Monitoring Of Process Parameters Research Articles

Application of Multivariate Data Analysis on Historical Recombinant Adenovirus Zoster Vaccine Production Data for Upstream Process Improvements

In recent years, multivariate data analysis (MVDA) has been widely used for process characterization and fault diagnosis in the biopharmaceutical industry. This study aims to investigate the feasibility of using MVDA for the development and scale-up of a perfusion process for HEK293 cell-based recombinant adenovirus zoster vaccine (Ad-HER) production. The Principal Component Analysis (PCA) results suggested comparable performance among the ATF, PATFP, and BFP perfusion systems in benchtop-scale stirred-tank bioreactor (STR). Then a Batch Evolution Model (BEM) was built using representative data from 10 L STR with a BFP system to assess the Ad-HER perfusion process performance at pilot-scale bioreactor (50 L STR and 50 L wave bioreactor). Furthermore, another BEM model and Batch Level Model (BLM) were built to monitor process parameters over time and predict the final adenovirus titer in 50 L wave bioreactor. The loading plot revealed that lactate dehydrogenase activity, viable cell diameter, and base-added during the virus production phase could be used as preliminary indicators of adenovirus yield. Finally, an adenovirus titer of 2.0±0.3×1010 IFU/mL was achieved in the 50 L wave bioreactor with BFP system, highlighting the robustness of the Ad-HER perfusion process at pilot-scale. Overall, this study emphasizes the effectiveness of MVDA as a tool for advancing the understanding of recombinant adenovirus vaccine perfusion production process development and scale-up.

Application of PAT Probes in Aluminum Phosphate Adjuvant Manufacturing.

This study is focused on monitoring process parameters and quality attributes of aluminum phosphate (AlPO4) using multiple in-line probes incorporated into an industrial-scale adjuvant suspension manufacturing unit. The manufacturing of aluminum adjuvant suspension was monitored at manufacturing scale using conductivity, turbidity, infrared, and particle sizing and count probes to follow the continuous evolution of particle formation and size distribution, and the reaction kinetics during the synthesis of AlPO4. The data showed that AlPO4 forms large particles at the early stages of mixing, followed by a decrease in size and then stabilization towards the later stages of mixing and pH adjustment. The results provided a complementary view of process events and assisted in optimizing several parameters, e.g., flow rate of reactants AlCl3 and Na3PO4 solutions, mixing rate, pH, and conductivity of AlPO4, as well as adjuvant quality attribute such as particle size, thus streamlining and shortening the process development stage. The results of this study showed the usefulness of the in-line probes to automate continuous assessment of AlPO4 batch-to-batch consistency during in-house adjuvant production at the industrial scale.

Direct Recycling of Hot‐Deformed Nd–Fe–B Magnet Scrap by Field‐Assisted Sintering Technology

Recycling of Nd–Fe–B magnets is an ongoing challenge regarding circular economy. State‐of‐the‐art magnet production methods, such as hot deformation, have limitations with respect to direct recycling of magnet scrap particles that differ from pristine melt‐spun Nd–Fe–B powder. Recent work has shown that a combination of presintering by field‐assisted sintering technology/spark plasma sintering (FAST/SPS) and hot deformation by flash spark plasma sintering (flash SPS) has the potential to directly produce Nd–Fe–B magnets from 100% scrap material. Both processes have the capability to adjust and monitor process parameters closely, resulting in recycled magnets with properties similar to commercial magnets but made directly from crushed and recycled Nd–Fe–B powder that partially or completely replaces pristine melt‐spun Nd–Fe–B powder. Herein, a systematic study is done inserting recycled magnet particles into a flash SPS deformed magnet, considering the effects of different weight percentages of scrap material of varied particle size fractions. In some cases, coercivity HcJ of >1400 kAm−1 and remanence Br of 1.1 T can be achieved with 20 wt% scrap material. The relationship between particle size fraction, oxygen uptake, and percentage of recyclate in a final magnet are all explored and discussed with respect to magnets made from pristine material.

