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Records with Subject: Process Monitoring
Showing records 1 to 25 of 140. [First] Page: 1 2 3 4 5 Last
Novel Carbon Dioxide-Based Method for Accurate Determination of pH and pCO2 in Mammalian Cell Culture Processes
Christian Klinger, Verena Trinkaus, Tobias Wallocha
July 2, 2020 (v1)
Keywords: Carbon Dioxide, cell culture, cell culture, CHO, off-gas measurement, pCO2, pH, reference
In mammalian cell culture, especially in pharmaceutical manufacturing, pH is a critical process parameter that has to be controlled as accurately as possible. Not only does pH directly affect cell culture performance, ensuring a comparable pH is also crucial for scaling and transfer of processes. A sample-based offline pH measurement is commonly used to ensure correct bioreactor pH probe signals after sterilization and as a detection measure for drifts of probe signals. However, the sample-based pH offline measurement does not necessarily deliver required accuracy. Offsets between bioreactor pH and sample pH heavily depend on equipment, local procedures and the offline measurement method that is used. This article adequately describes a novel, non-invasive method to determine pH and pCO2 in sterile bioreactors without the need to sample and measure offline. This method utilizes the chemical correlation between carbon dioxide in the gas phase, dissolved carbon dioxide, bicarbonate and d... [more]
Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries
Ana-Irina Stroe, Jinhao Meng, Daniel-Ioan Stroe, Maciej Świerczyński, Remus Teodorescu, Søren Knudsen Kær
June 23, 2020 (v1)
Keywords: equivalent electrical circuit, extended Kalman filter, hybrid pulse power characterization test, lithium titanate oxide (LTO) batteries, lithium-ion batteries, model parameterization, state of charge (SOC)
State of charge (SOC) is one of the most important parameters in battery management systems, as it indicates the available battery capacity at every moment. There are numerous battery model-based methods used for SOC estimation, the accuracy of which depends on the accuracy of the model considered to describe the battery dynamics. The SOC estimation method proposed in this paper is based on an Extended Kalman Filter (EKF) and nonlinear battery model which was parameterized using extended laboratory tests performed on several 13 Ah lithium titanate oxide (LTO)-based lithium-ion batteries. The developed SOC estimation algorithm was successfully verified for a step discharge profile at five different temperatures and considering various initial SOC initialization values, showing a maximum SOC estimation error of 1.16% and a maximum voltage estimation error of 44 mV. Furthermore, by carrying out a sensitivity analysis it was showed that the SOC and voltage estimation error are only slightl... [more]
FPGA-Based Online PQD Detection and Classification through DWT, Mathematical Morphology and SVD
Misael Lopez-Ramirez, Eduardo Cabal-Yepez, Luis M. Ledesma-Carrillo, Homero Miranda-Vidales, Carlos Rodriguez-Donate, Rocio A. Lizarraga-Morales
June 23, 2020 (v1)
Keywords: artificial neural networks, discrete wavelet transform, field programmable gate array, mathematical morphology, power quality disturbance, singular value decomposition
Power quality disturbances (PQD) in electric distribution systems can be produced by the utilization of non-linear loads or environmental circumstances, causing electrical equipment malfunction and reduction of its useful life. Detecting and classifying different PQDs implies great efforts in planning and structuring the monitoring system. The main disadvantage of most works in the literature is that they treat a limited number of electrical disturbances through personal computer (PC)-based computation techniques, which makes it difficult to perform an online PQD classification. In this work, the novel contribution is a methodology for PQD recognition and classification through discrete wavelet transform, mathematical morphology, decomposition of singular values, and statistical analysis. Furthermore, the timely and reliable classification of different disturbances is necessary; hence, a field programmable gate array (FPGA)-based integrated circuit is developed to offer a portable hard... [more]
Wind Turbine Condition Monitoring Strategy through Multiway PCA and Multivariate Inference
Francesc Pozo, Yolanda Vidal, Óscar Salgado
June 23, 2020 (v1)
Keywords: condition monitoring, Fault Detection, multivariate statistical hypothesis testing, principal component analysis, wind turbine
This article states a condition monitoring strategy for wind turbines using a statistical data-driven modeling approach by means of supervisory control and data acquisition (SCADA) data. Initially, a baseline data-based model is obtained from the healthy wind turbine by means of multiway principal component analysis (MPCA). Then, when the wind turbine is monitorized, new data is acquired and projected into the baseline MPCA model space. The acquired SCADA data are treated as a random process given the random nature of the turbulent wind. The objective is to decide if the multivariate distribution that is obtained from the wind turbine to be analyzed (healthy or not) is related to the baseline one. To achieve this goal, a test for the equality of population means is performed. Finally, the results of the test can determine that the hypothesis is rejected (and the wind turbine is faulty) or that there is no evidence to suggest that the two means are different, so the wind turbine can be... [more]
