Adsorption and wettability study of methyl ester sulphonate on precipitated asphaltene

Asphaltene precipitation from crude oil and its subsequent aggregation forms solid, which preferentially deposit on rock surfaces causing formation damage and wettability changes leading to loss of crude oil production. To resolve this problem, asphaltene inhibitor has been injected into the formation to prevent the precipitation of asphaltene. Asphaltene inhibitors that are usually employed are generally toxic and non-biodegradable. This paper presents a new environmentally friendly asphaltene inhibitor (methyl ester sulphonate), an anionic surfactant, which has excellent sorption on formation rock surfaces. Result from adsorption study validated by Langmuir and Freundlich models indicate a favourable adsorption. At low volumes injected, methyl ester sulphonate is capable of reverting oil-wet sandstone surface to water-wet surface. Biodegradability test profile shows that for concentrations of 100-5000ppm it is biodegradable by 65-80%

Grey-box model identification via evolutionary computing

This paper presents an evolutionary grey-box model identification methodology that makes the best use of a priori knowledge on a clear-box model with a global structural representation of the physical system under study, whilst incorporating accurate blackbox models for immeasurable and local nonlinearities of a practical system. The evolutionary technique is applied to building dominant structural identification with local parametric tuning without the need of a differentiable performance index in the presence of noisy data. It is shown that the evolutionary technique provides an excellent fitting performance and is capable of accommodating multiple objectives such as to examine the relationships between model complexity and fitting accuracy during the model building process. Validation results show that the proposed method offers robust, uncluttered and accurate models for two practical systems. It is expected that this type of grey-box models will accommodate many practical engineering systems for a better modelling accuracy

Experimental performance evaluation of a multi-diaphragm pump of a micro-ORC system

Abstract The performance of micro-scale ORC systems strongly depends on the performance of their key components. While the heat exchangers and expander have been extensively investigated, the pump has only received limited attention. The main purpose of this work is the experimental characterization of a multi-diaphragm positive displacement pump, integrated in an experimental ORC system with a rated power output of 4kWel. The study focuses on the experimental evaluation of the pump performance and on cavitation phenomena. A detailed presentation of the experimental procedure and results is supplied. A great effort has been spent in calculating the global and volumetric pump efficiencies for a wide range of operational conditions, which reach maximum values around 45-48% and 95%, respectively. With regards to cavitation issues, the effect of the available Net Positive Suction Head at the pump inlet has been deeply investigated both at partial and full load to obtain guidelines for stable operation. Finally, an extensive dataset of steady-state operating points has been used to calibrate an improved version of a semi-empirical model previously developed for positive displacement ORC pumps. Special attention has been given to the ability of the model to accurately predict the behaviour and performance of the pump at different, properly chosen, steady-state conditions. Relative errors in between 0.5%, for the outlet temperature, and 10%, for the electric power consumption, are achieved

The wavelet-NARMAX representation : a hybrid model structure combining polynomial models with multiresolution wavelet decompositions

A new hybrid model structure combing polynomial models with multiresolution wavelet decompositions is introduced for nonlinear system identification. Polynomial models play an important role in approximation theory, and have been extensively used in linear and nonlinear system identification. Wavelet decompositions, in which the basis functions have the property of localization in both time and frequency, outperform many other approximation schemes and offer a flexible solution for approximating arbitrary functions. Although wavelet representations can approximate even severe nonlinearities in a given signal very well, the advantage of these representations can be lost when wavelets are used to capture linear or low-order nonlinear behaviour in a signal. In order to sufficiently utilise the global property of polynomials and the local property of wavelet representations simultaneously, in this study polynomial models and wavelet decompositions are combined together in a parallel structure to represent nonlinear input-output systems. As a special form of the NARMAX model, this hybrid model structure will be referred to as the WAvelet-NARMAX model, or simply WANARMAX. Generally, such a WANARMAX representation for an input-output system might involve a large number of basis functions and therefore a great number of model terms. Experience reveals that only a small number of these model terms are significant to the system output. A new fast orthogonal least squares algorithm, called the matching pursuit orthogonal least squares (MPOLS) algorithm, is also introduced in this study to determine which terms should be included in the final model

Comparative analysis of NOAA REFM and SNB 3 GEO tools for the forecast of the fluxes of high-energy electrons at GEO

Reliable forecasts of relativistic electrons at geostationary orbit (GEO) are important for the mitigation of their hazardous effects on spacecraft at GEO. For a number of years the Space Weather Prediction Center at NOAA has provided advanced online forecasts of the fluence of electrons with energy >2 MeV at GEO using the Relativistic Electron Forecast Model (REFM). The REFM forecasts are based on real-time solar wind speed observations at L1. The high reliability of this forecasting tool serves as a benchmark for the assessment of other forecasting tools. Since 2012 the Sheffield SNB3GEO model has been operating online, providing a 24 h ahead forecast of the same fluxes. In addition to solar wind speed, the SNB3GEO forecasts use solar wind density and interplanetary magnetic field Bz observations at L1.The period of joint operation of both of these forecasts has been used to compare their accuracy. Daily averaged measurements of electron fluxes by GOES 13 have been used to estimate the prediction efficiency of both forecasting tools. To assess the reliability of both models to forecast infrequent events of very high fluxes, the Heidke skill score was employed. The results obtained indicate that SNB3GEO provides a more accurate 1 day ahead forecast when compared to REFM. It is shown that the correction methodology utilized by REFM potentially can improve the SNB3GEO forecast