New Machine-Learning Control Charts for Simultaneous Monitoring of Multivariate Normal Process Parameters with Detection and Identification

Simultaneous monitoring of the process parameters in a multivariate normal process has caught researchers’ attention during the last two decades. However, only statistical control charts have been developed so far for this purpose. On the other hand, machine-learning (ML) techniques have rarely been developed to be used in control charts. In this paper, three ML control charts are proposed using the concepts of artificial neural networks, support vector machines, and random forests techniques. These ML techniques are trained to obtain linear outputs, and then based on the concepts of memory-less control charts, the process is classified into in-control or out-of-control states. Two different input scenarios and two different training methods are used for the proposed ML structures. In addition, two different process control scenarios are utilized. In one, the goal is only the detection of the out-of-control situation. In the other one, the identification of the responsible variable (s)/process parameter (s) for the out-of-control signal is also an aim (detection–identification). After developing the ML control charts for each scenario, we compare them to one another, as well as to the most recently developed statistical control charts. The results show significantly better performance of the proposed ML control charts against the traditional memory-less statistical control charts in most compared cases. Finally, an illustrative example is presented to show how the proposed scheme can be implemented in a healthcare process.

Combine effect of feeding rate and modeling parameter on the extrusion pressure in material extrusion additive manufacturing

Numerical modeling of the complex flow through the hot-end in the material extrusion (ME) additive manufacturing (AM) system is presented. A modeling parameter known as the mushy zone parameter and feeding rate was varied to predict extrusion pressure and other flow variables during ME. The simulation shows that the mushy zone parameter (Amush) influences the extrusion pressure for tough-polylactic acid (TPLA) filament. At all the feeding rates (40, 60, and 80 mm/min) considered, the extrusion pressure and dampening of the flow increase with an increase in the mushy zone parameter. However, the pressure drop is minimum for the lowest feeding rate of 40 mm/min and increases with an increase in the feeding rate. Generally, the temperature distribution in the hot-end suggests that the impact of Amush on the thermal field is lower when compared to the feeding rate. This indicates that the additional thermal energy needed to raise the temperature of semicrystalline materials to melting temperature due to the melting enthalpy being small. The current model suggests that improving the understanding of the complex flow physics in the hot-end can increase the printing parts production rate, redesigning the hot-end, inline process parameter monitoring, and control.© 2023 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights reserved.This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)Peer-review under responsibility of the Scientific Committee of the NAMRI/SME.

Methods and Analysis of Biological Contaminants in the Biomanufacturing Industry

The advent of bioprocessing has revolutionized the biomanufacturing industry, leading to the rise of biotherapeutics derived from biologic products such as chimeric antigen receptor (CAR) T-cells used for targeted cancer treatment and the Vero cell line for the production of viral vectors and vaccines. Despite these promising developments, most biologic products are characterized by fragile macromolecular structures that are heterogenous with a purity profile that varies with each batch making them susceptible to microorganism contamination. Regulatory oversight of biologic products is imperative to ensure adherence to good manufacturing practices and compliance with quality management systems. Current quality assurance protocols during production include monoclonality during cell line development, real-time monitoring of process parameters, flow cytometry for microbial monitoring, polymerase chain reaction, and immunoassay techniques to amplify DNA sequences related to bacterial or biological contaminants. FDA guidance recommends the implementation of process analytical technology within biomanufacturing production to measure critical quality parameters, which includes screening for potential biological contamination. Future advancements in bioprocess monitoring and control should capitalize on providing cheap, real-time, and sensitive detection. Biosensors, mass spectrometry, and polymerase chain reaction present robust, rapid, and real-time capabilities for multiplexed detection of contaminant analytes and have shown promise in meeting these needs. This review discusses the main biological contaminants of bioprocesses, European Union and FDA regulatory guidelines for monitoring and control within biologics production, existing methods and their limitations, and future advancements for biological contamination detection.

Control System for Automated Technological Process of Hot Stamping-A Case Study.