Methodology for Detecting Malfunctions and Evaluating the Maintenance Effectiveness in Wind Turbine Generator Bearings Using Generic versus Specific Models from SCADA Data
Miguel A. Rodríguez-López, Luis M. López-González, Luis M. López-Ochoa, Jesús Las-Heras-Casas
June 23, 2020 (v1)
Keywords: condition monitoring, failure detection, generator bearing, normal behavior models, SCADA data, wind turbines
This article offers reasons to defend the use of generic behavior models as opposed to specific models in applications to determine component degradation. The term generic models refers to models based on operating data from various units, whereas specific models are calculated using operating data taken from a single unit. Moreover, generic models, used in combination with a status indicator, show excellent capacity for detecting anomalies in the equipment and for evaluating the effectiveness of the maintenance actions, resulting in lower development and maintenance costs for the operating firm. Artificial neural networks and moving means were used to calculate the degradation indicators, based on the remainders in the model. The models were developed from operating data from fourteen wind turbines monitored over several years, and applied to the detection of faults in the bearings on the non-drive end of the generator. The use of generic models may not be recommendable for detecting... [more]
Reliability Assessment of Wind Farm Electrical System Based on a Probability Transfer Technique
Hejun Yang, Lei Wang, Yeyu Zhang, Xianjun Qi, Lei Wang, Hongbin Wu
June 23, 2020 (v1)
Keywords: electrical topology, probability, reliability assessment, reliability indices, wind farm
The electrical system of a wind farm has a significant influence on the wind farm reliability and electrical energy yield. The disconnect switch installed in an electrical system cannot only improve the operating flexibility, but also enhance the reliability for a wind farm. Therefore, this paper develops a probabilistic transfer technique for integrating the electrical topology structure, the isolation operation of disconnect switch, and stochastic failure of electrical equipment into the reliability assessment of wind farm electrical system. Firstly, as the traditional two-state reliability model of electrical equipment cannot consider the isolation operation, so the paper develops a three-state reliability model to replace the two-state model for incorporating the isolation operation. In addition, a proportion apportion technique is presented to evaluate the state probability. Secondly, this paper develops a probabilistic transfer technique based on the thoughts that through transfe... [more]
Identifying Health Status of Wind Turbines by Using Self Organizing Maps and Interpretation-Oriented Post-Processing Tools
Alejandro Blanco-M., Karina Gibert, Pere Marti-Puig, Jordi Cusidó, Jordi Solé-Casals
June 23, 2020 (v1)
Keywords: clustering, data science, fault diagnosis, interpretation oriented tools, post- processing, Renewable and Sustainable Energy, self organizing maps (SOM), Supervisory Control and Data Acquisition(SCADA) data, wind farms
Background: Identifying the health status of wind turbines becomes critical to reduce the impact of failures on generation costs (between 25⁻35%). This is a time-consuming task since a human expert has to explore turbines individually. Methods: To optimize this process, we present a strategy based on Self Organizing Maps, clustering and a further grouping of turbines based on the centroids of their SOM clusters, generating groups of turbines that have similar behavior for subsystem failure. The human expert can diagnose the wind farm health by the analysis of a small each group sample. By introducing post-processing tools like Class panel graphs and Traffic lights panels, the conceptualization of the clusters is enhanced, providing additional information of what kind of real scenarios the clusters point out contributing to a better diagnosis. Results: The proposed approach has been tested in real wind farms with different characteristics (number of wind turbines, manufacturers, power,... [more]
Comparison among Methods for Induction Motor Low-Intrusive Efficiency Evaluation Including a New AGT Approach with a Modified Stator Resistance
Camila Paes Salomon, Wilson Cesar Sant’Ana, Germano Lambert-Torres, Luiz Eduardo Borges da Silva, Erik Leandro Bonaldi, Levy Ely de Lacerda de Oliveira
June 23, 2020 (v1)
Keywords: air-gap torque, condition monitoring, efficiency estimation, induction motors, stator resistance
