Effect of pulsating disintegrating airflow on electric arc depositing

To limit the losses in sprayed metal in the process of electric arc deposition, the disintegrating airflow is pulsated. In this work, the effect of changing the pulsation frequency was studied on the process performance, mainly, the efficiency of metal removal and rate of deposition. Additionally, the bonding strength of the resulting sprayed metal was evaluated at different pulsation frequencies. The application of air pulsations increases the productivity and efficiency of sprayed material by increasing the efficiency of material used up to 30% and enhancing the rate of deposition up to 32%, at a frequency range 70–80 Hz. Moreover, at the optimum frequency of air pulsations, the bond strength increased up to 69%, measured by Steffensen’s dowel method. The results found in this work will allow for more rational usage of the electrical arc energy and material.

A computer simulator for steel plant electrical arc furnace regulator

The function of the simulator is to imitate the behavior of the regulator loop, which is the main component of the Electrical Arc Furnace (EAF) control systems. In the past, the use of artificial intelligence methods, and in particular, the Adaptive Neuro Fuzzy Inference System (ANFIS) were successfully applied in the modeling and control of the EAF components individually. This research expands the use of ANFIS in building the full closed loop computer simulator for the three-phase regulator loop. THe ANFIS models inuts and outpus selected for this project were tried for the first time in this research. The simulator components were trained and verified by the use of plant recorded data in the open loop mode. The response of the closed loop simulator was tuned to follow the behavior of the plant EAF. Therefore the simulator works independent of the plant data or operation commands. The developed simulator, then, was used to measure the results of applying new controls in EAF such as fuzzy controllers, without disturbing the actual plant process.

A computer simulator for steel plant electrical arc furnace regulator

The function of the simulator is to imitate the behavior of the regulator loop, which is the main component of the Electrical Arc Furnace (EAF) control systems. In the past, the use of artificial intelligence methods, and in particular, the Adaptive Neuro Fuzzy Inference System (ANFIS) were successfully applied in the modeling and control of the EAF components individually. This research expands the use of ANFIS in building the full closed loop computer simulator for the three-phase regulator loop. THe ANFIS models inuts and outpus selected for this project were tried for the first time in this research. The simulator components were trained and verified by the use of plant recorded data in the open loop mode. The response of the closed loop simulator was tuned to follow the behavior of the plant EAF. Therefore the simulator works independent of the plant data or operation commands. The developed simulator, then, was used to measure the results of applying new controls in EAF such as fuzzy controllers, without disturbing the actual plant process.

A hybrid neuro-wavelet approach to electric arc furnace modeling.

This thesis proposes a hybrid neuro-wavelet based approach for modeling the dynamic voltage-current characteristics in electrical arc furnaces. This method uses the data obtained from an operational electrical arc furnace exclusively to describe the underlying process, and unlike conventional mathematical techniques it does not rely on presumed model structures or simplified assumptions. A comparison between the results that proceeded from the proposed method and the actual measurements has been made. The proposed method is demonstrated to be capable of modeling the EAF's dynamic voltage-current behaviour accurately.

A hybrid neuro-wavelet approach to electric arc furnace modeling.

This thesis proposes a hybrid neuro-wavelet based approach for modeling the dynamic voltage-current characteristics in electrical arc furnaces. This method uses the data obtained from an operational electrical arc furnace exclusively to describe the underlying process, and unlike conventional mathematical techniques it does not rely on presumed model structures or simplified assumptions. A comparison between the results that proceeded from the proposed method and the actual measurements has been made. The proposed method is demonstrated to be capable of modeling the EAF's dynamic voltage-current behaviour accurately.