Hot forming process has been used more and more frequently in the production of body structures of modern ultralight passenger cars for several years. This, unlike the commonly used cold stamping, is a complicated process, combining heat treatment and plastic-forming methods. For this reason, permanent control at each stage is required. This includes, among others, measurement of the blank thickness, monitoring its heating process in the suitable atmosphere in the furnace, control of the forming process itself, measurement of shape-dimensional accuracy as well as mechanical parameters of the finished drawpiece. This paper discusses the method of controlling the values of production parameters during the hot stamping process of a selected drawpiece. For this purpose, digital twins of the production line and the stamping process, made in accordance with the assumptions of Industry 4.0, have been used. Individual components of the production line with sensors for monitoring process parameters have been shown. The system's response to emerging threats has also been described. The correctness of the adopted values is confirmed via tests of mechanical properties and the assessment of the shape-dimensional accuracy of a drawpiece test series.

Multi-sensors data fusion for monitoring of powdered and granule products: Current status and future perspectives

Process Analytical Technology (PAT) is a systematic approach for monitoring of process parameters and product quality attributes and nowadays is considered for continuous processing of many industrial products. It is a mechanism to design, analyse and control manufacturing processes through on-line, in-line, at-line and off-line configurations for monitoring Critical Quality Attributes (CQAs). PAT systems include a combination of reliable in-line sensors, spectroscopic instruments and Multivariate Statistical Methods (MSMs) to provide informative knowledge for quality assessment of powdered and granule products. Nevertheless, monitoring programs of advanced manufacturing processes based on PAT systems typically provide large sets of data which are complex to interpret. The application of appropriate data-driven modelling techniques could assist in the interpretation of complex data matrices to better control of processes. Data fusion is a data-driven approach that could increase performance and robustness of models used for data interpretation to generate more accurate knowledge about process conditions and performance by merging related outputs collected from several instruments and considering synergies from multiple sources. This paper aims at presenting the current state of the art regarding the application of multi-sensors data fusion for powdered and granule manufacturing processes and making a critical review of recent progress and future possible perspectives in this field.

3D-Printed Microfluidic Chip for Real-Time Glucose Monitoring in Liquid Analytes

The connection of macrosystems with microsystems for in-line measurements is important in different biotechnological processes as it enables precise and accurate monitoring of process parameters at a small scale, which can provide valuable insights into the process, and ultimately lead to improved process control and optimization. Additionally, it allows continuous monitoring without the need for manual sampling and analysis, leading to more efficient and cost-effective production. In this paper, a 3D printed microfluidic (MF) chip for glucose (Glc) sensing in a liquid analyte is proposed. The chip made in Poly(methyl methacrylate) (PMMA) contains integrated serpentine-based micromixers realized via stereolithography with a slot for USB-like integration of commercial DropSens electrodes. After adjusting the sample's pH in the first micromixer, small volumes of the sample and enzyme are mixed in the second micromixer and lead to a sensing chamber where the Glc concentration is measured via chronoamperometry. The sensing potential was examined for Glc concentrations in acetate buffer in the range of 0.1-100 mg/mL and afterward tested for Glc sensing in a cell culturing medium. The proposed chip showed great potential for connection with macrosystems, such as bioreactors, for direct in-line monitoring of a quality parameter in a liquid sample.

Experimental Investigation of Microcontroller-Based Acoustic Temperature Transducer Systems

Temperature transducers are commonly used to monitor process parameters that are controlled by various types of industrial controllers. The purpose of this study is to design and model a simple microcontroller-based acoustic temperature transducer based on the variations of resonance conditions in a cylindrical resonance tube. The transducer's operation is based on the generation of an acoustic standing wave in the free resonance mode of generation within a cylindrical resonance tube which is converted into a train of pulses using Schmitt trigger circuit. The frequency of the generated standing wave (i.e., the train of pulses) is measured by the Arduino Uno microcontroller, where a digital pin is used to acquire pulses that are counted using a build-in software function in an Arduino IDE environment. Experimental results are performed for three sizes of diameters to investigate the effect of the diameter of resonance tube on the obtained results. The maximum nonlinearity error according to Full-Scale Deflection (FSD) is about 2.3 percent, and the relative error of the transducer is evaluated using experimental findings and the regression model. The circuit simplicity and design of the suggested transducer, as well as the linearity of its measurements, are notable.

