On-line Process Monitoring Research Articles

On-line untargeted metabolomics monitoring of an Escherichiacoli succinate fermentation process.

The real‐time monitoring of metabolites (RTMet) is instrumental for the industrial production of biobased fermentation products. This study shows the first application of untargeted on‐line metabolomics for the monitoring of undiluted fermentation broth samples taken automatically from a 5 L bioreactor every 5 min via flow injection mass spectrometry. The travel time from the bioreactor to the mass spectrometer was 30 s. Using mass spectrometry allows, on the one hand, the direct monitoring of targeted key process compounds of interest and, on the other hand, provides information on hundreds of additional untargeted compounds without requiring previous calibration data. In this study, this technology was applied in an Escherichia coli succinate fermentation process and 886 different m/z signals were monitored, including key process compounds (glucose, succinate, and pyruvate), potential biomarkers of biomass formation such as (R)‐2,3‐dihydroxy‐isovalerate and (R)‐2,3‐dihydroxy‐3‐methylpentanoate and compounds from the pentose phosphate pathway and nucleotide metabolism, among others. The main advantage of the RTMet technology is that it allows the monitoring of hundreds of signals without the requirement of developing partial least squares regression models, making it a perfect tool for bioprocess monitoring and for testing many different strains and process conditions for bioprocess development.

A Generic Online Nonparametric Monitoring and Sampling Strategy for High-Dimensional Heterogeneous Processes

With the rapid advancement of in-process measurements and sensor technology driven by zero-defect manufacturing applications, high-dimensional heterogeneous processes that continuously collect distinct physical characteristics frequently appear in modern industries. Such large-volume high-dimensional data place a heavy demand on data collection, transmission, and analysis in practice. Thus, practitioners often need to decide which informative data streams to observe given the resource constraints at each data acquisition time, which poses significant challenges for multivariate statistical process control and quality improvement. In this article, we propose a generic online nonparametric monitoring and sampling scheme to quickly detect mean shifts occurring in heterogeneous processes when only partial observations are available at each acquisition time. Our innovative idea is to seamlessly integrate the Thompson sampling (TS) algorithm with a quantile-based nonparametric cumulative sum (CUSUM) procedure to construct local statistics of all data streams based on the partially observed data. Furthermore, we develop a global monitoring scheme of using the sum of top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${r}$ </tex-math></inline-formula> local statistics, which can quickly detect a wide range of possible mean shifts. Tailored to monitoring the heterogeneous data streams, the proposed method balances between exploration that searches unobserved data streams for possible mean shifts and exploitation that focuses on highly suspicious data streams for quick shift detection. Both simulations and a case study are comprehensively conducted to evaluate the performance and demonstrate the superiority of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the critical challenge of online process monitoring by considering the cost-effectiveness and resource constraints in practice (e.g., limited number of sensors, limited transmission bandwidth or energy constraint, and limited processing time). Unlike the existing methodologies which rely on the restrictive assumptions (e.g., normally distributed, exchangeable data streams) or require historical full observations of all data streams to be available offline for training, this paper proposes a novel monitoring and sampling strategy that allows the practitioners to cost-effectively monitor high-dimensional heterogeneous data streams that contain distinct physical characteristics and follow different distributions. To implement the proposed methodology, it is necessary: (i) to identify sample quantiles for each data stream based on historical in-control data offline; (ii) to determine which data streams to observe at each acquisition time based on the resource constraints; and (iii) to automatically screen out the suspicious data streams to form the global monitoring statistic. Experimental results through simulations and a case study have shown that the proposed method has much better performance than the existing methods in reducing detection delay and effectively dealing with heterogeneous data streams.

