Improved Process Control Research Articles

Analytical Tools for HCP Detection, Identification, and Quantitation.

Identifying and quantifying host cell proteins (HCPs) are crucial in developing and manufacturing biopharmaceutical products. ELISA has been used widely for identifying and quantifying HCPs in biopharmaceutical products. Liquid chromatography-mass spectrometry (LC-MS) has recently emerged as an orthogonal tool for HCP analysis, providing comprehensive and quantitative information on individual HCPs. This chapter explores the advancements in LC-MS/MS methodologies for HCP analysis, including sample preparation, data acquisition modes, and data analysis strategies. It also discusses the challenges of HCP identification and quantitation, and recent innovations to overcome these limitations. The application of LC-MS/MS methods in HCP profiling holds significant promise for improving process control and ensuring product safety in biopharmaceutical manufacturing.

Development of an automated system for the purification of alkanolamine solutions

The alkanolamine solutions used in natural gas purification processes are inevitably degraded over time. This occurs due to accumulation of reaction products from the reactions with carbon dioxide, hydrogen sulphide, oxygen and other impurities present in the gas streams, as well as from thermal degradation. The degradation of solutions leads to the formation of thermostable compounds that not only reduce absorption efficiency, but also increase corrosive activity. Furthermore, part of the amine turns into a bound state and becomes ballast. The necessity of purification of alkanolamine solutions is becoming more and more urgent since the cost of maintaining efficient operation of gas treatment plants grows, and environmental safety requirements increase. Purification of solutions makes it possible to prolong their service life, reduce the cost of purchasing new reagents and reduce the negative impact on the equipment. In this work the method of anion-exchange technology of purification is used. Unlike vacuum distillation and electrodialysis, the considered technology does not require significant energy costs for generation and maintenance of vacuum, and heating of solutions, it can be easily integrated into the existing production process without any significant changes in the infrastructure. The described problem is relevant for Kazakhstan oil refineries, two of which use methyldiethanolamine, and one of which uses diethanolamine, and the existing practice of maintaining the operability of the systems is based on periodic replacement of part of the solution with pure amine. The development of automated systems for purification of such solutions makes it possible to significantly improve process control, minimise manual labour, and improve the overall economic efficiency of production.

Sequence Segmentation of Nematodes in Atlantic Cod with Multispectral Imaging Data.

Nematodes pose significant challenges for the fish processing industry, particularly in white fish. Despite technological advances, the industry still depends on manual labor for the detection and extraction of nematodes. This study addresses the initial steps of automatic nematode detection and differentiation from other common defects in fish fillets, such as skin remnants and blood spots. VideometerLab 4, an advanced Multispectral Imaging (MSI) System, was used to acquire 270 images of 50 Atlantic cod fillets under controlled conditions. In total, 173 nematodes were labeled using the Segment Anything Model (SAM), which is trained to automatically segment objects of interest from only few representative pixels. With the acquired dataset, we study the potential of identifying nematodes through their spectral signature. We incorporated normalized Canonical Discriminant Analysis (nCDA) to develop segmentation models trained to distinguish between different components within the fish fillets. By incorporating multiple segmentation models, we aimed to achieve a satisfactory balance between false negatives and false positives. This resulted in 88% precision and 79% recall for our annotated test data. This approach could improve process control by accurately identifying fillets with nematodes. Using MSI minimizes unnecessary inspection of fillets in good condition and concurrently boosts product safety and quality.

