Monitoring Of Chemical Processes Research Articles

Broadband and High‐Resolution Mid‐Infrared Spectroscopy Enabled by a Single Phase Change Metasurface

AbstractThe mid‐infrared (MIR) spectral region is crucial for various applications due to its unique properties, but traditional spectrometers are often bulky. Miniaturized spectrometers face a trade‐off between spectral and spatial resolution. Here, a novel approach to MIR spectroscopy is numerically demonstrated by employing an electrically controlled phase‐change metasurface. This method fully exploits the high optical contrast and the quasi‐continuous phase change characteristics of chalcogenide phase change materials, enabling the construction of a set of spectral responses that provide broad spectral coverage with low correlation, utilizing a single metasurface pixel. With this innovative strategy, a broadband and high‐resolution spectral reconstruction is numerically demonstrated with a full width at half maximum (FWHM) resolution of 20 nm and a dual‐peak resolution of 160 nm within a 2400 nm bandwidth. Furthermore, the potential of the spectral detection scheme is underscored by the successful numerical reconstruction of the absorption peaks of methane and carbon dioxide, highlighting its capability for gas analysis and molecular identification. The integration of the spectral detection method into the field of spectral imaging is anticipated to have significant implications, suggesting substantial improvements in chemical process monitoring, and rapid diagnostic techniques in combustion environments.

Data-driven Studies on Automation and Operational Monitoring of Fine Chemical Processes

Data-driven Studies on Automation and Operational Monitoring of Fine Chemical Processes

How Two‐Dimensional Heterocovariance Spectroscopy and Data Fusion Level out the Inadequacies of Compact Spectrometers

ABSTRACTOver the past decade, sensors and compact spectrometers have emerged as a powerful means for real‐time monitoring of chemical and biochemical processes in the field of process analytical technologies (PATs). This is largely attributed to cost‐efficiency and their robustness in near‐line applications. In comparison to more sophisticated laboratory instruments, these devices exhibit limited sensitivity and resolution. In this study, a combination of near‐infrared, low‐field 1H nuclear magnetic resonance and compact Raman spectrometers were employed for the process monitoring of the acid‐catalyzed esterification of isoamyl alcohol and acetic acid to isoamyl acetate. The resulting real‐time data were transformed and visualized for interpretation using two‐dimensional heterocovariance spectroscopy. The data were also subjected to pretreatment, concatenation, and multivariate analysis in accordance with low‐ and mid‐level data fusion. The spectral interpretation derived from heterocovariance spectroscopy provided support for the data pretreatment during data fusion. By applying these computational techniques, the inherent limitations of sensitivity and resolution associated with compact spectroscopic instruments could be overcome, thereby facilitating the interpretation of data and yielding further insights into the process under study. A comparison of the process parameters resulting from the applied methods indicated that consistent data could be obtained. This study demonstrates that heterocovariance spectroscopy and data fusion allow to enhance compact, less expensive analytical instruments for process monitoring and to acquire process knowledge. This, in turn, enables material, financial, and energy resources to be conserved.

Combinatorial Order Pre-processing Search (COPS): A new pre-processing strategy for large-scale interpretable data analysis in process analytical technologies

Combinatorial Order Pre-processing Search (COPS), a novel approach for optimizing data pre-processing is proposed in this work. Unlike simultaneous hyperparameter optimization, COPS employs a priori optimization to reduce computational time while refining the search space for preprocessing sequences and combinations. It allows for setting a maximum number of pre-processing methods, while efficiently searching through combinations of methods with chemically relevant knowledge. In this work, 67 calibration datasets across various analytical techniques, including fluorescence spectroscopy, gas chromatography (GC), near-infrared spectroscopy (NIR), mid-infrared spectroscopy (MIR), visible-near-infrared spectroscopy (Vis-NIR), Raman spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, and voltammetry were evaluated. COPS yielded significant improvements over existing methodologies based on design of experiment and compounded pre-processing approaches. The COPS outperformed the other methods, resulting in an average root mean square error of prediction (RMSEP) reduction of 31.7%, while also reduced the complexity (number of latent variables) of the model which allows for easier interpretation. This underscores the importance of combinatorial order set theory for the search of pre-processing method combinations (without fixing the sequence of pre-processing methods) to enhance model performance and interpretation. The novel COPS approach can be employed in process analytical technology (such as inline, online or at-line chemical sensing analytics) to enhance predictive accuracy and operational efficiency, fundamentally transforming the quality and reliability of chemical process monitoring and control.

