FUTURISTIC AIR COMPRESSOR SYSTEM DESIGN AND OPERATION BY USING ARTIFICIAL INTELLIGENCE.pdf (9.34 MB)
FUTURISTIC AIR COMPRESSOR SYSTEM DESIGN AND OPERATION BY USING ARTIFICIAL INTELLIGENCE
thesisposted on 2020-01-16, 19:05 authored by Babak Bahrami AslBabak Bahrami Asl
The compressed air system is widely used throughout the industry. Air compressors are one of the most costly systems to operate in industrial plants in therms of energy consumption. Therefore, it becomes one of the primary target when it comes to electrical energy and load management practices. Load forecasting is the first step in developing energy management systems both on the supply and user side. A comprehensive literature review has been conducted, and there was a need to study if predicting compressed air system’s load is a possibility.
System’s load profile will be valuable to the industry practitioners as well as related software providers in developing better practice and tools for load management and look-ahead scheduling programs. Feed forward neural networks (FFNN) and long short-term memory (LSTM) techniques have been used to perform 15 minutes ahead prediction. Three cases of different sizes and control methods have been studied. The results proved the possibility of the forecast. In this study two control methods have been developed by using the prediction. The first control method is designed for variable speed driven air compressors. The goal was to decrease the maximum electrical load for the air compressor by using the system's full operational capabilities and the air receiver tank. This goal has been achieved by optimizing the system operation and developing a practical control method. The results can be used to decrease the maximum electrical load consumed by the system as well as assuring the sufficient air for the users during the peak compressed air demand by users. This method can also prevent backup or secondary systems from running during the peak compressed air demand which can result in more energy and demand savings. Load management plays a pivotal role and developing maximum load reduction methods by users can result in more sustainability as well as the cost reduction for developing sustainable energy production sources. The last part of this research is concentrated on reducing the energy consumed by load/unload controlled air compressors. Two novel control methods have been introduced. One method uses the prediction as input, and the other one doesn't require prediction. Both of them resulted in energy consumption reduction by increasing the off period with the same compressed air output or in other words without sacrificing the required compressed air needed for production.
- Master of Science in Mechanical Engineering
- Mechanical Engineering
Advisor/Supervisor/Committee ChairAli Razban
Additional Committee Member 2Jie Chen
Additional Committee Member 3David W. Goodman
Air Compressor System DesignAir Compressor System Electrical Load PredictionNeural NetworksAir Compressor System ControlAir Compressor Energy EfficiencyDemand ManagementAir Compressor System OperationPeak ShavingElectrical Load ManagementAir Compressor SystemEnergy Usage ReductionLoad ForecastingFeed-Forward Neural Network (FFNN)Long Short-Term Memory (LSTM)Mechanical EngineeringPower and Energy Systems Engineering (excl. Renewable Power)Energy Generation, Conversion and Storage EngineeringProcess Control and Simulation