On-line monitoring and defect detection of arc-welding via plasma optical spectroscopy and LIBS

Plasma optical spectroscopy and laser-induced breakdown spectroscopy (both on and off-line) are used in this work to analyze the effects of different perturbations applied to a tungsten inert gas welding process. This approach enables the analysis and comparison of the spectra acquired with the two setups, thus providing more information about the process evolution and dynamics and the resulting chemical composition of the seams. It will be demonstrated that, in agreement with previous studies, the analysis of different elements gives rise to rich information regarding the resulting weld quality, thus enabling the on-line monitoring of arc-welding processes. Elements such as lithium or sodium, not commonly employed in spectroscopic analysis of welding processes, will prove to provide interesting information on surface defects and contamination. Additionally, LIBS 2D scanning of the performed seams will enable a detailed analysis of the resulting elemental distributions.

Raman spectroscopy for on-line monitoring of botanical extraction process using convolutional neural network with background subtraction

Aqueous extraction is the most common and cost-effective means of obtaining active ingredients from medicinal plants. However, botanical extracts generally contain high pigment content and complex chemical composition posing a challenge for the process analysis of aqueous extraction. Here, we employed Raman spectroscopy to monitor the physical and chemical properties during the extraction process using convolution neural network (CNN) with background subtraction. Real-time spectra were first preprocessed to eliminate fluorescence background interference. Next, two types of CNN models, the one-dimensional CNN (1D-CNN) based on one preprocessing method, and two-dimensional CNN (2D-CNN) based on a concatenation of differentially pretreated data blocks, were used to receive the preprocessed spectra data. Two case studies were conducted for 1D- and 2D-CNN: the extraction of Aurantii fructus, and the co-extraction of Radix Salvia miltiorrhiza and Rhizoma Ligusticum chuanxiong. Furthermore, partial least squares (PLS) models and sequential preprocessing through orthogonalization (SPORT) models were developed and compared with 1D-CNN and 2D-CNN, respectively. CNN-based methods were superior to other models in terms of prediction accuracy, with 2D-CNN yielding the best results. These results indicated that preprocessing and CNN methods were highly complementary, and could effectively remove the fluorescence effect and artefacts introduced by pretreatment in spectral profile. To the best of our knowledge, this is the first study to demonstrate that a combination of preprocessing and CNN leads to improved prediction performance of analytes when using Raman spectroscopy for online monitoring high-pigmented samples.

Spatio-temporal process monitoring using exponentially weighted spatial LASSO

Spatio-temporal process monitoring (STPM) has received a considerable attention recently due to its broad applications in environment monitoring, disease surveillance, streaming image processing, and more. Because spatio-temporal data often have complicated structure, including latent spatio-temporal data correlation, complex spatio-temporal mean structure, and nonparametric data distribution, STPM is a challenging research problem. In practice, if a spatio-temporal process has a distributional shift (e.g., mean shift) started at a specific time point, then the spatial locations with the shift are usually clustered in small regions. This kind of spatial feature of the shift has not been considered in the existing STPM literature yet. In this paper, we develop a new STPM method that takes into account the spatial feature of the shift in its construction. The new method combines the ideas of exponentially weighted moving average in the temporal domain for online process monitoring and spatial LASSO in the spatial domain for accommodating the spatial feature of a future shift. It can also accommodate the complicated spatio-temporal data structure well. Both simulation studies and a real-data application show that it can provide a reliable and effective tool for different STPM applications.

An unsupervised hourly weather status pattern recognition and blending fitting model for PV system fault detection

Detecting PV system faults in a timely fashion is important to ensure the safe operation of equipment and reduce their impact on the economy of the PV systems. It is necessary to further improve the time-sensitive performance evaluation of the system. However, the hourly weather scenario segmentations are seldom considered during the hour-level online monitoring process. Therefore, a hybrid method based on unsupervised hourly weather status pattern recognition and blending fitting model is proposed for hourly fault detection to improve the performance evaluation of PV systems. The proposed solution includes three parts, firstly, in the data preprocessing stage, the measured power with the errors and noise under normal operation situation caused by the environment changes is corrected by monthly linear fitting. Secondly, an unsupervised hourly weather status pattern recognition method is constructed using the measured radiation data, including unsupervised clustering and the Multiclass-GBDT-LR classification process. Finally, after eliminating the anomalies and errors, the blending fitting model of the hourly sub-weather status is established. Through the analysis of power plants in Australia and China, the proposed solutions are validated and evaluated to be superior to existing data-driven solutions in terms of fitting accuracy, detection validity, and response time. Numerical results of case studies indicate that the developed methodology under sub-weather has improved the detection accuracy up to 97.71% and 99.29% compared to benchmark models.

