Transactions of the Institute of Measurement and Control

Salt particles dissolution in slurry stirred tanks provides an ambitious challenge for the application of Electrical Resistance Tomography in the process industry, because the presence of high loadings of inert particles requires a purposely developed post-processing method of the experimental data. For the optimization of the working conditions of the dissolution process, two characteristic times are required: the time for the liquid homogenization in the tank and the time required for the complete dissolution of the salt particles. The former time has been experimentally determined in previous investigations both in stirred tanks working with single-phase and with multiphase mixtures. The latter characteristic time has not been analyzed so far, due to the lack of experimental procedures for distinguishing it from the former. In this work, a novel approach for the simultaneous identification of the two characteristic times is presented. The impact of the new procedure is significant for the production processes, since it offers a tool for identifying when the soluble particle size has an impact on the dissolution dynamics, and when the stirred tank dynamics is influenced by the liquid homogenization only, and therefore a reduction of the particle size does not speed up the process accomplishment.

With the increase of capacity, most of the circulating fluidized beds (CFB) risers are constricted to have rectangular cross section. Therefore, it is important to find out the cross-sectional aspect ratio effect on the gas-solids flow characteristics. In this study, a lab-scale CFB with two rectangular risers, which have the aspect ratio of 1:1 and 3:1, respectively, were studied by the electrical capacitance tomography (ECT), with the aided of pressure measurements and computational particle fluid dynamics (CPFD) simulation. Key issues related with ECT sensor design and image reconstruction, such as sensitivity map and excitation frequency, are also discussed. The results show that ECT image quality is affected by the sensitivity map and excitation frequency, and high excitation frequency and voltage are not equivalent of high image quality. In the riser bottom region, cross-sectional aspect ratio has big effect on ECT measured particle distribution and pressure drop, fewer particles are brought away from the bottom when the aspect ratio is larger. Multiple bubbles exist in the bottom region at Ve= 2.7 m/s, and bubble size decreases in the rectangular riser with larger cross-sectional aspect ratio. Static bed height influences the bubble behaviour that bubbles with smaller size scattered around and behave collapse or coalescence in the moving process when the static bed height is high.

Two-phase flow is a regime that commonly existed in many applications of process industry, on which the technique of ultrasonic tomography (UT) could provide reliable measurement with benefits of non-invasion, non-radiation and low-cost. In order to provide considerable visualization of low acoustic impedance contrast configurations in two-phase flow, a regular weighted simultaneous algebraic reconstruction technique (RW-SART) using continuous-wave excitation is proposed and presented in detail. With measurement strategy and formulized reconstruction model introduced, the measurement system is introduced with modification on continuous-wave excitation module and fan-beam angle transducers. A regularized weighted least square (RWLS) framework combining total variation (TV) regularization and SART reconstruction is derived and presented in detail to address the problem that under-determined and ill-conditioned situation couldn’t be resolved simultaneously. Both numerical simulation and experimental investigation are conducted to validate the feasibility of continuous-wave excitation and the improvement of proposed reconstruction strategy, from which the distribution of oil-water two-phase flow is effectively reconstructed. The implementation of measurement system provides an option of continuous high-voltage excitation and measurement in UT. By utilizing both the geometric information and penalty constraint, the proposed reconstruction approach provides a framework that introduces regularization information to the algebraic iteration reconstruction. Consequently, it has better reconstruction result than TV, Landweber, L1, filtered back projection (FBP) and SART method.

The article presents non-destructive testing based on electrical impedance tomography (EIT) for spatial (3D) monitoring of flood embankments. Therefore, to solve the inverse problem of the EIT, an effective algorithm based on multiple elastic nets has been developed. The originality of the solution is based on the application of many elastic net algorithms as functions, each of which, based on the vector of all measurements, generates the value of a single pixel for the reconstructed image. In this way, the set of elastic nets is equal to the resolution of the image output. Such an approach, although requiring more computing power, yields high resolution images. In addition, the presented algorithms are characterized by high noise immunity and distortion of measurement data. Five different electrode systems were tested in the samples and compared with each other in two measurement variants (stimulations). A reconstruction made on the basis of actual measurements obtained from the physical model was also presented.

