Pipeline Inspection Gauge's Velocity Simulation Based on Pressure Differential Using Artificial Neural Networks.

Industrial pipelines must be inspected to detect typical failures, such as obstructions and deformations, during their lifetime. In the petroleum industry, the most used non-destructive technique to inspect buried pipelines is pigging. This technique consists of launching a Pipeline Inspection Gauge (PIG) inside the pipeline, which is driven by the pressure differential produced by fluid flow. The purpose of this work is to study the application of artificial neural networks to calculate the PIG's velocity based on the pressure differential. We launch a prototype PIG inside a testing pipeline, where this PIG gathers velocity data from an odometer-based system, while a supervisory system gathers pressure data from the testing pipeline. Then we train a Multilayer Perceptron (MLP) and a Nonlinear Autoregressive Network with eXogenous Inputs (NARX) network with the gathered data to predict velocity. The results suggest it is possible to use a neural network to model the PIG's velocity from pressure differential measurements. Our method is a new approach to the typical speed measurements based only on odometer, since the odometer is prone to fail and present poor results under some circumstances. Moreover, it can be used to provide redundancy, improving reliability of data obtained during the test.

Controle adaptativo da pressäo arterial com restriçöes / Adaptative control and arterial pressure with restrictions

Este trabalho apresenta um método para o controle da pressão arterial usando vasodilatadores como o nitropussiato de sódio (NPS). Para este propósito, um controlador adaptativo baseado em uma lei de controle de mínima variância é utilizado. O controlador é projetado para otimizar o desempenho do sistema em malha fechada levando em conta restrições clínicas impostas na taxa de infusão de NPS e na taxa de variação da pressão arterial. Simulação mostraram o bom desempenho do controlador proposto na presença de ruído.

Abstract - This paper presents an approach for adaptive control of blood pressure using vasodilators as sodium nitroprusside. For this purpose, an adaptive controller based upon a minimum variance law is used. The controller is proposed in order to optirnize the performance of the closed loop system while meeting clinicai constraints imposed on infusion rate and on rate of change of the patient's blood pressure. Computer simulations show the good performance ofthe proposed controller in the presence ofnoise.