Records with Subject: Process Monitoring
Showing records 1 to 25 of 186. [First] Page: 1 2 3 4 5 Last
Detection and Diagnosis of Ring Formation in Rotary Lime Kilns
Lee D Rippon, Barry Hirtz, Carl Sheehan, Travis Reinheimer, Cilius van der Merwe, Philip Loewen, Bhushan Gopaluni
October 21, 2021 (v1)
Keywords: data visualization, Fault Detection, fault diagnosis, process monitoring, pulp and paper, rotary kiln
Rotary lime kilns are large-scale, energy-intensive unit operations that serve critical functions in a variety of industrial processes including cement production, pyrometallurgy, and kraft pulping. As massive expensive vessels that operate at high temperatures it is imperative from economic, environmental, and safety perspectives to optimize preventative maintenance and production efficiency. To achieve these objectives rotary kilns are increasingly outfitted with more sophisticated sensing technology that can provide additional operating insights. Although increasingly intricate data is collected from industrial operations the extent to which value is extracted from this data is often far from optimal. Our research aims to improve this situation by developing data analytics methods that leverage advanced industrial sensor data to address outstanding process faults. Specifically, this research investigates the use of infrared thermal cameras to detect and diagnose ring formation in r... [more]
Fault Monitoring of Chemical Process Based on Sliding Window Wavelet DenoisingGLPP
Fan Yang, Yuancun Cui, Feng Wu, Ridong Zhang
October 14, 2021 (v1)
Keywords: global local preserving projections, principal component analysis, process monitoring, sliding window, Tennessee Eastman, wavelet denoising
In industrial process fault monitoring, it is very important to collect accurate data, but in the actual process, there are often various noises that are difficult to eliminate in the collected data due to sensor accuracy, measurement errors, or human factors. Existing statistical process monitoring methods often ignore the problem of data noise. To solve this problem, a sliding window wavelet denoising-global local preserving projections (SWWD-GLPP) process monitoring method is proposed. In the offline stage, the wavelet denoising method is used to denoise the offline data, and then, the GLPP method is used for offline modeling, and then, the control limit is obtained by the kernel density estimation method. In the online phase, the sliding window wavelet denoising method is used to denoise the online data in real time. Then, use the model of the GLPP method to find the statistics, compare them with the control limit, judge the fault situation, and finally, use the contribution graph... [more]
Research on Rotating Machinery Fault Diagnosis Method Based on Energy Spectrum Matrix and Adaptive Convolutional Neural Network
Yiyang Liu, Yousheng Yang, Tieying Feng, Yi Sun, Xuejian Zhang
October 14, 2021 (v1)
Keywords: convolutional neural network, dynamic adjustment of the learning rate, energy spectrum matrix, hierarchical fault diagnosis, rotating machinery
Traditional intelligent fault diagnosis methods focus on distinguishing different fault modes, but ignore the deterioration of fault severity. This paper proposes a new two-stage hierarchical convolutional neural network for fault diagnosis of rotating machinery bearings. The failure mode and failure severity are modeled as a hierarchical structure. First, the original vibration signal is transformed into an energy spectrum matrix containing fault-related information through wavelet packet decomposition. Secondly, in the model training method, an adaptive learning rate dynamic adjustment strategy is further proposed, which adaptively extracts robust features from the spectrum matrix for fault mode and severity diagnosis. To verify the effectiveness of the method, the bearing fault data was collected using a rotating machine test bench. On this basis, the diagnostic accuracy, convergence performance and robustness of the model under different signal-to-noise ratios and variable load env... [more]
Method and Device Based on Multiscan for Measuring the Geometric Parameters of Objects
Michael Yurievich Alies, Yuriy Konstantinovich Shelkovnikov, Milan Sága, Milan Vaško, Ivan Kuric, Evgeny Yurievich Shelkovnikov, Aleksandr Ivanovich Korshunov, Anastasia Alekseevna Meteleva
September 21, 2021 (v1)
Keywords: discrete–continuous structure, measurement, multiscan, photodiode cell, Vernier method, video signal
