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The use of pneumatic conveying of solid bulk over long distance has become a popular technique due to low operational cost, low maintenance requirement, layout flexibility and ease of automation. The purpose of this paper is to identifity the flow regime in a pneumatic conveyor system by electrodynamic sensor placed around the pipe using fuzzy logic tools.

Electrical charge tomography is used to detect the existence of inherent charge on the moving particles through the pipe. Linear back projection algorithm and filtered back projection algorithm are employed to produce tomography image. Baffles of different shapes are inserted to create various flow regimes, such as full flow, three quarter flow, half flow and quarter flow. Fuzzy logic tools are used to identify different flow regimes and produce filtered back concentration profiles for each flow regime.

The results show significant improvement in the pipe flow image resolution and measurement.

This paper presents a flow identifier method using electrical charge tomography and fuzzy logic to monitor solid particles flow in pipeline.

An identification model for materials flow through a pipeline is presented in this paper. The development of the model involves fuzzy C-means clustering, in which different flow regimes can be identified by every adaptive network-based fuzzy inference system (ANFIS). The paper aims to discuss these issues.

For experimentation, 16 electrodynamic sensors were used to monitor and measure the charge carried by dense particles flow through a pipeline in a vertical gravity flow rig system. Four ANFIS models were also used simultaneously to provide the expected output on thresh-holding and were evaluated for ten different flow regimes, which produced satisfactory results at high flow rate.

The observations made on the four ANFIS models in the flow identification experimentation (in ten different flow regimes) have shown convincing and satisfactory results at high-flow rate of the particles.

Electrodynamic sensors have shown strong sensing capability in identification of dense-particle flows within a conveyor; and also proven capability to operate effectively in harsh industrial environments due to their firm and simple structures. Moreover, it has been verified that these sensors can conveniently be applied in flow regime identification of solid particles.

Solid particles flowing in a pipeline is a common mode of transport in industries. This is because pipeline transportation can avoid waste through spillage and minimizes the risk of handling of hazardous materials. Pharmaceutical industries, food stuff manufacturing industries, cement, and chemical industries are a few industries to exploit this transportation technique. For such industries, monitoring and controlling material flow through the pipe is an essential element to ensure efficiency and safety of the system. The purpose of this paper is to present electrical charge tomography, which is one of the most efficient, robust, cost‐effective, and non‐invasive tomographic methods of monitoring solid particles flow in a pipeline.

Process flow data are captured by fitting an array of 16 discrete electrodynamic sensors about the circumference of the flow pipe. The captured data are processed using two tomographic algorithms to obtain tomographic images of the flow. Then a neural network tool is used to improve image resolution and accuracy of measurements.

The results from the above technique show significant improvements in the pipe flow image resolution and measurements.

The paper presents electrical charge tomography, which is one of the most efficient, robust, cost‐effective, and non‐invasive tomographic methods of monitoring solid particles flow in a pipeline.

Circular pipelines are mostly used for pneumatic conveyance in industrial processes. For optimum and efficient production in industries that use a pipeline for conveyance, tomographic image of the transport particles is paramount. Sensing mechanism plays a vital role in process tomography. The purpose of this paper is to present a two‐dimensional (2‐D) model for sensing the characteristics of electrostatic sensors for electrical charge tomography system. The proposed model uses the finite‐element method.

The domain is discretized into discrete shapes, called finite elements, by using a MATLAB. Each of these elements is taken as image pixels, on which the electric charges carried by conveyed particles are transformed into equations. The charges' interaction and the sensors installed around the circumference, at the sensing zone of the conveying pipeline are related by the proposed model equations. A matrix compression technique was also introduced to solve the problem of unevenly sensing characteristics of the sensors due to elements' number's concentration. The model equations were used to simulate the modeled electrostatic charge distribution carried by the particles moving in the pipeline.

The simulated results show that the proposed sensors are highly sensitive to electrostatic charge at any position in the sensing zone, thereby making it a good candidate for tomographic image reconstruction.

Tomographic imaging using finite element method is found to be more accurate and reliable compared to linear and filtered back projection method.

The purpose of this paper is to conduct a review of types of tomographic systems that have been widely researched within the past 10 years. Decades of research on non-invasively and non-intrusively visualizing and monitoring gas-liquid multi-phase flow in process plants in making sure that the industrial system has high quality control. Process tomography is a developing measurement technology for industrial flow visualization.

A review of types of tomographic systems that have been widely researched especially in the application of gas-liquid flow within the past 10 years was conducted. The sensor system operating fundamentals and assessment of each tomography technology are discussed and explained in detail.