Induction motors consume a great portion of the generated electrical energy. Moreover, most of them work at underloaded conditions, so they have low efficiencies and waste a lot of energy. Because of this, the efficiency estimation of in-service induction motors is a matter of great importance. This efficiency estimation is usually performed through indirect methods, which do not require invasive measurements of torque or speed. One of these methods is the modified Air-Gap Torque (AGT) method, which only requires voltage and current data, the stator resistance value, and the mechanical losses. This paper approaches the computation of a modified stator resistance including the mechanical losses effect to be applied in the AGT method for torque and efficiency estimation of induction motors. Some improvements are proposed in the computation of this resistance by using a direct method, as well as the possibility to estimate this parameter directly from the nameplate data of the induction m... [more]
A Geometric Observer-Assisted Approach to Tailor State Estimation in a Bioreactor for Ethanol Production
Silvia Lisci, Massimiliano Grosso, Stefania Tronci
June 23, 2020 (v1)
Keywords: bioreactor, continuous system, extended Kalman filter, geometric observer, model-based sensor, nonlinear state estimation
In this work, a systematic approach based on the geometric observer is proposed to design a model-based soft sensor, which allows the estimation of quality indexes in a bioreactor. The study is focused on the structure design problem where the set of innovated states has to be chosen. On the basis of robust exponential estimability arguments, it is found that it is possible to distinguish all the unmeasured states if temperature and dissolved oxygen concentration measurements are combined with substrate concentrations. The proposed estimator structure is then validated through numerical simulation considering two different measurement processor algorithms: the geometric observer and the extended Kalman filter.
Leak Detection in Gas Mixture Pipelines under Transient Conditions Using Hammerstein Model and Adaptive Thresholds
Syed Muhammad Mujtaba, Tamiru Alemu Lemma, Syed Ali Ammar Taqvi, Titus Ntow Ofei, Seshu Kumar Vandrangi
June 23, 2020 (v1)
Keywords: adaptive thresholds, data-driven leak detection, Hammerstein model, OLGA simulator, pipeline system identification, pseudo-random binary signals
Conventional leak detection techniques require improvements to detect small leakage (<10%) in gas mixture pipelines under transient conditions. The current study is aimed to detect leakage in gas mixture pipelines under pseudo-random boundary conditions with a zero percent false alarm rate (FAR). Pressure and mass flow rate signals at the pipeline inlet were used to estimate mass flow rate at the outlet under leak free conditions using Hammerstein model. These signals were further used to define adaptive thresholds to separate leakage from normal conditions. Unlike past studies, this work successfully detected leakage under transient conditions in an 80-km pipeline. The leakage detection performance of the proposed methodology was evaluated for several leak locations, varying leak sizes and, various signal to noise ratios (SNR). Leakage of 0.15 kg/s—3% of the nominal flow—was successfully detected under transient boundary conditions with a F-score of 99.7%. Hence, it can be conclude... [more]
A Chemometric Tool to Monitor and Predict Cell Viability in Filamentous Fungi Bioprocesses Using UV Chromatogram Fingerprints
Philipp Doppler, Lukas Veiter, Oliver Spadiut, Christoph Herwig, Vignesh Rajamanickam
June 23, 2020 (v1)
Keywords: cell viability, chromatogram fingerprinting, filamentous fungi, HPLC-SEC, Penicillium chrysogenum, prediction, Trichoderma reesei Rut-C30
Monitoring process variables in bioprocesses with complex expression systems, such as filamentous fungi, requires a vast number of offline methods or sophisticated inline sensors. In this respect, cell viability is a crucial process variable determining the overall process performance. Thus, fast and precise tools for identification of key process deviations or transitions are needed. However, such reliable monitoring tools are still scarce to date or require sophisticated equipment. In this study, we used the commonly available size exclusion chromatography (SEC) HPLC technique to capture impurity release information in Penicillium chrysogenum bioprocesses. We exploited the impurity release information contained in UV chromatograms as fingerprints for development of principal component analysis (PCA) models to descriptively analyze the process trends. Prediction models using well established approaches, such as partial least squares (PLS), orthogonal PLS (OPLS) and principal component... [more]
Stacked Auto-Encoder Based CNC Tool Diagnosis Using Discrete Wavelet Transform Feature Extraction
Jonggeun Kim, Hansoo Lee, Jeong Woo Jeon, Jong Moon Kim, Hyeon Uk Lee, Sungshin Kim
June 23, 2020 (v1)
Keywords: auto-encoder, condition based maintenance, discrete wavelet transform, feature extraction, tool diagnosis
