Intelligent energy management of steam generators

TL;DR

This paper presents a smart energy management model for steam generators using neural networks, aiming to optimize efficiency and reduce emissions through simulation and control of air-fuel ratios.

Contribution

It introduces a neural network-based control system integrated into a steam generator model to improve energy efficiency and environmental impact.

Findings

Neural network effectively controls air-fuel ratio.

Simulation shows improved efficiency and reduced emissions.

Control system adapts to different load conditions.

Abstract

This paper introduces a smart model for intelligent energy management of steam generators which are utilized for steam generator and controlling the air to fuel ratio for steam generator all over the firing curve and transient mode operation. Nowadays, the environment faces a lot of pollution and global warming phenomena. With the spread of electrical devices, electric cars with conventional electrical generation sources, and the increase in electrical consumption, instead of minimizing the pollution level the situation becomes disastrous. Steam generators have a lot of pros which cannot be neglected, such as: high efficiency, reliable operation, low emission (with regular maintenance), and big variety of fuel source. However, regular maintenance overlooks some parameters, especially the air to fuel ratio that achieves green environment, high efficiency and low fuel consumption. The steam generator system is simulated utilizing Simulink/MATLAB. The system is operated at different loading and generation conditions to determine the variation of air to fuel ratio against power variation. Neural Network (NN) unit is added in different locations and scenarios. It is effective in controlling the main bus of air, fuel, auxiliary and inverter speed. By testing the NN on the simulated tested system, the results are satisfied.