A hybrid intelligent system for PID controller using in a steel rolling process

With the aim to improve the steel rolling process performance, this research presents a novel hybrid system for selecting the best parameters for tuning in open loop a PID controller. The novel hybrid system combines rule based system and Artificial Neural Networks. With the rule based system, it is modeled the existing knowledge of the PID controller tuning in open loop and, with Artificial Neural Network, it is completed the rule based model that allow to choose the optimal parameters for the controller. This hybrid model is tested with a long dataset to obtain the best fitness. Finally, the novel research is validated on a real steeling roll process applying the hybrid model to tune a PID controller which set the input speed in each of the gearboxes of the process

Modeling the Electromyogram (EMG) of Patients Undergoing Anesthesia During Surgery

All fields of science have advanced and still advance significantly. One of the facts that contributes positively is the synergy between areas. In this case, the present research shows the Electromyogram (EMG) modeling of patients undergoing to anesthesia during surgery. With the aim of predicting the patient EMG signal, a model that allows to know its performance from the Bispectral Index (BIS) and the Propofol infusion rate has been developed. The proposal has been achieved by using clustering combined with regression techniques and using a real dataset obtained from patients undergoing to anesthesia during surgeries. Finally, the created model has been tested with very satisfactory results

An intelligent fault detection system for a heat pump installation based on a geothermal heat exchanger

The heat pump with geothermal exchanger is one of the best methods to heat up a building. The heat exchanger is an element with high probability of failure due to the fact that it is an outside construction and also due to its size. In the present study, a novel intelligent system was designed to detect faults on this type of heating equipment. The novel approach has been successfully empirically tested under a real dataset obtained during measurements of one year. It was based on classification techniques with the aim of detecting failures in real time. Then, the model was validated and verified over the building; it obtained good results in all the operating conditions ranges

Intelligent Model to Obtain Initial and Final Conduction Angle of a Diode in a Half Wave Rectifier with a Capacitor Filter

The half wave rectifier with a capacitor filter circuit is a typically non-linear case of study. It requires a hard work to solve it on analytic form. The main reason is due to the fact that the output voltage comes alternatively from the source and from the capacitor. This study describes a novel intelligent model to obtain the time when the changes of the sources occur. For the operation range, a large set of work points are calculated to create the dataset. To achieve the final solution, several simple regression methods have been tested. The novel model is verified empirically by using CAD software to simulate electronic circuits and by analytical methods. The novel model allows to obtain good results in all the operating range

Intelligent Model for Fault Detection on Geothermal Exchanger of a Heat Pump

The Heat Pump with geothermal exchanger is one of the best methods to heat a building. The heat exchanger is an element with probabilities of failure due its size and due it is outside construction. The present study shows a novel intelligent system design to detect faults on this type of heating equipment. The novel approach has been successfully empirically tested under a real dataset obtained during measurements along one year. It is based on classification techniques with the aim to detect failures in real time. Then the model is validated and verified over the building; it allows to obtain good results in all the operating conditions ranges

Hybrid Intelligent Model to Predict the SOC of a LFP Power Cell Type

Nowadays, batteries have two main purposes: to enable mobility and to buffer intermitent power generation facilities. Due to their electromechaminal nature, several tests are made to check battery performance, and it is very helpful to know a priori how it works in each case. Batteries, in general terms, have a complex behavior. This study describes a hybrid intelligent model aimed to predict the State Of Charge of a LFP (Lithium Iron Phosphate - LiFePO4) power cell type, deploying the results of a Capacity Confirmation Test of a battery. A large set of operating points is obtained from a real system to create the dataset for the operation range of the power cell. Clusters of the different behavior zones have been obtained to achieve the final solution. Several simple regression methods have been carried out for each cluster. Polynomial Regression, Artificial Neural Networks and Ensemble Regression were the combined techniques to develop the hybrid intelligent model proposed. The novel model allows achieving good results in all the operating range