Development of Correlation for Efficiency of Incineration Plants Using Deep Neural Network Model
01 Jan 2021-pp 141-151
TL;DR: In this article, an attempt has been made to develop a correlation to calculate efficiency of the plant using composition of waste, in order to develop this correlation, concept of deep neural network model from machine learning has been used in this paper.
Abstract: In the present era, production of municipal solid waste (MSW) has become unrestrained due to rapid growth in population and urbanization. Therefore, people are facing various challenges such as health and environmental safety. But, this huge potential of MSW can be used as a promising source for electricity production to reduce the greenhouse gas (GHG) emissions. Incineration is well-known technique which has been extensively used to produce economically affordable energy from MSW. The purpose of the incineration plant is to get the maximum desirable outputs (heat and power) out of waste and minimize undesirable outputs (emissions and bottom ash). The value of heat or power recovered from waste burning in incineration plant depends on the heating value of the waste. Determining this heating value of each waste sample has been considered as complex and time consuming task due to different moisture, ash, and chemical composition. Under the present study, an attempt has been made to develop a correlation to calculate efficiency of the plant using composition of waste. In order to develop this correlation, concept of deep neural network model from machine learning has been used in this paper. The developed application may be useful for plant design engineer to predict the performance of plant for given range of parameters.
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TL;DR: The objectives of this overview article are to motivate why researchers and practitioners from business analytics should utilize deep neural networks and review potential use cases, necessary requirements, and benefits, and investigate the added value to operations research in different case studies with real data from entrepreneurial undertakings.
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