Information enhanced slow feature analysis integrated with prior fault data for sensitive monitoring of chemical processes

As an effective feature extraction method, slow feature analysis (SFA) has been successfully applied in the domain of chemical process monitoring. However, as an unsupervised method, the basic SFA ignores the key information carried by prior fault data so that many complicated faults cannot be sensitively detected. To address this issue, an enhanced SFA method, termed Information Enhanced SFA (IE-SFA), is proposed by integrating available fault discriminant information to achieve more sensitive detection of complicated chemical process faults. The proposed method builds a primary-auxiliary SFA modeling framework. On the one hand, the normal training data are analyzed to establish the primary SFA model. On the other, prior fault data are integrated to develop an auxiliary monitoring model for providing extra fault-sensitive information. In the auxiliary model, a sample-variable weighted Fisher discriminant analysis method is performed on normal data, fault data, and their respective slow feature components, thereby extracting fault-sensitive features for enhancing the fault detection capability. For full-view fault monitoring, the Bayesian fusion strategy is used to fuse the outcomes from both primary and auxiliary models. The applications on a benchmark Tennessee Eastman chemical process and a three-phase flow process demonstrate that the proposed IE-SFA method provides superior fault detection performance compared to the basic SFA method.

Toward understandable semi-supervised learning fault diagnosis of chemical processes based on long short-term memory ladder autoencoder (LSTM-LAE) and self-attention (SA)

Fault diagnosis and localization play vital role in chemical process monitoring. Finding the root cause of fault accurately and timely is the key to avoid serious accidents and ensure process safety. Unfortunately, most deep learning-based models only involves in predicting fault state and interpretability is still not fully explored. Besides, lack of labeled samples in practical situations makes supervised learning difficult to implement. To circumvent the obstacle, semi-supervised learning based on long short-term memory ladder autoencoder is combined with self-attention mechanism, which is intended to establish interpretable model by explicitly clarifying the corresponding relationship between abnormal variables and faults. Using Tennessee Eastman process and practical high-purity carbonate production process as benchmark, fault diagnosis and identification performance of proposed LSTM-LAE-SA is validated, and interpretability analysis is performed to demonstrate its capabilities in abnormal variable locations. The developed understandable model could improve operator's trust and industrial application in fault diagnosis system.

Simultaneous state‐estimator tuning and parameter estimation for systems with nonstationary disturbances, multi‐rate data, and measurement delays

AbstractModel‐based monitoring and control of chemical and biochemical processes rely on state estimators such as extended Kalman filters (EKFs) to ensure accurate online model predictions. Accurate predictions depend on appropriate model parameters and suitable state‐estimator tuning factors. Extensions to our previously developed simultaneous parameter estimation and tuning (SPET) method are proposed so that SPET can be used for systems with nonstationary disturbances, time‐varying parameters, multi‐rate data, and measurement delays. A continuous stirred tank reactor (CSTR) case study with simulated data is used to illustrate and test the proposed method. Superior online model predictions and state‐estimator performance are achieved using SPET compared to a traditional approach for parameter estimation and EKF tuning, with improvements in the average sum‐of‐squared prediction errors ranging from 3% to 52% for the scenarios tested. The SPET approach will also be useful for more‐advanced state estimators that require the same tuning information as EKFs.

Innovative Non-Invasive and Non-Intrusive Precision Thermometry in Stainless-Steel Tanks Using Ultrasound Transducers.

Measuring temperature inside chemical reactors is crucial to ensuring process control and safety. However, conventional methods face a number of limitations, such as the invasiveness and the restricted dynamic range. This paper presents a novel approach using ultrasound transducers to enable accurate temperature measurements. Our experiments, conducted within a temperature range of 28.8 to 83.8 °C, reveal a minimal temperature accuracy of 98.6% within the critical zone spanning between 70.5 and 75 °C, and an accuracy of over 99% outside this critical zone. The experiments focused on a homogeneous environment of distilled water within a stainless-steel tank. This approach will be extended in a future research in order to diversify the experimental media and non-uniform environments, while promising broader applications in chemical process monitoring and control.

Real-Time Monitoring of Multistep Batch and Flow Synthesis Processes of a Key Intermediate of Lifitegrast by a Combined Spectrometer System with Both Near-Infrared and Raman Spectroscopies Coupled to Partial Least-Squares.

