Brewing alcohol 101: An undergraduate experiment utilizing benchtop NMR for quantification and process monitoring

In recent years there has been a renewed interest in benchtop NMR. Given their lower cost of ownership, smaller footprint, and ease of use, they are especially suited as an educational tool. Here, a new experiment targeted at upper-year undergraduates and first-year graduate students follows the conversion of D-glucose into ethanol at low-field. First, high and low-field data on D-glucose are compared and students learn both the Hz and ppm scales and how J-coupling is field-independent. The students then acquire their own quantitative NMR datasets and perform the quantification using an Electronic Reference To Access In Vivo Concentration (ERETIC) technique. To our knowledge ERETIC is not currently taught at the undergraduate level, but has an advantage in that internal standards are not required; ideal for following processes or with future use in flow-based benchtop monitoring. Using this quantitative data, students can relate a simple chemical process (fermentation) back to more complex topics such as reaction kinetics, bridging the gaps between analytical and physical chemistry. When asked to reflect on the experiment, students had an overwhelmingly positive experience, citing agreement with learning objectives, ease of understanding the protocol, and enjoyment. Each of the respondents recommended this experiment as a learning tool for others. This experiment has been outlined for other instructors to utilize in their own courses across institutions, with the hope that a continued expansion of low-field NMR will increase accessibility and learning opportunities at the undergraduate level.

Quantitative at-line monitoring of enzymatic hydrolysis using benchtop diffusion nuclear magnetic resonance spectroscopy

Benchtop diffusion nuclear magnetic resonance (NMR) spectroscopy was used to perform quantitative monitoring of enzymatic hydrolysis. The study aimed to test the feasibility of the technology to characterize enzymatic hydrolysis processes in real time. Diffusion ordered spectroscopy (DOSY) was used to measure the signal intensity and apparent self-diffusion constant of solubilized protein in hydrolysate. The NMR technique was tested on an enzymatic hydrolysis reaction of red cod, a lean white fish, by the endopeptidase alcalase at 50°C. Hydrolysate samples were manually transferred from the reaction vessel to the NMR equipment. Measurement time was approximately 3 min per time point. The signal intensity from the DOSY experiment was used to measure protein concentration and the apparent self-diffusion constant was converted into an average molecular weight and an estimated degree of hydrolysis. These values were plotted as a function of time and both the rate of solubilization and the rate of protein breakdown could be calculated. In addition to being rapid and noninvasive, DOSY using benchtop NMR spectroscopy has an advantage compared with other enzymatic hydrolysis characterization methods as it gives a direct measure of average protein size; many functional properties of proteins are strongly influenced by protein size. Therefore, a method to give protein concentration and average size in real time will allow operators to more tightly control production from enzymatic hydrolysis. Although only one type of material was tested, it is anticipated that the method should be applicable to a broad variety of enzymatic hydrolysis feedstocks.

Polarization transfer methods for quantitative analysis of flowing mixtures with benchtop 13C NMR spectroscopy

Benchtop NMR spectroscopy is attractive for process monitoring; however, there are still drawbacks that often hamper its use, namely, the comparatively low spectral resolution in 1H NMR, as well as the low signal intensities and problems with the premagnetization of flowing samples in 13C NMR. We show here that all these problems can be overcome by using 1H-13C polarization transfer methods. Two ternary test mixtures (one with overlapping peaks in the 1H NMR spectrum and one with well-separated peaks, which was used as a reference) were studied with a 1 T benchtop NMR spectrometer using the polarization transfer sequence PENDANT (polarization enhancement that is nurtured during attached nucleus testing). The mixtures were analyzed quantitatively in stationary as well as in flow experiments by PENDANT enhanced 13C NMR experiments, and the results were compared with those from the gravimetric sample preparation and from standard 1H and 13C NMR spectroscopy. Furthermore, as a proxy for a process monitoring application, continuous dilution experiments were carried out, and the composition of the mixture was monitored in a flow setup by 13C NMR benchtop spectroscopy with PENDANT. The results demonstrate the high potential of polarization transfer methods for applications in quantitative process analysis with benchtop NMR instruments, in particular with flowing samples.

Near-Infrared Spectroscopy and Chemometrics for Effective Online Quality Monitoring and Process Control during Pelagic Fishmeal and Oil Processing

Near-infrared spectroscopy has become a common quality assessment tool for fishmeal products during the last two decades. However, to date it has not been used for active online quality monitoring during fishmeal processing. Our aim was to investigate whether NIR spectroscopy, in combination with multivariate chemometrics, could actively predict the changes in the main chemical quality parameters of pelagic fishmeal and oil during processing, with an emphasis on lipid quality changes. Results indicated that partial least square regression (PLSR) models from the NIR data effectively predicted proximate composition changes during processing (with coefficients of determination of an independent test set at RCV2 = 0.9938, RMSECV = 2.41 for water; RCV2 = 0.9773, RMSECV = 3.94 for lipids; and RCV2 = 0.9356, RMSECV = 5.58 for FFDM) and were successful in distinguishing between fatty acids according to their level of saturation (SFA (RCV2=0.9928, RMSECV=0.24), MUFA (RCV2=0.8291, RMSECV=1.49), PUFA (RCV2=0.8588, RMSECV=2.11)). This technique also allowed the prediction of phospholipids (PL RCV2=0.8617, RMSECV=0.11, and DHA (RCV2=0.8785, RMSECV=0.89) and EPA content RCV2=0.8689, RMSECV=0.62) throughout processing. NIR spectroscopy in combination with chemometrics is, thus, a powerful quality assessment tool that can be applied for active online quality monitoring and processing control during fishmeal and oil processing.

Metal Laser-Based Powder Bed Fusion Process Development Using Optical Tomography

In this study, a set of 316 L stainless steel test specimens was additively manufactured by laser-based Powder Bed Fusion. The process parameters were varied for each specimen in terms of laser scan speed and laser power. The objective was to use a narrow band of parameters well inside the process window, demonstrating detailed parameter engineering for specialized additive manufacturing cases. The process variation was monitored using Optical Tomography to capture light emissions from the layer surfaces. Process emission values were stored in a statistical form. Micrographs were prepared and analyzed for defects using optical microscopy and image manipulation. The results of two data sources were compared to find correlations between lack of fusion, porosity, and layer-based energy emissions. A data comparison of Optical Tomography data and micrograph analyses shows that Optical Tomography can partially be used independently to develop new process parameters. The data show that the number of critical defects increases when the average Optical Tomography grey value passes a certain threshold. This finding can contribute to accelerating manufacturing parameter development and help meet the industrial need for agile component-specific parameter development.

Convolutional Neural Networks for Raw Signal Classification in CNC Turning Process Monitoring

This study addresses the need for advanced machine learning-based process monitoring in smart manufacturing. A methodology is developed for near-real-time part quality prediction based on process-related data obtained from a CNC turning center. Instead of the manual feature extraction methods typically employed in signal processing, a novel one-dimensional convolutional architecture allows the trained model to autonomously extract pertinent features directly from the raw signals. Several signal channels are utilized, including vibrations, motor speeds, and motor torques. Three quality indicators-average roughness, peak-to-valley roughness, and diameter deviation-are monitored using a single model, resulting in a compact and efficient classifier. Training data are obtained via a small number of experiments designed to induce variability in the quality metrics by varying feed, cutting speed, and depth of cut. A sliding window technique augments the dataset and allows the model to seamlessly operate over the entire process. This is further facilitated by the model's ability to distinguish between cutting and non-cutting phases. The base model is evaluated via k-fold cross validation and achieves average F1 scores above 0.97 for all outputs. Consistent performance is exhibited by additional instances trained under various combinations of design parameters, validating the robustness of the proposed methodology.

