Process Monitoring Strategy Research Articles

KT-Biologics I (KTB1): A dynamic simulation model for continuous biologics manufacturing

The pharmaceutical industry's shift towards biological therapeutics has led to a transition from conventional batch production to continuous manufacturing. This change highlights the crucial need for effective process monitoring and control strategies to ensure consistent product quality and stability. Open-source benchmark simulation models have become essential tools for refining these processes, offering a platform for testing research hypotheses. This study uses the production of Lovastatin as a case study for continuous biopharmaceutical production. A comprehensive dynamic model covering upstream and downstream components provides an integrated perspective of the production process. The study introduces a basic control system emphasizing realistic sensor and actuator integration to enhance simulation accuracy. It assesses the benchmark through open-loop and closed-loop simulations, offering an in-depth analysis of the KTB1 model's dynamic response and functionality. KTB1 represents a model-driven decision support tool that enables the evaluation of monitoring strategies, process design, process optimization, and control for biomanufacturing.

Validation set sampling strategies for predictive process monitoring

Previous studies investigating the efficacy of long short-term memory (LSTM) recurrent neural networks in predictive process monitoring and their ability to capture the underlying process structure have raised concerns about their limited ability to generalize to unseen behavior. Event logs often fail to capture the full spectrum of behavior permitted by the underlying processes. To overcome these challenges, this study introduces innovative validation set sampling strategies based on control-flow variant-based resampling. These strategies have undergone extensive evaluation to assess their impact on hyperparameter selection and early stopping, resulting in notable enhancements to the generalization capabilities of trained LSTM models. In addition, this study expands the experimental framework to enable accurate interpretation of underlying process models and provide valuable insights. By conducting experiments with event logs representing process models of varying complexities, this research elucidates the effectiveness of the proposed validation strategies. Furthermore, the extended framework facilitates investigations into the influence of event log completeness on the learning quality of predictive process models. The novel validation set sampling strategies proposed in this study facilitate the development of more effective and reliable predictive process models, ultimately bolstering generalization capabilities and improving the understanding of underlying process dynamics.

Advancements and Innovations in Harnessing Microbial Processes for Enhanced Biogas Production from Waste Materials

Biogas production from waste materials has emerged as a promising avenue for sustainable energy generation, offering a dual benefit of waste management and renewable energy production. The selection and preparation of waste feedstocks, including agricultural residues, food waste, animal manure, and municipal solid wastes, are important for this process, while the microbial communities are majorly responsible for bioconversions. This review explores the role of complex microbial communities and their functions responsible for the anaerobic digestion of wastes. It covers the crucial physiological processes including hydrolysis, acidogenesis, acetogenesis, and methanogenesis, elucidating the microbial activities and metabolic pathways involved in the prospects of improving the efficiency of biogas production. This article further discusses the influence of recent progress in molecular techniques, including genomics, metagenomics, meta-transcriptomics, and stable isotope probing. These advancements have greatly improved our understanding of microbial communities and their capabilities of biogas production from waste materials. The integration of these techniques with process monitoring and control strategies has been elaborated to offer possibilities for optimizing biogas production and ensuring process stability. Microbial additives, co-digestion of diverse feedstocks, and process optimization through microbial community engineering have been discussed as effective approaches to enhance the efficiency of biogas production. This review also outlines the emerging trends and future prospects in microbial-based biogas production, including the utilization of synthetic biology tools for engineering novel microbial strains and consortia, harnessing microbiomes from extreme environments, and integrating biogas production with other biotechnological processes. While there are several reviews regarding the technical aspects of biogas production, this article stands out by offering up-to-date insights and recommendations for leveraging the potential of microbial communities, and their physiological roles for efficient biogas production. These insights emphasize the pivotal role of microbes in enhancing biogas production, ultimately contributing to the advancement of a sustainable and carbon-neutral future.