Machining processes are critical and widely used components in the manufacturing industry because they help to precisely make products and reduce production time. To keep the previous advantages, a machine tool should be installed at the designated place and condition of the machine tool should be maintained appropriately to working environment. In various maintenance methods for keeping the condition of machine tool, condition-based maintenance can be robust to unpredicted accidents and reduce maintenance costs. Tool monitoring and diagnosis are some of the most important components of the condition based maintenance. This paper proposes stacked auto-encoder based CNC machine tool diagnosis using discrete wavelet transform feature extraction to diagnose a machine tool. The diagnosis model, which only uses cutting force data, cannot sufficiently reflects tool condition. Hence, we modeled diagnosis model using features extracted from a cutting force, a current signal, and coefficients o... [more]
Study of Blockage Diagnosis for Hydrocyclone Using Vibration-Based Technique Based on Wavelet Denoising and Discrete-Time Fourier Transform Method
Guanghui Wang, Qun Liu, Chuanzhen Wang, Lulu Dong, Dan Dai, Liang Shen
June 22, 2020 (v1)
Keywords: blockage diagnosis, discrete-time Fourier transform, hydrocyclone, vibration-based technique, wavelet denoising
Hydrocyclones are extensively known as important separation devices which are used in many industrial fields. However, the general method to estimate device performance is time-consuming and has a high cost. The aim of this paper was to investigate the blockage diagnosis for a lab-scale hydrocyclone using a vibration-based technique based on wavelet denoising and the discrete-time Fourier transform method. The results indicate that the farther away the installation location from feed inlet the more regular the frequency is, which reveals that the installation plane near to the spigot generated the regular frequency distribution. Furthermore, the acceleration amplitude under blockage degrees 0%, 50% and 100% fluctuates as a sine shape with increasing time, meanwhile the vibration frequency of the hydrocyclone rises with increasing throughput. Moreover, the distribution of four dimensional and five non-dimensional parameters for the time domain shows that the standard deviation, compared... [more]
Estimation of Biomass Enzymatic Hydrolysis State in Stirred Tank Reactor through Moving Horizon Algorithms with Fixed and Dynamic Fuzzy Weights
Vitor B. Furlong, Luciano J. Corrêa, Fernando V. Lima, Roberto C. Giordano, Marcelo P. A. Ribeiro
June 10, 2020 (v1)
Keywords: artificial neural network, biomass enzymatic hydrolysis, fuzzy logic, local linear model tree, moving horizon estimation, process monitoring, soft sensing
Second generation ethanol faces challenges before profitable implementation. Biomass hydrolysis is one of the bottlenecks, especially when this process occurs at high solids loading and with enzymatic catalysts. Under this setting, kinetic modeling and reaction monitoring are hindered due to the conditions of the medium, while increasing the mixing power. An algorithm that addresses these challenges might improve the reactor performance. In this work, a soft sensor that is based on agitation power measurements that uses an Artificial Neural Network (ANN) as an internal model is proposed in order to predict free carbohydrates concentrations. The developed soft sensor is used in a Moving Horizon Estimator (MHE) algorithm to improve the prediction of state variables during biomass hydrolysis. The algorithm is developed and used for batch and fed-batch hydrolysis experimental runs. An alteration of the classical MHE is proposed for improving prediction, using a novel fuzzy rule to alter th... [more]
Improvement of Productivity through the Reduction of Unexpected Equipment Faults in Die Attach Equipment
You-Jin Park, Sun Hur
June 3, 2020 (v1)
Keywords: back grinding process, die attach process, loss, overall equipment effectiveness (OEE), productivity analysis system, unexpected equipment fault, unit per equipment hour (UPEH), wafer sawing process
As one of the semiconductor back-end processes, die attach process is the process that attaches an individual non-defective die (or chip) produced from the semiconductor front-end production to the lead frame on a strip. With most other processes of semiconductor manufacturing, it is very important to improve productivity by lessening the occurrence of defective products generally represented as losses, and then find the fault causes which lower productivity of the die attach process. Thus, as the case study to analyze quantitatively the faults of the die attach process equipment, in this research, we developed analysis systems including statistical analysis functions to improve the productivity of die attach process. This research shows that the developed system can find the causes of equipment faults in die attach process equipment and help improve the productivity of the die attach process by controlling the critical parameters which cause unexpected equipment faults and losses.
