Enhancing the Efficiency of Industrial Boilers and Burners Using Deep Learning Techniques

Boilers are an important unit in many industrial processes. They are responsible for generating energy for all purposes. There are various kinds of boilers. It depends on numerous factors such as tube content and numbers and the position of the furnace. In other words, boilers can be classified in many ways based on their design, fuel source, and purpose. Fire tube boiler, water tube boiler, gas boiler, oil boiler and industrial boilers are common types of boilers used in industries. The boiler efficiency can be affected by different factors such oxygen content in the flue gas. When there is sufficient oxygen in the air to burn the fuel, the burning process in the furnace is perfect. Even yet, when there is an improper mixture of fuel and oxygen, the oxygen supply will not be sufficient to burn the entire amount of fuel. Moreover, there is another unit which plays a crucial role in any industry. This unit is called burner. Burners are devices that provide the fuel to be burnt fully by mixing it with air in a specific ratio. It is considered a significant product for heating tools. Many issues can affect the burner work and efficiency. Nowadays, deep learning machines can produce a novel model to solve industrial issues. Hence, this work aims to produce a model by using deep learning techniques using (MATLAB and PYTHON) to enhance the efficiency of industrial burners and boilers. The outcomes of this research show that the anticipated and proposed models performed satisfactory and good in enhancing the boiler efficiency, measuring oxygen content, and enhancing the burner efficiency. Where PYTHON and MATLAB Both used to propose a model to predict oxygen content. The results show 96% accuracy. Moreover, PYTHON used to propose a model for flame image. The accuracy was 100%. This proves that the proposed models work well in achieving the desired aims