Krzysztof Grudzień

ECT Image Analysis Methods for Shear Zone Measurements during Silo Discharging Process

Abstract The paper covers the electrical capacitance tomography (ECT) data analysis on shear zones formed during silo discharging process. This is due to the ECT aptitude for detection of slight changes of material concentration. On the basis of ECT visualisations, wall-adjacent shear zone profiles are analysed for different wall roughness parameters. The analysis on changes of material concentration, based on ECT images, enables the calculation for the characteristic parameters of shear zones—size and material concentration inside the shear zone in a dynamic process of silo discharging. In order to verify the methodology a series of experiments on gravitational flow of bulk solids under various conditions were conducted with different initial granular material packing densities and silo wall roughness. The investigation shows that the increase in container wall roughness is an effective method for reducing the dynamic effects during the material discharging, since these effects are resulted from the resonance between hopper construction and trembling material. Such effects will damage industrial equipment in practical applications and need further investigation.

Using Crowdsourcing for Scientific Analysis of Industrial Tomographic Images

In this article, we present a novel application domain for human computation, specifically for crowdsourcing, which can help in understanding particle-tracking problems. Through an interdisciplinary inquiry, we built a crowdsourcing system designed to detect tracer particles in industrial tomographic images, and applied it to the problem of bulk solid flow in silos. As images from silo-sensing systems cannot be adequately analyzed using the currently available computational methods, human intelligence is required. However, limited availability of experts, as well as their high cost, motivates employing additional nonexperts. We report on the results of a study that assesses the task completion time and accuracy of employing nonexpert workers to process large datasets of images in order to generate data for bulk flow research. We prove the feasibility of this approach by comparing results from a user study with data generated from a computational algorithm. The study shows that the crowd is more scalable and more economical than an automatic solution. The system can help analyze and understand the physics of flow phenomena to better inform the future design of silos, and is generalized enough to be applicable to other domains.

Simulation of gravitational solids flow process and its parameters estimation by the use of Electrical Capacitance Tomography and Artificial Neural Networks

The paper presents a new approach to monitoring changes of characteristic parameters of gravitational solids flow. Electrical Capacitance Tomography (ECT) is applied for non-invasive process monitoring. Artificial Neural Networks (ANN) are used to estimate important flow parameters knowing the measured capacitances. The proposed approach solves the ECT inverse problem in a direct manner and provides a rapid parameterization of the funnel flow. The simulation of the silo discharging process is performed relying on real flow behaviour obtained from the authors’ previous work. The simulated data are used to new approach testing and verification. The obtained results proved that proposed ANN-based method will allow for on-line gravitational solids flow monitoring.

Towards Supporting Remote Cheering during Running Races with Drone Technology

The increasing availability of drones produces a number of opportunities for integrating them in everyday settings and using drones to create engaging experiences for users. In this paper, we investigate how drones can support amateur runners in their endeavours. We explore the possible roles for drones during amateur running races. Through two field studies and multiple semi-structured interviews, we gain new insights on how drones could augment the experience of both runners and supporters during organised races. Finally, we contribute a set of future directions for integrating drones into the sports experience.

Neural network approach to ECT inverse problem solving for estimation of gravitational solids flow

A new method to solve the inverse problem of electrical capacitance tomography is proposed. Our method is based on artificial neural network to estimate the radius of an object present inside a pipeline. This information is useful to predict the distribution of material inside the pipe. The capacitance data used to train and test the neural network is simulated on Matlab using the electrical capacitance tomography toolkit ECTsim. The provided accuracy is promising and shows efficiency to solve the inverse problem in a simple manner and on reduced computational time about 120 times when compared to the existing Landweber iterative algorithm for tomographic image reconstruction that can be encouraging for dynamic industrial applications.

Contextual processing of ECT measurement information towards detection of process emergency states

This papers shows attempt of using prior knowledge in order to learn about the industrial process system behaviour. The system being studied here is the pneumatic conveying of solids monitored with electrical capacitance tomography (ECT). The prior knowledge is the non-invasive measurement information derived on the basis of intentionally introduced structural change in the experimental process flow rig. The focus is on the examination of pseudo-emergency states of bulk flow. Real case pneumatic transport failure usually consists in pipeline blockage. Such blockage typically develops starting as material slug build up. In contrast, our experiments investigate artificially induced flow pre-blockage states monitored with ECT measurement system. Collected measurement data are contextually processed in order to detect the pipeline state in sense of flow regime and flow behaviour. ECT raw data analysis strives to uncover a relation between measured values and flow change tendency. This research is aimed at giving rise to conclusions about possibility of contextual data processing in order to properly adjust pneumatic conveying control and a possible role of ECT records in this aspect. Authors show here that for now it is possible to detect abnormal process state such as blockage signal and the presented methodology coupled with nowadays computational techniques are anticipated to lead to abnormal states forecasting such as blockage threat recognition.

Statistical method for analysis of multidimensional tomographic data

The paper presents the investigations of gas/solids flow in an opaque system by using industrial process tomography. The statistical analysis of such processes permits the decomposition of a multiphase flow into separated components. The components of the flow are defined as areas, in which changes in the material concentration are very similar. Changes in the position of individual components permit to investigate the dynamics of the processes.

Visualization System for Large-Scale Silo Flow Monitoring Based on ECT Technique

This paper addresses the issues of monitoring and diagnostics of flow processes in gravitational flows of friable materials within large-scale silos. The author shows how the electrical capacitance tomography (ECT) may be used for monitoring large-scale industrial reactors dedicated for dense granular flow. The ECT sensors under investigation are specially designed for large-scale measurements and accompanied by a dedicated measurement protocol. The proposed ECT sensor allows visualizing, in form of 3-D image, material concentration distribution inside silo wall area for 1.5 m silo container. The developed method provides possibility of analyze effective transition point, boundary between stagnant zone and flowing material.

Advanced statistical computing for capacitance tomography as a monitoring and control tool

Advanced statistical modelling such as Bayesian framework is a powerful methodology and gives great flexibility in terms of physical phenomena modelling. Unfortunately it is usually associated with very time and resource consuming computing. Therefore it was avoided by engineers in the past. Nowadays, rapid development of computer capabilities enables use of such methods. Algorithms reported here are based on Markov chain Monte Carlo (MCMC) methods applied to Bayesian modelling. The important factor is highly iterative approach enabling direct desired parameters estimation, hence omitting the phase of image reconstruction. This property has an important feature of making feasible implementation of automatic industrial process control systems based on process tomography.

Design of crowdsourcing system for analysis of gravitational flow using X-ray visualization

The paper describes application of the crowdsourcing system to pre-process X-ray tomography images. In this paper, we show the analysis of the crowdsourcing system applied to process tomography imaging to investigate granular flow with aid of tracking particles. Applying crowdsourcing approach coupled with a proper system interface design enhances the performance of the workers and elevates the attitude. We show here how analysis of the proposed systems in terms of user adoption and performance. Proposed interface features are designed based on previous work evaluation in order to reduce the cognitive and physical workload demands.