Online Monitoring Approach Research Articles

Multivariate Image Analysis (MIA) for Industrial Flare Combustion Control

A new approach for flare monitoring is proposed so that flare combustion efficiency can be predicted online in industrial plants. Multivariate image analysis (MIA), which is based on principal component analysis (PCA) and projection to latent structures (PLS), has been applied to flare combustion systems in order to predict their resulting combustion efficiencies, as a function of the crosswind velocity, using simulated results, and as a function of steam or air flow rates, using experimental tests of a full-size flare. The results show that a multivariate regression model based on flare color images can be used to predict the flare performance over a range of operating conditions for steam-assisted flares. Therefore, simple two-dimensional color images of industrial flares may be a fast, accurate, and inexpensive approach for online monitoring of these industrial combustion systems. This would allow for developing effective flare control and mitigation strategies.

Modeling and monitoring for handling nonlinear dynamic processes

In this paper, a new online monitoring approach is proposed for handling the dynamical multimode problem in the industrial processes. The contributions are as follows: (1) extracting method of the common characteristics from different modes is proposed; (2) nonlinear dynamic monitoring method is proposed; and (3) a new model analysis method is proposed. There are both similarity and dissimilarity in the underlying correlations of different modes. After two different subspaces are separated, models of the common and specific subspaces are built respectively. Then the common subspace and specific subspace are analyzed, where the monitoring process is carried out in each subspace. When the mode switches, the specific monitoring model is changed. The corresponding confidence regions are constructed according to their models respectively. The effectiveness of the proposed method has been demonstrated via simulated examples.

Fabrication of Submicron-Diameter and Taper Fibers Using Chemical Etching

The thin, long length and high smoothness silica photonic nanowires and taper optical fiber were fabricated using a simple and low cost chemical etching method. A two-steps wet etch process were used consisting of etching with 30% HF acid to remove cladding and 24% HF acid to decrease fiber core diameter. An approach for on-line monitoring of etching using 1300 nm light power transmitted in the optical fiber was used to determine the diameter of the remaining core and showed a transition between two different operation regimes of nanofiber from the embedded regime, where the mode was isolated from the environment, to the evanescent regime. The data indicated that the diameter of the silica fiber decreased linearly for both 30% and 24% HF acid with 1.2 and 0.1 μm/min grad diameter, respectively at room temperature, and more than 70% of the mode intensity could propagate outside fiber when the core diameter was less than 1 μm. The results of fiber taper showed that the fiber was tapered by a factor of 20 while retaining a thin core structure and leaving about more than 85% of core structure.

An acoustic technique for measurement of bubble solids mass loading: (b) Monitoring of Jameson cell flotation performance by passive acoustic emissions

A passive acoustic emission (AE) monitoring system has been developed and tested in Australian coal washeries. The system detects naturally occurring stress waves in the pulp and froth zones of a flotation unit, utilising volumetric (hydrophone) and surface (broadband accelerometer) sensing technologies. The analyser has been proof-of-concept trialled on a coal washery Jameson cell. AE signals associated with both hydrophones mounted near and accelerometers mounted on a downcomer are shown to be sensitive to cell process conditions including aeration, feed solids and reagent addition rates. Accelerometers mounted on a Jameson cell downcomer are shown to be particularly sensitive to aeration rate with AE signals containing features that can be interpreted in terms of Minnaert bubble resonance theory. The frequencies of peaks in the Fourier power spectra are in this case used to estimate dominant bubble size and solids loading inside the downcomer. Passive AE monitoring is a promising approach for flotation cell performance on-line monitoring.

Modeling and monitoring of dynamic processes.

In this paper, a new online monitoring approach is proposed for handling the dynamic problem in industrial batch processes. Compared to conventional methods, its contributions are as follows: (1) multimodes are separated correctly since the cross-mode correlations are considered and the common information is extracted; (2) the expensive computing load is avoided since only the specific information is calculated when a mode is monitored online; and (3) after that, two different subspaces are separated, and the common and specific subspace models are built and analyzed, respectively. The monitoring is carried out in the subspace. The corresponding confidence regions are constructed according to their respective models.