Development of a Multi-block Modified Independent Component Analysis based Process Monitoring Strategy

Development of a Multi-block Modified Independent Component Analysis based Process Monitoring Strategy

Multimode process monitoring strategy based on improved just‐in‐time‐learning associated with locality preserving projections

AbstractIn this paper, a multimode process monitoring strategy based on improved just‐in‐time‐learning associated with locality preserving projections (IJITL‐LPP) is proposed. First, raw data are projected into the feature space using locality preserving projections (LPP). Second, IJITL searches for similar samples of the query sample in the feature space by introducing a variational inference Gaussian mixture model (VIGMM). Finally, the new statistic named average distance is created to complete process monitoring. In the IJITL, the introduced VI can automatically determine the number of modes, thereby accelerating the efficiency of selecting similar samples. In the process monitoring phase, the average distance can reduce the impact of different mode dispersion on fault detection. In addition, LPP can render the model less sensitive to outliers. Compared with principal component analysis (PCA), LPP, K nearest neighbour rules, Gaussian mixture model (GMM), K‐means based‐PCA, and just‐in‐time‐learning (JITL)‐based LPP, the proposed method has better performance in a numerical case, the Tennessee Eastman process, and the semiconductor etching process.

Advances in Manufacturing Cardiomyocytes from Human Pluripotent Stem Cells.

The emergence of human pluripotent stem cell (hPSC) technology over the past two decades has provided a source of normal and diseased human cells for a wide variety of in vitro and in vivo applications. Notably, hPSC-derived cardiomyocytes (hPSC-CMs) are widely used to model human heart development and disease and are in clinical trials for treating heart disease. The success of hPSC-CMs in these applications requires robust, scalable approaches to manufacture large numbers of safe and potent cells. Although significant advances have been made over the past decade in improving the purity and yield of hPSC-CMs and scaling the differentiation process from 2D to 3D, efforts to induce maturation phenotypes during manufacturing have been slow. Process monitoring and closed-loop manufacturing strategies are just being developed. We discuss recent advances in hPSC-CM manufacturing, including differentiation process development and scaling and downstream processes as well as separation and stabilization.

Remote Sensing Approaches for Meteorological Disaster Monitoring: Recent Achievements and New Challenges.

Meteorological disaster monitoring is an important research direction in remote sensing technology in the field of meteorology, which can serve many meteorological disaster management tasks. The key issues in the remote sensing monitoring of meteorological disasters are monitoring task arrangement and organization, meteorological disaster information extraction, and multi-temporal disaster information change detection. To accurately represent the monitoring tasks, it is necessary to determine the timescale, perform sensor planning, and construct a representation model to monitor information. On this basis, the meteorological disaster information is extracted by remote sensing data-processing approaches. Furthermore, the multi-temporal meteorological disaster information is compared to detect the evolution of meteorological disasters. Due to the highly dynamic nature of meteorological disasters, the process characteristics of meteorological disasters monitoring have attracted more attention. Although many remote sensing approaches were successfully used for meteorological disaster monitoring, there are still gaps in process monitoring. In future, research on sensor planning, information representation models, multi-source data fusion, etc., will provide an important basis and direction to promote meteorological disaster process monitoring. The process monitoring strategy will further promote the discovery of correlations and impact mechanisms in the evolution of meteorological disasters.

Using Technology to Assess Individualized Education Plan Goal Progress During Virtual Learning

The purpose of this article is to provide practical, research-based strategies for using technology to monitor Individualized Education Plan (IEP) goal progress. Steps in the assessment process and progress monitoring strategies are provided to support educators in meeting the needs of students and to communicate the performance results with families. Case studies, resources, and sample materials are provided.

Real-Time Fault Detection and Diagnosis of CaCO3 Reactive Crystallization Process by Electrical Resistance Tomography Measurements.

In the present research work, an electrical resistance tomography (ERT) system is utilized as a means for real-time fault detection and diagnosis (FDD) during a reactive crystallization process. The calcium carbonate crystallization is part of the carbon capture and utilization scheme where process monitoring and malfunction diagnostics strategies are presented. The graphical logic representation of the fault tree analysis methodology is used to develop the system failure states. The measurement consistency due to the use of a single electrode from a set of ERT electrodes for malfunction identification is experimentally and quantitatively investigated based on the sensor sensitivity and standard deviation criteria. Electrical current measurements are employed to develop a LabVIEW-based process automation program by using the process-specific knowledge and historical process data. Averaged electrical current is correlated to the mechanical failure of the stirrer through standard deviation evaluation, and slopes of the measured data are used to monitor the pump and concentrations status. The performance of the implemented methodology for detecting the induced faults and abnormalities is tested at different operating conditions, and a basic signal-based alarming technique is developed.

