Records with Subject: Process Monitoring
Showing records 1 to 25 of 103. [First] Page: 1 2 3 4 5 Last
Evolutionary Observer Ensemble for Leak Diagnosis in Water Pipelines
A. Navarro, J. A. Delgado-Aguiñaga, J. D. Sánchez-Torres, O. Begovich, G. Besançon
January 7, 2020 (v1)
Keywords: fault diagnosis, Genetic Algorithm, leak isolation, nonlinear observer
This work deals with the Leak Detection and Isolation (LDI) problem in water pipelines based on some heuristic method and assuming only flow rate and pressure head measurements at both ends of the duct. By considering the single leak case at an interior node of the pipeline, it has been shown that observability is indeed satisfied in this case, which allows designing an observer for the unmeasurable state variables, i.e., the pressure head at leak position. Relying on the fact that the origin of the observation error is exponentially stable if all parameters (including the leak coefficients) are known and uniformly ultimately bounded otherwise, the authors propose a bank of observers as follows: taking into account that the physical pipeline parameters are well-known, and there is only uncertainty about leak coefficients (position and magnitude), a pair of such coefficients is taken from a search space and is assigned to an observer. Then, a Genetic Algorithm (GA) is exploited to minim... [more]
ABC-ANFIS-CTF: A Method for Diagnosis and Prediction of Coking Degree of Ethylene Cracking Furnace Tube
Zhiping Peng, Junfeng Zhao, Zhaolin Yin, Yu Gu, Jinbo Qiu, Delong Cui
January 7, 2020 (v1)
Keywords: ABC, ANFIS, coking diagnosis and prediction, coking-time factor, ethylene cracking furnace tube
The carburizing and coking of ethylene cracking furnace tubes are the important factors that affect the energy efficiency of ethylene production. To realize the diagnosis and prediction of the different coking degrees of cracking furnace tubes, and then take corresponding treatment measures, is of great significance for improving ethylene production and prolonging the service life of the furnace tube. Therefore, a fusion diagnosis and prediction method based on artificial bee colony (ABC) and adaptive neural fuzzy inference system (ANFIS) is proposed, which also introduces a coking-time factor (CTF). The actual data verification shows that the method not only improves the training efficiency and diagnosis accuracy of the coking diagnosis and inference system of the cracking furnace tube, but also realizes the prediction of the development trend of the coking degree of the furnace tube.
Electrically Conductive Electrospun Polymeric Mats for Sensing Dispersed Vegetable Oil Impurities in Wastewater
Abdolali Moghaddasi, Patrik Sobolčiak, Anton Popelka, Kishor Kumar Sadasivuni, Zdeno Spitalsky, Igor Krupa
January 7, 2020 (v1)
Keywords: carbon nanotubes, nanocomposites, sensors
This paper addresses the preparation of electrically conductive electrospun mats on a base of styrene-isoprene-styrene copolymer (SIS) and multiwall carbon nanotubes (CNTs) and their application as active sensing elements for the detection of vegetable oil impurities dispersed within water. The most uniform mats without beads were prepared using tetrahydrofuran (THF)/dimethyl formamide (DMF) 80:20 (v/v) as the solvent and 13 wt.% of SIS. The CNT content was 10 wt.%, which had the most pronounced changes in electrical resistivity upon sorption of the oil component. The sensors were prepared by deposition of the SIS/CNT layer onto gold electrodes through electrospinning and applied for sensing of oil dispersed in water for 50, 100, and 1000 ppm.