Process analytical technology (PAT) has been successfully applied in numerous chemical synthesis cases and is an important tool in pharmaceutical process research and development. PAT brings new methods and opportunities for the real-time monitoring of chemical processes. In multistep synthesis, real-time monitoring of the complex reaction mixtures is a significant challenge but provides an opportunity to enhance reaction understanding and control. In this study, a combined multichannel spectrometer system with both near-infrared and Raman spectroscopy was built, and calibration models were developed to quantify the desired products, intermediates, and impurities in real-time at multiple points along the synthetic pathway. The capabilities of the system have been demonstrated by operating dynamic experiments in both batch and continuous-flow processes. It represents a significant step forward in data-driven, multistep pharmaceutical ingredient synthesis.

Robust Fault Detection in Monitoring Chemical Processes Using Multi-Scale PCA with KD Approach

Effective fault detection in chemical processes is of utmost importance to ensure operational safety, minimize environmental impact, and optimize production efficiency. To enhance the monitoring of chemical processes under noisy conditions, an innovative statistical approach has been introduced in this study. The proposed approach, called Multiscale Principal Component Analysis (PCA), combines the dimensionality reduction capabilities of PCA with the noise reduction capabilities of wavelet-based filtering. The integrated approach focuses on extracting features from the multiscale representation, balancing the need to retain important process information while minimizing the impact of noise. For fault detection, the Kantorovich distance (KD)-driven monitoring scheme is employed based on features extracted from Multiscale PCA to efficiently detect anomalies in multivariate data. Moreover, a nonparametric decision threshold is employed through kernel density estimation to enhance the flexibility of the proposed approach. The detection performance of the proposed approach is investigated using data collected from distillation columns and continuously stirred tank reactors (CSTRs) under various noisy conditions. Different types of faults, including bias, intermittent, and drift faults, are considered. The results reveal the superior performance of the proposed multiscale PCA-KD based approach compared to conventional PCA and multiscale PCA-based monitoring methods.

Split-ring resonator with interdigital Split electrodes as detector for liquid and ion chromatography

The analysis of food and drugs as well as the monitoring of chemical processes and bioreactors by high-performance liquid chromatography and ion chromatography requires universal, cost-effective, and sensitive detectors. Therefore, this work presents a split-ring resonator that detects changes in the electrical and dielectric properties of the eluate from liquid chromatography or ion chromatography by monitoring the amplitude of the transmitted signal using an envelope detector. The used split-ring resonator consists of a simple printed circuit board featuring two microstrip lines. One is formed to a ring with interdigital split electrodes. The interdigital split electrodes forming the sensitive area significantly enhance the sensitivity to changes in relative permittivity, dielectric losses and electrical conductivity of the eluate In addition, the split-ring resonator is a small, inexpensive and easy-to-use detector that uses several dielectric parameters simultaneously for the measurement. Furthermore, the split-ring resonator measures these parameters at a significantly lower frequency than optical detectors and therefore measures different changes in frequency-dependent permittivity. For demonstrating feasibility of a split-ring resonator as a detector for high-performance liquid chromatography and ion chromatography, various anion and cation standards, basic and acidic amino acids, sugars, as well as sugar alcohols were separated and detected. A conductivity detector for ion chromatography and a refractive index detector for high-performance liquid chromatography were used as reference detectors.

An emerging tool in healthcare: wearable surface-enhanced Raman Spectroscopy

This perspective explores the progressive domain of wearable surface-enhanced Raman spectroscopy (SERS), underscoring its potential to revolutionize healthcare. As an advanced variation of traditional Raman spectroscopy, SERS offers heightened sensitivity in detecting molecular vibrations. Applied in wearable technology, it provides a mechanism for continuous, non-invasive, real-time monitoring of chemical and biomolecular processes in the human body through biofluids such as sweat and tears. This underscores its immense potential in enabling early disease detection and facilitating personalized medicine. However, the adoption of wearable SERS is not without challenges, which include device miniaturization, reliable biofluid sampling, user comfort, biocompatibility, and data interpretation. Nevertheless, this perspective emphasizes that the fast-paced advancements in nanotechnology and data sciences render these challenges surmountable. In summary, the perspective presents wearable SERS as a promising innovation in healthcare’s future landscape. It has the potential to enhance individual health outcomes significantly and lower healthcare costs by promoting a preventive health management approach.