Жизненный цикл изделия: мониторинг процессов механической обработки и фильтрация виброакустических сигналов

Introduction. In modern manufacturing, the product life cycle comprises various stages, from conception to disposal. Among these stages, machining plays a significant role, as it directly influences the durability and functionality of the finished product. With increasing competition and the need to reduce production costs, optimizing machining processes has become a crucial task. Traditionally, conservative technological approaches have been used to ensure processing quality. However, this often leads to decreased productivity and higher costs. Modern monitoring and diagnostic techniques can significantly improve process control, particularly through tool condition monitoring. The subject. This paper discusses the stages of the product life cycle and emphasizes the importance of monitoring machining processes. It explores the potential of using vibroacoustic signals to continuously monitor equipment and product conditions. Special attention is paid to the use of vibroacoustic signals for diagnostics and quality control. Modern approaches to filtering these signals, including the use of the fast Fourier transform and various window functions, are analyzed in order to improve the accuracy of the analysis and identify potential defects. The purpose of this work is to develop an algorithm for an online monitoring system that will monitor the condition of cutting tools based on the creation of a digital shadow using a vibroacoustic complex. The main tasks to be solved are to establish the ranges of applicability of frequency response of acoustic signals and optimal window functions, as well as to establish the relationship between the degree of wear on the cutting tool and the results of vibration diagnostics and surface roughness. The methods and technologies for filtering vibroacoustic signals and their application in real–world production settings are discussed. Special attention is given to the role of digital twins in integrating monitoring and filtering data, allowing for the creation of a virtual model of a product to predict its behavior and optimize processes throughout the life cycle. A comparison of various monitoring methods and technologies is conducted, as well as an analysis of practical examples of digital twin implementation in production processes and its impact on improved control. Results and discussion are presented, identifying current research and practical advancements, while also proposing existing challenges and promising areas for future research in the fields of monitoring, signal filtering, and the use of digital twins in mechanical manufacturing.

Impact of Measurement Uncertainty on Fault Diagnosis Systems: A Case Study on Electrical Faults in Induction Motors.

Classification systems based on machine learning (ML) models, critical in predictive maintenance and fault diagnosis, are subject to an error rate that can pose significant risks, such as unnecessary downtime due to false alarms. Propagating the uncertainty of input data through the model can define confidence bands to determine whether an input is classifiable, preferring to indicate a result of unclassifiability rather than misclassification. This study presents an electrical fault diagnosis system on asynchronous motors using an artificial neural network (ANN) model trained with vibration measurements. It is shown how vibration analysis can be effectively employed to detect and locate motor malfunctions, helping reduce downtime, improve process control and lower maintenance costs. In addition, measurement uncertainty information is introduced to increase the reliability of the diagnosis system, ensuring more accurate and preventive decisions.

Additively Manufactured Continuous Processing Reactor System for Producing Liquid-Based Pharmaceutical Substances

In this study, an AM-based continuous processing reactor system was designed, manufactured, and assembled on a laboratory scale for the generation of pharmaceutical substances with an improved process control. The developed AM-based (additively manufactured) continuous pharmaceutical reactor system for the synthesis of metronidazole derivatives aimed to optimize both the physical and the chemical processes with time savings. Using AM, we were able to build reactor subcomponents with complex designs and precise dimensions, which facilitated the precise control of the reaction parameters and reduced the amount of chemicals required compared to macroscale reactors. The assembly of the whole reactor system consisted of main reactor bodies, mixers, valves, heat exchangers, electrical motors, and a microcontroller system. The assembled reactor system revealed a continuous flow of reagents and ensured uniform mixing and reaction conditions, thereby increasing the process efficiency and product quality. Five metronidazole derivatives were synthesized via two continuous processes, involving metronidazole reduction and its subsequent reactions with terephthalic aldehyde and anthracen-9(10H)-one to form Schiff bases. The optimal conditions were determined as follows: compound A (72% yield, 120 min, 55 °C), compounds B and C (63% and 68% yield, respectively, 8 h, 65 °C), and compounds D and E (74% and 85% yield, respectively, 8 h, 45 °C).

Optimizing virus filtration for continuous processing using serial filtration at high area ratio.