An End-to-End Inclination State Monitoring Method for Collaborative Robotic Drilling Based on Resnet Neural Network

The collaborative robot can complete various drilling tasks in complex processing environments thanks to the high flexibility, small size and high load ratio. However, the inherent weaknesses of low rigidity and variable rigidity in robots bring detrimental effects to surface quality and drilling efficiency. Effective online monitoring of the drilling quality is critical to achieve high performance robotic drilling. To this end, an end-to-end drilling-state monitoring framework is developed in this paper, where the drilling quality can be monitored through online-measured vibration signals. To evaluate the drilling effect, a Canny operator-based edge detection method is used to quantify the inclination state of robotic drilling, which provides the data labeling information. Then, a robotic drilling inclination state monitoring model is constructed based on the Resnet network to classify the drilling inclination states. With the aid of the training dataset labeled by different inclination states and the end-to-end training process, the relationship between the inclination states and vibration signals can be established. Finally, the proposed method is verified by collaborative robotic drilling experiments with different workpiece materials. The results show that the proposed method can effectively recognize the drilling inclination state with high accuracy for different workpiece materials, which demonstrates the effectiveness and applicability of this method.

Development of a Nuclear Fuel Dissolution Monitor Based on Raman Spectroscopy

The processing of spent nuclear fuel and other nuclear materials is a critical component of nuclear material management with implications for global security. The first step of fuel processing is dissolution, with several charges of fuel sequentially added to a batch of solvent. The incomplete dissolution of a charge precludes the addition of the next charge. As the dissolution takes place in a heated, highly corrosive and radiological vessel, direct monitoring of the process is not possible. We discuss the use of Raman spectroscopy to indirectly monitor dissolution through an analysis of the gaseous emissions from the dissolver. Challenges associated with the implementation of Raman spectroscopy include the composition and physical characteristics of the offgas stream and the impact of operating conditions. Nonetheless, we observed that NO2 concentrations serve as a reliable indicator of process activity and correlate to the amount of fuel material that remains undissolved. These results demonstrate the promise of the method for monitoring nuclear material dissolution.

Machine Learning-Enabled NIR Spectroscopy. Part 3: Hyperparameter by Design (HyD) Based ANN-MLP Optimization, Model Generalizability, and Model Transferability

Data variations, library changes, and poorly tuned hyperparameters can cause failures in data-driven modelling. In such scenarios, model drift, a gradual shift in model performance, can lead to inaccurate predictions. Monitoring and mitigating drift are vital to maintain model effectiveness. USFDA and ICH regulate pharmaceutical variation with scientific risk-based approaches. In this study, the hyperparameter optimization for the Artificial Neural Network Multilayer Perceptron (ANN-MLP) was investigated using open-source data. The design of experiments (DoE) approach in combination with target drift prediction and statistical process control (SPC) was employed to achieve this objective. First, pre-screening and optimization DoEs were conducted on lab-scale data, serving as internal validation data, to identify the design space and control space. The regression performance metrics were carefully monitored to ensure the right set of hyperparameters was selected, optimizing the modelling time and storage requirements. Before extending the analysis to external validation data, a drift analysis on the target variable was performed. This aimed to determine if the external data fell within the studied range or required retraining of the model. Although a drift was observed, the external data remained well within the range of the internal validation data. Subsequently, trend analysis and process monitoring for the mean absolute error of the active content were conducted. The combined use of DoE, drift analysis, and SPC enabled trend analysis, ensuring that both current and external validation data met acceptance criteria. Out-of-specification and process control limits were determined, providing valuable insights into the model's performance and overall reliability. This comprehensive approach allowed for robust hyperparameter optimization and effective management of model lifecycle, crucial in achieving accurate and dependable predictions in various real-world applications.

Features of Self-Organization during the Process of Mindfulness-Based Stress Reduction: A Single Case Study

Compared to the extensive evidence of the effectiveness of mindfulness-based interventions, there is only a limited understanding of their mechanisms of change. The three aims of this study are (1) to identify features of self-organization during the process (e.g., pattern transitions), (2) to obtain an impression of the effects of continuous self-assessments and feedback sessions on mindfulness-related stress reduction, and (3) to test the feasibility of high-frequency process monitoring and process feedback. Concerning aim (1), the specific hypothesis is that change will occur as a cascade of discontinuous pattern transitions emerging spontaneously in the sense of not being a reaction to external input. This single case study describes changing patterns of multiple time series that were produced by app-based daily self-assessments during and after an 8-week mindfulness-based stress reduction program. After this MBSR program, the participant (a female nurse) continued the self-assessment and the mindfulness practice for a further 10 months. The results confirm findings on the positive effects of mindfulness programs for healthcare professionals, especially on coping with work-related stress. The analysis of the time series data supports the hypothesis of self-organization as a possible mechanism of change manifesting as a cascade of phase transitions in the dynamics of a biopsychosocial system. At the end of the year, the participant reported a beneficial impact of daily monitoring and systematic feedback on the change process. The results underline the feasibility and usefulness of continuous high-frequency monitoring during and after mindfulness interventions.

A Random Forest Classifier for Anomaly Detection in Laser-Powder Bed Fusion Using Optical Monitoring

Metal additive manufacturing (AM) is a disruptive production technology, widely adopted in innovative industries that revolutionizes design and manufacturing. The interest in quality control of AM systems has grown substantially over the last decade, driven by AM's appeal for intricate, high-value, and low-volume production components. Geometry-dependent process conditions in AM yield unique challenges, especially regarding quality assurance. This study contributes to the development of machine learning models to enhance in-process monitoring and control technology, which is a critical step in cost reduction in metal AM. As the part is built layer upon layer, the features of each layer have an influence on the quality of the final part. Layer-wise in-process sensing can be used to retrieve condition-related features and help detect defects caused by improper process conditions. In this work, layer-wise monitoring using optical tomography (OT) imaging was employed as a data source, and a machine-learning (ML) technique was utilized to detect anomalies that can lead to defects. The major defects analyzed in this experiment were gas pores and lack of fusion defects. The Random Forest Classifier ML algorithm is employed to segment anomalies from optical images, which are then validated by correlating them with defects from computerized tomography (CT) data. Further, 3D mapping of defects from CT data onto the OT dataset is carried out using the affine transformation technique. The developed anomaly detection model's performance is evaluated using several metrics such as confusion matrix, dice coefficient, accuracy, precision, recall, and intersection-over-union (IOU). The k-fold cross-validation technique was utilized to ensure robustness and generalization of the model's performance. The best detection accuracy of the developed anomaly detection model is 99.98%. Around 79.40% of defects from CT data correlated with the anomalies detected from the OT data.