The article deals with the issues of improving the accuracy of measurements of the geometric parameters of objects by optoelectronic systems, based on a television multiscan. A mathematical model of a multiscan with scanistor activation is developed, expressions for its integral output current and video signal are obtained, and the mechanism of their formation is investigated. An expression for the video signal is obtained that reflects the dual nature of the discrete−continuous multiscan structure: the video signal can have a discrete (pulse) or analog (continuous) form, depending on the step voltage between the photodiode cells of the multiscan. A Vernier discrete−analog method for measuring the parameters of the light zone on a multiscan is proposed, in which in order to increase the accuracy of the measurements, the location of the video pulse is determined relative to the neighboring reference pulses of a rigid geometric raster due to the slope of the discrete structure of the mul... [more]
Quantitative Determination of Vitamins A and E in Ointments Using Raman Spectroscopy
Sylwester Mazurek, Kamil Pichlak, Roman Szostak
August 2, 2021 (v1)
Keywords: chemometrics, multivariate calibration, ointments, quantitative analysis, Raman spectroscopy, vitamin A, vitamin E
A quantitative analysis of vitamins A and E in commercial ointments containing 0.044% and 0.8% (w/w) of active pharmaceutical ingredients, respectively, was performed using partial least squares models based on FT Raman spectra. Separate calibration systems were prepared to determine the amount of vitamin A in a petrolatum base ointment and to quantify vitamins A and E in a eucerin base one. Compositions of the laboratory-prepared and commercial samples were controlled through a principal component analysis. Relative standard errors of prediction were calculated to compare the predictive ability of the obtained regression models. For vitamin A determination, these errors were found to be in the 3.8−5.0% and 5.7−5.9% ranges for the calibration and validation data sets, respectively. In the case of vitamin E modeling, these errors amounted to 3.7% and 4.4%. On the basis of elaborated models, vitamins A and E were successfully quantified in two commercial products with recoveries in the 9... [more]
Prototype of the Runway Monitoring Process at Smaller Airports: Edvard Rusjan Airport Maribor
Boštjan Kovačič, Damjan Želodec, Damjan Doler
July 29, 2021 (v1)
Keywords: airport, deformations, FWD, geo-information model, geodesy, measurements, monitoring, vertical deviations
The last 20-year announcement predicts a 3.5% increase in the number of yearly passengers which will result in the doubling of the number of passengers in air transport by 2037. Such anticipation indicates the need for efficient monitoring of airport infrastructure as the support of opportune and efficient maintenance works. The novelties of this article are a process model of maintenance and monitoring, suitable for smaller and less burdened airports, and the methodology of monitoring of runways by implementation of the geodetic and geomechanics falling weight deflectometer (FWD) method. In addition, the results confirm the assumption that a specific environment such as an airport allows for sufficiently reliable determination of deformation areas or areas of vertical deviations of runways in a relative short time period available for measurements by using geodetic methods only or by combining other methods; our research model includes the FWD method. With the research, we have also s... [more]
Characterization of a Wireless Vacuum Sensor Prototype Based on the SAW-Pirani Principle
Sofia Toto, Mazin Jouda, Jan G. Korvink, Suparna Sundarayyan, Achim Voigt, Hossein Davoodi, Juergen J. Brandner
July 29, 2021 (v1)
Keywords: compact, Pirani, SAW, sensing, vacuum, wireless
A prototype of a wireless vacuum microsensor combining the Pirani principle and surface acoustic waves (SAW) with extended range and sensitivity was designed, modelled, manufactured and characterised under different conditions. The main components of the prototype are a sensing SAW chip, a heating coil and an interrogation antenna. All the components were assembled on a 15 mm × 11 mm × 3 mm printed circuit board (PCB). The behaviour of the PCB was characterised under ambient conditions and in vacuum. The quality of the SAW interrogation signal, the frequency shift and the received current of the coil were measured for different configurations. Pressures between 0.9 and 100,000 Pa were detected with sensitivities between 2.8 GHz/Pa at 0.9 Pa and 1 Hz/Pa close to atmospheric pressure. This experiment allowed us to determine the optimal operating conditions of the sensor and the integration conditions inside a vacuum chamber in addition to obtaining a pressure-dependent signal.