Reflective fiber-optic sensor for on-line nondestructive monitoring of Aspergillus on the surface of cultural paper relics.

A reflective fiber-optic sensor was created to realize on-line nondestructive monitoring of the growth process of Aspergillus on the surface of cultural paper relics. The sensor consisted of one tapered input and six output optical fibers. The operating principle of the device was established. The sensitivity of the sensor was checked. Sensors were used to monitor the growth of Aspergillus niger, Aspergillus flavus, and Aspergillus tamarrii on the papers. The morphology of Aspergillus was characterized. The sensor reveals a linear relationship between the output signal of the sensor and the thickness of Aspergillus biofilm with a detection limit of 10 µm.

A method for melt pool state monitoring in laser-based direct energy deposition based on DenseNet

Detecting and classifying the melt pool states in laser-based direct energy deposition (L-DED) is crucial for reducing defects and enhancing the mechanical properties of L-DED metal parts. Although physics-based modeling methods and traditional machine learning algorithms such as convolutional neural network have been introduced to monitor the melt pool states, improving the low accuracy of these methods remains to be challenging. To address this issue, we developed a DenseNet-39 model to classify the melt pool states. 80 single-track samples were fabricated using a linear scan strategy by L-DED and using a coaxial high-speed camera to capture the melt pool images in-process. Experimental results have demonstrated the superior performance of DenseNet-39 in classifying the melt pool states with 99.3% accuracy, achieved a lower computation burden, and less processing time. We used CAM to explain the mechanism of classification by DenseNet-39. DenseNet-39 provides the potential applications of online process monitoring in L-DED.

Do Struggling Adult Readers Monitor Their Reading? Understanding the Role of Online and Offline Comprehension Monitoring Processes During Reading

Comprehension monitoring is a meta-cognitive skill that is defined as the ability to self-evaluate one’s comprehension of text. Although it is known that struggling adult readers are poor at monitoring their comprehension, additional research is needed to understand the mechanisms underlying comprehension monitoring and their role in reading comprehension in this population. This study used a comprehension monitoring task with struggling adult readers, which included online eye movements (reread and regression path durations) and an offline verbal protocol (oral explanations of key information). We examined whether eye movements predicted accuracy on the passages’ reading comprehension questions, a norm-referenced reading assessment, and an offline verbal protocol after controlling for age and traditional component skills (i.e., decoding, oral language, working memory). Regression path duration uniquely predicted accuracy on the questions; however, decoding and oral vocabulary were the most salient predictors of the norm-referenced reading comprehension measure. Regression path duration also predicted the offline verbal protocol, such that those who exhibited longer regression path duration were also better at explaining key information. These results contribute to the literature regarding struggling adults’ reading component skills, eye movement behaviors involved in processing connected text, and future considerations in assessing comprehension monitoring.

Resin infusion process monitoring using graphene coated glass fabric sensors and infusible thermoplastic and thermoset matrices

AbstractIn this study, an online process monitoring system based on rGO coated glass fabric sensors was developed to monitor different stages of the resin infusion process. The developed sensors were able to monitor reinforcement compaction response, resin flow, race tracking, and resin cure when using thermoset resin and a novel low viscosity thermoplastic resin (i.e., Elium®). The coated glass fabric patches and strips were embedded at different locations within the laminate. The electrical resistance changes occurring within the embedded sensors during different stages of the manufacturing process were continuously monitored. The relative drop in overall resistance of the sensors upon contact with the resin systems was different for epoxy resin and Elium® resin due to differences in their electrical conductivity. It was found that the coating quality, dimensions, and spatial position of the sensors within the laminate and the type of resin system affected the performance of the sensors.

