Process Control Research Articles

Removal of Residual Additive Enabling Perfect Crystallization of Photovoltaic Perovskites

Achieving high‐efficiency perovskite solar cells (PSCs) hinges on the precise control of the perovskite film crystallization process, often improved by the inclusion of additives. While dimethyl sulfoxide (DMSO) is traditionally used to manage this process, its removal from the films is problematic. In this work, methyl phenyl sulfoxide (MPSO) was employed instead of DMSO to slow the crystallization rate, as MPSO is more easily removed from the perovskite structure. The electron delocalization associated with the benzene ring in MPSO decreases the electron density around the oxygen atom in the sulfoxide group, thus reducing its interaction with PbI2. This strategy not only sustains the formation of a crystallization‐slowing intermediate phase but also simplifies the elimination of the additive. Consequently, the optimized PSCs achieved a leading power conversion efficiency (PCE) of 25.95% along with exceptional stability. This strategy provides a novel method for fine‐tuning perovskite crystallization to enhance the overall performance of photovoltaic devices.

Robustness of Moving Average-Exponential Weighted Moving Average Control Chart with the Light-Tailed Distribution

Control charts are the most significant statistical process control tools for ensuring industrial process reliability and efficiency. This research uses moving average – exponentially weighted moving average (MA-EWMA) control charts to investigate the dispersion characteristics. The control chart performance compares the arithmetic mean of run length (AMRL) and standard deviation of run length (SDRL) profiles of MA-EWMA and synthetic charts. Student's t-distributions are compared for dispersion processes. Finally, we present a case to show how essential control charts are in practice.

Process Control of Multistep Surface Functionalization on Hydroxyethyl Starch Nanocapsules Determines the Reproducibility of the Biological Efficacy.

Nanocarrier synthesis is highly process-dependent, leading to potential batch-to-batch variability if it is not controlled at each step. This variability affects the reproducibility of subsequent biomodification, resulting in unpredictable biological effects, particularly for bioactive molecules such as interleukin-2 (IL-2). Inconsistent conjugation can lead to variable treatment outcomes and severe side effects. Therefore, precise control of each synthesis step is critical for ensuring a consistent quality and biological performance. Our study demonstrates that dividing nanocarrier synthesis into smaller, controlled steps improves reproducibility. Using this method, we achieved highly reproducible, concentration-dependent growth of CTLL-2 cells with hydroxyethyl starch (HES) nanocapsules functionalized with defined amounts of IL-2. We believe that such detailed, stepwise control in nanocarrier synthesis enhances batch consistency, improving the clinical applicability of the drug delivery systems.

Modeling the effect of land-use change on runoff water quality in the urban watershed: a case study of Settat city, Morocco

ABSTRACT The purpose of this study is to create a model of the quantity and quality of runoff from the urban watershed. The modeling was performed using a personal computer stormwater management model (PCSWMM) tool and was based on washoff and buildup for total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP) measured in the field. In addition, low impact development (LID) was used under four different scenarios to improve runoff control of the urban hydrologic cycle and reduce pollutant concentrations. Model performance was analyzed using the R2 and NSE goodness-of-fit indices. Thus, three typical rainfall events, as well as field measurement parameters (TSS, TN, and TP), were used for the model's calibration and validation. The base scenario quantified the runoff volume and pollutant load without any intervention in the runoff process control. Scenarios S2 (BR: bioretention) and S3 (PP: permeable pavement) contributed, separately, in reducing runoff and pollutant load but with relatively small percentages. In addition, the results show that scenario S4 (BR and PP) has the most significant impact on flow reduction, by about 10% compared with the base scenario, as well as on the pollutant load reduction, by about 31, 29, and 40% for the parameters TSS, TN, and TP, respectively.

Evaluating Chlorine Sanitization at Practical Concentrations for Controlling Listeria monocytogenes and Salmonella on Fresh Peaches.