Generalized Proportional Model of Relay Protection Based on Adaptive Homotopy Algorithm Transient Stability
Feng Zheng, Jiahao Lin, Jie Huang, Yanzhen Lin
January 7, 2020 (v1)
Keywords: adaptive homotopy algorithm, generalized proportional hazard model (GPHM), jacobi matrix, relay protection equipment, whole monitoring data
Relay protection equipment is important to ensure the safe and stable operation of power systems. The risks should be evaluated, which are caused by the failure of relay protection. At present, the fault data and the fault status monitoring information are used to evaluate the failure risks of relay protection. However, there is a lack of attention to the information value of monitoring information in the normal operation condition. In order to comprehensively improve monitoring information accuracy and reduce, a generalized proportional hazard model (GPHM) is established to fully exploit the whole monitoring condition information during the whole operation process, not just the monitoring fault condition data, with the maximum likelihood estimation (MLE) used to estimate the parameters of the GPHM. For solving the nonlinear equation in the process of parameter estimations, the adaptive homotopy algorithm is adopted, which could ensure the reversibility of the Jacobi matrix. Three test... [more]
Study on a Fault Identification Method of the Hydraulic Pump Based on a Combination of Voiceprint Characteristics and Extreme Learning Machine
Wanlu Jiang, Zhenbao Li, Jingjing Li, Yong Zhu, Peiyao Zhang
January 6, 2020 (v1)
Keywords: axial piston pump, extreme learning machine, fault diagnosis, voiceprint characteristics
Aiming at addressing the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an axial piston pump fault diagnosis method that is based on the combination of Mel-frequency cepstrum coefficients (MFCC) and the extreme learning machine (ELM) is proposed. Firstly, a sound sensor is used to realize contactless sound signal acquisition of the axial piston pump. The wavelet packet default threshold denoises the original acquired sound signals. Afterwards, windowing and framing are added to the de-noised sound signals. The MFCC voiceprint characteristics of the processed sound signals are extracted. The voiceprint characteristics are divided into a training sample set and test sample set. ELM models with different numbers of neurons in the hidden layers are established for training and testing. The relationship between the number of neurons in the hidden layer and the recognition accuracy rate is obtained. The ELM model with the optimal number of hi... [more]
Feature Extraction Method for Hydraulic Pump Fault Signal Based on Improved Empirical Wavelet Transform
Zhi Zheng, Zhijun Wang, Yong Zhu, Shengnan Tang, Baozhong Wang
December 16, 2019 (v1)
Keywords: empirical wavelet decomposition, fault signal, feature energy ratio, feature extraction, hydraulic pump, power spectrum density
There are many interference components in Fourier amplitude spectrum of a contaminated fault signal, and thus the segment obtained based on the spectrum can lead to serious over-decomposition of empirical wavelet transform (EWT). Aiming to resolve the above problems, a novel method named improved empirical wavelet transform (IEWT) is proposed. Because the power spectrum is less sensitive to the contaminated interference and manifests the presence of fault feature information, IEWT replaces the Fourier amplitude spectrum of EWT with power spectrum in segment acquirement, and threshold processing is also introduced to eliminate the bad influence on the acquirement, and thus the best decomposition result of IEWT can be obtained based on feature energy ratio (FER). The loose slipper fault signal of hydraulic pump is tested and verified. The result demonstrates that the proposed method is superior and can extract the fault feature information accurately.
The Rotating Components Performance Diagnosis of Gas Turbine Based on the Hybrid Filter
Li Zeng, Shaojiang Dong, Wei Long
December 16, 2019 (v1)
Keywords: gas turbine, hybrid filter, particle filter, Unscented Kalman Filter, weight optimization
Gas turbine converts chemical energy into mechanical energy and provide energy for aircraft, ships, etc. The performance diagnosis of rotating components of gas turbine are essential in terms of the high failure rate of these parts. A problem that the sudden changing of operation state of turbines may lead to the misdiagnosis due to the defect of gas turbine’s model. This paper constructs the strong tracking filter based on the unscented Kalman filter to achieve accurate estimation of gas turbine’s measured parameters when the state changes suddenly. In the strong tracking filter, a parameter optimization method based on the residual similarity of measured parameters is proposed. Next, adopt the measured parameters filtered by the strong tracking filter to construct the health parameters estimation algorithm based on the particle filter. The particle weight is optimized by the mean adjustment method. Performance diagnosis is realized by checking the changes of health parameters output... [more]
Dynamic Semi-Quantitative Risk Research in Chemical Plants
Qiusheng Song, Peng Jiang, Song Zheng, Yaguang Kong, Ye Zhao, Gang Shen
December 13, 2019 (v1)
Keywords: analytical hierarchy process, chemical plants, dynamic semi-quantitative calculation, risk value
