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.

人口激增和工业扩张导致更多的煤炭消费。许多环境保护机构提出环境问题、政府和卫生当局，特别是空气污染、土地使用、水管理和废物管理。机器视觉在工业系统控制和监测中的应用得到了极大改善。因此，本文提出了机器视觉辅助有效监测分析（MVEMA）模型来控制空气质量约束下的煤炭消耗总量。机器视觉辅助模型 (MVA) 是非侵入式的，可在潜在的恶劣环境中提供可靠的在线测量。MVA 模型是估算煤炭消耗总量，这会导致空气质量约束。直接使用煤炭对空气污染的制约因素包括空气质量、排放标准和位置限制。工业供暖系统具有基于机器视觉系统的空气污染控制选择和控制经济效果。空气质量限制是根据空气质量指数评估的。模拟结果表明，所提出的 MVEMA 方法诊断进展条件时，对于不同的操作条件，过程性能预测为 98.33%。