On-line monitoring of the dopamine-based molecular imprinting processes for protein templates with the assistance of a fluorescent indicator.

On-line monitoring of the dopamine (DA)-based molecular imprinting processes over Fe3O4@SiO2-NH2 nanoparticles (SiMNPs) is reportedby using a real-time quantitative PCR machine. Taking advantages of the efficient fluorescence quenching capability of polydopamine (PDA) and its high binding affinity to rhodamine B (RhB), we performed molecular imprinting against different proteins with free dopamine as the functional monomer and RhB as a fluorescent indicator. Along with the template molecules, the fluorescent indicators were continuously encapsulated into the PDA layer formed on the surface of the SiMNPs, resulting in immediate quenching of the fluorescence, which can be conveniently monitored in real time. As proteins showed sequence-dependent influences on the oxidation of dopamine and subsequent self-assembly on the surface of the SiMNPs, the observed fluorescence signals clearly indicated the polymerization progress in the presence of the template proteins, allowing precise control of the reaction time for different templates at a given initial concentration. The optimum end point of the reaction was found to be when 90 ± 3% of the templates had been encapsulated into the polymer, which offered the highest imprinting factor and selectivity. We applied the approach to prepare a primary PDA-based surface imprinted polymer for a multifunctional protein apurinic/apyrimidinic endonuclease/redox effector factor 1 (APE1). After further introduction of 3-hydroxyphenylboronic acid to the interfaces between APE1 and PDA, the resultant molecularly imprinted polymers (MIP-II) enabled quantitative isolation APE1 from cell lysate samples. The developed approach will be useful for the quantitative preparation of PDA-based MIPs for precious template proteins with limited input quantity. It is also applicable for further study on the effects of different proteins or peptides on the PDA formation reactions.

Online nonparametric monitoring of heterogeneous data streams with partial observations based on Thompson sampling

With the rapid advancement of sensor technology driven by Internet-of-Things-enabled applications, tremendous amounts of measurements of heterogeneous data streams are frequently acquired for online process monitoring. Such massive data, involving a large number of data streams with high sampling frequency, incur high costs on data collection, transmission, and analysis in practice. As a result, the resource constraint often restricts the data observability to only a subset of data streams at each data acquisition time, posing significant challenges in many online monitoring applications. Unfortunately, existing methods do not provide a general framework for monitoring heterogeneous data streams with partial observations. In this article, we propose a nonparametric monitoring and sampling algorithm to quickly detect abnormalities occurring to heterogeneous data streams. In particular, an approximation framework is incorporated with an antirank-based CUSUM procedure to collectively estimate the underlying status of all data streams based on partially observed data. Furthermore, an intelligent sampling strategy based on Thompson sampling is proposed to dynamically observe the informative data streams and balance between exploration and exploitation to facilitate quick anomaly detection. Theoretical justification of the proposed algorithm is also investigated. Both simulations and case studies are conducted to demonstrate the superiority of the proposed method.

Microfluidic In-Situ Spectrophotometric Approaches to Tackle Actinides Analysis in Multiple Oxidation States.

The study and development of present and future processes for the treatment/recycling of spent nuclear fuels require many steps, from design in the laboratory to setting up on an industrial scale. In all of these steps, analysis and instrumentation are key points. For scientific reasons (small-scale studies, control of phenomena, etc.) but also with regard to minimizing costs, risks, and waste, such developments are increasingly carried out on milli- or microfluidic devices. The logic is the same for the chemical analyses associated with their follow-up and interpretation. Due to this, over the last few years, opto-microfluidic analysis devices adapted to the monitoring of different processes (dissolution, liquid-liquid extraction, precipitation, etc.) have been increasingly designed and developed. In this work, we prove that photonic lab-on-a-chip (PhLoC) technology is fully suitable for all actinides concentration monitoring along the plutonium uranium refining extraction (plutonium, uranium, reduction, extraction, or Purex) process. Several PhLoC microfluidic platforms were specifically designed and used in different nuclear research and development (R&D) laboratories, to tackle actinides analysis in multiple oxidation states even in mixtures. The detection limits reached (tens of µmol·L-1) are fully compliant with on-line process monitoring, whereas a range of analyzable concentrations of three orders of magnitude can be covered with less than 150µL of analyte. Finally, this work confirms the possibility and the potential of coupling Raman and ultraviolet-visible (UV-Vis) spectroscopies at the microfluidic scale, opening the perspective of measuring very complex mixtures.

