Modelling an electrical capacitance tomography sensor with internal plate electrode

The electrical capacitance tomography (ECT) technique has been extensively studied for real-time tomographic imaging of various industrial processes. Due to the ‘soft-field’ and under-determined problems in the image reconstruction, the tomographic images of ECT are usually low in the spatial resolution when compared with its radioactive counterparts, i.e. $\gamma $ -ray tomography and computed tomography. This paper presents an ECT sensor that consists of 8 external electrodes and 8 internal electrodes, with enhanced image quality at the center of imaging region and better accuracy on phase fraction measurement. The internal electrodes are placed on cross planes inside the investigated vessel. In this way, the signal-to-noise ratio of measurement channels as well as image quality are improved. Numerical simulations show that the proposed sensor can achieve better reconstructed images than a conventional 12-electrode sensor. In addition, the phase fraction calculation using ECT data can also benefit from the internal electrodes and corresponding measurements, which is an important parameter for multiphase flow processes.

The practical application of human nose for fragrance recognition is severely limited by the fact that our sense of smell is subjective and gets tired easily. Consequently, there is a significant need for an instrument that can be a substitution of the human sense of smell. Development of an electronic nose devices is an active area of research starting from pioneering research of Dodd and Persuad in the mid-1980s. Such systems are used in growing number of applications. They comprise an array of several electrochemical gas sensors with partial specificity and a pattern recognition algorithms. Since most of the existing types of sensors are characterized by low selectivity, they can all be used in the construction of electronic noses. Most electronic nose systems are based only on one type of gas sensors. However, different types of sensors have different advantages and disadvantages. In this article, an electronic nose system for the purpose of incorporating different types of sensors has been proposed developed. Such approach allows to benefit from advantages of different type of sensors, and thus improve the operation of the entire system.

Electrical capacitance tomography (ECT) technique is a new technique for two-phase flow measurement. ECT is a complex nonlinear problem. To solve the ill-posed image reconstruction problem, image reconstruction algorithm based on wavelet neural networks (WNN) was presented. The principal component analysis (PCA) method was used to reduce the dimension of the input vectors. The transfer functions of the neurons in the WNN were wavelet base functions which were determined by retract and translation factors. The input measurement data were obtained using the ECT simulation software developed by the author. BP algorithm was used to train the WNN, and self-adaptive learning rate and momentum coefficient were also used to accelerate the learning speed. Experimental results showed the image quality has been improved markedly, compared with the typical linear back projection (LBP) algorithm and Landweber iteration algorithm.

Electrical capacitance tomography (ECT) has been developed successfully for industrial applications. While most ECT sensors employ a circular structure with a closed sensing area, some other structures like square and parallel are needed for some particular applications. This paper describes a parallel structure ECT sensor, which can provide incomplete measurement data only. The performance of this parallel ECT sensor is discussed based on simulation study with 2D FEM models. Images of different permittivity distributions are generated by linear back-projection (LBP) and Landweber iteration algorithms. The effect of the effective sensing area and distance of parallel plates are also discussed. Both capacitance residual and correlation coefficient are used to evaluate the image quality. The results show that (1) the width of the sensing area should be the same as the shielding layer in order to acquire high quality image and (2) the distance between two parallel plates is suggested being equal to the shielding layer to compromise the sensing space and the sensitivity of the central region.