Recent foodborne outbreaks and recalls involving Listeria monocytogenes and Salmonella-contaminated peaches have caused significant economic losses to the peach industry. This study evaluated the effectiveness of chlorine, a commonly used sanitizer in the fresh produce industry, against L. monocytogenes and Salmonella and its ability to control cross-contamination in fresh peaches. Peaches inoculated with L. monocytogenes or Salmonella (~6 log10 CFU/peach) were treated with 50-150 mg/L of free chlorine (FC, pH6.8) 24 h post-inoculation. The results revealed that chlorine had similar efficacy against L. monocytogenes and Salmonella on peaches (p > 0.05). A 30 s treatment at 50, 100, and 150 mg/L FC resulted in dose-dependent reductions (p < 0.05), achieving reductions of 0.88-0.92, 1.54-1.61, and 1.73-1.79 log10 CFU/peach, respectively. Extending the contact time to 2 min slightly but significantly enhanced the chlorine efficacy (p < 0.05). Additionally, a 30 s to 2 min exposure to chlorine with 50-150 mg/L FC resulted in a 1.05-1.43 log10 CFU/peach reduction in yeasts and molds. Tap water exposure led to substantial cross-contamination between inoculated and uninoculated fruits and processed water, with Salmonella exhibiting higher transfer rates than L. monocytogenes. The application of chlorine mitigated the cross-contamination of both pathogens but did not entirely prevent it. These findings offer valuable insights for the peach and other stone fruit industries to verify process controls.

Scenario Design for the Evaluation of Human–Machine Interaction in Modular Process Plants

AbstractOperators in modular process plants face new demands due to features like increasing automation and digitalization combined with an architecture allowing for reconfigurations in short time spans. However, operators must be able to master process control tasks at all times. For research purposes on topics like supporting operators in their performance in process control of modular process plants, this article aims at developing scenarios of such plants. Based on three conceptual levels describing the relation of chemical processes and their modularized realization and a neutralization process as use case, four scenarios were developed. Having the common task to increase the throughput, the scenarios differ in terms of complexity and constraints that need to be considered for process control.

AI-Driven Optimization of Roll Forming Parameters for Defect-Free Manufacturing in Advanced High-Strength Steels

This paper presents an innovative and novel AI-driven approach to optimizing the roll forming process for advanced high-strength steels (AHSS), which are prone to defects like wrinkling, springback, and cracking due to their high strength and low formability. Traditional trial-and-error methods for parameter selection are time-consuming and inefficient. The proposed framework integrates machine learning models, such as artificial neural networks (ANN), support vector machines (SVM), and random forests, with genetic algorithms for multi-objective optimization. The novelty of this paper lies in the real-time monitoring and adjustment capabilities, allowing for dynamic process control that adapts to variations in material properties. This approach not only predicts and reduces defects by 30%, but also decreases parameter selection time by 50%, significantly improving production efficiency. A case study on an AHSS automotive component demonstrates the effectiveness of this system in achieving defect-free manufacturing, reducing equipment costs, and enhancing throughput. The AI-based methodology represents a paradigm shift in roll forming, offering a more flexible, scalable, and cost-efficient solution for modern manufacturing. Future work will explore integration with finite element analysis (FEA) and reinforcement learning for further improvements in process control.

Multiscale Models of CVD Process: Review and Prospective.

Chemical vapor deposition (CVD) is a crucial technique in the preparation of high-quality thin films and coatings, and is widely used in various industries including semiconductor, optics, and nuclear fuel, due to its operation simplicity and high growth rate. The complexity of the CVD process arises from numerous parameters, such as precursor chemistry, temperature, pressure, gas flow dynamics, and substrate characteristics. These multiscale parameters make the optimization of the CVD process a challenging task. Numerical simulations are widely used to model and analyze the CVD complex systems, and can be divided into nanoscale, mesoscale, and macroscale methods. Numerical simulation is aimed at optimizing the CVD process, but the inter-scale parameters still need to be extracted in modeling processes. However, multiscale coupling modeling becomes a powerful method to solve these challenges by providing a comprehensive framework that integrates phenomena occurring at different scales. This review presents an overview of the CVD process, the common critical parameters, and an in-depth analysis of CVD models in different scales. Then various multiscale models are discussed. This review highlights the models in different scales, integrates these models into multiscale frameworks, discusses typical multiscale coupling CVD models applied in practice, and summarizes the parameters that can transfer information between different scales. Finally, the schemes of multiscale coupling are given as a prospective view. By offering a comprehensive view of the current state of multiscale CVD models, this review aims to bridge the gap between theory and practice, and provide insights that could lead to a more efficient and precise control of the CVD process.