Application of convolutional neural networks for prediction of disinfection by-products

Fluorescence spectroscopy can provide high-level chemical characterization and quantification that is suitable for use in online process monitoring and control. However, the high-dimensionality of excitation–emission matrices and superposition of underlying signals is a major challenge to implementation. Herein the use of Convolutional Neural Networks (CNNs) is investigated to interpret fluorescence spectra and predict the formation of disinfection by-products during drinking water treatment. Using deep CNNs, mean absolute prediction error on a test set of data for total trihalomethanes, total haloacetic acids, and the major individual species were all < 6 µg/L and represent a significant difference improved by 39–62% compared to multi-layer perceptron type networks. Heat maps that identify spectral areas of importance for prediction showed unique humic-like and protein-like regions for individual disinfection by-product species that can be used to validate models and provide insight into precursor characteristics. The use of fluorescence spectroscopy coupled with deep CNNs shows promise to be used for rapid estimation of DBP formation potentials without the need for extensive data pre-processing or dimensionality reduction. Knowledge of DBP formation potentials in near real-time can enable tighter treatment controls and management efforts to minimize the exposure of the public to DBPs.

Signal processing of airborne acoustic emissions from laser metal deposited structures

Laser Metal Deposition (LMD) is an additive manufacturing process that enables the production of complex structures on existing parts. To reduce the use of costly and energy-intensive produced materials like titanium, these structures can be selectively deposited on favorable but differing substrate materials. However due to differing material characteristics, this results in a high defect potential.Extensive process parameter development and online process monitoring helps to minimize the impact of defect formation. Monitoring of airborne acoustic process emissions can be used to identify critical defects early during the process and gain information about process stability.Two approaches for in-process time-frequency monitoring of the acquired acoustic data were compared within this work: The short time Fast Fourier Transformation and the Continuous Wavelet Transformation. Performance criteria based on detected distinct defect events were defined to evaluate both approaches and define specifications for an Acoustic Emission In-Process Monitoring system.

Extraction of Reduced Fault Subspace Based on KDICA and Its Application in Fault Diagnosis

Independent component analysis (ICA) is a commonly used non-Gaussian process fault diagnosis method. A fault detection algorithm of kernel dynamic ICA (KDICA) has been proposed for the non-Gaussian process with dynamic and nonlinear characteristics. However, a lack of studies tackling the fault reconstruction and fault diagnosis algorithm exists. Hence, a fault reconstruction model based on KDICA is proposed in this article. In this model, a reduced fault subspace extraction method is proposed. It consists in dividing the fault subspace into the kernel dynamic independent component reduced fault subspace and the residual reduced fault subspace (RRFS). Based on the RRFS, a fault diagnosis approach is designed for online process monitoring. Using the proposed method, the computational complexity can be efficiently reduced and the specific fault type can be accurately identified. The Tennessee–Eastman process is used to verify the feasibility and efficiency of the proposed method and its fault diagnosis application.

An Adaptive Threshold Strategy Based on Genetic Optimization for Nonlinear Dynamic Process Monitoring

Multivariate statistical analysis has been widely used in data-driven process monitoring and fault detection. However, the nonlinear and dynamic characteristics of industrial process data bring significant challenges to applying the method. In addition, the selection of hyperparameters has a significant impact on the monitoring results, but the selection process is tedious and random. For these reasons, an adaptive threshold strategy for nonlinear dynamic process monitoring based on genetic optimization is proposed in this paper. The proposed approach has two main contributions. Firstly, a research scheme combining kernel principal component analysis with adaptive threshold is proposed to handle industrial processes' nonlinear and dynamic characteristics. Then, the genetic algorithm selects the optimal hyperparameters for online process monitoring, which avoids the blindness of hyperparameters selection. The proposed strategy is tested by the Tennessee Eastman process dataset. The research results show that this method effectively reduces the false alarm rate and missed detection rate of process monitoring.