Immobilization of Enzyme Electrochemical Biosensors and Their Application to Food Bioprocess Monitoring

Electrochemical biosensors based on immobilized enzymes are among the most popular and commercially successful biosensors. The literature in this field suggests that modification of electrodes with nanomaterials is an excellent method for enzyme immobilization, which can greatly improve the stability and sensitivity of the sensor. However, the poor stability, weak reproducibility, and limited lifetime of the enzyme itself still limit the requirements for the development of enzyme electrochemical biosensors for food production process monitoring. Therefore, constructing sensing technologies based on enzyme electrochemical biosensors remains a great challenge. This article outlines the construction principles of four generations of enzyme electrochemical biosensors and discusses the applications of single-enzyme systems, multi-enzyme systems, and nano-enzyme systems developed based on these principles. The article further describes methods to improve enzyme immobilization by combining different types of nanomaterials such as metals and their oxides, graphene-related materials, metal-organic frameworks, carbon nanotubes, and conducting polymers. In addition, the article highlights the challenges and future trends of enzyme electrochemical biosensors, providing theoretical support and future perspectives for further research and development of high-performance enzyme chemical biosensors.

Interpretable Machine Learning Methods for Monitoring Polymer Degradation in Extrusion of Polylactic Acid

This work investigates real-time monitoring of extrusion-induced degradation in different grades of PLA across a range of process conditions and machine set-ups. Data on machine settings together with in-process sensor data, including temperature, pressure, and near-infrared (NIR) spectra, are used as inputs to predict the molecular weight and mechanical properties of the product. Many soft sensor approaches based on complex spectral data are essentially 'black-box' in nature, which can limit industrial acceptability. Hence, the focus here is on identifying an optimal approach to developing interpretable models while achieving high predictive accuracy and robustness across different process settings. The performance of a Recursive Feature Elimination (RFE) approach was compared to more common dimension reduction and regression approaches including Partial Least Squares (PLS), iterative PLS (i-PLS), Principal Component Regression (PCR), ridge regression, Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF). It is shown that for medical-grade PLA processed under moisture-controlled conditions, accurate prediction of molecular weight is possible over a wide range of process conditions and different machine settings (different nozzle types for downstream fibre spinning) with an RFE-RF algorithm. Similarly, for the prediction of yield stress, RFE-RF achieved excellent predictive performance, outperforming the other approaches in terms of simplicity, interpretability, and accuracy. The features selected by the RFE model provide important insights to the process. It was found that change in molecular weight was not an important factor affecting the mechanical properties of the PLA, which is primarily related to the pressure and temperature at the latter stages of the extrusion process. The temperature at the extruder exit was also the most important predictor of degradation of the polymer molecular weight, highlighting the importance of accurate melt temperature control in the process. RFE not only outperforms more established methods as a soft sensor method, but also has significant advantages in terms of computational efficiency, simplicity, and interpretability. RFE-based soft sensors are promising for better quality control in processing thermally sensitive polymers such as PLA, in particular demonstrating for the first time the ability to monitor molecular weight degradation during processing across various machine settings.

Evaluating Novel Quantification Methods for Infectious Baculoviruses

Accurate and rapid quantification of (infectious) virus titers is of paramount importance in the manufacture of viral vectors and vaccines. Reliable quantification data allow efficient process development at a laboratory scale and thorough process monitoring in later production. However, current gold standard applications, such as endpoint dilution assays, are cumbersome and do not provide true process analytical monitoring. Accordingly, flow cytometry and quantitative polymerase chain reaction have attracted increasing interest in recent years, offering various advantages for rapid quantification. Here, we compared different approaches for the assessment of infectious viruses, using a model baculovirus. Firstly, infectivity was estimated by the quantification of viral nucleic acids in infected cells, and secondly, different flow cytometric approaches were investigated regarding analysis times and calibration ranges. The flow cytometry technique included a quantification based on post-infection fluorophore expression and labeling of a viral surface protein using fluorescent antibodies. Additionally, the possibility of viral (m)RNA labeling in infected cells was investigated as a proof of concept. The results confirmed that infectivity assessment based on qPCR is not trivial and requires sophisticated method optimization, whereas staining of viral surface proteins is a fast and feasible approach for enveloped viruses. Finally, labeling of viral (m)RNA in infected cells appears to be a promising opportunity but will require further research.

A Feasibility Study towards the On-Line Quality Assessment of Pesto Sauce Production by NIR and Chemometrics

The food industry needs tools to improve the efficiency of their production processes by minimizing waste, detecting timely potential process issues, as well as reducing the efforts and workforce devoted to laboratory analysis while, at the same time, maintaining high-quality standards of products. This can be achieved by developing on-line monitoring systems and models. The present work presents a feasibility study toward establishing the on-line monitoring of a pesto sauce production process by means of NIR spectroscopy and chemometric tools. The spectra of an intermediate product were acquired on-line and continuously by a NIR probe installed directly on the process line. Principal Component Analysis (PCA) was used both to perform an exploratory data analysis and to build Multivariate Statistical Process Control (MSPC) charts. Moreover, Partial Least Squares (PLS) regression was employed to compute real time prediction models for two different pesto quality parameters, namely, consistency and total lipids content. PCA highlighted some differences related to the origin of basil plants, the main pesto ingredient, such as plant age and supplier. MSPC charts were able to detect production stops/restarts. Finally, it was possible to obtain a rough estimation of the quality of some properties in the early production stage through PLS.

Assessment of Variability Sources in Grape Ripening Parameters by Using FTIR and Multivariate Modelling

The variability in grape ripening is associated with the fact that each grape berry undergoes its own biochemical processes. Traditional viticulture manages this by averaging the physicochemical values of hundreds of grapes to make decisions. However, to obtain accurate results it is necessary to evaluate the different sources of variability, so exhaustive sampling is essential. In this article, the factors "grape maturity over time" and "position of the grape" (both in the grapevine and in the bunch/cluster) were considered and studied by analyzing the grapes with a portable ATR-FTIR instrument and evaluating the spectra obtained with ANOVA-simultaneous component analysis (ASCA). Ripeness over time was the main factor affecting the characteristics of the grapes. Position in the vine and in the bunch (in that order) were also significantly important, and their effect on the grapes evolves over time. In addition, it was also possible to predict basic oenological parameters (TSS and pH with errors of 0.3 °Brix and 0.7, respectively). Finally, a quality control chart was built based on the spectra obtained in the optimal state of ripening, which could be used to decide which grapes are suitable for harvest.

Online Prediction of Molded Part Quality in the Injection Molding Process Using High-Resolution Time Series

Process-data-supported process monitoring in injection molding plays an important role in compensating for disturbances in the process. Until now, scalar process data from machine controls have been used to predict part quality. In this paper, we investigated the feasibility of incorporating time series of sensor measurements directly as features for machine learning models, as a suitable method of improving the online prediction of part quality. We present a comparison of several state-of-the-art algorithms, using extensive and realistic data sets. Our comparison demonstrates that time series data allow significantly better predictions of part quality than scalar data alone. In future studies, and in production-use cases, such time series should be taken into account in online quality prediction for injection molding.

