Idris Ismail

Soil moisture detection using Electrical Capacitance Tomography (ECT) sensor

This paper briefly discusses the measurement on soil moisture distribution using Electrical Capacitance Tomography (ECT) technique. ECT sensor with 12 electrodes was used for visualization measurement of permittivity distribution. ECT sensor was calibrated using low and high permittivity material i.e. dry sand and saturated soils (sand and clay) respectively. The measurements obtained were recorded and further analyzed by using Linear Back Projection (LBP) image reconstruction. Preliminary result shows that there is a positive correlation with increasing water volume.

Real-time monitoring and measurement of wax deposition in pipelines via non-invasive electrical capacitance tomography

Tomographic analysis of the behavior of waxy crude oil in pipelines is important to permit appropriate corrective actions to be taken to remediate the wax deposit layer before pipelines are entirely plugged. In this study, a non-invasive/non-intrusive electrical capacitance tomography (ECT) system has been applied to provide real-time visualization of the formation of paraffin waxes and to measure the amount of wax fraction from the Malay Basin waxy crude oil sample under the static condition. Analogous expressions to estimate the wax fraction of the waxy crude oil across the temperatures range of 30–50 °C was obtained by using Otsus and Kuos threshold algorithms. Otsus method suggested that the wax fraction can be estimated by the correlation coefficient while Kuos method provides a similar correlation with . These correlations show good agreements with the results which are obtained from the conventional weighting method. This study suggested that Kuos threshold algorithm is more promising when integrated into the ECT system compared to Otsus algorithm because the former provides higher accuracy wax fraction measurement results below the wax appearance temperature for waxy crude oil. This study is significant because it serves as a preliminary investigation for the application of ECT in the oil and gas industry for online measurement and detection of wax fraction without causing disturbance to the process flow.

Fundamental modeling and simulation of a binary continuous distillation column

Distillation columns are important unit operations in chemical process plants. This paper reviews some techniques of modeling and simulation of distillation columns and describes a model for a lab-scale binary continuous distillation column. The focus is on the dynamic behavior of the product compositions under feed disturbances. The simulation results show that the composition responses to disturbances are close to the response of a first order system. The response to change in feed composition has larger gain than the response to change in feed flow rate. The paper also compares the simulation results with some of other works.

Application of electrical capacitance tomography and differential pressure measurement in an air-water bubble column for online analysis of void fraction

Among electrical tomography techniques, electrical capacitance tomography (ECT) has been the subject of extensive recent research due to its non intrusive and non-invasive nature. It is used for obtaining information about the distribution of the contents of closed pipes or vessels by measuring variations in the dielectric properties of the material inside the vessel. An experimental study was conducted by producing two-phase bubble flow regime in a vertical online ECT column. This experimental study measures the void fraction in a two-phase bubble flow regime by using ECT and differential pressure (ΔP). Differential pressure is also a simple and reliable approach. The experiments conducted for this study used compressed air as the gas phase and deionised water as a liquid phase. On the variation in superficial gas velocity from 0.0021 to 0.03 m/sec, and superficial liquid velocity from 0 to 0.034 m/sec, some different kinds of sub-bubble flow regimes were observed within the column. The experimental results show the influence of gas velocity on the void fraction in an increasing manner. The raw data obtained from ECT was also compared with the simulated data which shows a good agreement to each other.

Dielectric properties of slaughtered and non-slaughtered goat meat

Dielectric properties of goat meat were characterized over a frequency range from 50 Hz up to 4 MHz using impedance analyzer. The dielectric dispersion as a function of post-mortem time was also investigated. The results showed that a difference in dielectric constant in the order of 106 between slaughtered and non-slaughtered meat was clearly observed even after 168 hours of post-mortem time. The magnitude of the difference in dielectric constant, however, depends very much on the frequency used. The results indicated that 103 Hz is a proper frequency to detect such a difference. The consistent difference in dielectric constant observed in this work suggests that it is possible to distinguish slaughtered and non-slaughtered goat meat by measuring its dielectric properties.

