Solution For Quality Control Research Articles

Deep Learning Solution for Quality Control in a Die Casting Process

Industry 4.0 aims for a digital transformation of manufacturing and production systems, producing what is known as smart factories, where information coming from Cyber-Physical Systems (core elements in Industry 4.0) will be used in all the manufacturing stages to improve productivity. Cyber-physical systems through their control and sensor systems, provide a global view of the process, and generate large amounts of data that can be used for instance to produce datadriven models of the processes. However, having data is not enough, we must be able to store, visualize and analyze them, and to integrate induced knowledge in the whole production process. In this work, we present a solution to automate the quality control process of manufactured parts through image analysis. In particular, we present a Deep Learning solution to detect defects in manufactured parts from thermographic images of a die casting machine at an aluminum foundry.

Human Pluripotent Stem Cell Colony Migration Is Related to Culture Environment and Morphological Phenotype

Human pluripotent stem cells (hPSCs) are an important tool in the field of regenerative medicine due to their ability to differentiate towards all tissues of the adult organism. An important task in the study of hPSCs is to understand the factors that influence the maintenance of pluripotent and clonal characteristics of colonies represented by their morphological phenotype. Such factors include the ability of colonies to migrate during growth. In this work, we measured and analyzed the migration trajectories of hPSC colonies obtained from bright-field images of three cell lines, including induced hPSC lines AD3 and HPCASRi002-A (CaSR) and human embryonic stem cell line H9. To represent the pluripotent status, the colonies were visually phenotyped into two classes having a “good” or “bad” morphological phenotype. As for the migration characteristics, we calculated the colony speed and distance traveled (mobility measures), meandering index (motion persistence measures), outreach ratio (trajectory tortuosity characteristic), as well as the velocity autocorrelation function. The analysis revealed that the discrimination of phenotypes by the migration characteristics depended on both the cell line and growth environment. In particular, when the mTESR1/Matrigel culture environment was used, “good” AD3 colonies demonstrated a higher average migration speed than the “bad” ones. The reverse relationship between average speeds of “good” and “bad” colonies was found for the H9 line. The CaSR cell line did not show significant differences in the migration speed between the “good” and “bad” phenotypes. We investigated the type of motion exhibited by the colonies by applying two diffusion models to the mean squared displacement dynamics, one model corresponding to normal and the other to anomalous diffusion. The type of diffusion and diffusion parameter values resulting from the model fitting to data demonstrated a similar cell line, environment, and phenotype dependency. Colonies mainly showed a superdiffusive behavior for the mTESR1/Matrigel culture conditions, characterized by longer migration steps compared to the normal random walk. The specific properties of migration features and the patterns of their variation demonstrated in our work can be useful for the development and/or improvement of automated solutions for quality control of hPSCs.

QC Constellation: a cutting-edge solution for risk and patient-based quality control in clinical laboratories.

Clinical laboratories face limitations in implementing advanced quality control (QC) methods with existing systems. This study aimed to develop a web-based application to addresses this gap, and improve QC practices. QC Constellation, a web application built using Python 3.11, integrates various statistical QC modules. These include Levey-Jennings charts with Westgard rules, sigma-metric calculations, exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) charts, and method decision charts. Additionally, it offers a risk-based QC section and a patient-based QC module aligning with modern QC practices. The codes and the web application links for QC Constellation were shared at https://github.com/hikmetc/QC_Constellation, and http://qcconstellation.com, respectively. Using synthetic data, QC Constellation demonstrated effective implementation of Levey-Jennings charts with user-friendly features like checkboxes for Westgard rules and customizable moving averages graphs. Sigma-metric calculations for hypothetical performance values of serum total cholesterol were successfully performed using allowable total error and maximum allowable measurement uncertainty goals, and displayed on method decision charts. The utility of the risk-based QC module was exemplified by assessing QC plans for serum total cholesterol, showcasing the application's capability in calculating risk-based QC parameters including maximum unreliable final patient results, risk management index, and maximum run size andoffering risk-based QC recommendations. Similarly, the patient-based QC and optimization modules were demonstrated using simulated sodium results. In conclusion, QC Constellation emerges as a pivotal tool for laboratory professionals, streamlining the management of quality control and analytical performance monitoring, while enhancing patient safety through optimized QC processes.