Advances in bioreactor control for production of biotherapeutic products

Advanced control strategies are well established in chemical, pharmaceutical, and food processing industries. Over the past decade, the application of these strategies is being explored for control of bioreactors for manufacturing of biotherapeutics. Most of the industrial bioreactor control strategies apply classical control techniques, with the control system designed for the facility at hand. However, with the recent progress in sensors, machinery, and industrial internet of things, and advancements in deeper understanding of the biological processes, coupled with the requirement of flexible production, the need to develop a robust and advanced process control system that can ease process intensification has emerged. This has further fuelled the development of advanced monitoring approaches, modeling techniques, process analytical technologies, and soft sensors. It is seen that proper application of these concepts can significantly improve bioreactor process performance, productivity, and reproducibility. This review is on the recent advancements in bioreactor control and its related aspects along with the associated challenges. This study also offers an insight into the future prospects for development of control strategies that can be designed for industrial-scale production of biotherapeutic products.

Fault Monitoring Based on the VLSW-MADF Test and DLPPCA for Multimodal Processes

Actual industrial processes often exhibit multimodal characteristics, and their data exhibit complex features, such as being dynamic, nonlinear, multimodal, and strongly coupled. Although many modeling approaches for process fault monitoring have been proposed in academia, due to the complexity of industrial data, challenges remain. Based on the concept of multimodal modeling, this paper proposes a multimodal process monitoring method based on the variable-length sliding window-mean augmented Dickey-Fuller (VLSW-MADF) test and dynamic locality-preserving principal component analysis (DLPPCA). In the offline stage, considering the fluctuation characteristics of data, the trend variables of data are extracted and input into VLSW-MADF for modal identification, and different modalities are modeled separately using DLPPCA. In the online monitoring phase, the previous moment's historical modal information is fully utilized, and modal identification is performed only when necessary to reduce computational cost. Finally, the proposed method is validated to be accurate and effective for modal identification, modeling, and online monitoring of multimodal processes in TE simulation and actual plant data. The proposed method improves the fault detection rate of multimodal process fault monitoring by about 14% compared to the classical DPCA method.

Algorithms for Weld Depth Measurement in Laser Welding of Copper with Scanning Optical Coherence Tomography

In-process monitoring of weld penetration depth is possible with optical coherence tomography (OCT). The weld depth can be identified with OCT by statistical signal processing of the raw OCT signal and keyhole mapping. This approach is only applicable to stable welding processes and requires a time-consuming keyhole mapping to identify the optimal placement of a singular OCT measuring beam. In this work, we use an OCT measurement line for the identification of the weld depth. This approach shows the advantage that the calibration effort can be reduced as the measurement line requires only calibration in one dimension. As current literature focuses on weld depth measurement with a singular measurement point in the keyhole, no optimal algorithm exists for weld depth measurement with an OCT measurement line. We developed seven different weld depth processing pipelines and tested these algorithms under different weld conditions, such as stable deep penetration welding, unstable deep penetration welding, and heat conduction welding. We analyzed the accuracy of the weld depth processing algorithms by comparing the measured weld depth with metallographic weld depths. The intensity accumulation approach is identified as the most accurate algorithm for successful weld depth measurement with a scanning OCT measurement line.

In-Process Analysis of Melt Pool Fluctuations with Scanning Optical Coherence Tomography for Laser Welding of Copper for Quality Monitoring

Optical coherence tomography (OCT) is an inline process monitoring technology for laser welding with various applications in the pre-, in-, and post-process. In-process monitoring with OCT focuses on the measurement of weld depth by the placement of a singular measurement beam into the keyhole. A laterally scanned measurement beam gives the opportunity to measure the keyhole and melt pool width. The processing region can be identified by separating higher signal intensities on the workpiece surface from lower signal intensities from the keyhole and the melt pool. In this work, we apply a scanned measurement beam for the identification of keyhole fluctuations. Different laser processing parameters are varied for laser welding of copper to evoke welds in the heat conduction regime, stable deep penetration welding, and unstable deep penetration welding. As keyhole instabilities can be related to the generation of spatter and other defects, we identified a feature for the classification of different weld statuses. In consequence, feedback can be given about possible defects which are originated in keyhole fluctuations (e.g., spatter).

AI-Based Support System for Monitoring the Quality of a Product within Industry 4.0 Paradigm

Three-dimensional (3D) printing, also known as additive manufacturing (AM), has already shown its potential in the fourth technological revolution (Industry 4.0), demonstrating remarkable applications in manufacturing, including of medical devices. The aim of this publication is to present the novel concept of support by artificial intelligence (AI) for quality control of AM of medical devices made of polymeric materials, based on the example of our own elbow exoskeleton. The methodology of the above-mentioned inspection process differs depending on the intended application of 3D printing as well as 3D scanning or reverse engineering. The use of artificial intelligence increases the versatility of this process, allowing it to be adapted to specific needs. This brings not only innovative scientific and technological solutions, but also a significant economic and social impact through faster operation, greater efficiency, and cost savings. The article also indicates the limitations and directions for the further development of the proposed solution.

Temperature Dependence of Electrical Resistance in Carbon Nanotube Composite Film during Curing Process

Carbon nanotube (CNT) film possesses excellent mechanical and piezoresistivity, which may act as a sensor for process monitoring and reinforcement of the final composite. This paper prepared CNT/epoxy composite film via the solution dipping method and investigated the electrical resistance variation (ΔR/R₀) of CNT/epoxy composite film during the curing process. The temperature dependence of electrical resistance was found to be closely related to resin rheological properties, thermal expansion, and curing shrinkage. The results show that two opposing effects on electrical resistivity occur at the initial heating stage, including thermal expansion and condensation caused by the wetting tension of the liquid resin. The lower resin content causes more apparent secondary impregnation and electrical resistivity change. When the resin viscosity remains steady during the heating stage, the electrical resistance increases with an increase in temperature due to thermal expansion. Approaching gel time, the electrical resistance drops due to the crosslink shrinkage of epoxy resin. The internal stress caused by curing shrinkage at the high-temperature platform results in an increase in electrical resistance. The temperature coefficient of resistance becomes larger with an increase in resin content. At the isothermal stage, an increase in ΔR/R₀ value becomes less obvious with a decrease in resin content, and ΔR/R₀ even shows a decreasing tendency.

NIR Spectroscopy as an Online PAT Tool for a Narrow Therapeutic Index Drug: Toward a Platform Approach Across Lab and Pilot Scales for Development of a Powder Blending Monitoring Method and Endpoint Determination

An online near-infrared (NIR) spectroscopy platform system for real-time powder blending monitoring and blend endpoint determination was tested for a phenytoin sodium formulation. The study utilized robust experimental design and multiple sensors to investigate multivariate data acquisition, model development, and model scale-up from lab to manufacturing. The impact of the selection of various blend endpoint algorithms on predicted blend endpoint (i.e., mixing time) was explored. Spectral data collected at two process scales using two NIR spectrometers was incorporated in a single (global) calibration model. Unique endpoints were obtained with different algorithms based on standard deviation, average, and distributions of concentration prediction for major components of the formulation. Control over phenytoin sodium's distribution was considered critical due to its narrow therapeutic index nature. It was found that algorithms sensitive to deviation from target concentration offered the simplest interpretation and consistent trends. In contrast, algorithms sensitive to global homogeneity of active and excipients yielded the longest mixing time to achieve blending endpoint. However, they were potentially more sensitive to subtle uniformity variations. Qualitative algorithms using principal component analysis (PCA) of spectral data yielded the prediction of shortest mixing time for blending endpoint. The hybrid approach of combining NIR data from different scales presents several advantages. It enables simplifying the chemometrics model building process and reduces the cost of model building compared to the approach of using data solely from commercial scale. Success of such a hybrid approach depends on the spectroscopic variability captured at different scales and their relative contributions in the final NIR model.