The presented solution provides a visual analysis of seepages and leaks, which allows for quick and effective intervention and possible prevention of dangers. The research proved that the use of tomographic measurement techniques in combination with the image reconstruction algorithm based on elastic net allows for non-invasive and very accurate spatial assessment of leaks and damages of flood embankments. The received results confirm the effectiveness of the presented research.

In this paper, a novel approach for tracking moving structures in multiphase flows over larger axial ranges is presented, which at the same time allows imaging the tracked structures and their environment. For this purpose, ultrafast electron beam X-ray computed tomography (UFXCT) is being extended by an image-based position control. Application is scanning and tracking of, for example, bubbles, particles, waves and other features of multiphase flows within vessels and pipes. Therefore, the scanner has to be automatically traversed with the moving structure basing on real-time scanning, image reconstruction and image data processing. In this paper, requirements and different strategies for reliable object tracking in dual image plane imaging mode are discussed. Promising tracking strategies have been numerically implemented and evaluated.

This paper investigated the particle dispersion behaviours of Lithium-ion battery (LIB) slurry by using electrical impedance spectra-tomography (EIST) method from the perspective of experiment and simulation. In the experiment, an EIST system composed of Field—Programmable Gate Array (FPGA), multi-plexer, switch circuit and 8-electrode sensor is developed to measure the frequency response of LIB slurry under two different conditions, which are rotation speed n=0rpm with rotation time t=0min and n=100rpm with t=6min. In the simulation, four different geometry structure models, which are (a) Carbon Black (CB) linear formation, (b) CB aggregation, (c) CB & LiCoO₂ aggregation and (d) network dispersion, are established. Six frequencies, which are f=1 kHz, f=10 kHz, f=50 kHz, f=100 kHz, f=250 kHz and f=500 kHz, are used for the reconstructed conductivity images of LIB slurry in both the experiment and the simulation. The numerical simulation is used to verify the correctness of the experiment results. After combining the experiment and the simulation, it is concluded that the agglomeration behaviours of CB and LiCoO₂ particles appear within LIB slurry in the case of n=0rpm with t=0min, while CB path and part LiCoO₂ particles coated by CB particles appear in the case of n=100rpm with t=6min. Moreover, high frequencies are suitable to distinguish high conductive CB components from LIB slurry. Furthermore, the developed EIST system has the capability of monitoring particle dispersion behaviours in LIB slurry, which has the potential to be used for the on-line measurement of LIB slurry in order to improve the performance of LIB.

Pneumatic conveying systems have become a standard technique for the transport of bulk materials such as powdery or granulates. The spatial dependence of the material density and the stream velocity in such transport systems require a volumetric measurement principle for flow measurement. In this paper we analyse the capability to estimate the volume fraction from capacitive sensing data using electrical capacitance tomography (ECT). In particular, we investigate the capability of back-projection type imaging algorithms. The ill-posed nature of the imaging problem of ECT require the incorporation of prior knowledge in the design of the estimator. We analyse the different flow profiles in pneumatic conveying in order to generate specific sample-based prior information to improve the estimation performance and robustness. We discuss the construction of different linear image reconstruction algorithms and present a framework, which allows a detailed statistical analysis of the estimator performance. Simulation studies show the estimation behaviour of different algorithms with respect to the incorporated prior information. We demonstrate, that the incorporation of specific prior knowledge leads to an improved estimator behaviour; for example, reduced variance and unbiased estimates. We implemented laboratory experiments in order to analyse the presented approach for the application in real pneumatic conveying processes. We demonstrate the improved robust estimation behaviour by means of comparative reconstruction results obtained with different algorithms and priors. Furthermore, the uncertainty of the estimated volume fraction is analysed in steady state conveying processes. Hereby, it is demonstrated, that appropriate prior information improves the estimation performance also for measurements coming from real pneumatic conveying processes, making ECT a suitable tool for the volume fraction estimation in such transport systems.