Rapid online analysis of n-alkanes in gaseous streams via APCI mass spectrometry.

Online monitoring of dynamic chemical processes involving a wide volatility range of hydrocarbon species is challenging due to long chromatographic measurement times. Mass spectrometry (MS) overcomes chromatographic delays. However, the analysis of n-alkane mixtures by MS is difficult because many fragment ions are formed, which leads to overlapping signals of the homologous series. Atmospheric pressure chemical ionization (APCI) is suitable for the analysis of saturated hydrocarbons and is the subject of current research. Still, although APCI is a "soft ionization" technique, fragmentation is typically inevitable. Moreover, it is usually applied for liquid samples, while an application for online gas-phase monitoring is widely unexplored. Here, we present an automated APCI-MS method for an online gas-phase analysis of volatile and semi-volatile n-alkanes. Mass spectra for n-heptane and n-decane reveal [M-H]+, [M-3H]+ and [M-3H+H2O]+ as abundant ions. While [M-H]+ and [M-3H]+ show an excessive fragmentation pattern to smaller CnH2n+1+ and CnH2n-1+ cations, [M-3H+H2O]+ is the only relevant signal within the CnH2n+1O+ ion group, i.e., no chain cleavage is observed. This makes [M-3H+H2O]+ an analyte-specific ion that is suitable for the quantification of n-alkane mixtures. A calibration confirms the linearity of C7 and C10 signals up to concentrations of ~1000-1500 ppm. Moreover, validated concentration profiles are measured for a binary C7/C10 mixture and a five-alkane C7/C10/C12/C14/C20 mixture. Compared to the 40-min sampling interval of the reference gas chromatograph, MS sampling is performed within 5 min and allows dynamic changesto be monitored.

Multiscale monitoring of industrial chemical process using wavelet-entropy aided machine learning approach

In recent decades, machine learning (ML) techniques have been effectively applied for industrial process monitoring to assure safety and high-quality yield. Traditional process fault detection, identification, and diagnosis (FDI&D) approaches are insufficiently smart to address the modern complex challenges of real-time industrial chemical processes. The detection and diagnostic resolution of the traditional monitoring approach are less robust, inefficient, and produce the wrong interpretation of actual fault information. These approaches are based on a single-scale fault illustration and cannot effectively address multiple fault depiction roots. This study introduces a novel ML-aided methodological framework for industrial and manufacturing monitoring systems. The proposed ML framework is developed using Principal component analysis (PCA), Shannon information entropy (IE), wavelet transformation (WT), and signed directed graph (SDG). It includes fault detection, identification, and diagnostic propagation root-path interpretation to address the safety challenges of modern, real-time industrial chemical processes. The proposed methodological framework is validated using the Tennessee Eastman process (TEP) benchmark to highlight their performance and efficiency. The results of this study determined that the new proposed approach is more efficient in terms of accuracy, robustness, and actual propagation root cause than traditional techniques. It has a high fault detection rate (FDR), low fault alarm rate (FAR) that identifies and recognizes the actual faulty-correlated variables to establish the SDG model framework for determining the actual diagnostic propagation root path. It initially enables operators to react to unusual incidents, ensuring industrial safety, minimizing economic loss, and avoiding disasters.

Real-time quality control for chemical and biotechnological processes: a brief review

Monitoring critical process parameters of chemical and biotechnological processes is an essential tool at every stage of drug manufacturing technology. The aim of Process Analytical Technology (PAT) is to provide effective tools, such as multidimensional data analysis, modern analytical methods, and monitoring tools, for the continuous improvement of process understanding and knowledge. Among the methods of wide interest are optical and spectroscopic techniques that can be used in the control of chemical and biotechnological processes. The selection of the appropriate method is crucial and depends on many factors, including the nature of the process, the number of variables, and analytical limitations. This review focuses on a brief and precise characterization of spectroscopic and optical methods that can be applied to monitoring and control of chemical and biotechnological processes.