Compared to batch operation, continuous bioprocessing can offer numerous advantages, including increased productivity, improved process control, reduced footprint, and increased flexibility. However, integration of traditional batch operations into a connected process can be challenging. In contrast to batch operations run at constant pressure or high flux, virus filtration in continuous processes may be operated at very low flux. This change in operating conditions may reduce the viral retention performance of the filter which has inhibited adoption of truly continuous virus filtration. To overcome this limitation, a novel approach is described that utilizes serial virus filtration, with a high area ratio between first to second stage filters, to achieve virus retention targets. In this study, virus filters were operated continuously (except for planned process interruptions) for 200 h in a serial configuration at a first to second stage filter area ratio of 13:1 and at a first stage flux of 5 L/m2/h. While the minute virus of mice (MVM) retention performance of the first stage filter was about 4 log reduction value (LRV), there was no virus detected in the second stage filtrate, translating to an MVM LRV across the filtration train of ≥6.7. The second stage filter was the dominant flow resistance at the start of the run but, as it was protected from foulants by the first stage filter, it suffered minimal fouling and the life of the filter train was controlled by the first stage. A theoretical case study projected that continuous virus filtration using serial configuration at high area ratio would have about 30% longer filter changeout time, 14% higher productivity, and virus retention nearly sixLRV greater than single stage operation. The findings of this research are expected to provide valuable insights into optimizing virus filtration in continuous bioprocessing.

A progressive overview of the mainstream additive manufacturing of ceramic components for industrial advancement

Additive manufacturing (AM), well-known as 3D printing, has revolutionized traditional manufacturing methods, particularly in the production of ceramic components. These techniques offer unique advantages and challenges in ceramic component production. Developing specific ceramic materials with AM improves the finished products' mechanical, thermal, and electrical characteristics. Integrating artificial intelligence and machine learning shows promise in improving process control and design optimization, spurring innovation and opening previously unimaginable possibilities, although problems such as material shrinkage, microstructural control, and surface polish remain crucial research areas. This review article presents a comprehensive overview of the advancements and applications of AM for ceramic pieces in the industrial sector. This study highlights the fundamental principles of AM, focusing on their difficulties and potential in ceramic fabrication. A comprehensive review of AM will deliver an understanding of its ability to alter industrial manufacturing and open the door to a new era of ceramic fabrication and applications.

Investigating Non-Newtonian Fluid Behavior in Hydrocyclones Via Computational Fluid Dynamics

Expert researchers examine complex patterns of pressure, viscosity, and velocity in a CFD study of viscoelastic food inside hydrocyclones to obtain a detailed grasp of particle behavior and fluid dynamics. Velocity profiles show how fluids and particles flow through the hydrocyclone in complex ways, while pressure distributions show where high and low pressure is found, regions that are critical for maximizing separation efficiency. Furthermore, the analysis of viscosity fluctuations clarifies the intricate relationship between fluid rheology and flow dynamics, providing information on how food's viscoelastic characteristics affect particle trajectories and separation efficiency. Utilizing this comprehensive examination, scientists hope to optimize the design and functioning parameters of the hydrocyclones, which will in turn improve the efficacy and efficiency of particle separation procedures in viscoelastic food solutions. This will ultimately lead to improvements in food processing technology and product quality. Researchers look into the impact of geometric elements on flow patterns and separation efficiency in addition to these characteristics, such as hydrocyclone size and inlet configurations. Additionally, they investigate how different operating parameters, such rotational speed and flow rate, affect how well the hydrocyclone handles viscoelastic food items. Through the integration of these complex analyses, researchers hope to create all encompassing models that can precisely forecast and optimize the behavior of viscoelastic food flows inside hydrocyclones, opening the door to improved process control and food sector product quality.

Transfer Dynamic Latent Variable Modeling for Quality Prediction of Multimode Processes.