Biological Synthesis and Process Monitoring of an Aggregation-Induced Emission Luminogen-Based Fluorescent Polymer

As the most abundant and renewable biopolymer on earth, cellulose can be functionalized for various advanced applications by chemical modification. In addition, fluorescent polymers with aggregation-induced emission (AIE) are generally prepared using chemical approaches, and the biosynthesis of AIE-active polymers are rarely investigated. Herein, fluorescent cellulose was successfully synthesized by bacterial fermentation, where glucosamine-modified AIE luminogen was incorporated into cellulose to achieve AIE-active biopolymers. Excitingly, real-time visualization of the synthetic process was realized, which is crucial for investigating the process of bacterial fermentation. The biosynthesized cellulose exhibited better performance with uniform fluorescence distribution and high stability, compared with that prepared by physical absorption. Additionally, fluorescent mats were fabricated by electrospinning of AIE-active cellulose, demonstrating its great potential applications in flexible display and tissue engineering.

Domain Adaptation for In-Line Allergen Classification of Agri-Food Powders Using Near-Infrared Spectroscopy

The addition of incorrect agri-food powders to a production line due to human error is a large safety concern in food and drink manufacturing, owing to incorporation of allergens in the final product. This work combines near-infrared spectroscopy with machine-learning models for early detection of this problem. Specifically, domain adaptation is used to transfer models from spectra acquired under stationary conditions to moving samples, thereby minimizing the volume of labelled data required to collect on a production line. Two deep-learning domain-adaptation methodologies are used: domain-adversarial neural networks and semisupervised generative adversarial neural networks. Overall, accuracy of up to 96.0% was achieved using no labelled data from the target domain moving spectra, and up to 99.68% was achieved when incorporating a single labelled data instance for each material into model training. Using both domain-adaptation methodologies together achieved the highest prediction accuracies on average, as did combining measurements from two near-infrared spectroscopy sensors with different wavelength ranges. Ensemble methods were used to further increase model accuracy and provide quantification of model uncertainty, and a feature-permutation method was used for global interpretability of the models.

A Monitoring Method Based on FDALM and Its Application in the Sintering Process of Ternary Cathode Material

In industrial processes, the composition of raw material and the production environment are complex and changeable, which makes the production process have multiple steady states. In this situation, it is difficult for the traditional single-mode monitoring methods to accurately detect the process abnormalities. To this end, a multimode monitoring method based on the factor dynamic autoregressive hidden variable model (FDALM) for industrial processes is proposed in this paper. First, an improved affine propagation clustering algorithm to learn the model modal factors is adopted, and the FDALM is constructed by combining multiple high-order hidden state Markov chains through the factor modeling technology. Secondly, a fusion algorithm based on Bayesian filtering, smoothing, and expectation-maximization is adopted to identify model parameters. The Lagrange multiplier formula is additionally constructed to update the factor coefficients by using the factor constraints in the solving. Moreover, the online Bayesian inference is adopted to fuse the information of different factor modes and obtain the fault posterior probability, which can improve the overall monitoring effect of the model. Finally, the proposed method is applied in the sintering process of ternary cathode material. The results show that the fault detection rate and false alarm rate of this method are improved obviously compared with the traditional methods.

Strip-Type Embeddable Shape Sensor Based on Fiber Optics for In Situ Composite Consolidation Monitoring

Carbon fibers and resin used in manufacturing carbon fiber-reinforced plastic composite structures flow before the resin solidifies, resulting in disrupted fiber orientation and non-uniform thickness. This process, known as consolidation, is critical for the quality of the composite structure, but no technology exists to measure the deformation in situ. This study proposes a strip-type embeddable shape sensor based on fiber optics for in situ monitoring of consolidation deformation. The sensor consists of a thin, flexible sheet with optical fibers embedded in the upper and lower surfaces of the sheet, and it can monitor out-of-plane bending deformation in composite materials during consolidation. Finite element analysis and experiments are used to evaluate the basic performance of the shape sensor before it is applied to composite gap/lap monitoring. For the first time, the relaxation of consolidation deformation due to the flow of fiber-resin suspension is measured. The proposed sensor will be a powerful tool for elucidating consolidation mechanisms and for validating composite manufacturing simulations.

Process Monitoring Using Synchronized Path Infrared Thermography in PBF-LB/M

Additive manufacturing processes, particularly Laser-Based Powder Bed Fusion of Metals (PBF-LB/M), enable the development of new application possibilities due to their manufacturing-specific freedom of design. These new fields of application require a high degree of component quality, especially in safety-relevant areas. This is currently ensured primarily via a considerable amount of downstream quality control. Suitable process monitoring systems promise to reduce this effort drastically. This paper introduces a novel monitoring method in order to gain process-specific thermal information during the manufacturing process. The Synchronized Path Infrared Thermography (SPIT) method is based on two synchronized galvanometer scanners allowing high-speed and high-resolution observations of the melt pool in the SWIR range. One scanner is used to steer the laser over the building platform, while the second scanner guides the field of view of an IR camera. With this setup, the melting process is observed at different laser powers, scan speeds and at different locations with respect to the laser position, in order to demonstrate the positioning accuracy of the system and to initially gain thermal process data of the melt pool and the heat-affected zone. Therefore, the SPIT system shows a speed independent overall accuracy of ±2 Pixel within the evaluated range. The system further allows detailed thermal observation of the melt pool and the surrounding heat-affected zone.

Enhanced Multiscale Principal Component Analysis for Improved Sensor Fault Detection and Isolation

Multiscale PCA (MSPCA) is a well-established fault-detection and isolation (FDI) technique. It utilizes wavelet analysis and PCA to extract important features from process data. This study demonstrates limitations in the conventional MSPCA fault detection algorithm, thereby proposing an enhanced MSPCA (EMSPCA) FDI algorithm that uses a new wavelet thresholding criterion. As such, it improves the projection of faults in the residual space and the threshold estimation of the fault detection statistic. When tested with a synthetic model, EMSPCA resulted in a 30% improvement in detection rate with equal false alarm rates. The EMSPCA algorithm also relies on the novel application of reconstruction-based fault isolation at multiple scales. The proposed algorithm reduces fault smearing and consequently improves fault isolation performance. The paper will further investigate the use of soft vs. hard wavelet thresholding, decimated vs. undecimated wavelet transforms, the choice of wavelet decomposition depth, and their implications on FDI performance.The FDI performance of the developed EMSPCA method was illustrated for sensor faults. This undertaking considered synthetic data, the simulated data of a continuously stirred reactor (CSTR), and experimental data from a packed-bed pilot plant. The results of these examples show the advantages of EMSPCA over existing techniques.