Probabilistic Kernel Approach to Online Monitoring of Nuclear Power Plants

Nuclear power plants are complex engineering systems comprised of many interacting and interdependent mechanical components whose failure might lead to degraded plant performance or unplanned shutdown with loss of power generation and negative economic impact. As a result, continuous component surveillance and accurate prediction of their failing points is necessary for their on-time replacement. In this paper, a probabilistic kernel approach for intelligent online monitoring of mechanical components is presented. Specifically, the probabilistic kernel notion of Gaussian processes (GPs) is applied to the distribution prediction of a component's degradation trend. The proposed method exploits the learning ability of a GP and updates its prediction using a feedback mechanism. The methodology is tested on actual turbine blade degradation data for a variety of topologies (i.e., kernels). The GP estimations are compared to those obtained with a nonprobabilistic, kernel-based machine learning algorithm, the support vector regression (SVR). The comparison outcome clearly demonstrates that GP prediction accuracy outperforms SVR in the majority of the cases while providing a predictive distribution instead of point estimates as SVR does.

Efficient and scalable scheduling for performance heterogeneous multicore systems

Performance heterogeneous multicore processors (HMP for brevity) consisting of multiple cores with the same instruction set but different performance characteristics (e.g., clock speed, issue width), are of great concern since they are able to deliver higher performance per watt and area for programs with diverse architectural requirements than comparable homogeneous ones. However, such power and area efficiencies of performance heterogeneous multicore systems can only be achieved when workloads are matched with cores according to both the properties of the workload and the features of the cores.Several heterogeneity-aware schedulers were proposed in the previous work. In terms of whether properties of workloads are obtained online or not, those scheduling algorithms can be categorized into two classes: online monitoring and offline profiling. The previous online monitoring approaches had to trace threads’ execution on all core types, which is impractical as the number of core types grows. Besides, to trace all core types threads have to be migrated among cores, which may cause load imbalance and degrade the performance. The existing offline profiling approaches profile programs with a given input set before really executing them and thus remove the overhead associated with the number of core types. However, offline profiling approaches do not account for phase changes of threads. Moreover, since the properties they have collected are based on the given input set, those offline profiling approaches are hard to adapt to various input sets and therefore will drastically affect the program performance.To address the above problems in the existing approaches, we propose a new technique, ASTPI (Average Stall Time Per Instruction), to measure the efficiencies of threads in using fast cores. We design, implement and evaluate a new online monitoring approach called ESHMP, which is based on the metric. Our evaluation in the Linux 2.6.21 operating system shows that ESHMP delivers scalability while adapting to a wide variety of applications. Also, our experiment results show that among HMP systems in which heterogeneity-aware schedulers are adopted and there are more than one LLC (Last Level Cache), the architecture where heterogeneous cores share LLCs gain better performance than the ones where homogeneous cores share LLCs.

Bandwidth borrowing-based QoS approach for adaptive call admission control in multiclass traffic wireless cellular networks

SUMMARY This paper proposes a QoS approach for an adaptive call admission control (CAC) scheme for multiclass service wireless cellular networks. The QoS of the proposed CAC scheme is achieved through call bandwidth borrowing and call preemption techniques according to the priorities of the traffic classes, using complete sharing of the available bandwidth. The CAC scheme maintains QoS in each class to avoid performance deterioration through mechanisms for call bandwidth degradation, and call bandwidth upgrading based on min–max and max–min policies for fair resource deallocation and reallocation, respectively. The proposed adaptive CAC scheme utilizes a measurement-based online monitoring approach of the system performance, and a prediction model to determine the amount of bandwidth to be borrowed from calls, or the amount of bandwidth to be returned to calls. The simulation-based performance evaluation of the proposed adaptive CAC scheme shows the strength and effectiveness of our proposed scheme. Copyright © 2011 John Wiley & Sons, Ltd.