When a major accident occurs in a chemical industry park, it directly affects the personal safety of operators and neighboring residents and causes major losses; therefore, we should take measures to strengthen the management of chemical industry parks. This article proposes and analyzes a new dynamic semi-quantitative risk calculation model for chemical plants that can be applied digitally. This model provides a sustainable, standardized, and comprehensive management strategy for the safety management of chemical plants and chemical industry park managers. The model and its determined parameters were applied to the safety management of chemical companies within the chemical industry park of Quzhou, Zhejiang Province. From the point of view of the existing semi-quantitative model, the existing problems of the current model are analyzed, the current model is optimized, and a new dynamic semi-quantitative calculation model scheme is proposed. The new model uses an analytical hierarchy pr... [more]
Fault Classification Decision Fusion System Based on Combination Weights and an Improved Voting Method
Fanliang Zeng, Zuxin Li, Zhe Zhou, Shuxin Du
December 13, 2019 (v1)
Keywords: analytic hierarchy process, decision fusion, ensemble method, entropy weight method, fault classification
It is difficult to correctly classify all faults by using only one classifier, and the performance of most classifiers varies under different conditions. In view of this, a new decision fusion system is proposed to solve the problem of fault classification. The proposed decision fusion system is innovative in two aspects: the use of combined weights and a new improved voting method. The combined weights integrate the subjective and objective weights, where the analytic hierarchy process and entropy weight-technique for order performance by similarity to ideal solution are used to determine the subjective and objective weights of different base classifiers under multiple performance evaluation indicators. Moreover, a new improved voting method based on the concept of classifier validity is proposed to increase the accuracy of the decision system. Finally, the method is validated by the Tennessee Eastman benchmark process, and the classification accuracy of the new method is shown to be... [more]
Preliminary Study on Integration of Fiber Optic Bragg Grating Sensors in Li-Ion Batteries and In Situ Strain and Temperature Monitoring of Battery Cells
Aleksandra Fortier, Max Tsao, Nick D. Williard, Yinjiao Xing, Michael G. Pecht
December 10, 2019 (v1)
Keywords: fiber Bragg grating (FBG) sensors, in situ sensing, Li-ion batteries, safe batteries, strain, temperature, thermal runway
Current commercial battery management systems (BMSs) do not provide adequate information in real time to mitigate issues of battery cells such as thermal runway. This paper explores and evaluates the integration of fiber optic Bragg grating (FBG) sensors inside lithium-ion battery (LiB) coin cells. Strain and internal and external temperatures were recorded using FBG sensors, and the battery cells were evaluated at a cycling C/20 rate. The preliminary results present scanning electron microscope (SEM) images of electrode degradation upon sensor integration and the systematic process of sensor integration to eliminate degradation in electrodes during cell charge/discharge cycles. Recommendation for successful FBG sensor integration is given, and the strain and temperature data is presented. The FBG sensor was placed on the inside of the coin cell between the electrodes and the separator layers towards the most electrochemically active area. On the outside, the temperature of the coin ce... [more]
Development of a Diesel Engine Thermal Overload Monitoring System with Applications and Test Results
Sangram Kishore Nanda, Boru Jia, Andrew Smallbone, Anthony Paul Roskilly
December 10, 2019 (v1)
Keywords: diesel engine, lambda, monitoring system, thermal overload, thermocouple, wear rate
In this research, the development of a diesel engine thermal overload monitoring system is presented with applications and test results. The designed diesel engine thermal overload monitoring system consists of two set of sensors, i.e., a lambda sensor to measure the oxygen concentration and a fast response thermocouple to measure the temperature of the gas leaving the cylinder. A medium speed Ruston diesel engine is instrumented to measure the required engine process parameters, measurements are taken at constant load and variable fuel delivery i.e., normal and excessive injection. It is indicated that with excessive injection, the test engine is of high risk to be operated at thermal overload condition. Further tests were carried out on a Sulzer 7RTA84T engine to explore the influence of engine operating at thermal overload condition on exhaust gas temperature and oxygen concentration in the blow down gas. It is established that a lower oxygen concentration in the blow down gas corre... [more]
Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems
Hsueh-Hsien Chang, Nguyen Viet Linh
December 10, 2019 (v1)
Keywords: artificial intelligence (AI), distribution systems, feature extraction, non-intrusive fault monitoring (NIFM), wavelet transform