A benchmark simulator for quality-by-design and quality-by-control studies in continuous pharmaceutical manufacturing - Intensified filtration-drying of crystallization slurries

This article introduces ContCarSim, a benchmark simulator for the development and testing of quality-by-design and quality-by-control strategies in the continuous intensified filtration-drying of paracetamol/ethanol slurries on a novel carousel technology, developed by Alconbury Weston Ltd (United Kingdom). The simulator is based on a detailed mechanistic mathematical modeling framework, and has been validated with filtration and drying experiments on a prototype equipment. A set of design- and control-relevant challenges to be addressed through ContCarSim are proposed. A case study is developed, to demonstrate the features of the simulator and its suitability to design, test and optimize the unit operation. ContCarSim is expected to promote the transition to end-to-end continuous pharmaceutical manufacturing and the adoption of closed-loop quality control by the pharmaceutical industry. The simulator can also be employed as a benchmark for data analytics and process monitoring studies.

Improved Estimation of End-Milling Parameters from Acoustic Emission Signals Using a Microphone Array Assisted by AI Modelling

This paper presents the implementation of a measurement system that uses a four microphone array and a data-driven algorithm to estimate depth of cut during end milling operations. The audible range acoustic emission signals captured with the microphones are combined using a spectral subtraction and a blind source separation algorithm to reduce the impact of noise and reverberation. Afterwards, a set of features are extracted from these signals which are finally fed into a nonlinear regression algorithm assisted by machine learning techniques for the contactless monitoring of the milling process. The main advantages of this algorithm lie in relatively simple implementation and good accuracy in its results, which reduce the variance of the current noncontact monitoring systems. To validate this method, the results have been compared with the values obtained with a precision dynamometer and a geometric model algorithm obtaining a mean error of 1% while maintaining an STD below 0.2 mm.

Early Warning Signals in Phase Space: Geometric Resilience Loss Indicators From Multiplex Cumulative Recurrence Networks

The detection of Early Warning Signals (EWS) of imminent phase transitions, such as sudden changes in symptom severity could be an important innovation in the treatment or prevention of disease or psychopathology. Recurrence-based analyses are known for their ability to detect differences in behavioral modes and order transitions in extremely noisy data. As a proof of principle, the present paper provides an example of a recurrence network based analysis strategy which can be implemented in a clinical setting in which data from an individual is continuously monitored for the purpose of making decisions about diagnosis and intervention. Specifically, it is demonstrated that measures based on the geometry of the phase space can serve as Early Warning Signals of imminent phase transitions. A publicly available multivariate time series is analyzed using so-called cumulative Recurrence Networks (cRN), which are recurrence networks with edges weighted by recurrence time and directed towards previously observed data points. The results are compared to previous analyses of the same data set, benefits, limitations and future directions of the analysis approach are discussed.

Influence of Filament Winding Tension on the Deformation of Composite Flywheel Rotors with H-Shaped Hubs

The residual stress plays an important role in composite flywheel rotors composed of filament windings. The fiber tension during high-prestressed winding is the main source of the rotor deformation and residual stress of composite layers. In this study, the effect of the winding tension gradient on deformation was monitored in real-time. Two types of in-plane winding tension fluctuation methods were developed to investigate the effect of tension on deformation. Online and offline measurements were performed for the strain acquisition. A wireless strain instrument was used for online deformation monitoring and a laser scanner was used for the offline surface reconstruction. Additionally, different filament winding strategies were carried out to improve the efficiency of the winding tension by finite element analysis. The results indicated that the deviation between numerical and experimental results was within 8%. Based on the proposed numerical method, the influence of the in-plane and out-of-plane winding tension gradient distributions on the rotation process of the H-shaped rotor was analyzed. An in-plane winding strategy with variable tension was developed, which increased the initial failure speed by 160%.

Inline Quality Control through Optical Deep Learning-Based Porosity Determination for Powder Bed Fusion of Polymers

Powder bed fusion of thermoplastic polymers is a powder based additive manufacturing process that allows for manufacturing individualized components with high geometric freedom. Despite achieving higher mechanical properties compared to other additive manufacturing processes, statistical variations in part properties and the occurrence of defects cannot be avoided systematically. In this paper, a novel method for the inline assessment of part porosity is proposed in order to detect and to compensate for inherent limitations in the reproducibility of manufactured parts. The proposed approach is based on monitoring the parameter-specific decay of the optical melt pool radiance during the melting process, influenced by a time dependency of optical scattering within the melt pool. The underlying methodology compromises the regression of the time-dependent optical melt pool properties, assessed in visible light using conventional camera technology, and the resulting part properties by means of artificial neural networks. By applying deep residual neural networks for correlating time-resolved optical process properties and the corresponding part porosity, an inline assessment of the spatially resolved part porosity can be achieved. The authors demonstrate the suitability of the proposed approach for the inline porosity assessment of varying part geometries, processing parameters, and material aging states, using Polyamide 12. Consequently, the approach represents a methodological foundation for novel monitoring solutions, the enhanced understanding of parameter-material interactions and the inline-development of novel material systems in powder bed fusion of polymers.

Fault Detection and Diagnosis in Industrial Processes with Variational Autoencoder: A Comprehensive Study

This work considers industrial process monitoring using a variational autoencoder (VAE). As a powerful deep generative model, the variational autoencoder and its variants have become popular for process monitoring. However, its monitoring ability, especially its fault diagnosis ability, has not been well investigated. In this paper, the process modeling and monitoring capabilities of several VAE variants are comprehensively studied. First, fault detection schemes are defined in three distinct ways, considering latent, residual, and the combined domains. Afterwards, to conduct the fault diagnosis, we first define the deep contribution plot, and then a deep reconstruction-based contribution diagram is proposed for deep domains under the fault propagation mechanism. In a case study, the performance of the process monitoring capability of four deep VAE models, namely, the static VAE model, the dynamic VAE model, and the recurrent VAE models (LSTM-VAE and GRU-VAE), has been comparatively evaluated on the industrial benchmark Tennessee Eastman process. Results show that recurrent VAEs with a deep reconstruction-based diagnosis mechanism are recommended for industrial process monitoring tasks.

Short Time Correlation Analysis of Melt Pool Behavior in Laser Metal Deposition Using Coaxial Optical Monitoring

The development and improvement of monitoring and process control systems is one of the important ways of advancing laser metal deposition (LMD). The control of hydrodynamic, heat and mass transfer processes in LMD is extremely important, since these processes directly affect the crystallization of the melt and, accordingly, the microstructural properties and the overall quality of the synthesized part. In this article, the data of coaxial video monitoring of the LMD process were used to assess the features of melt dynamics. The obtained images were used to calculate the time dependences of the characteristics of the melt pool (MP) (temperature, width, length and area), which were further processed using the short-time correlation (STC) method. This approach made it possible to reveal local features of the joint behavior of the MP characteristics, and to analyze the nature of the melt dynamics. It was found that the behavior of the melt in the LMD is characterized by the presence of many time periods (patterns), during which it retains a certain ordered character. The features of behavior that are important from the point of view of process control systems design are noted. The approach used for the analysis of melt dynamics based on STC distributions of MP characteristics, as well as the method for determining the moments of pattern termination through the calculation of the correlation power, can be used in processing the results of online LMD diagnostics, as well as in process control systems.

