Abstract
The proliferation in population and industrial expansion leads to more coal consumption. Many environmental protection bodies raise environmental issues, governments, and health authorities, especially air pollution, land use, water management, and waste management. The utilization of machine vision for the control and monitoring of industrial systems has improved intensely. Hence, in this paper, machine vision-assisted effective monitoring analysis (MVEMA) model has been proposed to control total coal consumption under air quality constraints. Machine vision-assisted model (MVA) is nonintrusive and delivers reliable online measurements in a potentially harsh environment. MVA model is to estimate total coal consumption, which leads to air quality constraints. The constraints on air pollution from direct coal use include air quality, emission standards, and location restrictions. The industrial heating systems have air pollution control choices and control economic effects based on the machine vision system. The air quality constraints are evaluated based on the Air Quality Index. The simulation results show that the proposed MVEMA method diagnosing the progression conditions predicts the process performance at 98.33% for diverse operating conditions.
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This research has been financed by 2018 Project for Cultural Evolution and Creation—the Think Tank of the Energy Mining Economy (No. CUMT 2018WHCC01).
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PI was involved in conception and design of study and analysis and/or interpretation of data. YL performed acquisition of data and drafted the manuscript.
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Liu, Y., Isaev, P. Simulation of total coal consumption control under air quality constraints based on machine vision. Soft Comput 25, 12389–12400 (2021). https://doi.org/10.1007/s00500-021-05951-7
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DOI: https://doi.org/10.1007/s00500-021-05951-7