Wurster fluidised bed is commonly used for coating pellets in the pharmaceutical industry. Normally, the control in a Wurster fluidised bed for coating is based on point-based measurement, that is, pressure and optical probes. However, the point-based measurement methods only provide local flow information and cannot reveal the cross-sectional flow dynamics characteristics and it is difficult to control the whole process with limited data. In this paper, electrical capacitance tomography (ECT) is applied to reconstruct the solids distribution in a lab-scale Wurster fluidised bed for coating pellets and the measurements are used in a control loop of the process. The flow regime is identified from the ECT images using a simple but efficient approach. The objective of control is to keep stable minimum fluidisation and avoid undesired flow regime in the region between the outside wall and the Wurster tube, such as intermittent, plug and defluidisation. To achieve this target, a PID controller is applied to keep a low volume fraction in the annular region by adjusting the fluidisation air rate, and bang-bang control is applied to a peristaltic pump used for spraying coating solutions based on the detected flow regime. It has been found that the proposed control scheme based on the ECT measurement is effective for keeping a stable flow regime, reducing the degree of pellets agglomeration and avoiding defluidisation.

This paper describes the characterisation of the dynamic response of a range of pressure transducer systems. The transducers were subjected to virtually instantaneous pressure step inputs in the National Physical Laboratory’s shock tube facilities. The magnitudes of these pressure steps were derived from ideal gas theory, with prior commissioning tests having been performed to demonstrate the theory’s validity in this application. The results demonstrate a significant variation in response obtained from various combinations of transducer, instrumentation settings, and mounting arrangement.

In many engineering applications the phase angle of a signal is a key parameter. Especially when measuring small angles, the measurement accuracy is of a vital importance. Often, the absolute phase error of a digitizer, which is defined as the phase displacement between the digitized output and the input analog waveform and which represents a systematic measurement error, is neglected. Therefore, in this paper, a new measurement technique for the evaluation of this absolute phase error is discussed, along with a deep theoretical analysis on the uncertainty sources and how to handle them. The measurement technique is validated through a high accuracy experimental setup. Experimental tests demonstrate that even high accuracy digitizers can show non-linear behavior in the absolute phase errors.

Issues concerning natural gas pipeline leakage are becoming more prominent than ever because of the continuing expansion of natural gas pipeline networks. Although many scholars have extensively investigated generation and detection methods for pipeline leakage acoustic signals, systematic research on the characteristics of leakage and interference signals remains insufficient. Results show that the method based on the RBF kernel function is feasible for pipeline fault diagnosis, yielding 100% sensitivity, 92% specificity, and 96% accuracy.

In the practical oilfield production, it has great significance to realize timely and accurate measurement of the moisture content of crude oil. However, there are some drawbacks in the traditional measurement methods, such as: non-real time, high cost, labor-consume, vulnerability to environmental impacts, and so on. In order to solve these problems, a soft sensor model based on multi-kernel Gaussian process regression optimized by an adaptive variable population fruit fly optimization algorithm (APFOA-MKGPR) is presented in this paper. A multiple kernels-based Gaussian process regression method is utilized to deal with the practical production process characterised by multiple operating phases, noises, strong nonlinearity and dynamic. In the multi-kernel function, many parameters (five hyper-parameters in the multi-kernel function and three weights of each kernel function) need to be accurately given, which is difficult to be effectively optimized by the maximum likelihood estimation. So, a swarm intelligence-based adaptive variable population fruit fly optimization algorithm (APFOA) is proposed to train the best model parameters. A novel adaptive variable population mechanism is developed to adaptively adjust the population size and the random flight distance during the iterations, which can realize a combination of the global searching and the local searching for the optimal solutions. The proposed method is verified by four benchmark functions and the actual production data of one oil well, and experimental results show the effectiveness for accurate prediction of the moisture content of crude oil.

