Steam generating prediction of a biomass boiler using artificial neural network

Although the world is shifting toward using more renewable energy resources, combustion systems will still play an important role in the immediate future of global energy. To follow a sustainable path to the future and reduce global warming impacts, it is important to improve the efficiency and performance of combustion processes and minimize their emissions. Machine learning techniques are a cost-effective solution for improving the sustainability of combustion systems through modeling, prediction, forecasting, optimization, fault detection, and control of processes. The objective of this study is to provide a review and discussion regarding the current state of research on the applications of machine learning techniques in different combustion processes related to power generation. Depending on the type of combustion process, the applications of machine learning techniques are categorized into three main groups: (1) coal and natural gas power plants, (2) biomass combustion, and (3) carbon capture systems. This study discusses the potential benefits and challenges of machine learning in the combustion area and provides some research directions for future studies. Overall, the conducted review demonstrates that machine learning techniques can play a substantial role to shift combustion systems towards lower emission processes with improved operational flexibility and reduced operating cost.

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Steam generating prediction of a biomass boiler using artificial neural network

Cited by 3 publications

References 10 publications

Resource Allocation in Multiple Energy-Integrated Biorefinery Using Neuroevolution and Mathematical Optimization

Resource Allocation in Multiple Energy-Integrated Biorefinery Using Neuroevolution and Mathematical Optimization

The listing price prediction of used sailboats based on LM-BP Neural Network

A review on the application of machine learning for combustion in power generation applications

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