How to Model Condensate Banking in a Simulation Model to Get Reliable Forecasts? Case Story of Elgin/Franklin

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Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter

This paper investigates the state estimation of a high-fidelity spatially resolved thermal- electrochemical lithium-ion battery model commonly referred to as the pseudo two-dimensional model. The partial-differential algebraic equations (PDAEs) constituting the model are spatially discretised using Chebyshev orthogonal collocation enabling fast and accurate simulations up to high C-rates. This implementation of the pseudo-2D model is then used in combination with an extended Kalman filter algorithm for differential-algebraic equations to estimate the states of the model. The state estimation algorithm is able to rapidly recover the model states from current, voltage and temperature measurements. Results show that the error on the state estimate falls below 1 % in less than 200 s despite a 30 % error on battery initial state-of-charge and additive measurement noise with 10 mV and 0.5 K standard deviations.Comment: Submitted to the Journal of Power Source

New Technologies in the Oil and Gas Industry

Oil and gas are the most important non-renewable sources of energy. Exploring, producing and managing these resources in compliance with HSE standards are challenging tasks. New technologies, workflows and procedures have to be implemented.This book deals with some of these themes and describes some of the advanced technologies related to the oil and gas industry from HSE to field management issues. Some new technologies for geo-modeling, transient well testing and digital rock physics are also introduced. There are many more technical topics to be addressed in future books. This book is aimed at researchers, petroleum engineers, geoscientists and people working within the petroleum industry

Introduction of a Cost Effective Method for Analysing Engine Intake Ice Removal Device for Small Aircraft

As the need for personal air transport increases significantly, new aircraft and/or its components are required to be designed and developed together with expectations for guarantying the high level flight safety. Since smaller aircraft manufacturers don’t have the infrastructural and experimental resources for complex investigations, analysis of engine components with especial care for the behaviour of particle separation components in the inlet air duct for example, smarter, more efficient solutions have to be developed. CFD software gives an opportunity to simulate the trajectories of different type of particles, such as hailstones, dust, or even liquid water droplets. Hence, in this study an upper-wing type, two engines thrusted, small turboprop aircraft’s integrated engine air intake device has been analysed, to prove the effectivity of the aircraft performance in the considered raining and icing conditions. The flow field has been discretized with a detailed, hybrid mesh using hexa elements at the simpler parts, and tetra elements, where the geometry is more complex. Inflation layers have been inserted on the wall-type surfaces, with especial care to the problematic parts, where the y+ number is predictably higher. The inlet boundary conditions of the model have been extracted from a larger, complex pre-simulation, performed in a previous study. Standard Reynolds Averaged Navier-Stokes equations have been considered with Shear Stress Transport turbulence model. Solid (ice) and liquid particles have been defined, and their trajectories are investigated by using fully coupled model. The interaction of the wall-fluid particle has been taken into consideration

Plant monitoring and fault detection - Synergy between data reconciliation and principal component analysis

Data reconciliation and principal component analysis are tno recognised statistical methods used for plant monitoring and fault detection. We propose to combine them for increased efficiency. Data reconciliation is used in the first step of the determination of the projection matrix for principal component analysis (eigenvectors). principal component analysis can then be applied to raw process data for monitoring purpose. The combined use of these techniques aims at a better efficiency in fault detection. It relies mainly in a lower number of components to monitor. The method is applied to a modelled ammonia synthesis loop. (C) 2001 Elsevier Science Ltd. All rights reserved

Development of a Solution for Start-up Optimization of a Thermal Power Plant

This thesis covers optimizing the first phase of the start-up of a thermal power plant using Nonlinear Model Predictive Control (NMPC) and state estimation using an Unscented Kalman Filter (UKF). The start-up has been optimized in regards to time and fuel usage. The thesis is done as a joint project between Vattenfall and Modelon. Both NMPC and UKF are nonlinear methods and require a model of the power plant. The model used in this thesis has been developed in the language Modelica in a previous master thesis and has been extended and improved upon during this thesis. The optimization and simulation of the model required by the NMPC and UKF was done within the framework of JModelica.org. Another, more detailed, model of the power plant, developed by Vattenfall, was originally planned to be used as the process to be controlled. State estimation using the UKF has been successful, with a maximum mean absolute error of 0.7 % when estimating the states of the detailed model in a reference startup. When using the NMPC to control the optimization model itself, the simulated start-up time is 70 minutes faster compared to a reference start-up using the detailed model. This is more than half the time of the first phase of the start-up. The total firing power, which relates to the fuel amount, is also considerably less, with the optimized value being about 40 % of that in the reference soft start with the detailed model. Due to difficulties in initializing the detailed model, it was not possible to run it online together with the NMPC and UKF. Running the NMPC and UKF together on the optimization model worked, but the NMPC failed to find an optimal trajectory 8 out of 10 iterations. The conclusion is that the start-up has potential for optimization, but requires more robust models to work with