Falling is a major cause of serious injury or even death for the elderly population. To improve the safety of elderly people, a wide range of wearable fall detection devices have been developed over recent years, such as smart watches, waistbands and other wearable fall detectors. However, most of these fall detection devices are threshold-based and have a high rate of false alarm. This paper presents a novel fuzzy logic fall detection algorithm used in smart wristbands to reduce false alarms and achieve accurate fall detection. Experiments have been conducted in our laboratory and the results show that the proposed algorithm can accurately distinguish fall events from non-fall daily activities such as walking, jumping, clapping, and so forth. It shows good potential for commercial applications.

Optoelectronic tweezers (OET) utilizes the optically induced dielectrophoresis (ODEP) force to manipulate and assemble carbon nanotube (CNT) particles in an aqueous solution. This work can help us to gain exciting and promising applications in electronic devices and sensing areas. In this paper, a numerical model based on the Maxwell stress tensor (MST) method has been presented to study a single CNT particle subjected to both the ODEP force and torque in a non-uniform electric field. In addition, a single-sided OET, which is unlike traditional OET chips and enables the assembly and alignment of CNT particles, has been introduced and studied. The calculated results on the CNT particles analogy to non-spherical shapes demonstrate that the MST method can provide more accurate predictions than the effective dipole moment. Furthermore, both the DEP force and torque exerted on the CNT particle, as well as shell thickness, spatial position, and distance between CNT particle and electrode, have been studied in detail. These results are in agreement with those obtained by other researchers. This work can help us to gain new insights into the analysis of motions of the CNT particles suspended in OET chips.

The precision of particle size distribution (PSD) is a key indicator to evaluate inversion algorithms. In the light scattering particle sizing technology, constructed on the inversion method of the traditional non-negative Philips-Twomey (NNPT) algorithm, an iterative NNPT (INNPT) algorithm was proposed in this paper, applied for the inversion of PSD. In simulations, the inversion accuracy performance of NNPT and INNPT algorithms were compared and analyzed by using different widths of PSD that conform to Johnson’s S_B unimodal and bimodal functions. A small angle forward scattering method of PSD measurement system using Charge Coupled Device (CCD) as a photodetector was constructed and the national standard particles were tested. Both the results of simulations and experiments show that the inverse accuracy and stability of INNPT algorithm are superior to NNPT algorithm especially in the conditions of narrow distribution.

Piezo-actuated micro-/nanopositioning systems have been widely employed in diverse high-precision positioning applications. However, the inherent hysteresis nonlinearity seriously deteriorates the tracking performance of piezo-actuated stages. This paper presents the design, analysis, and validation of a novel control scheme termed model prediction control (MPC) with real-time modified reference based on disturbance observer (DOB) to suppress the hysteresis nonlinearity and model uncertainty, in which the nonlinear effects are treated as an unknown disturbance to the system. In order to remove the most of the interference and diminish the effect of noise, a DOB is designed for the non-minimum phase (NMP) system. Then the difference between the actual displacement and the output of the nominal model termed the residual error is estimated and used to modify the reference in real time for a better performance. By the proposed method, the model of the inherent hysteresis is not required and the controller is established based on the identified nominal model. Its effectiveness is validated through experimental investigations on a commercial nanopositioner. Experimental results show that the proposed method can improve the tracking performance of the piezo-actuated stage, as compared with the traditional MPC and DOB-based MPC.

Key comparison measurements serve as an ultimate tool of quality assurance of results. Whenever the inter-comparison results indicate inconsistency, the participating laboratory needs to take the corrective actions. Practically, the systematic errors involved in the measuring system confines the achievable accuracy. Therefore, the corrective action involves either empirically determine the influences afresh or intuitively reassigns these error values. Alternatively, an analytical method based on inter laboratory comparison results is proposed. The novelty of the proposal is considering task-specific errors in the model that is used for the analysis of interlaboratory comparison results. Without accounting the uncertainties of task-specific errors, the analysis grows complicated and even sometimes it is not feasible. To supplement the proposed method, task-specific errors due to the imperfect geometry of ring gauge, practical inability in implementing the measurement, and unattended environmental influences are explored. The proposed method is demonstrated using some internal diameter key comparison data. The systematic errors responsible for the outlier in the measurement comparison are clearly distinguished.

