90 research outputs found

    High-viscosity biphasic flow characterization in a pipeline: application to flow pattern classification and leak detection

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    Pipeline systems play an essential role in the oil industry. These systems connect ports, oil fields, refineries, and consumer markets[104]. Pipelines covering long distances require pumping stations, where products are propelled to the next pumping station, refinery, or deposit terminal, thus traveling through most of the country. The product considered in this research work is crude oil. It is usually transported with a combination of crude oil with viscosity reducers (DRA, drag reducer agent) and oil with gas in onshore/offshore pipelines. This mode of transport is efficient for large quantities and large product shipment distances. Problems may arrive when a leak occurs. In major incidents, large scale damage to humans and the environment is possible. Then, this research addresses the problem of how to detect the leak earlier to reduce the impact in the surrounding areas and economic losses, considering five research topics taking into account that the products inside the pipeline are water-glycerol and gas-glycerol mixtures (simulating oil-DRA and oil-gas in the laboratory test apparatus). The first research topic presents a mathematical model to describe the flow of a mixture of water and glycerol in pressurized horizontal pipelines, which emulates the mixture of heavy oil and a viscosity reducer. The model is based on the mass and momentum conservation principles and empirical correlations for the mixture’s density and viscosity. The set of partial differential equations is solved using finite differences. These equations were implemented in a computer platform to be able to simulate a system. This simulation platform is a tool to simulate leak cases for different fractions of water and glycerol to evaluate algorithms for leak detection and localization before their implementation in a laboratory setting.DoctoradoDoctor en Ingeniería Mecánic

    Nonlinear Model Predictive Control Of A Distillation Column Using Hammerstein Model And Nonlinear Autoregressive Model With Exogenous Input.

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    Turus penyulingan adalah unit proses penting dalam industri penapisan petroleum dan kimia. Ia perlu dikawal hampir dengan keadaan-keadaan pengendalian yang optima demi insentif- nsentif ekonomi. Distillation column is an important processing unit in petroleum refining and chemical industries, and needs to be controlled close to the optimum operating conditions because of economic incentives

    Sliding mode observer design for decentralized multi-phase flow estimation

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    Robust flow measurement in multi-phase systems has extensive applications in understanding, design, and operation of complex environmental, energy and industrial processes. The nonlinearity and spatiotemporal variability of the interactions between different flow phases makes the multi-phase flow measurement a challenging task. Two Sliding Mode Observer (SMO) schemes are proposed in this study for the state estimation of a decentralized multi-phase flow measurement system. The developed observers are shown to be theoretically valid and numerically applicable for a real-life case study data. The multi-phase flow system considered in this paper can be described as two interconnected sub-systems including fluid and gas sub-systems, and two scenarios are considered in the design of the observers. The first scenario considers the interconnections as bounded disturbance (SMOD), while the second scenario considers the interconnections as an uncertainty (SMOU). Hence, the Sliding Mode Observers are adopted to mitigate the effects of disturbance in the system and uncertainties in the parameters. Numerical simulations are conducted using MATLAB and dynamic HYSYS simultaneously, using the data obtained from field-based multi-phase flow measurements. The results demonstrate the appropriateness and robustness of the proposed Sliding Mode Observer (SMO) for estimation of the multi-phase fluid specifications including the density, velocity, and the volume phases fraction in each subsystem. The analysis of the results highlights that the proposed model is computationally efficient with fast transient response, accurate tracking capability of the real process data, and very low steady-state error. This study shows that choosing an appropriate Lyapunov-Krasovsky function results in the asymptotic stability of the decentralized system and improves the performance of the proposed observers. Uncertainty analysis is conducted on the velocity estimation results obtained from the Sliding Observers. Overall, SMOU method shown better performance with RMSE of 0.24%, while RMSE of 0.46% was achieved for the SMOD. Comparison of the numerical results with the field-based flow measurement, as a benchmark, shows that although uncertainty in SMOU is approximately half of the uncertainty in SMOD, state estimation for both schemes was achieved in a finite time with high order of precision. It was shown that both observers developed in this study are well capable of estimating the multi-phase flow variables and states

    Dynamic Modeling, Optimization, and Advanced Control for Large Scale Biorefineries

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    Multiphase Flow Estimation using Digital Image Processing

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    Application of Soft Computing Techniques to Multiphase Flow Measurement: A Review

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    After extensive research and development over the past three decades, a range of techniques have been proposed and developed for online continuous measurement of multiphase flow. In recent years, with the rapid development of computer hardware and machine learning, soft computing techniques have been applied in many engineering disciplines, including indirect measurement of multiphase flow. This paper presents a comprehensive review of the soft computing techniques for multiphase flow metering with a particular focus on the measurement of individual phase flowrates and phase fractions. The paper describes the sensors used and the working principle, modelling and example applications of various soft computing techniques in addition to their merits and limitations. Trends and future developments of soft computing techniques in the field of multiphase flow measurement are also discussed

    Multiphase Flow Estimation Using Image Processing

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