Quality prediction is beneficial to intelligent inspection, advanced process control, operation optimization, and product quality improvements of complex industrial processes. Most of the existing work obeys the assumption that training samples and testing samples follow similar data distributions. The assumption is, however, not true for practical multimode processes with dynamics. In practice, traditional approaches mostly establish a prediction model using the samples from the principal operating mode (POM) with abundant samples. The model is inapplicable to other modes with a few samples. In view of this, this article will propose a novel dynamic latent variable (DLV)-based transfer learning approach, called transfer DLV regression (TDLVR), for quality prediction of multimode processes with dynamics. The proposed TDLVR can not only derive the dynamics between process variables and quality variables in the POM but also extract the co-dynamic variations among process variables between the POM and the new mode. This can effectively overcome data marginal distribution discrepancy and enrich the information of the new mode. To make full use of the available labeled samples from the new mode, an error compensation mechanism is incorporated into the established TDLVR, termed compensated TDLVR (CTDLVR), to adapt to the conditional distribution discrepancy. Empirical studies show the efficacy of the proposed TDLVR and CTDLVR methods in several case studies, including numerical simulation examples and two real-industrial process examples.

Numerical investigation of heat transfer and temperature distribution in a Microwave-Heated Heli-Flow reactor and experimental validation

Microwave-heated flow systems (MWFSs), offering improved process control, enhanced product yields, and the potential for scalable and sustainable manufacturing routes, have gained significant popularity in continuously manufacturing nanomaterials and their hybrids. Temperature control in a microwave zone can maintain fine control over the manufactured materials' properties; however, the current state-of-the-art designs lack monitoring of the temperature distribution throughout the flow reactor. Herein, a unique model of numerical estimations of temperature distribution in a microwave-heated system featuring a helical flow milli-reactor, Heli-Flow, is described and validated by experimentally acquired temperature profiles. The proposed numerical model accurately predicts the temperature profile along the Heli-Flow and the steady-state outlet temperature at the MW cavity's exit. The helical design of the reactor promotes homogeneous heating and eliminates orientation-related discrepancies. The maximum outlet temperatures achieved at different microwave powers and FRs align well with expected trends. The modeled temperatures closely match the experimental and analytical measurements, indicating the effectiveness of the simulation approach. This research contributes to understanding temperature distribution in MWFSs and offers insights for optimizing nanomaterial synthesis and other applications.

The effect of conditioning pH behaviour on spodumene flotation with fatty acids

ABSTRACT Spodumene - the primary lithium mineral - is often concentrated by flotation with fatty acid collectors. However, producing high-grade concentrates at high recoveries is challenging and a risk for future operations. Poor spodumene selectivity is largely driven by the sensitive properties and poor solubility of fatty acids and requires high-density conditioning for desired flotation performance. This study related pH behaviour and operating parameters during fatty acid conditioning to flotation performance. The best pH characteristics for >85% lithium recovery at >5.25% Li2O were a minimum pH 6.5–6.8, final pH 6.7–7.1, and a pH change rate of 0.07 ΔpH/min. Regression analysis predicted optimizated conditions of initial pH 8.2, 30 W/L power density, 8 min conditioning and 550 g/t collector. Follow-up testing confirmed a higher W/L improved grade, and recovery was maintained when combined with decreased residence time and pH. Better defining these relationships can improve process control n developing industrial spodumene flotation circuits.

Total ammonia aeration control (TAAC) theory – An innovative ammonia-based aeration controller

Abstract Water resource recovery facilities (WRRFs) need optimized and robust solutions to ensure efficient and reliable operation for this critical environmental service. Secondary treatment aeration control is a prime example as the activated sludge treatment process consumes the largest amount of energy for WRRFs, which require oxygen to biologically remove the ammonia content through nitrification. The selected control strategy will directly impact system efficiency and ability to maintain discharge permit compliance levels. The use of an ammonia-based aeration controller has two major benefits for these systems: (1) cost savings, through minimization of energy usage, and (2) enhanced performance from a steady effluent ammonia concentration. These benefits come from an increase in the system biological kinetics. The process control improvements result in a higher rate of total nitrogen removal, via simultaneous nitrification/denitrification, through delivery of the minimum instantaneous oxygen necessary over time. The thesis contained herein is a novel controller algorithm, which leverages the relationship between primary input and output variables of this complex treatment process. The approach provides continuous output stability and a substantial reduction of the overall system costs, through decreased wear of large-budget equipment and by requiring fewer algorithm input data sources than any other possible solution.