Solar-thermal cold-wall chemical vapor deposition reactor design and characterization for graphene synthesis

Manufacturing processes are often highly energy-intensive, even when the energy is primarily used for direct heating processes. The required energy tends to derive from local utilities, which currently employ a blend of sources ranging from fossil fuels to renewable wind and solar photovoltaics, among others, when the end manufacturing need is thermal energy. Direct solar-thermal capture provides a compelling alternative that utilizes renewable energy to reduce greenhouse gas emissions from industrial processes, but one that has rarely been employed to date. In this study, a 10 kWe custom-built high flux solar simulator (HFSS) that closely approximates the solar spectrum produces a heat flux distribution with an adjustable peak between 1.5 and 4.5 MW/m2. The HFSS system is coupled to a cold-wall chemical vapor deposition (CVD) system that is equipped to automate graphene synthesis while providing safe operation, precise control, and real-time monitoring of process parameters. A numerical heat transfer model of a thin copper substrate is derived and validated to compute the substrate’s temperature profile prior to the synthesis process. The peak substrate temperature is correlated to the HFSS supply current and vacuum pressure, as it serves as a critical design parameter during graphene synthesis. We report the synthesis of high-quality graphene films on copper substrates with an average Raman peak intensity ratio ID/IG of 0.17. Backscattered electron microscopy reveals a characteristic grain size of 120 μm, with an area ratio of 16 when compared to that of low-quality graphene on copper. The reported solar-thermal CVD system demonstrates the ability to produce a high-value product, namely, graphene on copper, directly from a renewable energy resource with process control and automation that enables synthesis under a variety of conditions.

Efficient Auxiliary Information–Based Control Charting Schemes for the Process Dispersion with Application of Glass Manufacturing Industry

The hybrid exponentially weighted moving average (HEWMA) control chart is an enhanced version of the EWMA control chart that monitors the process parameters effectively. Similarly, the auxiliary information-based (AIB) EWMA control charts are very efficient for monitoring process parameters. The purpose of this paper is to propose two new control charts for the improved monitoring of process dispersion referred to as HEWMA AIB 1 and HEWMA AIB 2 control charts. A simulation study is carried out to assess the performance of the proposed HEWMA AIB 1 and HEWMA AIB 2 control charts. Average run length, extra quadratic loss, relative average run length, and performance comparison index are used to compare the performance of the proposed control charts against the existing counterparts. The comparisons reveal the superiority of the proposed control charts against other competing control charts, particularly for small to moderate shifts in the process dispersion. Finally, a real-life data set from the glass industry is used to demonstrate the practical implementation of the proposed control charts.

Automated melt electrowritting platform with real-time process monitoring

Melt electrowriting (MEW) is an additive manufacturing (AM) technology with the ability to fabricate complex designs with high-resolution. The utility of MEW is studied in many fields including tissue engineering and soft robotics. However, current MEW hardware offers only basic functionality and is often designed and built in-house. This affects results replication across different MEW devices and slows down the technological advancement. To address these issues, we present an automated MEW platform with real-time process parameter monitoring and control. We validate the developed platform by demonstrating the ability to accurately print polymer structures and successfully measure and adjust parameters during the printing process. The platform enables the collection of large volumes of data that can be subsequently used for further analysis of the system. Ultimately, the concept will help MEW to become more accessible for both research laboratories and industry and allow advancing the technology by leveraging the process monitoring, control and data collection.

Space-Filling Curve Resistor on Ultra-Thin Polyetherimide Foil for Strain Impervious Temperature Sensing.