SensorSCAN: Self-supervised learning and deep clustering for fault diagnosis in chemical processes

Modern industrial facilities generate large volumes of raw sensor data during the production process. This data is used to monitor and control the processes and can be analyzed to detect and predict process abnormalities. Typically, the data has to be annotated by experts in order to be used in predictive modeling. However, manual annotation of large amounts of data can be difficult in industrial settings.In this paper, we propose SensorSCAN, a novel method for unsupervised fault detection and diagnosis, designed for industrial chemical process monitoring. We demonstrate our model's performance on two publicly available datasets of the Tennessee Eastman Process with various faults. The results show that our method significantly outperforms existing approaches (+0.2-0.3 TPR for a fixed FPR) and effectively detects most of the process faults without expert annotation. Moreover, we show that the model fine-tuned on a small fraction of labeled data nearly reaches the performance of a SOTA model trained on the full dataset. We also demonstrate that our method is suitable for real-world applications where the number of faults is not known in advance. The code is available at https://github.com/AIRI-Institute/sensorscan.

A Disposable Soft Magnetic Ribbon Impedance-Based Sensor for Corrosion Monitoring

We present a new approach for the real-time monitoring of chemical corrosion based on radio-frequency (RF) impedance technology and soft ferromagnetic ribbons. The impedance (Z) of a commercial METGLAS® 2714A ribbon was measured in real time for 5 μL of drop-casted HNO3 of various concentrations. Variations in the concentration of the drop-casted acid were assessed by considering the difference in Z (η) with and without the acid treatment. We found a large and linear increase in η (from ~5 to ~35 mΩ) and a large linear decrease in measurement time (from ~240 to 70 s) with increases in acid concentration (from 0.9 to 7.4 Molar), which is promising for the development of disposable chemical sensors for the strength estimation of corrosive chemicals and for the monitoring of time-dependent chemical corrosion processes. Since the ribbon used is commercially available at a low cost and as the measurement system is quick and low power-consuming, the proposed sensor can be used as an easy, quick, and low-cost chemical probe in industry and for environmental hazard management purposes.

Studying kinetics of a surface reaction using elastocapillary effect

HypothesisWhen a liquid is inserted inside a microfluidic channel, embedded within a soft elastomeric layer, e.g. poly(dimethylsiloxane) (PDMS), the thin wall of the channel deforms, due to change in solid–liquid interfacial energy. This phenomenon is known as Elastocapillary effect. The evolution of a new species at this interface too alters the interfacial energy and consequently the extent of deformation. Hence, it should be possible to monitor dynamics of physical and chemical events occurring near to the solid–liquid interface by measuring this deformation by a suitable method, e.g., optical profilometer. ExperimentsAqueous solution of a metal salt inserted into these channels reacts with Silicon-hydride present in PDMS, yielding metallic nanoparticles at the channel surface. The kinetics of this reaction was captured in real time, by measuring the wall deformation. Similarly, physical adsorption of a protein: Bovine Serum Albumin, on PDMS surface too was monitored. FindingThe rate of change in deformation can be related to rate of these processes to extract the respective reaction rate constant. These results show that Elastocapillary effect can be a viable analytical tool for in-situ monitoring of many physical and chemical processes for which, the reaction site is inaccessible to conventional analytical methods.

Real‐Time Monitoring of Dynamic Chemical Processes in Microbial Metabolism with Optical Sensors

AbstractMonitoring microbial metabolism is vital for revealing the mechanism of disease related to microbial metabolism and providing guidance for biomanufacturing processes optimization. However, it remains a grand challenge to offer real‐time insights into microbial metabolism owing to the complex and dynamic process. In this paper, the recent advances and prospects of optical biosensors including the organic, genetic coding and inorganic optical biosensors are briefly described for real‐time monitoring of dynamic microbial metabolism. This paper points out that challenges remain in microbial heterogeneity. We believe that this work will inspire the application of developing new methods for single cell real‐time analysis.

Single-Molecule Interconversion between Chiral Configurations of Boronate Esters Observed in a Nanoreactor.

Isomers of some chemical compounds may be dynamically interconvertible. Due to a lack of sensing methods with a sufficient resolution, however, direct monitoring of such processes can be difficult. Engineered Mycobacterium smegmatis porin A (MspA) nanopores can be applied as nanoreactors so that chemical reactions can be directly monitored. Here, an MspA modified with a phenylboronic acid (PBA) adapter was prepared and was used to observe dynamic interconversion between chiral configurations of boronate esters, which appears as telegraphic switching on top of nanopore events. The mechanism of this behavior was further confirmed by trials with different halogenated catechols, dopamine, adenosine, 1,2-propanediol, and (2R,3R)-2,3-butanediol, and its generality has been demonstrated. These results suggest that an engineered MspA possesses an exceptional resolution in its monitoring of chemical reaction processes and may inspire the future design of nanopore small-molecule sensors.