Acoustic chemometric prediction of total solids in bioslurry: A full-scale feasibility study for on-line biogas process monitoring

Dry matter is an important process control parameter in the bioconversion application field. Acoustic chemometrics, as a Process Analytical Technology (PAT) modality for quantitative characterisation of dry matter in complex bioslurry systems (biogas fermentation), has not been successful despite several earlier dedicated attempts. A full-scale feasibility study based on standard addition experiments involving natural plant biomass was conducted using multivariate calibration (Partial Least Squares Regression, PLS-R) of acoustic signatures against dry matter content (total solids, TS). Prediction performance of the optimised process implementation was evaluated using independent test set validation, with estimates of accuracy (slope of predicted vs. reference values) and precision (squared correlation coefficient, r 2) of 0.94 and 0.97 respectively, with RMSEP of 0.32% w/w (RMSEP rel = 3.86%) in the range of 5.8–10.8% w/w dry matter. Based on these excellent prediction performance measures, it is concluded that acoustic chemometrics has come of age as a full grown PAT approach for on-line monitoring of dry matter (TS) in complex bioslurry, with a promising application potential in other biomass processing industries as well.

Online condition-based shaft faults diagnosis with multiscale entropy

The conventional approach for online monitoring of a machine’s operating condition is based on linear time–frequency analysis and is therefore limited by the point that the vibrations are non-linear and non-stationary in nature. This problem has been addressed by the proposal of the multiscale entropy (MSE) approach to non-linear time series analysis. This paper proposes an online feature extractor to allow the vibration signal of shafts experiencing different problems to be differentiated using the MSE approach.

Statistical Analysis and Monitoring For Handling Different Modes Batch Processes

AbstractIn the paper, a new online monitoring approach is proposed for handling the multimode problem in the industrial batch processes. The contributions are as follows: (1) Extracting method of the common characteristics from different modes is proposed; (2) A new model analysis method is proposed. There are both similarity and dissimilarity in the underlying correlations of different modes. By an adequate decomposition, one part of the underlying variations in each subspace stay invariable over subspaces while the other part changes with the alternation of subspaces. After that two different subspaces are separated, modeling the common and specific subspaces respectively. The common subspace and specific subspace are analyzed respectively, and the monitoring process is carried out in each subspace. The corresponding confidence regions are constructed according to their models respectively. Then the online monitoring system is set up, which can track different types of variations closely.

On-line HPLC Analysis System for Metabolism and Inhibition Studies in Precision-Cut Liver Slices

A novel approach for on-line monitoring of drug metabolism in continuously perifused, precision-cut liver slices (PCLS) in a microfluidic system has been developed using high-performance liquid chromatography with UV detection (HPLC-UV). In this approach, PCLS are incubated in a microfluidic device made of poly(dimethylsiloxane) (PDMS) by continuous, single-pass perifusion with fresh medium. Two syringe pumps are incorporated into the system to infuse substrates or inhibitors at varying concentrations into the perfusion medium just before the chip entrance. The medium containing the metabolites produced by the PCLS is directed toward an injection loop. Once filled, the content of this injection loop is automatically injected onto an HPLC for analysis. The on-line analysis of metabolites was tested by using the substrate, 7-hydroxycoumarin (7-HC). Rapid switching between substrate and solvent control was possible, and a direct metabolic response of the liver slice to perifusion with substrate was detected. Very stable phase II metabolism over a period of 24 h was observed. The inhibitory effect of phloxine B on the formation of 7-hydroxycoumarin glucuronide (phase II product of 7-HC) was also investigated. Phloxine B was injected into the incubation medium in increasing concentrations varying from 0 to 200 μM. The results showed a concentration-dependent inhibition of 7-HC glucuronide formation and allowed the calculation of an IC50 value (concentration in which 50% of the enzyme is inhibited) of ∼85 μM using one single liver slice. On-line detection was also shown to be advantageous for the detection of unstable metabolites. This was demonstrated by determination of the metabolites of the drug diclofenac. The reactive metabolite, acyl glucuronide, was detected at relatively high concentrations which remained very constant over a period of 4 h. In contrast, only low and decreasing amounts of diclofenac acyl glucuronide could be measured in the conventional well-plate incubation system. The advantages of this novel on-line analysis system for PCLS include the capability to obtain direct information about tissue function, assess the concentration dependence of drug-drug interactions in one single slice, and detect unstable metabolites. The system also enables fast analysis without the need to store samples, thus eliminating the associated freeze-thaw problems, and allows the simultaneous analysis of multiple metabolites.