This paper proposes statistical feature extraction methods combined with artificial intelligence (AI) approaches for fault locations in non-intrusive single-line-to-ground fault (SLGF) detection of low voltage distribution systems. The input features of the AI algorithms are extracted using statistical moment transformation for reducing the dimensions of the power signature inputs measured by using non-intrusive fault monitoring (NIFM) techniques. The data required to develop the network are generated by simulating SLGF using the Electromagnetic Transient Program (EMTP) in a test system. To enhance the identification accuracy, these features after normalization are given to AI algorithms for presenting and evaluating in this paper. Different AI techniques are then utilized to compare which identification algorithms are suitable to diagnose the SLGF for various power signatures in a NIFM system. The simulation results show that the proposed method is effective and can identify the fault... [more]
Research on Partial Discharge Source Localization Based on an Ultrasonic Array and a Step-by-Step Over-Complete Dictionary
Shuguo Gao, Ying Zhang, Qing Xie, Yuqiang Kan, Si Li, Dan Liu, Fangcheng Lü
December 10, 2019 (v1)
Keywords: direction of arrival (DOA), matching pursuit (MP), partial discharge (PD) localization, step-by-step over-complete dictionary
Partial discharge (PD) in electrical equipment is one of the major causes of electrical insulation failures. Fast and accurate positioning of PD sources allows timely elimination of insulation faults. In order to improve the accuracy of PD detection, this paper mainly studies the direction of arrival (DOA) estimation of PD ultrasonic signals based on a step-by-step over-complete dictionary. The simulation results show that the step by step dictionary can improve the operation speed and save signal processing time. Firstly, a step-by-step over-complete dictionary covering all the angles of space is established according to the expression of the steering vector for a matching pursuit direction finding algorithm, which can save computation time. Then, the step-by-step complete dictionary is set up according to the direction vector, and the atomic precision is respectively set to 10°, 1° and 0.1°. The matching pursuit algorithm is used to carry out the sparse representation of the received... [more]
In Situ Stress Measurement Techniques on Li-ion Battery Electrodes: A Review
Ximing Cheng, Michael Pecht
December 10, 2019 (v1)
Keywords: electrodes, in situ measurements, Li-ion batteries, mechanical stress, review
Li-ion batteries experience mechanical stress evolution due in part to Li intercalation into and de-intercalation out of the electrodes, ultimately resulting in performance degradation. In situ measurements of electrode stress can be used to analyze stress generation factors, verify mechanical deformation models, and validate degradation mechanisms. They can also be embedded in Li-ion battery management systems when stress sensors are either implanted in electrodes or attached on battery surfaces. This paper reviews in situ measurement methods of electrode stress based on optical principles, including digital image correlation, curvature measurement, and fiber optical sensors. Their experimental setups, principles, and applications are described and contrasted. This literature review summarizes the current status of these stress measurement methods for battery electrodes and discusses recent developments and trends.
A New Method of Ground Fault Location in 2 × 25 kV Railway Power Supply Systems
Jesús Serrano, Carlos A. Platero, Máximo López-Toledo, Ricardo Granizo
December 10, 2019 (v1)
Keywords: 2 × 25 kV, fault location, ground faults, protection, railways
Owing to the installation of autotransformers at regular intervals along the line, distance protection relays cannot be used with the aim of locating ground faults in 2 × 25 kV railway power supply systems. The reason is that the ratio between impedance and distance to the fault point is not linear in these electrification systems, unlike in 1 × 25 kV power systems. Therefore, the location of ground faults represents a complicated task in 2 × 25 kV railway power supply systems. Various methods have been used to localize the ground fault position in 2 × 25 kV systems. The method described here allows the location of a ground fault to be economically found in an accurate way in real time, using the modules of the circulating currents in different autotransformers when the ground fault occurs. This method first needs to know the subsection and the conductor (catenary or feeder) with the defect, then localizes the ground fault’s position.
Signal-Based Gas Leakage Detection for Fluid Power Accumulators in Wind Turbines
Jesper Liniger, Nariman Sepehri, Mohsen Soltani, Henrik C. Pedersen
December 10, 2019 (v1)
Keywords: FaultDetectionandIsolation (FDI), fluidpower, leakage, pistonaccumulator, wavelet transform, windturbinepitchsystem
This paper describes the development and application of a signal-based fault detection method for identifying gas leakage in hydraulic accumulators used in wind turbines [...]
Fundamental Analysis of Thermal Overload in Diesel Engines: Hypothesis and Validation
Sangram Kishore Nanda, Boru Jia, Andrew Smallbone, Anthony Paul Roskilly
December 10, 2019 (v1)
Keywords: diesel engine, flame visualisation, thermal overload, validation
‘Thermal Overload’ can be defined as a condition under which design threshold values such as the surface temperature of combustion chamber components is exceeded [...]