Quality-Analysis-Based Process Monitoring for Multi-Phase Multi-Mode Batch Processes

In batch processing, not only the characteristics of different phases are different, but also there may be different characteristics between batches. These characteristics of different phases and batches will have different effects on the final product quality. In order to enhance the safety of batch processes, it is necessary to establish an appropriate monitoring system to monitor the production process based on quality-related information. In this work, based on multi-phase and multi-mode quality prediction, a new quality-analysis-based process-monitoring strategy is developed for batch processes. Firstly, the time-slice models are established to determine the critical-to-quality phases. Secondly, a multi-phase residual recursive model is established using each quality residual of the phase mean models. Subsequently, a new process-monitoring strategy based on quality analysis is proposed for a single mode. After that, multi-mode quality analysis is carried out to judge the relevance between the historical modes and the new mode. Further, online quality prediction is achieved applying the selected model based on multi-mode quality analysis, and an according process-monitoring strategy is developed. The simulation results show the availability of this method for multi-phase multi-mode batch processes.

Cost-benefit analysis of calibration model maintenance strategies for process monitoring

The long-term prediction performance of spectroscopic calibration models is a critical factor to monitor or control many production processes. Over time, new variations may emerge that deteriorate prediction performance. Therefore, models have to be maintained to retain or improve their prediction performance through time, requiring considerable resources and data. Maintenance should improve relevant predictions but also needs to be resource and cost efficient. Current approaches do not consider these trade-offs. We propose a new method to quantify the effectiveness and cost of model maintenance strategies based on historical data. Model performance over time for past, imminent and future samples is evaluated as these may react differently to maintenance. The model performance and required updating resources are translated into relative cost and benefit to compare strategies and determine optimal maintenance parameters. We used this method to evaluate a maintenance strategy that combines adding incoming samples to the calibration data with re-optimization of spectral preprocessing and modelling parameters. Continuously adding samples to the calibration data is shown to improve prediction performance and leads to more robust and generic models for emerging variations in all investigated data streams. Selectively adding incoming sample variations showed a reduced prediction performance but saves considerably in resources. Comparing model performance on the different sampling windows can also be used to determine an optimal updating frequency. This novel strategy to evaluate the expected performance and determine an optimal maintenance strategy is generally applicable and should lead to robust and consistently high prospective and/or retrospective model performance through time, which can be crucial for optimal operation and fault detection in industrial processes.

Dynamic system modelling and process monitoring based on long-term dependency slow feature analysis

Modern industrial processes are large-scale, highly complex systems with many units and equipment. The complex flow of mass and energy, as well as the compensation effects of closed-loop control systems, cause significant cross-correlation and autocorrelation between process variables. To operate the process systems stably and efficiently, it is crucial to uncover the inherent characteristics of both the variance structure and dynamic relationship. Compared with the original slow feature analysis (SFA) that can only model the one-step time dependence, long-term dependency slow feature analysis (LTSFA) proposed in this paper can understand the longer-term dynamics by an explicit expression of latent states of the process. An iterative algorithm is developed for the model parameter optimization and its convergency is proved. The model properties and theoretical comparison with existing dynamic models are presented. A process monitoring strategy is designed based on LTSFA. The results of two simulation case studies show that LTSFA has better system dynamics extraction capability, which reduces the violation rate of the residual for the 95% confidence interval from 40.4% to 3.2% compared to the original SFA, and can disentangle the quickly- and slowly-varying features. Several typical disturbances can be correctly identified by LTSFA. The monitoring results on the Tennessee Eastman process benchmark show the overall advantages of the proposed method both in the dynamic and nominal deviation detection and the monitoring accuracy

Detection and analysis of photo-acoustic emission in Direct Laser Interference Patterning