Deep Learning and Statistical Process Control Approach to Improve Quality in Immigration Services

Technological advancements have profoundly influenced various sectors, including tourism, by simplifying international travel and increasing the demand for passports. In this context, public feedback on the Surabaya Immigration Office's services, gathered from Google Maps reviews, presents an invaluable dataset for analysis. This study introduces a novel approach to quality management systems in immigration services by applying the Convolutional Long Short-Term Memory (Co-LSTM) for sentiment analysis of public reviews and p-attribute control charts for statistical process control. Focusing on the Surabaya Immigration Office, we analyze public feedback from Google Maps to assess service quality. Our methodology preprocesses and classifies opinion data into sentiment classes, employing Co-LSTM for its superior accuracy over traditional models. The sentiment analysis reveals a predominance of positive over negative reviews, with classification accuracy demonstrating Area Under Curve (AUC) values of 98.61% for training and 85.66% for testing data. Furthermore, the p-attribute control charts are utilized to monitor service defects, identifying areas of uncontrolled variability and suggesting the necessity for service improvement interventions. The study uncovers that the primary public grievance relates to the perceived lack of friendliness and politeness from office staff. By integrating sentiment analysis with statistical process control, this research offers a comprehensive approach for immigration service providers to enhance service quality, respond proactively to public sentiment, and ensure customer satisfaction.

Coffee and Cases (C&C) - Enhancing Knowledge Creation and Sharing for Organizational Learning From Clinical Debriefs in a Helicopter Emergency Medical Service: A Quality Improvement Study

Objectives The objective of this quality improvement (QI) study was to improve organizational learning from clinical debriefs known as “Coffee and Cases” (C&C) in a helicopter emergency medical service (HEMS) by increasing weekly learning summaries (LS) and documented learning points (DLP) as well as the dissemination thereof by at least 50% from baseline. Methods The problem analysis for sub-optimal organizational learning from C&C identified several factors, including lack of responsibility, poor documentation quality, and limited sharing of learning points. Using the Model for Improvement (MFI), interventions enhanced the learning environment, and improved documentation and dissemination. Changes included dedicated computers, introducing standardized processes, a newsletter, and a searchable DLP database. Statistical process control (SPC) charts were used to assess the effectiveness of interventions. Results Prior to interventions (August 2022 to January 2023), baseline mean counts of weekly DLPs, LSs, DLPs internally disseminated, and DLPs openly disseminated were 3.18, 2.67, 3.18, and 2.96, respectively. Plan-Do-Study-Act (PDSA) cycles included declaring C&C as a non-clinical portfolio, redesigning documentation processes, initiating monthly newsletters, developing a DLP repository, and designing a C&C visual brand. Signals of special cause variation showed improvements, with mean counts increasing. Weekly DLPs increased to 14.92, LSs to 5.2, internally disseminated C&C Snippets to 11.88, and openly disseminated C&C Snippets to 8.64. Conclusions Recognizing barriers to effective knowledge creation and sharing, our QI study aimed to increase weekly DLPs and LSs by 50% from baseline. It aligned with the relationship between knowledge management and organizational learning, emphasizing the importance of utilizing knowledge for improved performance. Our interventions enhanced the learning environment, ensured robust capturing of learning points and effective communication thereof, ultimately contributing toward improving organizational dissemination of learning from clinical debriefs. Our QI study demonstrates how enhanced knowledge creation and sharing can widen the benefits of learning from clinical team debriefs.

Conductive metal-organic framework for ppb-concentration and highly selective SAW hydrogen sulfide sensing at room temperature

Hydrogen sulfide (H2S) sensing is utilized for monitoring the concentration in environment protection and industrial process control, of which high selectivity and ppb detection limit are required. Here, a ppb-concentration and highly selective surface acoustic wave (SAW) H2S sensing has been developed with the Cu3(HHTP)2 MOFs conductive metal-organic frameworks (MOFs), which have been synthesized by connecting Cu2+ with the organic ligands via hydrothermal process. Typically, as-prepared Cu3(HHTP)2 MOFs take on shapes including nanoparticles and nanorods. Beneficially, the SAW H2S sensing presents excellent selectivity to H2S and enables to detection of H2S as low as 6 ppb at room temperature (∼ 26 °C). Remarkably, the SAW sensor prototypes exhibit high sensitivity (0.02 mV/ppb) to 50–2000 ppb H2S, fast response (T90: 281 s) and 46 day-long stability at room temperature. Theoretically, such excellent H2S SAW sensing performance might be attributed to the interaction between Cu ions and H2S molecules and the strong acoustoelectric effect of SAW. Practically, our ppb-concentration and highly selective H2S SAW sensing have the potential in the real-time detection of H2S.

Pure Shift NMR in Continuous Flow.

