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Caracas, Venezuela

Pilipovik V.,JC Engineering | Riverol C.,University of the West Indies
Journal of Petroleum Exploration and Production Technology | Year: 2016

New recovery technologies are having an impact on heavy oil production which makes many marginal projects profitable. Before applying any technology or investing any capital in that, it is very important to have a model that can predict the results or oil recovery ratios. This paper deals with a model for predicting the flow in porous media of heavy oil using one of these emerging technologies (electromagnetic field) for heavy oil recovery. Some experiments were performed using different scenarios and thus evaluate the accuracy of the model developed in this article. It was found that the time and the frequency of the waves are key factors in promoting oil production. Also the article presents preliminary results of the model which will be useful for selecting the optimal frequency and time to stimulate heavy oil production to industrial scale. © 2015, The Author(s).


Riverol C.,University of the West Indies | Carosi C.,JC Engineering
Journal of Food Process Engineering | Year: 2010

In this study, the use of fuzzy logic for signal validation is introduced. This tool uses the sensor readings at the current time step to predict system state at the next time step using observers. The deviation between the sensor reading and the model prediction rises, leading to fault detection and isolation. The fault is accommodated by replacing the reading of the faulty sensor by the model prediction. The results presented in this article demonstrate that a simple scheme can carry out sensor fault detection, isolation and accommodation using a fuzzy rule-based system based on experience of an operator into heuristics. Although the method lacks the mathematical basis that other analytical methods provide, this method is simple, effective and useful. © 2008 Wiley Periodicals, Inc.


Riverol C.,University of the West Indies | Pilipovik M.V.,JC Engineering
Desalination | Year: 2011

The environmental characteristics of the seawater and its impacts on planning and operating RO desalination plants were always the concern of several scientists around the world. In this article, a study has been performed to investigate the seasonal characteristic of seawater in the Caribbean. The Radial Basis Function Networks (RBFN) was the methodology used in this paper where the redistribution of centres and the input training data possess significant effects in the model. The proposed method is based on clustering of input space vectors and computing weights of Euclidian distances and histogram equalization within each cluster for determining the centre and width of each receptive field. The model allows predicting the behaviour of the SDI using field data (turbidity and salinity) taking into account the tides (the Gulf of Paria) and seasonal changes (hurricane or dry seasons). The results indicated that during the hurricane season, the parameters can change up to 32% between seasons along year due to the change of direction of the inshore currents. Also values higher than 6 in the SDI indicated a very high potential for fouling. This article offers to the project engineers a new door in the application of precautionary principles in the design of desalination plants. © 2010 Elsevier B.V.


A case study involving analyses of the reliability was made from a selected pasteurization plant. The study involved a probability of failure and mitigation of selected typical installation. Recently, new studies indicate that the RBFN is a good tool for prediction of parameters such that the aim of this paper is to apply a RBFN approach as predictive model focus on the context of reliability. The proposed method herein is based on clustering of input space vectors and computing weights of Euclidian distances and histogram equalization within each cluster will determine the centre and width of each receptive field. The results of this paper indicate that the accuracy of the prediction obtained can be around 99.7%. Also, the model indicates that there is 9.2% additional risk in extending the operation from fourth to fifth year for major components having survived 3 years. © 2013.


Riverol-Canizares C.,Campus Universitario del Parque Tecnologico Walqa | Riverol-Canizares C.,University of the West Indies | Pilipovik V.,JC Engineering
Expert Systems with Applications | Year: 2010

The quality of seawater is very important in the design of the pre-treatment unit of any desalination plant. It is worth noting that the operational parameters (salinity and TDS) depend on the seasons (dry or hurricane) such that they are not constants along all year. Recently, new studies indicate that the RBFN is a good tool for prediction of parameters such that the aim of this paper is to apply a RBFN approach as predictive model focus in the context of the Seawater in the Caribbean. The model is obtained taking into account the behaviour of salinity and total dissolved solids content according to the season (dry or hurricane season). The methodology is based on redistributions of centres to locations where input training data possess significant effects can lead to more efficient RBFN. The proposed method herein is based on clustering of input space vectors and computing weights of Euclidian distances and histogram equalization within each cluster will determine the centre and width of each receptive field. The results of this paper indicate that the parameters can change up to 30% along year and the accurate of the prediction obtained can be around 96.7%. © 2010 Elsevier Ltd. All rights reserved.

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