Potential future research on gas-liquid flow in a conducting vessel using ultrasonic tomography sensor system is addressed.

The authors would like to undertake that the above-mentioned manuscript is original, has not been published elsewhere, accepted for publication elsewhere or under editorial review for publication elsewhere and that my Institute’s Universiti Teknologi Malaysia representative is fully aware of this submission.

Electrostatic sensors are applied to measure velocity of solid particles in many industries because controlling the velocity particles improves product quality and process efficiency. The purpose of current paper is optimization of these sensors which is required to achieve maximum spatial sensitivity and minimum statistical error.

Different electrode of electrostatic sensors with different length, thickness and sensor separations were experimentally applied in laboratory. Then, correlation velocity, signal bandwidth and statistical error were calculated.

High sensor separation is a crucial factor because it would lead to increase signal similarity and decrease statistical error. This paper focuses on the effect of sensor separation on optimization of electrostatic sensors.

From observations, the optimal value for length, thickness and sensor separations was 0.6, 0.5 and 15 cm, respectively. Consequently, statistical error has improved by about 17 per cent. These results provided a significant basis of optimization of electrostatic sensors.

The effect of viscosity on the performance of disk-shaped electromechanical resonators has been studied and investigated in the past. The vibration frequency of a disk-shaped resonator changes according to the viscosity of the liquid which the resonator is in contact with. Therefore, the purpose of this paper is based on design a sensor for measuring the viscosity of liquids using a disk-shaped electromechanical resonator. The viscosity of liquids is of interest to researchers in industry and medicine.

In this paper, a viscosity sensor for liquids is proposed, which is designed based on a disk-shaped electromechanical resonator. In this proposed sensor, two comb drives are used as electrostatic actuators to stimulate the resonator. Also, two other comb drives are used as electrostatic sensors to monitor the frequency changes of the proposed resonator. The resonance frequency of the resonator in response to different fluids under test varies according to their viscosity.

After calibration of the proposed sensor by nonlinear weights, the viscosity of some liquids are calculated using this sensor and results confirm its accuracy according to the liquids real viscosity.

The design of the proposed sensor and its simulated performance are reported. Also, the viscosity of several different liquids are evaluated with simulations of the proposed sensor and presented.

Intelligent pneumatic actuator (IPA) is a new generation of actuator developed for Research and Development (R&D) purposes in the academic and industrial fields. The purpose of this paper is to show the application of optical encoder and pressure sensor in IPA, to develop a real-time model similar to the existing devices, and to assess the position control performance using a proportional-integrative (PI) controller and a bang-bang controller in real-time.

A micro optical encoder chip is used to detect cylinder rod position by reading constructed laser stripes on a guide rod, whereas a pressure sensor is used to detect the chamber pressure reading. To control the cylinder movements by manipulating pulse-width modulation (PWM) cycles, two unit valves of two ports and two positions were used. A PI controller and a bang-bang controller are used with suitable gain value to drive the valve using PWM to achieve the target actuator position.

The results show the experimental results of the closed-loop position tracking performance of the system using a data acquisition (DAQ) card over MATLAB software.

This paper presents a real-time model used to replace the microcontroller-based system from previous IPA design. The paper proposes two control strategies, PI and bang-bang, to control position using encoder and pressure reading.

The purpose of this paper is to review the development of energy harvesting techniques and their use with wireless, self‐powered sensors.

This paper first considers the need for wireless sensors and then discusses a number of products and recent development activities.

Energy harvesting devices based on electrodynamic, piezoelectric and thermoelectric effects, implemented through electromechanical, microelectromechanical systems and nanotechnological approaches, are attracting strong academic and commercial interest. A limited number of sensors and systems exploiting these effects are in production and many more are under development. Some actual and anticipated applications include industrial condition monitoring, structural monitoring and healthcare.

This provides a technical insight into energy harvesting techniques and their applications to wireless sensing.

This paper aims to provide details of the technology and applications of the sensors and instruments used in condition monitoring practices.

Following an introduction to condition monitoring, this paper discusses the sensors and instruments used in the major techniques, notably vibration, acoustic emission, shock pulse monitoring, wear debris analysis and thermal methods. Applications are also discussed.

Condition monitoring techniques play a vital role in reducing in-service failures and unscheduled down-time and allow the implementation of condition-based maintenance. These techniques are reliant on a range of sensors based principally on piezoelectric, electrodynamic, eddy current, inductive, magnetic and thermal technologies.

This paper provides details of the sensors used in machinery condition monitoring and discuses their applications.