Development of a Real-Time Virtual Nitric Oxide Sensor for Light-Duty Diesel Engines
Seungha Lee, Youngbok Lee, Gyujin Kim, Kyoungdoug Min
December 10, 2019 (v1)
Keywords: control-oriented, diesel engine, in-cylinder pressure, NOx emissions, predictive model, real-time
This study describes the development of a semi-physical, real-time nitric oxide (NO) prediction model that is capable of cycle-by-cycle prediction in a light-duty diesel engine. The model utilizes the measured in-cylinder pressure and information obtained from the engine control unit (ECU). From the inputs, the model takes into account the pilot injection burning and mixing, which affects the in-cylinder mixture formation. The representative in-cylinder temperature for NO formation was determined from the mixture composition calculation. The selected temperature and mixture composition was substituted using a simplified form of the NO formation rate equation for the cycle-by-cycle estimation. The reactive area and the duration of NO formation were assumed to be limited by the fuel quantity. The model predictability was verified not only using various steady-state conditions, including the variation of the EGR rate, the boost pressure, the rail pressure, and the injection timing, but al... [more]
A Redundancy Mechanism Design for Hall-Based Electronic Current Transformers
Kun-Long Chen, Ren-Shuo Wan, Yi Guo, Nanming Chen, Wei-Jen Lee
December 10, 2019 (v1)
Keywords: coreless Hall-effect current transformer (HCT), current measurement, current transformer (CT), Hall sensor, multiple sensor module, redundancy
Traditional current transformers (CTs) suffer from DC and AC saturation and remanent magnetization in many industrial applications. Moreover, the drawbacks of traditional CTs, such as closed iron cores, bulky volume, and heavy weight, further limit the development of an intelligent power protection system. In order to compensate for these drawbacks, we proposed a novel current measurement method by using Hall sensors, which is called the Hall-effect current transformer (HCT). The existing commercial Hall sensors are electronic components, so the reliability of the HCT is normally worse than that of the traditional CT. Therefore, our study proposes a redundancy mechanism for the HCT to strengthen its reliability. With multiple sensor modules, the method has the ability to improve the accuracy of the HCT as well. Additionally, the proposed redundancy mechanism monitoring system provides a condition-based maintenance for the HCT. We verify our method with both simulations and an experimen... [more]
A Novel High-Frequency Voltage Standing-Wave Ratio-Based Grounding Electrode Line Fault Supervision in Ultra-High Voltage DC Transmission Systems
Yufei Teng, Xiaopeng Li, Qi Huang, Yifei Wang, Shi Jing, Zhenchao Jiang, Wei Zhen
December 10, 2019 (v1)
Keywords: fault supervision, grounding electrode line, injected current source, UHVDC transmission system, voltage standing-wave ratio
In order to improve the fault monitoring performance of grounding electrode lines in ultra-high voltage DC (UHVDC) transmission systems, a novel fault monitoring approach based on the high-frequency voltage standing-wave ratio (VSWR) is proposed in this paper. The VSWR is defined considering a lossless transmission line, and the characteristics of the VSWR under different conditions are analyzed. It is shown that the VSWR equals 1 when the terminal resistance completely matches the characteristic impedance of the line, and when a short circuit fault occurs on the grounding electrode line, the VSWR will be greater than 1. The VSWR will approach positive infinity under metallic earth fault conditions, whereas the VSWR in non-metallic earth faults will be smaller. Based on these analytical results, a fault supervision criterion is formulated. The effectiveness of the proposed VSWR-based fault supervision technique is verified with a typical UHVDC project established in Power Systems Compu... [more]
Using a Hybrid Cost-FMEA Analysis for Wind Turbine Reliability Analysis
Nacef Tazi, Eric Châtelet, Youcef Bouzidi
December 10, 2019 (v1)
Keywords: criticality, expected failure cost, failure mode and effects analysis (FMEA), reliability analysis, wind turbine
Failure mode and effects analysis (FMEA) has been proven to be an effective methodology to improve system design reliability. However, the standard approach reveals some weaknesses when applied to wind turbine systems. The conventional criticality assessment method has been criticized as having many limitations such as the weighting of severity and detection factors. In this paper, we aim to overcome these drawbacks and develop a hybrid cost-FMEA by integrating cost factors to assess the criticality, these costs vary from replacement costs to expected failure costs. Then, a quantitative comparative study is carried out to point out average failure rate, main cause of failure, expected failure costs and failure detection techniques. A special reliability analysis of gearbox and rotor-blades are presented.