For intermittent transonic wind tunnel (ITWT), Mach number and total pressure are crucial features of the flow field in the test section. However, they are typically difficult to control due to system nonlinearities, uncertainties, coupling and delays. This paper proposes an output feedback model predictive control (MPC) strategy to ensure the tracking of Mach number and total pressure of the ITWT to a specified reference trajectory. The proposed approach includes open-loop prediction, steady state target calculation (SSTC) and dynamic control, where the lower layer dynamic control tracks the steady state target obtained from the upper SSTC layer. By adding an artificial disturbance in the identified linear parameter varying (LPV) model for ITWT, and utilizing the resulting LPV model in both SSTC and dynamic control layers, a heuristic open-loop output feedback MPC approach is proposed. The experimental results for the control of Mach number and total pressure for ITWT demonstrate the effectiveness of the proposed approach.

A new wind sensing device based on bimorphs array is designed for apperceiving wind direction and velocity, whose structure is an L-shaped cantilevers array. A coupling model of the array is proposed to predict the response voltage stimulated by wind. By extracting the response voltage ( U m ) on each bimorph cantilever, the values of wind direction ( α n , cal ) and velocity ( v n , cal ) can be calculated in terms of the geometric relation. Further, the relations among wind errors, array amounts (m) and array radius (r) are acquired, and m & r can be utilized to decreasing angle error ( E α n ¯ ) and velocity error ( E v n ¯ ) of wind. The calculated and experimental results show that, increasing m initially yields decreasing these two errors, then it begins to increase the errors when m is larger than 6. The minimal wind angle and velocity errors of the array are 0.14° and 0.34%, respectively, at actual wind of 13.4 m/s, m = 6 and r = 20 mm. Meanwhile, increasing r can decrease E v n ¯ and E α n ¯ , Besides, for calculating 20 random incidence wind angles, respectively, the range of wind velocity error is from 0.34% to 0.87%, the range of wind angle error is from 0.12°to 0.38°, and the response time is 10 ms.

The primary output for any health monitoring system that offers telecardiology services is the recovery of the electrocardiographic (ECG) signal from the noised signal. The mechanized investigation of the ECG signal is the most inspiring challenge for accurate detection of cardiac disease. This could be accomplished by eliminating the various noises from the acquired signal. In this paper, a noise reduction approach employing DTCWT is executed on an ECG signal by proposing a noise estimator along with a detailed assessment of the effect of the choice of the threshold value, threshold algorithm and distribution function. The thresholding technique is executed by varying the threshold value (γ) and its function (f_n) applied to the proposed estimator (α*). The proposed estimator is scaled by 2ⁿ factor to study its impact on performance metrics and the nature of the reconstructed signal utilizing different distribution functions. The best combination of threshold function with threshold value selection has been chosen in this work from eight different sets of threshold value selection rules along with six distinct threshold functions. The experimental results show that the proposed noise reduction approach using a universal modified threshold level-dependent threshold with non-negative garrote threshold function for normal distribution with n = 3 delivers 80.72dB SNR with a subsequent reduction in MSE and PRD as compared with other standard techniques. An elaborate empirical analysis for selecting the distribution function for obtaining the best possible threshold function and technique is the prime objective and novelty of this research work.

Electronic tongue mimics human gustatory sensation and is used to characterize and discriminate beverages and foods. Feature extraction plays a key role in improving the classification accuracy by preserving the distinct characteristics while reducing high dimensionality of data generated from electronic tongue. This paper presents a new feature extraction method based on stationary wavelet singular entropy for a developed electronic tongue system to classify pasteurized cow milk. The electronic tongue consists of an array of five working electrodes along with a reference and a counter electrode to characterize milk sample. The feature extraction of acquired data is done by computing stationary wavelet transform to obtain detail and approximate coefficients at different level of decomposition. These coefficients are processed using singular value decomposition followed by calculation of entropy to obtain stationary wavelet singular entropy values. These values form the feature set and feed to two classifiers, k-nearest neighbor and back propagation artificial neural network, and their classification accuracy is evaluated with variation in their model parameters. The proposed method is compared with other wavelet transform-entropy methods in terms of classification accuracy, which indicates that the proposed method is more effective in discriminating milk samples.

