control system within thermal power generating units. Focused on bridging the physical and digital realms, our research establishes DTs of the combustion process control system (CPCS), situated within the Networked Control System Laboratory (NCSLab). We propose a novel five-dimensional (5D) DTs model encompassing the physical, data, service, connection, and virtual models, thereby facilitating a comprehensive fusion of physical and digital information. By analyzing the demands and technical intricacies of the combustion process control system, we design and implement a virtual 3D model, enriching the visualization and analytical capabilities of the DT environment. Furthermore, through controlled experiments on the combustion process and rigorous comparison with offline simulations, we validate the efficacy and feasibility of our approach. Importantly, our research opens avenues for future exploration, particularly in harnessing industrial real-time data to drive the virtual 3D model. Through the integration of Artificial Intelligence (AI) techniques such as Deep Learning, we elevate the capabilities of our DT framework. This enhanced predictive analytics, dynamic optimization, human–machine interaction, and virtual model realism. This research contributes to the advancement of DT technology in the context of thermal power plants and holds promise for driving innovation and efficiency across diverse industrial sectors.