Fault Detection and Identification of Blast Furnace Ironmaking Process Using the Gated Recurrent Unit Network
Hang Ouyang, Jiusun Zeng, Yifan Li, Shihua Luo
June 3, 2020 (v1)
Keywords: fault detection and identification, gated recurrent unit, support vector data description, time sequence prediction
It is of critical importance to keep a steady operation in the blast furnace to facilitate the production of high quality hot metal. In order to monitor the state of blast furnace, this article proposes a fault detection and identification method based on the multidimensional Gated Recurrent Unit (GRU) network, which is a kind of recurrent neural network and is highly effective in handling process dynamics. Comparing to conventional recurrent neural networks, GRU has a simpler structure and involves fewer parameters. In fault detection, a moving window approach is applied and a GRU model is constructed for each process variable to generate a series of residuals, which is further monitored using the support vector data description (SVDD) method. Once a fault is detected, fault identification is performed using the contribution analysis. Application to a real blast furnace fault shows that the proposed method is effective.
Intelligent Colored Token Petri Nets for Modeling, Control, and Validation of Dynamic Changes in Reconfigurable Manufacturing Systems
Husam Kaid, Abdulrahman Al-Ahmari, Zhiwu Li, Reggie Davidrajuh
May 22, 2020 (v1)
Keywords: colored Petri net, Modelling, Reconfigurable manufacturing system, Simulation
The invention of reconfigurable manufacturing systems (RMSs) has created a challenging problem: how to quickly and effectively modify an RMS to address dynamic changes in a manufacturing system, such as processing failures and rework, machine breakdowns, addition of new machines, addition of new products, removal of old machines, and changes in processing routes induced by the competitive global market. This paper proposes a new model, the intelligent colored token Petri net (ICTPN), to simulate dynamic changes or reconfigurations of a system. The main idea is that intelligent colored tokens denote part types that represent real-time knowledge about changes and status of a system. Thus, dynamic configurations of a system can be effectively modeled. The developed ICTPN can model dynamic changes of a system in a modular manner, resulting in the development of a very compact model. In addition, when configurations appear, only the changed colored token of the part type from the current mo... [more]
Single-Use Printed Biosensor for L-Lactate and Its Application in Bioprocess Monitoring
Lorenz Theuer, Judit Randek, Stefan Junne, Peter Neubauer, Carl-Fredrik Mandenius, Valerio Beni
May 22, 2020 (v1)
Keywords: at-line measurement, enzyme electrode, in-line monitoring, lactate biosensor, off-line monitoring, screen-printing
There is a profound need in bioprocess manufacturing for low-cost single-use sensors that allow timely monitoring of critical product and production attributes. One such opportunity is screen-printed enzyme-based electrochemical sensors, which have the potential to enable low-cost online and/or off-line monitoring of specific parameters in bioprocesses. In this study, such a single-use electrochemical biosensor for lactate monitoring is designed and evaluated. Several aspects of its fabrication and use are addressed, including enzyme immobilization, stability, shelf-life and reproducibility. Applicability of the biosensor to off-line monitoring of bioprocesses was shown by testing in two common industrial bioprocesses in which lactate is a critical quality attribute (Corynebacterium fermentation and mammalian Chinese hamster ovary (CHO) cell cultivation). The specific response to lactate of the screen-printed biosensor was characterized by amperometric measurements. The usability of th... [more]
Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data
Iftikhar Ahmad, Ahsan Ayub, Manabu Kano, Izzat Iqbal Cheema
April 14, 2020 (v1)
Keywords: big data analytics, internet of things, Machine Learning, sensor 4.0
Virtual sensors, or soft sensors, have greatly contributed to the evolution of the sensing systems in industry. The soft sensors are process models having three fundamental categories, namely white-box (WB), black-box (BB) and gray-box (GB) models. WB models are based on process knowledge while the BB models are developed using data collected from the process. The GB models integrate the WB and BB models for addressing the concerns, i.e., accuracy and intuitiveness, of industrial operators. In this work, various design aspects of the GB models are discussed followed by their application in the process industry. In addition, the changes in the data-driven part of the GB models in the context of enormous amount of process data collected in Industry 4.0 are elaborated.