Fiber optic data transmission has caused revolutionary developments in the current information society. It was also an eye opener for the Oil & Gas industry when fiber optic-based Distributed Temperature Sensing was introduced in the nineties. Temperature profiles over the entire length of the wellbore could suddenly be monitored in real-time to deliver data that is used for troubleshooting and production optimization. Ongoing research in fiber optic sensing technology for applications with extreme operational conditions (such as aerospace or nuclear fusion) will further boost the development of fiber optic multi-parameter sensing systems. Innovations like these will particularly benefit the Oil & Gas industry in situations where conventional electronic sensors are not technically feasible or cost efficient. In multi-parameter sensing systems of the future, different types of fiber optic sensors (e.g. flow, pressure, acoustic, chemical, vibration, gravity) can be manufactured and read out via one single optical fiber. Data from the different types of sensors will be merged and processed via special data-mining algorithms to generate unambiguous information for faster and better decision making. Judging on the speed of developments witnessed today, we believe there will be a second fiber optic sensing revolution in the Oil & Gas industry in the near future. In this paper, we will give some in-house examples of fiber optic innovations from both in and outside the Oil & Gas industry to demonstrate the versatility and potential of fiber optic technology. Introduction Increasing energy demand combined with scarcity means that oil must be produced from new and often more complex reservoirs. These reservoirs may consist of many sections separated by geological faults or the oil may be very viscous, making production more difficult or economically impossible with current technology. This implies that well and production strategies are becoming increasingly complex and require accurate and reliable input for decision making. Maturing assets develop complex ‘time critical’ process dynamics such as production decline, liquid loading and salt precipitation creating the need for real-time monitoring and control. This measurement and control technology must give a more accurate understanding of the key processes involved. It should also improve asset diagnostics to lead to improved production control and recovery strategies without compromising reliability or safety. Furthermore the industry has to deal with increasingly complex environmental and safety regulations and an increasing shortage of expert personnel. We believe that innovations in fiber optic sensing technology have the potential to help better understand the key processes behind decision-making in the oil and gas industry. This insight is based on many projects both inside and outside the oil and gas industry related to monitoring, process modeling and data processing. These new insights are essential to control and optimize the ‘new’ Oil & Gas industry when one considers the challenges that lie ahead. Before we focus on these upcoming fiber optic technologies for the oil and gas industry, we would like to illustrate the versatility and potential of fiber optic technology based on applications from other industries and their applicability to the oil and gas industry. Fiber optic technology is a proven technology but needs to be adapted and modified to the restricting and demanding applications of the oil and gas industry. These applications also give rise to innovations as will be discussed later.

Flex sensors are frequently used as wearable tools for unobtrusively tracking human joint movements. Among all the flex sensor types, the resistive ones are the mostly adopted thanks to their electrical and mechanical properties capable to furnish electrical resistance values related to the amount of mechanical flexion. In particular, resistive flex sensors have been finding many applications when embedded into gloves, in order to evaluate fine flexion/extension movements of the finger joints. Within this frame, here we investigate the possible utilization of a different type of flex sensors embedded into gloves, i.e., the stretch ones, since the stretch sensors change in resistance proportionally to their stretch that can be just obtained when laid on-top of a finger joint. In such a view, here we compare the characteristics of commercial flex and stretch sensors obtained by means of an ad-hoc setup and protocol. Results demonstrate the different peculiarities of the two different types of sensors, so to determine when it is convenient to adopt one type instead of the other.

Nowadays wireless sensor networks (WSNs) have attracted a great deal of study due to their low cost and wide-range applications. Most of the sensors used so far are point sensors which have a disc-shaped sensing region. In this paper, we introduce a new type of sensor: the fiber optic cable sensor. Unlike a traditional point sensor, this type of sensor has a rectangular sensing region with a processor installed on it to do processing and communication. Like wireless sensor networks with point sensors, energy-efficient communication is still an important issue in wireless sensor networks with cable sensors because of the need to efficiently use limited resources. To address the issue, we propose a Cable Mode Transition (CMT) algorithm, which determines the minimal number of active sensors to maintain $K$-coverage of a terrain as well as $K$-connectivity of the network. Specifically, it allocates periods of inactivity for cable sensors without affecting the coverage and connectivity requirements of the network based only on local information. Before presenting CMT, we first show the relationship between coverage and connectivity, then the eligibility algorithm permitting a cable sensor to decide whether to stay active. CMT calls the eligibility algorithm to schedule cables. Simulation results show that our scheme is efficient in saving energy and thus can prolong the lifetime of wireless cable sensor networks.

The Wave Crest Sensor Inter-comparison Study (WACSIS) employed many different types of sensors to measure ocean surface waves. These sensors were attached to a steel jacket platform located in 18 m deep water about 9 km from the Dutch coast. To investigate the suitability and consistency of different wave sensors, wave characteristics at the locations of some sensors were deterministically predicted based on three other wave measurements using a Directional Hybrid Wave Model (DHWM). The comparisons between the predictions and related measurements were applied to the examination of the consistency among different sensors. As an example, a mistake of the orientation of a current meter has been found through the comparisons. This study also demonstrates that consistency among different types of wave sensors is satisfactory and the DHWM is valuable to the analysis of field wave measurements.

This paper presents a variable gain potetiostat designed for the electrochemical control of Dissolved Oxygen (DO) sensors. The design is targeted for implementation using MIMOS 0.35 um CMOS process technology at 3.3V. The potentiostat amplifier for dissolved oxygen utilizes three electrodes (working, reference and counter) which work together to form the electrochemical reaction. There are several types of DO sensor available including membrane-based, also known as Clark electrode, and microelectrode-based. This results in different sensitivity and output current when different types of sensor are used on the same test solution. The variable gain feature is added in the design to cater to different DO sensors that are available in the market. This design implements four different gain values to accommodate four different current ranges. The resistor values chosen largely depend on the type of DO sensor used and the range of output current it produces. The transimpedance amplifier used has also been simulated in the post-extracted view across process corners to ensure its compatibility with the different feedback resistor values. The complete integrated design is then simulated to be working within a power supply of 2.2 V to 3.6 V, temperature of 0 to 90°C and has an operating range of 0 to 100 uA sensor current. The final chip has an area of 1.7 mm × 1.7 mm and has been simulated to be in working order.