Fault diagnosis in semiconductor manufacturing processes using a CNN-based generative adversarial network1

In the current industry, quality inspection in semiconductor manufacturing is of immense significance. Significant achievements have been made in fault diagnosis in fabricated semiconductor wafer manufacturing due to the development of machine learning. Since real-time intermediate signals are non-linear and time-varying, the signals undergo various distortions due to changes in equipment, material, and process. This leads to a drastic change in information in intermediate signals. This paper presents a fault diagnosis model for semiconductor manufacturing processes using a generative adversarial network (GAN). The study aims to address the challenges associated with efficient and accurate fault identification in these complex processes. Our approach involves the extraction of relevant components, development of a paired generator model, and implementation of a deep convolutional neural network. Experimental evaluations were conducted using a comprehensive dataset and compared against six existing models. The results demonstrate the superiority of our proposed model, showcasing higher accuracy, specificity, and sensitivity across various shift tasks. This research contributes to the field by introducing a novel approach for fault diagnosis, paving the way for improved process control and product quality in semiconductor manufacturing. Future work will focus on further optimizing the model and extending its applicability to other manufacturing domains.

Statistical process control and PDCA for quality improvement in the mexican automotive industry

Despite the advancements witnessed in the realm of quality improvement, Statistical Process Control (SPC) perseveres as a universally embraced and extensively employed tool within manufacturing companies across the globe. This enduring popularity can be attributed to its adherence to the time-tested principles established by Shewhart, which have withstood the test of time since their inception in 1924. Over the years, SPC has consistently proven its efficacy in optimizing manufacturing processes, enhancing product quality, and fostering continuous improvement initiatives. Quality is an utmost necessity in the automotive industry, which ranks as the 3rd most significant industry in Mexico and the 5th globally in terms of volume and transactional activity. This research focuses on a successful real case study that demonstrates the enhancement of capability in a critical machining operation within an automotive plant located in Mexico, specifically dedicated to truck engine remanufacturing. This improvement was achieved through the analysis of x̅ and R control charts, followed by the implementation of the Plan-Do-Check-Act (PDCA) cycle. The results obtained were derived from a comparative analysis of data from two distinct time periods in the machining process of bushing diameters. This analysis revealed a significant increase in process capacity, with a 13.08% improvement in the Process Capability Index (Cpk) and a 17.2% enhancement in the Process Performance Index (Ppk). The decrease in expected out-of-specification parts showed a remarkable improvement of 72.7%. Additionally, there was an enhancement in process variability, with a notable 8.33% reduction in the mean range. The significance of this research lies in its empirical identification of gaps and opportunities within the realms of quality assurance and quality control, utilizing methodologies for quality improvement. By uncovering these gaps and opportunities, this study provides valuable insights that can guide future efforts to enhance quality practices and drive continuous improvement in the field.

Application of neural networks to predict the quality of iron ore concentrate based on flotation data

This paper presents a study aimed at developing and testing a neural network model for predicting the percentage of silica in iron ore concentrate obtained during flotation. The problem of precise control of the silica content is critical for the mining industry, since the quality of the final product and, accordingly, its market value depend on it. During the study, data was collected from the flotation plant, their preliminary processing was carried out, including standardization and elimination of missing values. The developed neural network model included two hidden layers and was trained on real data. The evaluation of the model quality showed high results, which was confirmed by the metrics of mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2). Additionally, an analysis of the visualizations of the residuals and predicted values confirmed the accuracy and stability of the model. The results of the study demonstrate that the proposed model can be effectively used in production conditions to improve process control and improve product quality in the mining industry.