An Online Contaminant Classification Method Based on MF-DCCA Using Conventional Water Quality Indicators
Yanni Zhu, Kexin Wang, Youxin Lin, Hang Yin, Dibo Hou, Jie Yu, Pingjie Huang, Guangxin Zhang
April 1, 2020 (v1)
Keywords: abnormal fluctuation analysis, cosine distance classification, D–S evidential theory, MF-DCCA, online contaminant classification
Emergent contamination warning systems are critical to ensure drinking water supply security. After detecting the existence of contaminants, identifying the types of contaminants is conducive to taking remediation measures. An online classification method for contaminants, which explored abnormal fluctuation information and the correlation between 12 water quality indicators adequately, is proposed to realize comprehensive and accurate discrimination of contaminants. Firstly, the paper utilized multi-fractal detrended fluctuation analysis (MF-DFA) to select indicators with abnormal fluctuation, used multi-fractal detrended cross-correlation analysis (MF-DCCA) to measure the cross-correlation between indicators. Subsequently, the algorithm fused the abnormal probability of each indicator and constructed the abnormal probability matrix to further judge the abnormal fluctuation of indicators using D−S evidence theory. Finally, the singularity index of the cross-correlation function and th... [more]
Estimation of Ice Cream Mixture Viscosity during Batch Crystallization in a Scraped Surface Heat Exchanger
Alejandro De la Cruz Martínez, Rosa E. Delgado Portales, Jaime D. Pérez Martínez, José E. González Ramírez, Alan D. Villalobos Lara, Anahí J. Borras Enríquez, Mario Moscosa Santillán
March 11, 2020 (v1)
Keywords: crystallization, ice-cream, Modelling, scraped surface heat exchanger, viscosity
Ice cream viscosity is one of the properties that most changes during crystallization in scraped surface heat exchangers (SSHE), and its online measurement is not easy. Its estimation is necessary through variables that are easy to measure. The temperature and power of the stirring motor of the SSHE turn out to be this type of variable and are closely related to the viscosity. Therefore, a mathematical model based on these variables proved to be feasible. The development of this mathematical relationship involved the rheological study of the ice cream base, as well as the application of a method for its in situ melting in the rheometer as a function of the temperature, and the application of a mathematical model correlating the SSHE stirring power and the ice cream viscosity. The result was a coupled model based on both the temperature and stirring power of the SSHE, which allowed for online viscosity estimation with errors below 10% for crystallized systems with a 30% ice fraction at... [more]
Quality-Relevant Monitoring of Batch Processes Based on Stochastic Programming with Multiple Output Modes
Feifan Shen, Jiaqi Zheng, Lingjian Ye, De Gu
March 11, 2020 (v1)
Keywords: bagging algorithm, batch processes, Bayesian fusion, data-driven modeling, quality-relevant monitoring, stochastic programming
To implement the quality-relevant monitoring scheme for batch processes with multiple output modes, this paper presents a novel methodology based on stochastic programming. Bringing together tools from stochastic programming and ensemble learning, the developed methodology focuses on the robust monitoring of process quality-relevant variables by taking the stochastic nature of batch process parameters explicitly into consideration. To handle the problem of missing data and lack of historical batch data, a bagging approach is introduced to generate individual quality-relevant sub-datasets, which are used to construct the corresponding monitoring sub-models. For each model, stochastic programming is used to construct an optimal quality trajectory, which is regarded as the reference for online quality monitoring. Then, for each sub-model, a corresponding control limit is obtained by computing historical residuals between the actual output and the optimal trajectory. For online monitoring,... [more]