Monitoring process parameters in the manufacture of composite structures is key to ensuring product quality and safety. Ideally, this can be done by sensors that are embedded during production and can remain as devices to monitor structural health. Extremely thin foil-based sensors weaken the finished workpiece very little. Under ideal conditions, the foil substrate bonds with the resin in the autoclaving process, as is the case when polyetherimide is used. Here, we present a temperature sensor as part of an 8 µm thick multi-sensor node foil for monitoring processing conditions during the production and structural health during the lifetime of a construction. A metallic thin film conductor was shaped in the form of a space-filling curve to suppress the influences of resistance changes due to strain, which could otherwise interfere with the measurement of the temperature. FEM simulations as well as experiments confirm that this type of sensor is completely insensitive to the direction of strain and sufficiently insensitive to the amount of strain, so that mechanical strains that can occur in the composite curing process practically do not interfere with the temperature measurement. The temperature sensor is combined with a capacitive sensor for curing monitoring based on impedance measurement and a half-bridge strain gauge sensor element. All three types are made of the same materials and are manufactured together in one process flow. This is the key to cost-effective distributed sensor arrays that can be embedded during production and remain in the workpiece, thus ensuring not only the quality of the initial product but also the operational reliability during the service life of light-weight composite constructions.

Real-time identification of out-of-control and instability in process parameter for gasification process: Integrated application of control chart and kalman filter

Due to the parameters’ complex variation in gasification process, traditional monitoring method detecting the observed data directly may case missing alarm. This paper proposed a real-time identification model with high accuracy using Kalman filter and control chart jointly. The observed data is divided into state value and deviation value using Kalman filter, then they are separately monitored by control char. Simulation experiments on gasifier temperature monitoring in three typical fluctuation modes and field application on H2 and CO monitoring for a gasifier under there different starting condition were carried out. The proposed method respectively identified 4, 6 and 2 alarms in simulation experiments, and identified 10, 3, 12, 68, 2 and 4 alarms in filed application. Besides, the proposed method also accurately identified the presented the preset sinusoidal fluctuations and upward shift when monitoring temperature and 4 systematic variations and 23 special variations when monitoring H2 and CO. The performance of the proposed method is significantly more accuracy than that of traditional methods. So the integrated application of control chart and Kalman filter in gasification process parameter monitoring has the advantages of high sensitivity of outlier alarm, high identification of variation and high applicability of multimode fluctuations under various conditions.

Experimental investigation and process optimization of the ultrasonic welding applied to papers

AbstractUltrasonic welding is a serious candidate in the development of methods to assemble papers and paperboards without using additional substances. However, the ultrasonic welding of papers remains a technological challenge considering the low weldability of lignocellulosic materials. This study aims to investigate on the ultrasonic welding process applied to papers in order to identify the processing conditions which favor the formation of strong welded joints. To reach such purpose, an experimental strategy was developed by combining the characterization of welded materials and the monitoring of process parameters. Experimentations were performed using a reference paper displaying a good weldability to specifically highlight the contribution of process parameters. Results indicate that the process is highly sensitive to vibration amplitude, power supplied by the high frequency generator, and sample thickness. Power seems to be a reliable indicator of the severity of the process. A strong decrease in the strength of the welded joints is observed when working with low thicknesses. It seems that the layers coated on top of papers are the main part of the material contributing in the development of adhesion at the welding joint. Overall, instrumenting the device has led to a better understanding of the ultrasonic welding of papers.

Thermography for performance evaluation of a compact, air disperser equipped hot air dryer

AbstractA hot air dryer has been developed for achieving uniform temperature distribution throughout the drying trays with the use of an air disperser. Comparative experiments were conducted with and without the use of the disperser. Then, 1 mm thick potato slices were dried at 50, 55, and 60°C and 1.5 m/s air velocity at different locales of the tray. Validation of drying rate uniformity was done by studying the temperature distribution on the drying tray using thermal imaging and measuring the chromatic, textural, and morphological properties of dried potato slices. Without the air disperser, the average temperature and average initial drying rate measured after 30 min at five different points on the bottom tray varied from 42.5 to 52.2°C and 0.086 to 0.185 g water/min, respectively, whereas the variation was only in the range of 49.0–49.6°C and 0.182 and 0.206 g water/min when the air disperser was used at a set dryer temperature of 50°C. Similarly, drying rates at all the positions in different trays were observed to be in close proximity throughout the drying period with the presence of air disperser; however, without it, the drying rate was found to be higher at the center of the tray than other four points. Similar observations were also made for all the qualitative parameters of potato slices at all drying temperatures.Practical ApplicationsThe dryer has provision for placement of different types of imaging devices and sensors for online monitoring of process parameters and the product being dried. It has been designed for nondestructive study of the drying progression using machine vision system. Such a dryer will be especially useful for controlled drying experiments and laboratory simulation of different hot air drying processes.

