摘要：Due to rapid growth in Industrialization and Urbanization, there is an exponential increase in the air pollution. This increased air pollution has major impact on environment and human health. The growth in industrialization and urbanization has everlasting demand of electricity. Most of the electricity (almost 60%) to meet the said demand comes from Thermal Power Plants (TPP). Almost all the TPPs generate electricity by burning fossil fuels which releases poisonous gas in the air contributing to pollution. The air pollution caused by TPPs may be reduced by using non-conventional energy source like solar and wind, unfortunately they are in their infancy, and thus the tuning and optimizing existing TPPs are of prime importance to reduce the air pollution. To device the alternate strategies, one need to have a mathematical model that depicts the performance of existing TPPs. There are many mathematical ways to model optimization of air pollution in TPPs. Since such models turns out to be nonlinear, complex and multimodal, hence most of the classical optimization methods fail to give appropriate solution. This paper integrates an efficient variant of Nature Inspired (NI) algorithm namely weighted distance grey Wolf Optimizer (WdGWO) for minimizing air pollution caused by Delhi Thermal Power Plants. The results show that the WdGWO has reduced total pollution by 8.2350% as compared to the state of-the-art which has reduced even less than 4% only. Thus results support the application and superiority of WdGWO algorithm for Delhi TPPs.
关键词：Thermal power plant; Grey wolf optimizer; Constraints; Air pollution