Electron-Optical In Situ Imaging for the Assessment of Accuracy in Electron Beam Powder Bed Fusion

The current study evaluates the capabilities of electron-optical (ELO) in situ imaging with respect to monitoring and prediction of manufacturing precision in electron beam powder bed fusion. Post-process X-ray computed tomography of two different as-built parts is used to quantitatively evaluate the accuracy and limitations of ELO imaging. Additionally, a thermodynamic simulation is performed to improve the understanding of ELO data and to assess the feasibility of predicting dimensional accuracy numerically. It is demonstrated that ELO imaging captures the molten layers accurately (deviations <100 μm) and indicates the creation of surface roughness. However, some geometrical features of the as-built parts exhibit local inaccuracies associated with thermal stress-induced deformation (deviations up to 500 μm) which cannot be captured by ELO imaging. It is shown that the comparison between in situ and post-process data enables a quantification of these effects which might provide the possibility for developing effective countermeasures in the future.

Fusing NIR and Process Sensors Data for Polymer Production Monitoring

Process analytical technology and multivariate process monitoring are nowadays the most effective approaches to achieve real-time quality monitoring/control in production. However, their use is not yet a common practice, and industries benefit much less than they could from the outcome of the hundreds of sensors that constantly monitor production in industrial plants. The huge amount of sensor data collected are still mostly used to produce univariate control charts, monitoring one compartment at a time, and the product quality variables are generally used to monitor production, despite their low frequency (offline measurements at analytical laboratory), which is not suitable for real-time monitoring. On the contrary, it would be extremely advantageous to benefit from predictive models that, based on online sensors, will be able to return quality parameters in real time. As a matter of fact, the plant setup influences the product quality, and process sensors (flow meters, thermocouples, etc.) implicitly register process variability, correlation trends, drift, etc. When the available spectroscopic sensors, reflecting chemical composition and structure, consent to monitor the intermediate products, coupling process, and spectroscopic sensor and extracting/fusing information by multivariate analysis from this data would enhance the evaluation of the produced material features allowing production quality to be estimated at a very early stage. The present work, at a pilot plant scale, applied multivariate statistical process control (MSPC) charts, obtained by data fusion of process sensor data and near-infrared (NIR) probes, on a continuous styrene-acrylonitrile (SAN) production process. Furthermore, PLS regression was used for real-time prediction of the Melt Flow Index and percentage of bounded acrylonitrile (%AN). The results show that the MSPC model was able to detect deviations from normal operative conditions, indicating the variables responsible for the deviation, be they spectral or process. Moreover, predictive regression models obtained using the fused data showed better results than models computed using single datasets in terms of both errors of prediction and R². Thus, the fusion of spectra and process data improved the real-time monitoring, allowing an easier visualization of the process ongoing, a faster understanding of possible faults, and real-time assessment of the final product quality.

Monitoring SEIRD model parameters using MEWMA for the COVID-19 pandemic with application to the state of Qatar

During the current COVID-19 pandemic, decision-makers are tasked with implementing and evaluating strategies for both treatment and disease prevention. In order to make effective decisions, they need to simultaneously monitor various attributes of the pandemic such as transmission rate and infection rate for disease prevention, recovery rate which indicates treatment effectiveness as well as the mortality rate and others. This work presents a technique for monitoring the pandemic by employing an Susceptible, Exposed, Infected, Recovered, Death model regularly estimated by an augmented particle Markov chain Monte Carlo scheme in which the posterior distribution samples are monitored via Multivariate Exponentially Weighted Average process monitoring. This is illustrated on the COVID-19 data for the State of Qatar.

Ultrasonic Vibration-Assisted Ball Burnishing Tool for a Lathe Characterized by Acoustic Emission and Vibratory Measurements

This paper focuses on a resonant system used to induce a low-amplitude movement and ultrasonic frequency to complement a ball burnishing process on a lathe. The system was characterized through the combination of different techniques. A full vibratory characterization of this process was undertaken with the purpose of demonstrating that the mechanical system-composed of the tool and the machine-does not present resonance phenomena during the execution of the operation that could lead to eventual failure. This dynamic analysis validates the adequateness of the tool when attached to an NC lathe, which is important to guarantee its future implementation in actual manufacturing contexts. A further aim was to confirm that the system succeeds in transmitting an oscillating signal throughout the material lattice. To this end, different static and dynamic techniques that measure different vibration ranges-including impact tests, acoustic emission measurement, and vibration measurement-were combined. An operational deflection shape model was also constructed. Results demonstrate that the only high frequency appearing in the process originated in the tool. The process was not affected by the presence of vibration assistance, nor by the burnishing preload or feed levels. Furthermore, the frequency of the assisting ultrasonic vibration was characterized and no signal due to possible damage in the material of the specimens was detected. These results demonstrate the suitability of the new tool in the vibration-assisted ball burnishing process.

In Situ Quantification of Polyhydroxybutyrate in Photobioreactor Cultivations of Synechocystis sp. Using an Ultrasound-Enhanced ATR-FTIR Spectroscopy Probe

Polyhydroxybutyrate (PHB) is a very promising alternative to most petroleum-based plastics with the huge advantage of biodegradability. Biotechnological production processes utilizing cyanobacteria as sustainable source of PHB require fast in situ process analytical technology (PAT) tools for sophisticated process monitoring. Spectroscopic probes supported by ultrasound particle traps provide a powerful technology for in-line, nondestructive, and real-time process analytics in photobioreactors. This work shows the great potential of using ultrasound particle manipulation to improve spectroscopic attenuated total reflection Fourier-transformed mid-infrared (ATR-FTIR) spectra as a monitoring tool for PHB production processes in photobioreactors.

Distributed Fibre Optic Sensing (DFOS) for Deformation Assessment of Composite Collectors and Pipelines

Due to the low costs of distributed optical fibre sensors (DFOS) and the possibility of their direct integration within layered composite members, DFOS technology has considerable potential in structural health monitoring of linear underground infrastructures. Often, it is challenging to truly simulate the actual ground conditions at all construction stages. Thus, reliable measurements are required to adjust the model and verify theoretical calculations. The article presents a new approach to monitor displacements and strains in Glass Fiber Reinforced Polymer (GFRP) collectors and pipelines using DFOS. The research verifies the effectiveness of the proposed monitoring solution for health monitoring of composite pipelines. Optical fibres were installed over the circumference of a composite tubular pipe, both on the internal and external surfaces, while loaded externally. Analysis of strain profiles allowed for calculating the actual displacements (shape) of the pipe within its cross-section plane using the Trapezoidal method. The accuracy of proposed approach was positively verified both with reference spot displacement transducer as well as numerical simulations using finite element method (FEM). DFOS could obtain a comprehensive view of structural deformations, including both strains and displacements under externally applied load. The knowledge gained during research will be ultimately used for renovating existing collectors.

Experimental Study on the Raman Spectra of Imine Emulsification with Chemometrics

In this study, we aimed to investigate imine emulsification using Raman spectroscopy with chemometrics. The imine emulsification samples were obtained by mixing aldehydes and amines in methanol and aqueous methanol. The Raman spectra of the samples were measured over time between 400 and 2300 cm^-1 every 40 s using a Raman spectrometer. The obtained spectra were regarded as a dataset matrix. A multivariate curve resolution with alternating least squares was applied to the dataset. A multivariate analysis based on the Raman spectrum revealed that raw materials, emulsions, and products were decomposed when the water-rich samples were emulsified. Additionally, we evaluated the kinetics of the synthesis. The effect of water content on emulsification was investigated using Raman spectroscopy. The molecular dynamics of the co-solvent model were also investigated. The phase-layer construction was consistent with the phase transition in the water-methanol imine samples.