Thermal Hazard Analysis of Styrene Polymerization in Microreactor of Varying Diameter
Junjie Wang, Lei Ni, Jiawei Cui, Juncheng Jiang, Kuibin Zhou
July 29, 2021 (v1)
Keywords: Computational Fluid Dynamics, microreactor, styrene polymerization, thermal runaway
Polymerization is a typical exothermic reaction in the fine chemical industry, which is easy to cause thermal runaway. In order to lower the thermal runaway risk of polymerization, a microreactor was adopted in this study to carry out styrene thermal polymerization. The hydrodynamic model and the fluid−solid coupling model of thermal polymerization of styrene were combined by using the computation fluid dynamics (CFD) method to build a three-dimensional steady-state model of the batch and the microreactor and compare. The results indicated that the maximum temperature of the polymerization in the microreactor was only 150.23 °C, while in the batch reactor, it was up to 371.1 °C. Therefore, the reaction temperature in the microreactor could be controlled more effectively compared with that in the batch reactor. During the reaction process, jacket cooling may fail, which would lead to an adiabatic situation. According to the divergence criterion (DIV), the thermal runaway of the polymeri... [more]
First Principles Statistical Process Monitoring of High-Dimensional Industrial Microelectronics Assembly Processes
Tiago J. Rato, Pedro Delgado, Cristina Martins, Marco S. Reis
June 10, 2021 (v1)
Keywords: artificial generation of variability, data augmentation, high-dimensional data, Industry 4.0, statistical process monitoring
Modern industrial units collect large amounts of process data based on which advanced process monitoring algorithms continuously assess the status of operations. As an integral part of the development of such algorithms, a reference dataset representative of normal operating conditions is required to evaluate the stability of the process and, after confirming that it is stable, to calibrate a monitoring procedure, i.e., estimate the reference model and set the control limits for the monitoring statistics. The basic assumption is that all relevant “common causes” of variation appear well represented in this reference dataset (using the terminology adopted by the founding father of process monitoring, Walter A. Shewhart). Otherwise, false alarms will inevitably occur during the implementation of the monitoring scheme. However, we argue and demonstrate in this article, that this assumption is often not met in modern industrial systems. Therefore, we introduce a new approach based on the r... [more]
A Wavelet Transform-Assisted Convolutional Neural Network Multi-Model Framework for Monitoring Large-Scale Fluorochemical Engineering Processes
Xintong Li, Kun Zhou, Feng Xue, Zhibing Chen, Zhiqiang Ge, Xu Chen, Kai Song
May 27, 2021 (v1)
Keywords: convolutional neural network (CNN), deep learning, fault detection and diagnosis (FDD), fluorochemical engineering processes, wavelet transform
The barely satisfactory monitoring situation of the hypertoxic fluorochemical engineering processes requires the application of advanced strategies. In order to deal with the non-linear mechanism of the processes and the highly complicated correlation among variables, a wavelet transform-assisted convolutional neural network (CNN) based multi-model dynamic monitoring method was proposed. A preliminary CNN model was first trained to detect faults and to diagnose part of them with minimum computational burden and time delay. Then, a wavelet assisted secondary CNN model was trained to diagnose the remaining faults with the highest possible accuracy. In this step, benefitting from the scale decomposition capabilities of the wavelet transform function, the inherent noise and redundant information could be filtered out and the useful signal was transformed into a higher compact space. In this space, a well-designed secondary CNN model was trained to further improve the fault diagnosis perfor... [more]
Quantitative Methods to Support Data Acquisition Modernization within Copper Smelters
Alessandro Navarra, Ryan Wilson, Roberto Parra, Norman Toro, Andrés Ross, Jean-Christophe Nave, Phillip J. Mackey
May 27, 2021 (v1)
Keywords: adaptive finite differences, copper smelter, discrete event simulation, Industry 4.0, matte-slag chemistry, nickel-copper smelter, Peirce-smith converting, radiometric sensors