Flow NMR is an expanding analytical approach with applications that include in-line analysis for process control and optimisation, and real-time reaction monitoring. The samples monitored by flow NMR are typically mixtures that yield complex 1D1H spectra. "Pure shift" NMR is a powerful approach to simplifying1H NMR spectra, but its standard implementation is not compatible with continuous flow because of interference between sample motion and the position-dependent spin manipulations that are required in pure shift NMR. Here we show that pure shift NMR spectra can be successfully collected for continuously flowing samples, thanks to an adapted acquisition scheme, robust solvent suppression, and a velocity-compensation strategy. The resulting method is used to collect ultrahigh resolution reaction monitoring data. Pure shift NMR spectra are expected to benefit many applications of flow NMR.

ნიადაგით გამოწვეული ელექტროქიმიური კოროზია ტენიან, ნაკლებ მჟავიან, ნეიტრალურ და ტუტოვან გარემოში

The article analyzes the anodic, transient and cathodic processes of a galvanic cell. Electrochemical corrosion caused by soil in a moist, less acidic, neutral and alkaline environment is discussed. The scheme of corrosion of underground pipelines under various conditions of soil aeration is given. The cases of corrosion process control for various soil corrosion conditions are considered: soil corrosion with prevailing cathodic control; corrosion in loose, dry soils (anodic control); corrosion over long stretches (prevailing ohmic control).

Bayesian control chart using variable sample size with engineering applications

In this study, we suggested an innovative approach by introducing an Adaptive Exponential Weighted Moving Average (AEWMA) control chart utilizing Variable Sample Size (VSS) under Bayesian methodology. The proposed methodology utilized an integer linear function to dynamically adjust sample sizes according to the AEWMA statistic. Another appealing feature of our adaptive framework is the integration of the smoothing constant of an EWMA chart, which enhances monitoring responsiveness. We reveal the superiority of our recommended control chart by extensive simulations to existing Bayesian EWMA and Bayesian AEWMA control charts using Fixed sample size (FSS). The offered Bayesian VAEWMA control chart is more sensitive to detection improvement, a decrease in the false alarm rate, and overall more effective than the existing methods. These findings provide additional justification for the basic notion that process control statistical tools needed to be dynamic, as the manufacturing process itself was dynamic. The results suggest the importance of introducing adaptive SPC methods in dynamic manufacturing environments. A real data application is performed to evaluate the validity and optimal performance of our recommended chart."Please check article if captured correctly."="Dear Editor we have checked and found corrrect."As per standard instruction, city is required for affiliations; however, this information is missing in affiliations [1, 5]. Please check if the provided city is correct and amend if necessary."Dear Editor we have checked and found correct. thanks youPlease check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary."Dear Editor we have checked and found correct. thank you"

Biofuser: a multi-source data fusion platform for fusing the data of fermentation process devices.

In the past decade, the progress of traditional bioprocess optimization technique has lagged far behind the rapid development of synthetic biology, which has hindered the industrialization process of synthetic biology achievements. Recently, more and more advanced equipment and sensors have been applied for bioprocess online inspection to improve the understanding and optimization efficiency of the process. This has resulted in large amounts of process data from various sources with different communication protocols and data formats, requiring the development of techniques for integration and fusion of these heterogeneous data. Here we describe a multi-source fusion platform (Biofuser) that is designed to collect and process multi-source heterogeneous data. Biofuser integrates various data to a unique format that facilitates data visualization, further analysis, model construction, and automatic process control. Moreover, Biofuser also provides additional APIs that support machine learning or deep learning using the integrated data. We illustrate the application of Biofuser with a case study on riboflavin fermentation process development, demonstrating its ability in device faulty identification, critical process factor identification, and bioprocess prediction. Biofuser has the potential to significantly enhance the development of fermentation optimization techniques and is expected to become an important infrastructure for artificial intelligent integration into bioprocess optimization, thereby promoting the development of intelligent biomanufacturing.

The design and control of heat-integrated EDWC processes for the separation of THF/IPA/water

Extractive dividing wall columns (EDWC) and heat integration are effective process intensification strategies for separating azeotropes. In this study, the steady-state and dynamic control for the separation of tetrahydrofuran/isopropanol/water using a combination of EDWC and heat integration with intermediate reboilers are systematically investigated. The feasibility of the extractive distillation process is evaluated by analyzing the thermodynamic characteristics of the mixture through phase diagrams, and the three-column extractive distillation (TCED) process is established as the basic process. To enhance the energy and economic efficiencies, as well as environmental sustainability, three improved processes are proposed. Compared with TCED, the optimal process (E-EDWC-HI2) reduces the total annual costs, energy consumption, and CO2 emissions by 22.35 %, 41.77 %, and 26.28 %, respectively. In addition, a dynamic control structure is proposed for the E-EDWC-HI2, which exhibits robustness against disturbances in the feed flow rate and composition. This study provides guidance for the design and dynamic control of complex distillation processes.