Virtual multiphase flow metering using diverse neural network ensemble and adaptive simulated annealing

Development of data-driven virtual flow meter (VFM) using diverse neural network ensembles.Adaptive simulated annealing is used for pruning and combining strategy selection.VFM can provide real-time monitoring for fields with common metering infrastructure.Achieved 4.7% and 2.5% mean absolute errors for gas and liquid flow rate estimations.The proposed method outperforms standard stacking and bagging techniques. Real-time production monitoring in oil and gas industry has become very significant particularly as fields become economically marginal and reservoirs deplete. Virtual flow meters (VFMs) are intelligent systems that infer multiphase flow rates from ancillary measurements and are attractive and cost-effective solutions to meet monitoring demands, reduce operational costs, and improve oil recovery efficiency. Current VFMs are very challenging to develop and very expensive to maintain, most of which were developed for wells with dedicated physical meters where there exists an abundance of well test data. This study proposes a VFM system based on ensemble learning for fields with common metering infrastructure where data generated is very limited. The proposed method generates diverse neural network (NN) learners by manipulating training data, NN architecture and learning trajectory. Adaptive simulated annealing optimization is proposed to select the best subset of learners and the optimal combining strategy. The proposed method was evaluated using actual well test data and managed to achieve a remarkable performance with average errors of 4.7% and 2.4% for liquid and gas flow rates respectively. The accuracy of the developed VFM was also analyzed using cumulative deviation plot where the predictions are within a maximum deviation of 15%. Furthermore, the proposed ensemble method was compared to standard bagging and stacking and remarkable improvements have been observed in both accuracy and ensemble size. The proposed VFM is expected to be easier to develop and maintain than model-driven VFMs since only well test samples are required to tune the model. It is hoped that the developed VFM can augment and backup physical meters, improve data reconciliation, and assist in reservoir management and flow assurance ultimately leading to a more efficient oil recovery and less operating and maintenance costs.

Study of bubble flow in an air-water two-phase flow by using Electrical Capacitance Tomography

Electrical Capacitance Tomography (ECT) is an efficient and inexpensive type of Process Tomography, which is being widely used nowadays. It is a technique for obtaining information about the distribution of the contents of closed pipes or vessels by measuring variations in the dielectric properties of the material inside the vessel. An experimental study was conducted by producing two-phase bubble flow regime in a vertical online ECT column. By changing the flow rates of air and water, different bubble flow regimes were established in the test loop which is then compared with (a) the flow observed through transparent section of the column and (b) the flow regime map for two-phase bubble flow. This study also measures the values of raw capacitance and voltages of air and water flow. The raw data obtained from ECT images are analysed by using distribution models and MATLAB which is in good agreement with the experimental data.

Radial Basis Function Network to Predict Gas Flow Rate in Multiphase Flow

Estimation of individual phase flow rates in multiphase flow is of great significance to production optimization and reservoir management in oil and gas industry. This paper proposes radial basis function network to develop a virtual flow meter (VFM) that can estimate gas flow rate in multiphase flow production lines. The model is validated with actual well test measurements, and testing results reveal excellent performance and generalization capability of the developed VFM. The paper also discusses the significance of bottom-hole and choke valve measurements to attain accurate predictions. Proposed VFM model potentially offers an attractive and cost-effective solution to meet real-time production monitoring demands, and reduces operational and maintenance costs.

Optimal Camera Placement for 3D Environment

Efficient camera placement is important in order to make sure the cost of a monitoring system is not higher than what it should be. This is also to ensure the maintenance of that system will not be complex and take longer time. Based on these issues, it has become an important requirement to optimize the number of the camera in camera placement system inside particular environment. This problem is based on the well-known Art Gallery Problem but most of previous works only proposed solution to this problem in 2D. We propose a method for finding the minimum number of cameras that can observe maximum space of 3D environment. In this method we assume that each of the cameras has limited field of view of 90o and only to be placed on the wall of the environment. Placement in 3D environment uses volume approach that takes frustum’s volume and space’s volume to calculate minimum number of camera.

ANFIS identification model of an Advanced Process Control (APC) pilot plant

Fuzzy Inference System structured in form of adaptive networks is an intelligent technique being used for modeling not only linear systems but also for ill-conditioned systems. Adaptive Network Based Fuzzy Inference System (ANFIS) uses a hybrid computational algorithm for modeling systems. This paper discusses the system identification model developed for an Advanced Process Control (APC) pilot plant (continuous binary distillation column) located in APC laboratory of Universiti Teknologi PETRONAS, Malaysia, using ANFIS technique. Estimation and validation of the models was performed using the experimental data collected from the pilot plant. The developed model has been validated using the best fit criteria against the measured data of the pilot plant. The result shows that the Multi Input Single Output (MISO) ANFIS model developed is capable of modeling the non-linear APC plant by means of the input-output pairs obtained from the plant experiment.