Design of S2N—NEWMA Control Chart for Monitoring Process having Indeterminate Production Data
Muhammad Aslam, Rashad A. R. Bantan, Nasrullah Khan
December 10, 2019 (v1)
Keywords: monitoring, neutrosophic, neutrosophic logarithmic transformation, numbers, variance
The existing charts for monitoring the variance are designed under the assumption that all production data must consist of exact, precise, and determined observations. This paper presents the design of a new neutrosophic exponentially weighted moving average (NEWMA) combining with a neutrosophic logarithmic transformation chart for monitoring the variance having neutrosophic numbers. The computation of the neutrosophic control chart parameters is done through the neutrosophic Monte Carlo simulation (NMCS). The performance of the proposed chart is discussed with the existing charts.
Prediction of Lithium-ion Battery Thermal Runaway Propagation for Large Scale Applications Fire Hazard Quantification
Mohamad Syazarudin Md Said, Mohd Zahirasri Mohd Tohir
December 9, 2019 (v1)
Keywords: cascade failure, fire and explosion, lithium-ion battery, thermal runaway propagation
The high capacity and voltage properties demonstrated by lithium-ion batteries render them as the preferred energy carrier in portable electronic devices. The application of the lithium-ion batteries which previously circulating and contained around small-scale electronics is now expanding into large scale emerging markets such as electromobility and stationary energy storage. Therefore, the understanding of the risk involved is imperative. Thermal runaway is the most common failure mode of lithium-ion battery which may lead to safety incidents. Transport process of immense amounts of heat released during thermal runaway of lithium-ion battery to neighboring batteries in a module can lead to cascade failure of the whole energy storage system. In this work, a model is developed to predict the propagation of lithium-ion battery in a module for large scale applications. For this purpose, kinetic of material thermal decomposition is combined with heat transfer modelling. The simulation is... [more]
A Fault Feature Extraction Method for the Fluid Pressure Signal of Hydraulic Pumps Based on Autogram
Zhi Zheng, Xianze Li, Yong Zhu
December 9, 2019 (v1)
Keywords: Autogram, feature extraction, fluid pressure, hydraulic pump, kurtosis
Center spring wear faults in hydraulic pumps can cause fluid pressure fluctuations at the outlet, and the fault feature information on fluctuations is often contaminated by different types of fluid flow interferences. Aiming to resolve the above problems, a fluid pressure signal method for hydraulic pumps based on Autogram was applied to extract the fault feature information. Firstly, maximal overlap discrete wavelet packet transform (MODWPT) was adopted to decompose the contaminated fault pressure signal of center spring wear. Secondly, based on the squared envelope of each node, three kinds of kurtosis of unbiased autocorrelation (AC) were computed in order to describe the fault feature information comprehensively. These are known as standard Autogram, upper Autogram and lower Autogram. Then a node corresponding to the biggest kurtosis value was selected as a data source for further spectrum analysis. Lastly, the data source was processed by threshold values, and then the fault could... [more]
Combining Mechanistic Modeling and Raman Spectroscopy for Monitoring Antibody Chromatographic Purification
Fabian Feidl, Simone Garbellini, Martin F. Luna, Sebastian Vogg, Jonathan Souquet, Hervé Broly, Massimo Morbidelli, Alessandro Butté
December 9, 2019 (v1)
Keywords: chromatography, downstream processing, extended Kalman filter, flow cell, Raman spectroscopy
Chromatography is widely used in biotherapeutics manufacturing, and the corresponding underlying mechanisms are well understood. To enable process control and automation, spectroscopic techniques are very convenient as on-line sensors, but their application is often limited by their sensitivity. In this work, we investigate the implementation of Raman spectroscopy to monitor monoclonal antibody (mAb) breakthrough (BT) curves in chromatographic operations with a low titer harvest. A state estimation procedure is developed by combining information coming from a lumped kinetic model (LKM) and a Raman analyzer in the frame of an extended Kalman filter approach (EKF). A comparison with suitable experimental data shows that this approach allows for the obtainment of reliable estimates of antibody concentrations with reduced noise and increased robustness.
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