Analysis of disease burden and trends of noise-induced hearing loss in China from 1990 to 2019

Objective: To analyze the disease burden of Noise-Induced Hearing Loss (NIHL) in China from 1990 to 2019, forecast the disease burden of NIHL from 2020 to 2030, and provide data support for the prevention and control of NIHL. Methods: In July 2022, the disease burden data of NIHL in different age groups and genders in China during 1990-2019 were selected from the GBD database. The Jointpoint regression model was established to analyze the trend of the disease burden of NIHL in China. An age-period-cohort model was constructed to analyze the changing trend of NIHL in terms of age, period, and cohort, and a Bayesian age-period-cohort model was developed to predict the disease burden of NIHL in China from 2020 to 2030. Results: From 1990 to 2019, the disability adjusted life year (DALY) of China's NIHL increased from 1361600 to 2327700 years. The coarse rate of DALY increased from 115.03/100000 to 163.65/100000 (AAPC=1.23, P<0.001), and the normalization rate of DALY decreased from 127.67/100000 to 119.83/100000 (AAPC=-0.21, P<0.001). It is predicted that from 2020 to 2030, the DALYs of China's NIHL will increase from 2412900 in 2020 to 2655000 in 2030, and the DALY normalization rate will decrease from 241.29/100000 in 2020 to 125.71/100000 in 2030. Conclusion: The burden of noise-induced hearing loss (NIHL) in China is significant and should not be overlooked. To reduce this burden, we need to focus on strengthening source management, process control, personal protection, and comprehensive prevention and treatment through various methods.

Stress-Induced Evolution of the Nucleolus: The Role of Ribosomal Intergenic Spacer (rIGS) Transcripts.

It became clear more than 20 years ago that the nucleolus not only performs the most important biological function of assembling ribonucleic particles but is also a key controller of many cellular processes, participating in cellular adaptation to stress. The nucleolus's multifunctionality is due to the peculiarities of its biogenesis. The nucleolus is a multilayered biomolecular condensate formed by liquid-liquid phase separation (LLPS). In this review, we focus on changes occurring in the nucleolus during cellular stress, molecular features of the nucleolar response to abnormal and stressful conditions, and the role of long non-coding RNAs transcribed from the intergenic spacer region of ribosomal DNA (IGS rDNA).

Social Genomic Determinants of Health: Understanding the Molecular Pathways by Which Neighborhood Disadvantage Affects Cancer Outcomes.

Neighborhoods represent complex environments with unique social, cultural, physical, and economic attributes that have major impacts on disparities in health, disease, and survival. Neighborhood disadvantage is associated with shorter breast cancer recurrence-free survival (RFS) independent of individual-level (race, ethnicity, socioeconomic status, insurance, tumor characteristics) and health system-level determinants of health (receipt of guideline-concordant treatment). This persistent disparity in RFS suggests unaccounted mechanisms such as more aggressive tumor biology among women living in disadvantaged neighborhoods compared with advantaged neighborhoods. The objective of this article was to provide a clear framework and biological mechanistic explanation for how neighborhood disadvantage affects cancer survival. Development of a translational epidemiological framework that takes a translational disparities approach to study cancer outcome disparities through the lens of social genomics and social epigenomics. The social genomic determinants of health, defined as the physiological gene regulatory pathways (ie, neural/endocrine control of gene expression and epigenetic processes) through which contextual factors, particularly one's neighborhood, can affect activity of the cancer genome and the surrounding tumor microenvironment to alter disease progression and treatment outcomes. We propose a novel, multilevel determinants of health model that takes a translational epidemiological approach to evaluate the interplay between political, health system, social, psychosocial, individual, and social genomic determinants of health to understand social disparities in oncologic outcomes. In doing so, we provide a concrete biological pathway through which the effects of social processes and social epidemiology come to affect the basic biology of cancer and ultimately clinical outcomes and survival.

Intelligente Anlaufsteuerung für die Batteriezellenproduktion

Abstract The ramp-up of production lines as a result of changing product variants or production processes puts battery cell manufacturers under enormous quality and cost pressure due to high reject rates. A control system based on artificial intelligence methods can help to speed up the start-up processes. The InTeAn research project developed a procedure for developing a process control system to reduce ramp-up times and costs in battery cell production.