The use of multiple sensors typically requires the fusion of data from different type of sensors. The combined use of such a data has the potential to give an efficient, high quality and reliable estimation. Input data from different sensors allows the introduction of target attributes (target type, size) into the association logic. This requires a more general association logic, in which both the physical position parameters and the target attributes can be used simultaneously. Although, the data fusion from a number of sensors could provide better and reliable estimation but abundance of information is to be handled. Therefore, more extensive computer resources are needed for such a system. The parallel processing technique could be an alternative for such a system. The main objective of this research is to provide a real time task allocation strategy for data processing using multiple processing units for same type of multiple sensors, typically radar in our case.KeywordsTarget trackingData fusionSensor levelParallel processing

The accuracy of reconstructed images of electrical capacitance tomography (ECT) is a bottleneck concerning the successful application of ECT. An image data fusion algorithm based on wavelet transform was proposed in this paper to improve the accuracy of reconstructed images. First, reconstructed images were obtained using conjugate gradient least square algorithm and Landweber iterative algorithm, respectively. Secondly, reconstructed images were decomposed by wavelet transform. After that, the approximate component was processed according to the weighted average fusion rule. The detail component was processed according to the maximum fusion rule of absolute value. Finally, the new reconstructed images were obtained by wavelet reconstruction. Simulation and static experimental results showed that the reconstructed images with higher accuracy can be obtained after fusion and the artefacts were decreased obviously.

Choosing the number of electrodes is crucial for a multi-plane electrical capacitance tomography (ECT) sensor to obtain satisfactory capacitance data and good image quality. In this paper, issues with the number of electrodes in three-plane ECT sensors are discussed. The number of electrodes on each plane for the three sensors is 4, 8, and 12, respectively. Three quality measures of resolution matrix, i.e. the amplitude response, relative position error, and spatial resolution, are defined and used for evaluating the preliminary performance of ECT sensors. Based on numerical simulation, the noise-free and noisy capacitance data obtained from the three sensors are used for image reconstruction by linear back projection (LBP) and Landweber iteration algorithms. The image quality is evaluated quantitatively in terms of image error and correlation coefficient. Based on static experiment, the effect of the number of electrodes on the quality of the capacitance data and reconstructed images is also analyzed. According to the numerical simulation and experiment results, the selection of the number of electrodes under different conditions is suggested.

In the design of electrical capacitance tomography (ECT) sensors, the internal wall thickness can vary with specific applications, and it is a key factor that influences the sensitivity distribution and image quality. This paper will discuss the effect of the wall thickness of ECT sensors on image quality. Three flow patterns are simulated for wall thicknesses of 2.5 mm to 15 mm on eight-electrode ECT sensors. The sensitivity distributions and potential distributions are compared for different wall thicknesses. Linear back-projection and Landweber iteration algorithms are used for image reconstruction. Relative image error and correlation coefficients are used for image evaluation using both simulation and experimental data.

Identification of inlet pipe blockage level in centrifugal pump over a range of speeds by deep learning algorithm using multi-source data

An intelligent multisensor integration and fusion model that uses fuzzy logic is developed. Measurement data from different types of sensors with different resolutions are integrated and fused based on the confidence in them derived from information not usually used in data fusion, such as operating temperature, frequency range, fatigue cycles, etc. These are fed as additional inputs to a fuzzy inference system (FIS) that has predefined membership functions for each of these variables. The output of the FIS are weights that are assigned to the different sensor measurement data that reflect the confidence in the sensor's behavior and performance. A modular approach is adopted. It allows adding or deleting a sensor, along with its fuzzy logic controller (FLC), anytime without affecting the entire data fusion system. This paper presents a preliminary model that fuses the data from three different types of sensors that monitor the strain at a single location in a cantilever beam. This will be later extended to sensors that will be fixed at different locations on the same beam. The results from the proposed work are a stepping stone toward the development of generic autonomous sensor models that are capable of data interpretation, self-calibration, data fusion from other sources, and even learning so as to improve their performance with time. This work is aimed at the development of smart structural health monitoring systems, but has applications in diverse fields such as robotics, controls, target tracking, and biomedical imaging.