Functional laser texturing by means of Direct Laser Interference Patterning is one of the most efficient approaches to fabricate well-defined micro textures which mimic natural surfaces, such as the lotus effect for self-cleaning properties or shark skin for reduced friction. While numerous technical and theoretical improvements have been demonstrated, strategies for process monitoring are yet to be implemented in DLIP, for instance aiming to treat complex and non-plane surfaces. Over the last 35 years, it has been shown that the sound pressure generated by a laser beam hitting a surface and producing ablation can be detected and analysed using simple and commercially available transducers and microphones. This work describes the detection and analysis of photo-acoustic signals acquired from airborne acoustic emission during DLIP as a direct result of the laser–material interaction. The study includes the characterization of the acoustic emission during the fabrication of line-like micro textures with different spatial periods and depths, the interpretation the spectral signatures deriving from single spot and interference ablation, as well as a detailed investigation of the vertical extent of the interference effect based on the ablated area and its variation with the interference period. The results show the possibility to develop an autofocusing system using only the signals from the acoustic emission for 3D processing, as well as the possibility to predict deviations in the DLIP processing parameters.

Design of Smart Classroom System Based on Internet of Things Technology and Smart Classroom

Smart classroom teaching is one of the new teaching methods. With the support of technology, teaching is carried out with the help of smart teaching tools to enhance teacher-student communication, enhance students’ learning autonomy, and provide new ideas for the realization of students’ deep learning. How to promote the overall intelligence of the teaching environment so that the teaching equipment can be used more efficiently and managed more effectively has become the main concern of schools. This article mainly studies the smart classroom system based on the Internet of Things technology and smart classroom. For temperature detection, we mainly use the DS18B20 chip to detect the temperature in the classroom. For the light intensity of the classroom, we use a photoresistor to collect the light data, and after amplification by the amplifier, the A/D sampling process of the single-chip microcomputer is used to obtain the light intensity, combined with the clock module to distinguish the influence of the classroom light. The data collection adopts the method of directly observing the source data, and the data format has not undergone secondary conversion, which ensures the accuracy of the source data. This test uses the USR-TCP232 network debugging assistant to debug the data collection. To optimize the safety and reliability of the system, dual-computer backup switching is adopted on the hardware, and process monitoring and management strategies are adopted on the software. At the same time, the amount of data interaction in the smart classroom is relatively large, so it is necessary to build a highly available cluster server, so that the system not only has a certain degree of stability, but also can quickly respond to users’ access requests. We can calculate that the average transmission time is about 10 ms, and 99.9% of the data transmission delay is less than 30 ms. The results show that the Internet of Things and smart classroom provide great convenience for future smart campus construction, daily teaching, and campus management and can also provide reference for the construction of smart classrooms in other universities.

Multi-block dynamic weighted principal component regression strategy for dynamic plant-wide process monitoring

To properly monitor dynamic large-scale processes, a new distributed dynamic process monitoring strategy named multi-block dynamic weighted principal component regression (DWPCR) is developed in this paper. Because complex plant-wide processes have multiple operation units and complex correlations among variables, traditional global process monitoring models may suppress local fault information and fail to identify incipient faults and local faults for large-scale processes. Besides, product quality determines the economic benefits of the enterprise. Motivated by these problems, this work studies the distributed quality monitoring strategy. At first, the idea of community partition in complex networks is used for multiple subblock division for a large number of process variables in this new monitoring framework. Then, the monitoring model for each subblock is established by the proposed DWPCR approach. Moreover, a novel weighting key components strategy based on fault information is proposed to monitor the process. Finally, the comprehensive monitoring result is fused by Bayesian inference. The superiority of the proposed distributed DWPCR strategy is testified in the case study.

Towards an adaptable quality monitoring process for self-piercing riveting

In the automotive industry self-piercing riveting (SPR) is a standard joining technology, especially for vehicles with a high mix of materials. The applied quality control system and the underlying quality decisions have hardly changed in the recent years. The commonly used combination of process monitoring and rework strategies leads to a large number of false positives and a high amount of manual work. The collected process data is not used comprehensively and misses the potential to improve the SPR process. This article introduces a quality monitoring method for SPR to show a proof of concept by using machine learning to predict faulty joining points. Based on numerous technical and statistical features extracted from the force-displacement curve of SPR, the trained model categorizes the observations in two rivet head height (RHH) classes. An evolutionary algorithm is used for the feature selection and a random forest model for classification. The resulting accuracy scores up to 84.4% and shows the potential of the developed random forest model. The potential application of this approach in the context of serial body-shop production improves the prediction of joining quality and the process availability significantly. This enables the adaptable rework of non-critical joining points depending on the classified RHH.