Sensors and process control systems are essential for process automation and optimization. Many sectors have adapted to the Industry 4.0 paradigm, but copper smelters remain hesitant to implement these technologies without appropriate justification, as many critical functions remain subject to ground operator experience. Recent experiments and industrial trials using radiometric optoelectronic data acquisition, coupled with advanced quantitative methods and expert systems, have successfully distinguished between mineral species in reactive vessels with high classification rates. These experiments demonstrate the increasing potential for the online monitoring of the state of a charge in pyrometallurgical furnaces, allowing data-driven adjustments to critical operational parameters. However, the justification to implement an innovative control system requires a quantitative framework that is conducive to multiphase engineering projects. This paper presents a unified quantitative framewor... [more]
Challenges and Opportunities on Nonlinear State Estimation of Chemical and Biochemical Processes
Ronald Alexander, Gilson Campani, San Dinh, Fernando V. Lima
May 27, 2021 (v1)
Keywords: extended Kalman filter, moving horizon estimation, nonlinear system, state estimation
This paper provides an overview of nonlinear state estimation techniques along with a discussion on the challenges and opportunities for future work in the field. Emphasis is given on Bayesian methods such as moving horizon estimation (MHE) and extended Kalman filter (EKF). A discussion on Bayesian, deterministic, and hybrid methods is provided and examples of each of these methods are listed. An approach for nonlinear state estimation design is included to guide the selection of the nonlinear estimator by the user/practitioner. Some of the current challenges in the field are discussed involving covariance estimation, uncertainty quantification, time-scale multiplicity, bioprocess monitoring, and online implementation. A case study in which MHE and EKF are applied to a batch reactor system is addressed to highlight the challenges of these technologies in terms of performance and computational time. This case study is followed by some possible opportunities for state estimation in the f... [more]
Denoising of Hydrogen Evolution Acoustic Emission Signal Based on Non-Decimated Stationary Wavelet Transform
Zazilah May, Md Khorshed Alam, Noor A’in A. Rahman, Muhammad Shazwan Mahmud, Nazrul Anuar Nayan
May 26, 2021 (v1)
Keywords: acoustic emission, denoising, hydrogen evolution, SHM, stationary wavelet transform
Monitoring the evolution of hydrogen gas on carbon steel pipe using acoustic emission (AE) signal can be a part of a reliable technique in the modern structural health-monitoring (SHM) field. However, the extracted AE signal is always mixed up with random extraneous noise depending on the nature of the service structure and experimental environment. The noisy AE signals often mislead the obtaining of the desired features from the signals for SHM and degrade the performance of the monitoring system. Therefore, there is a need for the signal denoising method to improve the quality of the extracted AE signals without degrading the original properties of the signals before using them for any knowledge discovery. This article proposes a non-decimated stationary wavelet transform (ND-SWT) method based on the variable soft threshold function for denoising hydrogen evolution AE signals. The proposed method filters various types of noises from the acquired AE signal and removes them efficiently... [more]
DOA Estimation in Non-Uniform Noise Based on Subspace Maximum Likelihood Using MPSO
Jui-Chung Hung
May 25, 2021 (v1)
Keywords: direction of arrival estimation, memetic algorithms, non-uniform noise, Particle Swarm Optimization, subspace maximum-likelihood
In general, the performance of a direction of arrival (DOA) estimator may decay under a non-uniform noise and low signal-to-noise ratio (SNR) environment. In this paper, a memetic particle swarm optimization (MPSO) algorithm combined with a noise variance estimator is proposed, in order to address this issue. The MPSO incorporates re-estimation of the noise variance and iterated local search algorithms into the particle swarm optimization (PSO) algorithm, resulting in higher efficiency and a reduction in non-uniform noise effects under a low SNR. The MPSO procedure is as follows: PSO is initially utilized to evaluate the signal DOA using a subspace maximum-likelihood (SML) method. Next, the best position of the swarm to estimate the noise variance is determined and the iterated local search algorithm to reduce the non-uniform noise effect is built. The proposed method uses the SML criterion to rebuild the noise variance for the iterated local search algorithm, in order to reduce non-un... [more]
Analysis of Soot Deposition Mechanisms on Nickel-Based Anodes of SOFCs in Single-Cell and Stack Environment
