A Bio-inspired knowledge system for improving combined cycle plant control tuning

This study presents a novel bio-inspired knowledge system, based on closed loop tuning, for calculating the Proportional-Integral-Derivative (PID) controller parameters of a real combined cycle plant. The aim is to automatically achieve the best parameters according to the work point and the dynamics of the plant. To this end, several typical expressions and systems were taken into account to build the model for this multidisciplinary study. Each of these expressions is appropriated for a particular system. The novel method is empirically verified under a real case study based on an auxiliary steam system of a combined cycle plant

A bio-inspired robust controller for a refinery plant process

This research presents a novel bio-inspired knowledge method, based on gain scheduling, for the calculation of Proportional-Integral-Derivative controller parameters that will prevent system instability. The aim is to prevent a transition to control system instability due to undesirable controller parameters that may be introduced manually by an operator. Each significant operation point in the system is identified first. Then, a solid stability structure is calculated, using transfer functions, in order to program a bio-inspired model by using an artificial neural network. The novel method is empirically verified under working conditions in a real refinery plant process

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

A Hybrid Regression System Based on Local Models for Solar Energy Prediction

The aim of this study is to predict the energy generated by a solar thermal system. To achieve this, a hybrid intelligent system was developed based on local regression models with low complexity and high accuracy. Input data is divided into clusters by using a Self Organization Maps; a local model will then be created for each cluster. Different regression techniques were tested and the best one was chosen. The novel hybrid regression system based on local models is empirically verified with a real dataset obtained by the solar thermal system of a bioclimatic house

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

Simplified method based on an intelligent model to obtain the extinction angle of the current for a single-phase half wave controlled rectifier with resistive and inductive load

With the aim of calculating the extinction angle of the current of a single-phase half wave controlled rectifier with resistive and inductive load, present work shows a method to obtain a regression model based on intelligent methods. This type of circuit is a typical non-linear case of study that requires a hard work to solve it by hand. To create the intelligent model, a dataset has been obtained with a computational method for the working range of the circuit. Then, with the dataset, to achieve the final solution, several methods of regression were tested from traditional to intelligent types. The model was verified empirically with electronic circuit software simulation, analytical methods and with a practical implementation. The advantage of the proposed method is its low computational cost. Then, the final solution is very appropriate for applications where high computational requirements are not possible, like low-performance microcontrollers or web applications

A bio-knowledge based method to prevent control system instability

This study presents a novel bio-inspired method, based on gain scheduling, for the calculation of Proportional-Integral-Derivative (PID) controller parameters that will prevent system instability. The aim is to prevent a transition to control system instability due to undesirable controller parameters that may be introduced manually by an operator. Each significant operation point in the system is firstly identified. Then, a solid stability structure is calculated, using transfer functions, in order to program a bio-inspired model by using an artificial neural network. The novel method is empirically verified under working conditions in a liquid-level laboratory plant

A Novel Method to Prevent Control System Instability Based on a Soft Computing Knowledge System

The aim of this study is to present a novel soft computing method to assure PID tuning parameters place the system into a stable region by applying the gain scheduling method. First the system is identified for each significant operation point. Then using transfer functions solid structures of stability are calculated to program artificial neural networks, whose object is to prevent system from transitioning to instability. The method is verified empirically under a data set obtained by a pilot plant

A Novel Hybrid Intelligent Classifier to Obtain the Controller Tuning Parameters for Temperature Control

This study presents a novel hybrid classifier method to obtain the best parameters of a PID controller for desired specifications. The study presents a hybrid system based on the organization of existing rules and classifier models that select the optimal expressions to improve specifications. The model achieved chooses the best controller parameters among different closed loop tuning methods. The classifiers are based on ANN and SVM. The proposal was tested on the temperature control of a laboratory stove