Flow virometry for process monitoring of live virus vaccines-lessons learned from ERVEBO

Direct at line monitoring of live virus particles in commercial manufacturing of vaccines is challenging due to their small size. Detection of malformed or damaged virions with reduced potency is rate-limited by release potency assays with long turnaround times. Thus, preempting batch failures caused by out of specification potency results is almost impossible. Much needed are in-process tools that can monitor and detect compromised viral particles in live-virus vaccines (LVVs) manufacturing based on changes in their biophysical properties to provide timely measures to rectify process stresses leading to such damage. Using ERVEBO, MSD’s Ebola virus vaccine as an example, here we describe a flow virometry assay that can quickly detect damaged virus particles and provide mechanistic insight into process parameters contributing to the damage. Furthermore, we describe a 24-h high throughput infectivity assay that can be used to correlate damaged particles directly to loss in viral infectivity (potency) in-process. Collectively, we provide a set of innovative tools to enable rapid process development, process monitoring, and control strategy implementation in large scale LVV manufacturing.

Potential applications of halophilic microorganisms for biological treatment of industrial process brines contaminated with aromatics.

Saline wastewater contaminated with aromatic compounds can be frequently found in various industrial sectors. Those compounds need to be degraded before reuse of wastewater in other process steps or release to the environment. Halophiles have been reported to efficiently degrade aromatics, but their application to treat industrial wastewater is rare. Halophilic processes for industrial wastewater treatment need to satisfy certain requirements: a continuous process mode, low operational expenditures, suitable reactor systems and a monitoring and control strategy. The aim of this review is to provide an overview of halophilic microorganisms, principles of aromatic biodegradation, and sources of saline wastewater containing aromatics and other contaminants. Finally, process examples for halophilic wastewater treatment and potential process monitoring strategies are discussed. To further illustrate the significant potential of halophiles for saline wastewater treatment and to facilitate development of ready-to-implement processes, future research should focus on scale-up and innovative process monitoring and control strategies.

Process monitoring and control strategies in extrusion-based bioprinting to fabricate spatially graded structures

Extrusion-based bioprinting is the most common printing technology used in regenerative medicine. Despite recent technological advances, a pressing challenge for extrusion printing is low spatial resolution, which limits the functionality of printed constructs. One of the reasons for the low spatial resolution is a lack of process monitoring and control strategies to monitor fabrication and correct for print errors. Few research efforts implement process control and investigate the relationship between extrusion process parameters and printing fidelity. The lack of understanding between process parameters and print results ultimately limits the complexity of the possible structures. For example, fabrication of structures whose topologies vary spatially within the part is not possible without advanced process control. Here we enable fabrication of advanced spatially graded structures by implementing process monitoring and control strategies. We develop material models to better understand the relationship between process parameters and printing outcomes. We also present an experimental procedure to generate a process map that provides insight into the regions of the processing space that produce the desired extrusion features (e.g., width of the filament). After generation of a process map and material models, we implement a process monitoring and control strategy that measures the feature error and intelligently updates the process control inputs to reduce defects and improve spatial fidelity, which will lead to better functionality of the final construct.

A non-linear statistical process monitoring strategy – A case study of a steel making shop

Process monitoring strategies are an amalgamation of procedures and techniques for monitoring of the manufacturing process for the eventual goal of production of good quality end product. The study delves into the development of a monitoring strategy based on statistical techniques and taking into account the nonlinearity of the data. The case study involving a Steel making Shop has been chosen to showcase the methodology thus developed. The statistical monitoring strategy devised is based on multivariate Hotelling T2 chart and the nonlinearity of the data is addressed via the employment of Neural Network Fitting model. The data consisting of the quality characteristics observations of the steel billets is fed to Neural Network Model for removal of nonlinear pattern. Thereafter the complete or partial linear transformed observations are being tested for the presence of fault(s) by employment of Hotelling T2 Control Chart and upon the detection of fault represented by out-of-control observation appropriate actions ought to be initiated.