Ultrasound Sensors for Process Monitoring in Injection Moulding

Injection moulding is an extremely important industrial process, being one of the most commonly-used plastic formation techniques. However, the industry faces many current challenges associated with demands for greater product customisation, higher precision and, most urgently, a shift towards more sustainable materials and processing. Accurate real-time sensing of the material and part properties during processing is key to achieving rapid process optimisation and set-up, reducing down-times, and reducing waste material and energy in the production of defective products. While most commercial processes rely on point measurements of pressure and temperature, ultrasound transducers represent a non-invasive and non-destructive source of rich information on the mould, the cavity and the polymer melt, and its morphology, which affect critical quality parameters such as shrinkage and warpage. In this paper the relationship between polymer properties and the propagation of ultrasonic waves is described and the application of ultrasound measurements in injection moulding is evaluated. The principles and operation of both conventional and high temperature ultrasound transducers (HTUTs) are reviewed together with their impact on improving the efficiency of the injection moulding process. The benefits and challenges associated with the recent development of sol-gel methods for HTUT fabrication are described together with a synopsis of further research and development needed to ensure a greater industrial uptake of ultrasonic sensing in injection moulding.

Development of Gas Sensor Array for Methane Reforming Process Monitoring

The article presents a new method of monitoring and assessing the course of the dry methane reforming process with the use of a gas sensor array. Nine commercially available TGS chemical gas sensors were used to construct the array (seven metal oxide sensors and two electrochemical ones). Principal Component Regression (PCR) was used as a calibration method. The developed PCR models were used to determine the quantitative parameters of the methane reforming process: Inlet Molar Ratio (IMR) in the range 0.6-1.5, Outlet Molar Ratio (OMR) in the range 0.6-1.0, and Methane Conversion Level (MCL) in the range 80-95%. The tests were performed on model gas mixtures. The mean error in determining the IMR is 0.096 for the range of molar ratios 0.6-1.5. However, in the case of the process range (0.9-1.1), this error is 0.065, which is about 6.5% of the measured value. For the OMR, an average error of 0.008 was obtained (which gives about 0.8% of the measured value), while for the MCL, the average error was 0.8%. Obtained results are very promising. They show that the use of an array of non-selective chemical sensors together with an appropriately selected mathematical model can be used in the monitoring of commonly used industrial processes.

Bioprocess Control: Current Progress and Future Perspectives

Typical bioprocess comprises of different unit operations wherein a near optimal environment is required for cells to grow, divide, and synthesize the desired product. However, bioprocess control caters to unique challenges that arise due to non-linearity, variability, and complexity of biotech processes. This article presents a review of modern control strategies employed in bioprocessing. Conventional control strategies (open loop, closed loop) along with modern control schemes such as fuzzy logic, model predictive control, adaptive control and neural network-based control are illustrated, and their effectiveness is highlighted. Furthermore, it is elucidated that bioprocess control is more than just automation, and includes aspects such as system architecture, software applications, hardware, and interfaces, all of which are optimized and compiled as per demand. This needs to be accomplished while keeping process requirement, production cost, market value of product, regulatory constraints, and data acquisition requirements in our purview. This article aims to offer an overview of the current best practices in bioprocess control, monitoring, and automation.

Soft Sensor Development for Real-Time Process Monitoring of Multidimensional Fractionation in Tubular Centrifuges

High centrifugal acceleration and throughput rates of tubular centrifuges enable the solid-liquid size separation and fractionation of nanoparticles on a bench scale. Nowadays, advantageous product properties are defined by precise specifications regarding particle size and material composition. Hence, there is a demand for innovative and efficient downstream processing of complex particle suspensions. With this type of centrifuge working in a semi-continuous mode, an online observation of the separation quality is needed for optimization purposes. To analyze the composition of fines downstream of the centrifuge, a UV/vis soft sensor is developed to monitor the sorting of polymer and metal oxide nanoparticles by their size and density. By spectroscopic multi-component analysis, a measured UV/vis signal is translated into a model based prediction of the relative solids volume fraction of the fines. High signal stability and an adaptive but mandatory calibration routine enable the presented setup to accurately predict the product's composition at variable operating conditions. It is outlined how this software-based UV/vis sensor can be utilized effectively for challenging real-time process analytics in multi-component suspension processing. The setup provides insight into the underlying process dynamics and assists in optimizing the outcome of separation tasks on the nanoscale.

In Situ Mapping of H2, O2, and H2O2 in Microreactors: A Parallel, Selective Multianalyte Detection Method

Determining local concentrations of the analytes in state-of-the-art microreactors is essential for the development of optimized and safe processes. However, the selective, parallel monitoring of all relevant reactants and products in a multianalyte environment is challenging. Electrochemical microsensors can provide unique information on the reaction kinetics and overall performance of the hydrogen peroxide synthesis process in microreactors, thanks to their high spatial and temporal resolution and their ability to measure in situ, in contrast to other techniques. We present a chronoamperometric approach which allows the selective detection of the dissolved gases hydrogen and oxygen and their reaction product hydrogen peroxide on the same platinum microelectrode in an aqueous electrolyte. The method enables us to obtain the concentration of each analyte using three specific potentials and to subtract interfering currents from the mixed signal. While hydrogen can be detected independently, no potentials can be found for a direct, selective measurement of oxygen and hydrogen peroxide. Instead, it was found that for combined signals, the individual contribution of all analytes superimposes linearly additive. We showed that the concentrations determined from the subtracted signals correlate very well with results obtained without interfering analytes present. For the first time, this approach allowed the mapping of the distribution of the analytes hydrogen, oxygen, and hydrogen peroxide inside a multiphase membrane microreactor, paving the way for online process control.

Coaxial Monitoring of AISI 316L Thin Walls Fabricated by Direct Metal Laser Deposition

Direct metal laser deposition (DMLD) is an additive manufacturing technique suitable for coating and repair, which has been gaining a growing interest in 3D manufacturing applications in recent years. However, its diffusion in the manufacturing industry is still limited due to technical challenges to be solved—both the sub-optimal quality of the final parts and the low repeatability of the process make the DMLD inadequate for high-value applications requiring high-performance standards. Thus, real-time monitoring and process control are indispensable requirements for improving the DMLD process. The aim of this study was the optimization of deposition strategies for the fabrication of thin walls in AISI 316L stainless steel. For this purpose, a coaxial monitoring system and image processing algorithms were employed to study the melt pool geometry. The comparison tests carried out highlighted how the region-based active contour algorithm used for image processing is more efficient and stable than others covered in the literature. The results allowed the identification of the best deposition strategy. Therefore, it is shown how this monitoring methodology proved to be suitable for designing and implementing the right building strategy for DMLD manufactured 3D components. A fast and stable image processing method was achieved, which can be considered for future closed-loop monitoring in real-time applications.