The accurate prediction of force is very important in the present scenario of aerodynamic force measurement. The high accuracy of force prediction during calibration facilitates a better accuracy of force measurement in aerodynamic facilities like shock tunnels and wind tunnels. The present study describes the force prediction in an accelerometer force balance system using support vector regression (SVR). The comparison of SVR with the existing force prediction techniques namely, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) has also been carried out. The accelerometer force balance used in the current experimentation consists of a tri-axial accelerometer to measure the response on an aluminium hemispherical model on the application of force. The impulse forces were applied along the axial, normal and azimuthal directions. The forces were predicted using the accelerations obtained from the tri-axial accelerometer. SVR method was able to predict the forces quite accurately as compared to ANFIS and ANN. However, SVR has the advantage over ANFIS and ANN in that it is independent of the magnitude of the training and testing data. It is capable of an accurate prediction of forces with any magnitude of training and testing data, unlike ANFIS and ANN.

In this paper, a novel robust online model predictive control (RMPC) method for image-based visual servoing (IBVS) in polar coordinates is proposed. First, the Jacobian matrix is transformed into a weighted combination of vertex matrices of convex polytopic by tensor product (TP) model transformation method. Then, a new IBVS control design condition for 6-DOF manipulator submitting to robot physical limitations and visibility constraints is obtained in polar coordinates by using RMPC technique. The optimal value of the control signal can be solved online when carrying out the convex optimization problem. The proposed control strategy can effectively improve the trajectory in the case that involves translation and rotation with fast response while averting the pseudo-inverse of the image Jacobian matrix. Conclusively, the effectiveness of the proposed scheme is validated by simulations and experiments on a 6-DOF manipulator.

In this paper, a non-fragile optimal observer is proposed for the decentralized multiphase flow measurement based on the interconnections between the two subsystems, that is, gas and liquid, constituting the whole system. Due to the dynamic model of system and presence of disturbances and slowly varying quantities, a non-fragile decentralized observer is designed and the states of the condensate and gas sub-systems were separately estimated. Lyapunov-based stability conditions are converted to linear matrix inequality (LMI) and observer gains are optimally selected from solution set such that the effect of the disturbance on the states’ estimation error becomes minimized. The estimation is conducted using the real-time measurements including lines pressures, single-phase gas flow, and single-phase liquid flow in the refinery outlet. To check the stability and performance of the system against the changes, the Lyapunov theory has been used. Finally, the estimation results are compared with real-world data from the industry showing the high accuracy of this method as the estimations were consistent with the operation data. In all stages, the investigations were based on the data collected from the actual process in the South Pars Gas Complex (SPGC), Iran. Additionally, the Extended Kalman Filter (EKF) based on the simplified drift flux model (DFM) was used to estimate the states then both methods’ results are compared and using the HYSYS simulator with the real process data, it is found that both observers are capable to identify the states with some differences in performance and DFM model is sufficient for estimation of parameters and states of the multiphase flow entering the gas refinery. As a result, these techniques not only can be substituted for the existing system at the gas refinery, but also can be as a backup for available measurement systems.

A modified inter-digital capacitive (IDC) transducer for relative humidity (RH) measurement is fabricated by coating a thick film of polyvinyl alcohol (PVA) on the structure. The effective capacitance of the sensor, measured at 1MHz, increases with RH at room temperature. The RH sensing characteristics of PVA coated IDC transducer are analysed in terms of its sensitivity, dynamic range, frequency response, repeatability and recovery time. It is found that the stability of such a PVA based RH transducer improves remarkably by diffusing nanoparticles of CoFe₂O₄ in the PVA layer. A standard deviation of ± 0.05 and ± 0.01 for a sensitive layer with pure PVA and PVA-CoFe₂O₄ combination respectively results in a measurement error of ± 0.005 and ± 0.003.