Enhancing Operations Quality Improvement through Advanced Data Analytics

This study focuses on the application of data analytics algorithms for real-time monitoring in additive manufacturing processes. The utilization of advanced analytics plays a pivotal role in enhancing the quality control and efficiency of these manufacturing techniques. The research explores how data-driven insights can be harnessed to identify, analyze, and rectify deviations in the manufacturing process, ensuring optimal performance and product quality. By integrating sophisticated monitoring algorithms, the study aims to create a robust framework that continuously analyzes various parameters during additive manufacturing. This includes monitoring factors such as temperature, pressure, and material properties in real-time. The collected data is processed through advanced analytics tools to detect anomalies or deviations from the expected standards. The implementation of machine learning algorithms further facilitates predictive maintenance and proactive adjustments, contributing to the overall reliability and effectiveness of additive manufacturing processes. The outcomes of this research hold significant implications for industries relying on additive manufacturing technologies, providing a foundation for improved process control and product quality. The study contributes to the growing field of Industry 4.0 by showcasing the integration of data analytics as a key enabler for efficient and reliable additive manufacturing.

Fast spatial laser beam modulation for improved process control in Laser Powder Bed Fusion

We report on the implementation of a high frequency beam oscillation (wobbling) strategy for improving process control during laser powder bed fusion of single weld tracks on Inconel 625. Oscillation frequencies ranging from ~ 600 Hz to 7000 Hz, and different oscillation trajectories (circular, parallel or perpendicular to the direction of scanning) were explored. Highspeed imaging was used to elucidate the dynamics of the melt pool induced by the wobble beams, along with in situ absorptivity measurements to substantiate our hypothesis that the dynamic nature of wobble beams reduces absorptive losses due to laser-vapor interactions and results in improved coupling at the melt pool. Operando X-ray radiography was carried out to visualize sub-surface melt flow dynamics, correlate to spatter mechanisms and optimize the window for improving process stability. Our observations indicate that wobble beams increase the aspect ratio of the melt pool by up to 4×, depending on the oscillation frequency and energy input. Highspeed imaging and X-ray radiography reveal an optimized process parameter window for reducing spatter, improving absorptivity and creating a stable melt pool at high (several kHz) oscillation frequencies.

Pulse/Pulse Reverse Electrolysis for Electrochemical Conversion Technologies

Faraday Technology Inc. is a research, development and electrochemical engineering firm developing electrochemical innovations based has pulse/pulse reverse electrolytic principles. [1,2] With that founding basis Faraday has been able to bridge the gap between academic understanding and small-scale demonstrations to commercialization of practical components for over 32 years of operation.This presentation will discuss techniques where pulse/pulse reverse operation enabled improved production rates, reduced energy demand, eliminated biofouling, and enabled recycling of complex structures. We will highlight some of the key factors that determine the usefulness and successfulness of using pulse/pulse reverse principles to improve process control of the electrochemical operations. For instances, we will discuss electrochemical destruction of PFAS, electrocatalytic operations, and carbon fiber recovery from composites.

Development and evaluation of a vision driven sensor for estimating fuel feeding rates in combustion and gasification processes

A machine vision driven sensor for estimating the instantaneous feeding rate of pelletized fuels was developed and tested experimentally in combustion and gasification processes. The feeding rate was determined from images of the pellets sliding on a transfer chute into the reactor. From the images the apparent area and velocity of the pellets were extracted. Area was determined by a segmentation model created using a machine learning framework and velocities by image registration of two subsequent images. The measured weight of the pelletized fuel passed through the feeding system was in good agreement with the weight estimated by the sensor. The observed variations in the fuel feeding correlated with the variations in the gaseous species concentrations measured in the reactor core and in the exhaust. Since the developed sensor measures the ingoing fuel feeding rate prior to the reactor, its signal could therefore help improve process control.