Konrad Motylinski, Marcin Blesznowski, Marek Skrzypkiewicz, Michal Wierzbicki, Agnieszka Zurawska, Arkadiusz Baran, Maciej Bakala, Jakub Kupecki
May 17, 2021 (v1)
Keywords: Boudouard reaction, carbon deposition, SOFC
Solid oxide fuel cells (SOFCs) can be fueled with various gases, including carbon-containing compounds. High operating temperatures, exceeding 600 °C, and the presence of a porous, nickel-based SOFC anode, might lead to the formation of solid carbon particles from fuels such as carbon monoxide and other gases with hydrocarbon-based compounds. Carbon deposition on fuel electrode surfaces can cause irreversible damage to the cell, eventually destroying the electrode. Soot formation mechanisms are strictly related to electrochemical, kinetic, and thermodynamic conditions. In the current study, the effects of carbon deposition on the lifetime and performance of SOFCs were analyzed in-operando, both in single-cell and stack conditions. It was observed that anodic gas velocity has an impact on soot formation and deposition, thus it was also studied in depth. Single-anode-supported solid oxide fuel cells were fueled with gases delivered in such a way that the initial velocities in the anodic... [more]
PEMFC Transient Response Characteristics Analysis in Case of Temperature Sensor Failure
Jaeyoung Han, Sangseok Yu, Jinwon Yun
May 11, 2021 (v1)
Keywords: controller, dynamic system model, fault scenario, fault tolerance control, fuel cell vehicle, thermal management system
In this study, transient responses of a polymer electrolyte fuel cell system were performed to understand the effect of sensor fault signal on the temperature sensor of the stack and the coolant inlet. We designed a system-level fuel cell model including a thermal management system, and a controller to analyze the dynamic behavior of fuel cell system applied with variable sensor fault scenarios such as stuck, offset, and scaling. Under drastic load variations, transient behavior is affected by fault signals of the sensor. Especially, the net power of the faulty system is 45.9 kW. On the other hand, the net power of the fault free system is 46.1 kW. Therefore, the net power of a faulty system is about 0.2 kW lower than that of a fault-free system. This analysis can help in understanding the transient behavior of fuel cell systems at the system level under fault situations and provide a proper failure avoidance control strategy for the fuel cell system.
Motor Fault Detection Using Wavelet Transform and Improved PSO-BP Neural Network
Chun-Yao Lee, Yi-Hsin Cheng
April 30, 2021 (v1)
Keywords: back propagation neural network, Fault Detection, induction motors, particle swarm optimization wavelet transform
This paper proposes a motor fault detection method based on wavelet transform (WT) and improved PSO-BP neural network which is combined with improved particle swarm optimization (PSO) and a back propagation (BP) neural network with linearly increasing inertia weight. First, this research used WT to analyze the current signals of the healthy motor, bearing damage motor, stator winding inter-turn short circuit motor, and broken rotor bar motor. Second, features after completing the signal analysis were extracted, and three types of classifiers were used to classify. The results show that the improved PSO-BP neural network can effectively detect the cause of failure. In addition, in order to simulate the actual operating environment of the motor, this study added white noise with signal noise ratios of 30 dB, 25 dB, and 20 dB to verify that this model has a better anti-noise ability.
Adaptive Monitoring of Biotechnological Processes Kinetics
Velislava Lyubenova, Maya Ignatova, Olympia Roeva, Stefan Junne, Peter Neubauer
April 30, 2021 (v1)
Keywords: adaptive monitoring, bioprocess kinetics, software sensor, stirred tank reactor, tuning procedure
In this paper, an approach for the monitoring of biotechnological process kinetics is proposed. The kinetics of each process state variable is presented as a function of two time-varying unknown parameters. For their estimation, a general software sensor is derived with on-line measurements as inputs that are accessible in practice. The stability analysis with a different number of inputs shows that stability can be guaranteed for fourth- and fifth-order software sensors only. As a case study, the monitoring of the kinetics of processes carried out in stirred tank reactors is investigated. A new tuning procedure is derived that results in a choice of only one design parameter. The effectiveness of the proposed procedure is demonstrated with experimental data from Bacillus subtilis fed-batch cultivations.