Development of PLC Program for Multi-Process Parameter and Multi Profile-based Control Logic for Heat Treatment Industrial Applications

The industrial heat treatment application is very critical generally. The implementation of process parameters like Temperature, Pressure, and vacuum should be controlled precisely along with pre-defined time slot, otherwise, it may impact the product quality in some applications. The process of vulcanization related to rubber, glass and aerospace products is done by hot air or steam autoclaves. The Temperature and Pressure are to be controlled with ramped setpoints and in some of the applications Vacuum is also considered. By using Industrial Autoclaves, there are two types of processes that can be carried out, namely the Vulcanization and Pre-Heating. Most of the Autoclaves were used for sterilization of medical equipment in the medical field also. The process parameters are controlled based on the Process time and pre-defined profile paths which are defined by the type of application used. Whatever the applications are available, the use of a control logic program remains same in all applications. The main agenda of control logic was to achieve the process set points and minimizing the amount of error and executing the Profile steps and logging the process data were purely depending upon the Control logic program and efficiency of logic. The development of the PLC program for Controlling the Autoclaves or Ovens, should capable to handle the Data acquisition, control and monitoring of Process parameters with the smooth functioning of associated sub-systems.

A very boring 120 h: 15 million tablets under a continuous state of control

The aim of this study is to show the robustness of a commercial direct compression system running continuously under relevant process conditions for over 120 h. The study will apply a representative commercial control strategy including real time monitoring of process parameters, rejection of OOS material, use of in-line NIR prediction for blend potency monitoring, application of mathematical RTD modelling for real-time prediction of product potency, and tablet concentration prediction by tablet NIR.The study showed that the process can produce in specification product robustly over significant operating times. Across 122 h of actual run time (including pauses) the process manufactured over 15 million tablets to give a product yield of 99.2%. Whilst the purpose of this study was to evaluate the process for over 120 h, this process is not limited to running longer periods of time given the performance of the equipment and the control strategy.

DIGITAL TECHNOLOGIES IN AGRICULTURAL PRODUCTION: IMPLEMENTATION BACKGROUND, CURRENT STATE AND DEVELOPMENT TRENDS

Digital technologies have been deeply integrated into our lives and found their application in all areas of human activity, including agriculture. Technological progress, the development of infrastructure in the IT industry, wider access to the Internet, a high level of education and growing computer literacy of the population contribute to the popularization and introduction of digital technologies in agricultural production. The review of the use of digital technologies in the agricultural sector has shown that existing software tools help plan the work of an agricultural organization, monitor the state of production, manage the farm, and sell products on virtual trading platforms. Most software products are able to store data in the cloud, and farmers can access the system via their personal account from any device with Internet access. There are also a number of programs on the market that allow farmers to plan, analyze and control the crop or livestock production. Leading manufacturers of agricultural machinery and equipment are actively integrating digital technologies into their products. It is now possible to manage a fl eet of vehicles used in the fi elds, and implement unmanned control. Use can be made of tractors equipped with automatic systems for driving along rows or laid paths, turning, and monitoring process parameters. The authors propose a set of digital control means to be used in agricultural production, based on the application of technologies of the Internet of things, cloud data storage, big data processing, and artifi cial intelligence. It is the technologies that digital agriculture will be based on, which use robotic monitoring tools to collect information and transmit it to “cloud data storage”. It is processed there and directed to the control system, which develops the optimal solution and transmits the control signal to the robotic actuators. In this regard, the development of agricultural machinery should be focused on the robotic tools designed for monitoring and performing technological operations. The most diffi cult task is to develop a control system, since it must have elements of artifi cial intelligence and replace humans in agricultural production.