Hypothesis Tests-Based Analysis for Anomaly Detection in Photovoltaic Systems in the Absence of Environmental Parameters
Silvano Vergura
February 24, 2020 (v1)
Keywords: ANOVA, Bartlett’s test, Hartigan’s dip test, Jarque-Bera’s test, Kruskal-Wallis’ test, Mood’s Median test, residential buildings, Tukey’s test, urban context
This paper deals with the monitoring of the performance of a photovoltaic plant, without using the environmental parameters such as the solar radiation and the temperature. The main idea is to statistically compare the energy performances of the arrays constituting the PV plant. In fact, the environmental conditions affect equally all the arrays of a small-medium-size PV plant, because the extension of the plant is limited, so any comparison between the energy distributions of identical arrays is independent of the solar radiation and the cell temperature, making the proposed methodology very effective for PV plants not equipped with a weather station, as it often happens for the PV plants located in urban contexts and having a nominal peak power in the 3÷50 kWp range, typically installed on the roof of a residential or industrial building. In this case, the costs of an advanced monitoring system based on the environmental data are not justified, consequently, the weather station is of... [more]
A Dynamic Active Safe Semi-Supervised Learning Framework for Fault Identification in Labeled Expensive Chemical Processes
Xuqing Jia, Wende Tian, Chuankun Li, Xia Yang, Zhongjun Luo, Hui Wang
February 12, 2020 (v1)
Keywords: active learning, chemical process, fault identification, feature selection, ontology, semi-supervised learning
A novel active semi-supervised learning framework using unlabeled data is proposed for fault identification in labeled expensive chemical processes. A principal component analysis (PCA) feature selection strategy is first given to calculate the weight of the variables. Secondly, the identification model is trained based on the obtained key process variables. Thirdly, the pseudo label confidence of identification model is dynamically optimized with an historical, current, and future pseudo label confidence mean. To increase the upper limit of the identification model that is self-learning with high entropy process data, active learning is used to identify process data and diagnosis fault causes by ontology. Finally, a PCA-dynamic active safe semi-supervised support vector machine (PCA-DAS4VM) for fault identification in labeled expensive chemical processes is built. The application in the Tennessee Eastman (TE) process shows that this hybrid technology is able to: (i) eliminate chemical... [more]
Electrical Conductivity for Monitoring the Expansion of the Support Material in an Anaerobic Biofilm Reactor
Oscar Marín-Peña, Alejandro Alvarado-Lassman, Norma A. Vallejo-Cantú, Isaías Juárez-Barojas, José Pastor Rodríguez-Jarquín, Albino Martínez-Sibaja
February 12, 2020 (v1)
Keywords: anaerobic biofilm reactor, anaerobic digestion, electrical conductivity, inverse fluidized bed reactor, organic solid wastes
This article describes the use of the electrical conductivity for measuring bed expansion in a continuous anaerobic biofilm reactor in order to prevent the exit of support material from the reactor with the consequent loss of biomass. The substrate used for the tests is obtained from a two-stage anaerobic digestion (AD) process at the pilot scale that treats the liquid fraction of fruit and vegetable waste (FVW). Tests were performed with the raw substrate before anaerobic treatment (S1), the effluent from the hydrolysis reactor (S2), and the effluent from the methanogenic reactor (S3) to evaluate its effect on the electrical conductivity values and its interaction with colonized support material. The tests were carried out in a 32 L anaerobic inverse fluidized bed reactor (IFBR), which was inoculated with colonized support material and using two industrial electrodes at different column positions. The results with the previously digested samples (S2 and S3) were satisfactory to detect... [more]
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