Real-Time Nanoplasmonic Sensor for IgG Monitoring in Bioproduction
Thuy Tran, Olof Eskilson, Florian Mayer, Robert Gustavsson, Robert Selegård, Ingemar Lundström, Carl-Fredrik Mandenius, Erik Martinsson, Daniel Aili
April 29, 2021 (v1)
Keywords: bioprocess, IgG titer, nanoplasmonic, on-line, PAT, real-time
Real-time monitoring of product titers during process development and production of biotherapeutics facilitate implementation of quality-by-design principles and enable rapid bioprocess decision and optimization of the production process. Conventional analytical methods are generally performed offline/at-line and, therefore, are not capable of generating real-time data. In this study, a novel fiber optical nanoplasmonic sensor technology was explored for rapid IgG titer measurements. The sensor combines localized surface plasmon resonance transduction and robust single use Protein A-modified sensor chips, housed in a flexible flow cell, for specific IgG detection. The sensor requires small sample volumes (1−150 µL) and shows a reproducibility and sensitivity comparable to Protein G high performance liquid chromatography-ultraviolet (HPLC-UV). The dynamic range of the sensor system can be tuned by varying the sample volume, which enables quantification of IgG samples ranging from 0.0015... [more]
Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery
Shengnan Tang, Shouqi Yuan, Yong Zhu
April 26, 2021 (v1)
Keywords: cyclic spectral, cyclostationarity, fault diagnosis, rotating machinery
In the light of the significance of the rotating machinery and the possible severe losses resulted from its unexpected defects, it is vital and meaningful to exploit the effective and feasible diagnostic methods of its faults. Among them, the emphasis of the analysis approaches for fault type and severity is on the extraction of useful components in the fault features. On account of the common cyclostationarity of vibration signal under faulty states, fault diagnosis methods based on cyclostationary analysis play an essential role in the rotatory machine. Based on it, the fundamental definition and classification of cyclostationarity are introduced briefly. The mathematical principles of the essential cyclic spectral analysis are outlined. The significant applications of cyclostationary theory are highlighted in the fault diagnosis of the main rotating machinery, involving bearing, gear, and pump. Finally, the widely-used methods on the basis of cyclostationary theory are concluded, an... [more]
High-Throughput Raman Spectroscopy Combined with Innovate Data Analysis Workflow to Enhance Biopharmaceutical Process Development
Stephen Goldrick, Alexandra Umprecht, Alison Tang, Roman Zakrzewski, Matthew Cheeks, Richard Turner, Aled Charles, Karolina Les, Martyn Hulley, Chris Spencer, Suzanne S. Farid
April 16, 2021 (v1)
Keywords: cation exchange chromatography, high-throughput, mammalian cell culture, monitoring, process analytical technology, Raman spectroscopy, scale-down technologies
Raman spectroscopy has the potential to revolutionise many aspects of biopharmaceutical process development. The widespread adoption of this promising technology has been hindered by the high cost associated with individual probes and the challenge of measuring low sample volumes. To address these issues, this paper investigates the potential of an emerging new high-throughput (HT) Raman spectroscopy microscope combined with a novel data analysis workflow to replace off-line analytics for upstream and downstream operations. On the upstream front, the case study involved the at-line monitoring of an HT micro-bioreactor system cultivating two mammalian cell cultures expressing two different therapeutic proteins. The spectra generated were analysed using a partial least squares (PLS) model. This enabled the successful prediction of the glucose, lactate, antibody, and viable cell density concentrations directly from the Raman spectra without reliance on multiple off-line analytical devices... [more]
Membrane System-Based Improved Neural Networks for Time-Series Anomaly Detection
Wenxiang Guo, Xiyu Liu, Laisheng Xiang
April 16, 2021 (v1)
Keywords: anomaly detection, convolutional neural networks, long short-term memory, membrane systems, time series