Model-based mid-infrared spectroscopy for on-line monitoring of volatile fatty acids in the anaerobic digester

The performance of anaerobic digestion is significantly governed by the concentration of volatile fatty acids (VFAs). Though the titration and near-infrared spectroscopy have been used to measure the VFAs in the digester, there is still lack of the establishment of on-line monitoring of VFAs in practical application. An effective quantification method based on mid-infrared (MIR) spectroscopy was developed, and used to measure the concentrations of VFAs in the anaerobic bioreactor nondestructively in parallel. The wavelet denoising (WD) spectra were used as the spectral preprocessing option. Compared with other pretreatment methods, the established calibration model built by WD spectra showed satisfactory results. Further, the model was verified using high performance liquid chromatography (HPLC), and predictions were made using real reactor effluent samples. Based on this theoretical work, a set of equipment for the in-situ online monitoring of VFAs was designed, which has high feasibility and effectively solves the problems with the current VFAs online monitoring process. These results provide a new solution for on-line monitoring of the anaerobic digestion, and have great potential for practical application.

Monitoring of Biopolymer Production Process Using Soft Sensors Based on Off-Gas Composition Analysis and Capacitance Measurement

This paper focuses on the design of soft sensors for on-line monitoring of the biotechnological process of biopolymer production, in which biopolymers are accumulated in bacteria as an intracellular energy storage material. The proposed soft sensors for on-line estimation of the biopolymer concentration represent an interesting alternative to the traditional off-line analytical techniques of limited applicability for real-time process control. Due to the complexity of biochemical reactions, which make it difficult to create reasonably complex first-principle mathematical models, a data-driven approach to the design of soft sensors has been chosen in the presented study. Thus, regression methods were used in this design, including multivariate statistical methods (PLS, PCR). This approach enabled the creation of soft sensors using historical process data from fed-batch cultivations of the Pseudomonas putida KT2442 strain used for the production of medium-chain-length polyhydroxyalkanoates (mcl-PHAs). Specifically, data from on-line measurements of off-gas composition analysis and culture medium capacitance were used as input to the soft sensors. The resulting soft sensors allow not only on-line estimation of the biopolymer concentration, but also the concentration of the cell biomass of the production bacterial culture. For most of these soft sensors, the estimation error did not exceed 5% of the measurement range. In addition, soft sensors based on capacitance measurement were able to accurately detect the end of the production phase. This study thus offers an innovative and practically relevant contribution to the field of monitoring of bioprocesses used for the production of medium-chain-length biopolymers.

Development and Validation of Near-Infrared Methods for the Quantitation of Caffeine, Epigallocatechin-3-gallate, and Moisture in Green Tea Production.

The quality of tea leaves (e.g., their color, appearance, and taste) can be directly influenced by the tea production process, which is closely connected with the content of a number of chemical components formed during the production of the tea leaves. However, the production process is now controlled by people's experience, making its quality significantly different. NIRS is a time-saving, cost-saving, and nondestructive method. Therefore, it is necessary to introduce NIRS technology into the quality control of the tea production process. In this study, a quantitative analysis model of caffeine, epigallocatechin-3-gallate (EGCG), and moisture content was established by near-infrared spectroscopy (NIRS) which was united simultaneously with partial least squares (PLSR) for online process monitoring of tea production. The model parameters show that the established model has fine robustness and outstanding measuring accuracy. Then, the feasibility of the established method is verified by the traditional method. Through the verification of the precision of the instrument and the stability of the sample, it is clarified that the model can be further utilized to monitor tea product quality online in a productive process.