An Online Monitoring Approach for Web Service Requirements

Web service technology aims to enable the interoperation of heterogeneous systems and the reuse of distributed functions in an unprecedented scale and has achieved significant success. There are still, however, challenges to realize its full potential. One of these challenges is to ensure the behavior of Web services consistent with their requirements. Monitoring events that are relevant to Web service requirements is, thus, an important technique. This paper introduces an online monitoring approach for Web service requirements. It includes a pattern-based specification of service constraints that correspond to service requirements, and a monitoring model that covers five kinds of system events relevant to client request, service response, application, resource, and management, and a monitoring framework in which different probes and agents collect events and data that are sensitive to requirements. The framework analyzes the collected information against the prespecified constraints, so as to evaluate the behavior and use of Web services. The prototype implementation and experiments with a case study shows that our approach is effective and flexible, and the monitoring cost is affordable.

Monitoring of froth stability using electrical impedance spectroscopy

An on-line monitoring approach for froth stability in flotation processes has been developed based on electrical impedance spectroscopy. The electrical impedance spectrum (EIS) of the froth phase has been found to be sensitive to the froth structure and characteristics. The froth structure variation or fluctuation caused by bubble coalescence and rupture can be sensed by the variation or fluctuation of electrical impedance spectrum in the froth phase. The EIS electrodes can monitor the froth stability at real flotation conditions in a nearly non-invasive manner. An approach in which a stability index ( SI) has been defined, has been developed to quantify the stability of the froth structure. A froth phase having a low SI value has been found to be stable and the corresponding operating conditions will be favourable in terms of the separation efficiency of flotation processes.

Access Control System: An Architecture Approach for Monitoring, Survivability and Scalability in FTTH-PON Application

This paper highlights on online monitoring, protection switching and fast restoration approach for fiber to the home-passive optical network (FTTH-PON) using Ac- cess Control System (ACS). ACS is developed for downwardly monitoring and failure detection from central office (CO) towards customer premises. ACS also provides activation of switching in the restoration scheme at the drop region of FTTH-PON. Customer protection device (CPD) comprises of programmable optical switches will be employed at the customer premises which stationed just before the ONU to ensure high survivability. We proposed and experimentally demonstrated a new in-line monitoring and testing system with the combination of passive in-line monitoring (PIM) device and Centralized Failure Detection System (CFDS) for transmission surveillance and failure detection without affecting the triple-play service delivery in fiber-to-the-home (FTTH) access network. PIM device is a fully passive device used for optical signals tapping purposes, meanwhile CFDS is a centralized monitoring and troubleshooting solution with graphical user interface (GUI) processing capabilities in MATLAB. ACS contains also a new optical splitting device named MROS for improving the efficiency and scalability of data delivery to the customer premises/ subscribers through optimizing the magnitude power dis- tribute to each line connected to ONU. All this approach has a bright prospect to improve the survivability and reliability as well as increase the efficiency and monitoring capabilities in an optical access network.

Robust Online Monitoring for Multimode Processes Based on Nonlinear External Analysis

A robust online monitoring approach based on nonlinear external analysis is proposed for monitoring multimode processes. External analysis was previously proposed to distinguish faults from normal changes in operating conditions. However, linear external analysis may not function well in nonlinear processes, in which correlations between external variables and main variables are generally nonlinear. Under the consideration of real-time monitoring, a moving window is used for sample selection and least-squares support vector regression is used as the model structure of nonlinear external analysis. When the influence of external variables is removed, the filtered information of main variables is extracted and monitored by a two-step independent component analysis−principal component analysis strategy. In addition, to improve the performance of modeling and monitoring, a robust scheme is developed. A benchmark study of the Tennessee Eastman process shows the efficiency of the proposed method.