Anomaly detection in time series has attracted much attention recently and is quite a challenging task. In this paper, a novel deep-learning approach (AL-CNN) that classifies the time series as normal or abnormal with less domain knowledge is proposed. The proposed algorithm combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) to effectively model the spatial and temporal information contained in time-series data, the techniques of Squeeze-and-Excitation are applied to implement the feature recalibration. However, the difficulty of selecting multiple parameters and the long training time of a single model make AL-CNN less effective. To alleviate these challenges, a hybrid dynamic membrane system (HM-AL-CNN) is designed which is a new distributed and parallel computing model. We have performed a detailed evaluation of this proposed approach on three well-known benchmarks including the Yahoo S5 datasets. Experiments show that the proposed method possessed a rob... [more]
Solid Circulating Velocity Measurement in a Liquid−Solid Micro-Circulating Fluidised Bed
Orlando L. do Nascimento, David A. Reay, Vladimir Zivkovic
April 16, 2021 (v1)
Keywords: circulating fluidised bed, digital PIV, liquid–solid fluidisation, micro-fluidised bed, wall effects
Liquid−solid circulating fluidised beds (CFB) possess many qualities which makes them useful for industrial operations where particle−liquid contact is vital, e.g., improved heat transfer performance, and consequent uniform temperature, limited back mixing, exceptional solid−liquid contact. Despite this, circulating fluidised beds have seen no application in the micro-technology context. Liquid−solid micro circulating fluidised bed (µCFBs), which basically involves micro-particles fluidisation in fluidised beds within the bed of cross-section or inner diameter at the millimetre scale, could find potential applications in the area of micro-process and microfluidics technology. From an engineering standpoint, it is vital to know the solid circulating velocity, since that dictates the bed capability and operability as processing equipment. Albeit there are several studies on solid circulating velocity measurement in CFBs, this article is introducing the first experimental study on solid c... [more]
Autonomous Indoor Scanning System Collecting Spatial and Environmental Data for Efficient Indoor Monitoring and Control
Dongwoo Park, Soyoung Hwang
March 14, 2021 (v1)
Keywords: autonomous scanning, environmental data, indoor spatial data, IoT (Internet of Things), mobile application, sensor
As various activities related to entertainment, business, shopping, and conventions are done increasingly indoors, the demand for indoor spatial information and indoor environmental data is growing. Unlike the case of outdoor environments, obtaining spatial information in indoor environments is difficult. Given the absence of GNSS (Global Navigation Satellite System) signals, various technologies for indoor positioning, mapping and modeling have been proposed. Related business models for indoor space services, safety, convenience, facility management, and disaster response, moreover, have been suggested. An autonomous scanning system for collection of indoor spatial and environmental data is proposed in this paper. The proposed system can be utilized to collect spatial dimensions suitable for extraction of a two-dimensional indoor drawing and obtainment of spatial imaging as well as indoor environmental data on temperature, humidity and particulate matter. For these operations, the sys... [more]
A Review on Fault Detection and Process Diagnostics in Industrial Processes
You-Jin Park, Shu-Kai S. Fan, Chia-Yu Hsu
March 14, 2021 (v1)
Keywords: data-driven methods, Fault Detection, fault diagnosis, fault prognosis, hybrid method, industrial process, knowledge-based methods, model-based methods
The main roles of fault detection and diagnosis (FDD) for industrial processes are to make an effective indicator which can identify faulty status of a process and then to take a proper action against a future failure or unfavorable accidents. In order to enhance many process performances (e.g., quality and throughput), FDD has attracted great attention from various industrial sectors. Many traditional FDD techniques have been developed for checking the existence of a trend or pattern in the process or whether a certain process variable behaves normally or not. However, they might fail to produce several hidden characteristics of the process or fail to discover the faults in processes due to underlying process dynamics. In this paper, we present current research and developments of FDD approaches for process monitoring as well as a broad literature review of many useful FDD approaches.
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