A filtration probe-free on-line monitoring of glycerol during fermentation by a biosensor device

A novel on-line monitoring approach implementing an improved sampling methodology is presented here. A sample is directly withdrawn from the bioreactor without a need for a filtration or a dialysis probe, extensively diluted in a flow-through fashion and mixed with a mobile phase consisting of a buffer and a soluble mediator. The developed sampling system efficiently controls dilution of the sample prior to biosensor assay covering full dynamic range of glycerol concentrations during its fermentation to 1,3-propanediol. The new sampling protocol minimizes problems associated with the use of traditional on-line sampling methods implementing membrane separation or filtration techniques. Concentration of glycerol in the sample is determined by a flow injection analysis (FIA) with an amperometric biosensor using glycerol dehydrogenase and diaphorase with a throughput of nine samples per hour. The biosensor performance was validated by a reference analytical method (HPLC) with a very good agreement.

ON-LINE ESTIMATION AND DETECTION OF ABNORMAL SUBSTRATE CONCENTRATIONS IN WWTPS USING A SOFTWARE SENSOR: A BENCHMARK STUDY

In this paper, a new approach for on-line monitoring and detection of abnormal readily biodegradable substrate (S S) and slowly biodegradable substrate (X S) concentrations, for example due to input of toxic loads from the sewer, or due to influent substrate shock load, is proposed. Considering that measurements of S S and X S concentrations are not available in real wastewater treatment plants, the S S | X S software sensor can activate an alarm with a response time of about 60 and 90 minutes, respectively, based on the dissolved oxygen measurement. The software sensor implementation is based on an extended Kalman filter observer and disturbances are modelled using fast Fourier transform and spectrum analyses. Three case studies are described. The first one illustrates the fast and accurate convergence of the extended Kalman filter algorithm, which is achieved in less than 2 hours. Furthermore, the difficulties of estimating X S when off-line analysis is not available are depicted, and the S S | X S software sensor performances when no measurements of S S and X S are available are illustrated. Estimation problems related to the death-regeneration concept of the activated sludge model no.1 and possible application of the software sensor in wastewater monitoring are discussed.

Characterization of NOM and its adsorption by iron oxide coated sand (IOCS) using UV and fluorescence spectroscopy

The removal of natural organic matter (NOM) by adsorption onto iron oxide coated sand (IOCS) can be interpreted based on analysis of the absorbance and fluorescence spectra of the water before and after treatment. The useful spectral parameters include SUVA254, the widths of the benzenoid and electron transfer absorbance bands (WBz, and WET, respectively), the ratio of the absorbance intensities of these two bands (AET/ABz), and the wavelength of the emission maximum (λmaxem). These parameters appear to reflect both the activated aromatic content in the sample and its average molecular weight, and are good indicators of the adsorbability of NOM onto IOCS. A multi-parametric spectroscopic approach for online monitoring of NOM and its reactions can be valuable for both practical treatment studies and for fundamental explorations of NOM reactivity. Key words: natural organic matter, UV absorbance, adsorption, activated aromatics.

A practical investigation to solving the inverse problem of crack identification through vibration measurements

PurposeTo investigate the feasibility of using single/multi variable optimisation techniques with vibration measurements in solving the inverse crack identification problem.Design/methodology/approachThe finite element method is used to solve the forward crack problem with a special nodal crack force approach. The multi‐variable optimisation approach is reduced to a much more efficient single‐variable one by decoupling the physical variables in the problem.FindingsIt is shown that, for the crack identification problem, global optimisation algorithms perform much better than other algorithms relying heavily on objective function gradients. Simultaneous identification of crack size and location proved to be difficult. Decoupling of the physical variable is introduced and proved to provide efficient results with single‐variable optimisation algorithms.Research limitations/implicationsNeed for improving the reliability and accuracy of the procedure for smaller crack sizes. Need for developing and investigation more rigorous and robust multi‐variable optimisation algorithm.Practical implicationsAny information about approximate crack size and location provides significant aid in the maintenance and online monitoring of rotating equipment.Originality/valueThe paper offers practical approach and procedure for online monitoring and crack identification of slow rotating equipment.