Modified rat pup cerebrospinal fluid collection method

BackgroundCerebrospinal fluid (CSF) reflects biochemical changes in the brain due to its direct contact with brain interstitial fluid, making it a valuable tool for diagnosing and monitoring disease progression and therapeutic effectiveness in clinical practice. However, collecting CSF in animal studies, particularly from small animals like rat pups or mice, poses significant challenges. New methodAfter attempting various reported protocols, we encountered difficulties in consistently obtaining sufficient CSF from rat pups (P7-P42). Consequently, we modified these methods and developed a protocol with controllable and precise parameters for each step, enhancing reproducibility across different researchers. ResultsThe newly developed method enables rapid, single-operator, and reproducible CSF extraction while ensuring high-quality (the absorbance of the “quality control solution” at 415 nm < 0.05 AU, an indicator of oxyhemoglobin contamination for the collected CSF samples) and high-yield samples (33 ± 2.128 μL for P7 pups, 34.10 ± 2.747 μL for P8 pups, 36.67 ± 3.997 μL for P9 pups, 36.90 ± 1.946 μL for P10 pups, 35.11 ± 3.285 μL for P10 hypoxic-ischemic brain damage (HIBD) pups and 51.70 ± 5.256 μL for P42 pups, respectively). Comparison with existing methodsUnlike existing methods of CSF extraction in rat pups, our protocol has reproducible capillary pipette pulling parameters, controllable CSF quality indexes, and can be operated by a single person with high yield in a short time. ConclusionsThis paper provides a step-by-step comparison and discussion of the CSF collection process, establishing a method that enables a single operator to collect CSF rapidly, consistently, sufficiently, and with controlled quality.

Quality control of elbow joint radiography using a YOLOv8-based artificial intelligence technology

BackgroundTo explore an artificial intelligence (AI) technology employing YOLOv8 for quality control (QC) on elbow joint radiographs.MethodsFrom January 2022 to August 2023, 2643 consecutive elbow radiographs were collected and randomly assigned to the training, validation, and test sets in a 6:2:2 ratio. We proposed the anteroposterior (AP) and lateral (LAT) models to identify target detection boxes and key points on elbow radiographs using YOLOv8. These identifications were transformed into five quality standards: (1) AP elbow positioning coordinates (XA and YA); (2) olecranon fossa positioning distance parameters (S17 and S27); (3) key points of joint space (Y3, Y4, Y5 and Y6); (4) LAT elbow positioning coordinates (X2 and Y2); and (5) flexion angle. Models were trained and validated using 2,120 radiographs. A test set of 523 radiographs was used for assessing the agreement between AI and physician and to evaluate clinical efficiency of models.ResultsThe AP and LAT models demonstrated high precision, recall, and mean average precision for identifying boxes and points. AI and physicians showed high intraclass correlation coefficient (ICC) in evaluating: AP coordinates XA (0.987) and YA (0.991); olecranon fossa parameters S17 (0.964) and S27 (0.951); key points Y3 (0.998), Y4 (0.997), Y5 (0.998) and Y6 (0.959); LAT coordinates X2 (0.994) and Y2 (0.986); and flexion angle (0.865). Compared to manual methods, using AI, QC time was reduced by 43% for AP images and 45% for LAT images (p < 0.001).ConclusionYOLOv8-based AI technology is feasible for QC of elbow radiography with high performance.Relevance statementThis study proposed and validated a YOLOv8-based AI model for automated quality control in elbow radiography, obtaining high efficiency in clinical settings.Key PointsQC of elbow joint radiography is important for detecting diseases.Models based on YOLOv8 are proposed and perform well in image QC.Models offer objective and efficient solutions for QC in elbow joint radiographs.Graphical

Efficient fabric anomaly detection: A transfer learning framework with expedited training times

In industrial quality control, anomaly detection plays a critical role in identifying defective products. However, because of the rarity and time-consuming nature of defect collection, training models often rely solely on defect-free samples. This necessitates the use of unsupervised anomaly-detection techniques trained exclusively on defect-free data. Alternatively, defect data can be synthesized to augment the dataset with defective samples. In the textile industry, expeditious model training is crucial to ensure a smooth production flow. Unfortunately, most unsupervised methods require extensive training time. This paper proposes a novel transfer learning approach designed to achieve training times in seconds while effectively adapting the model to the target domain of fabric anomaly detection. The key contributions of our method include significantly reduced training times, up to 10 times faster than current state-of-the-art methods, and comparable performance in anomaly detection, achieving results on par with state-of-the-art approaches on benchmark datasets (MVTEC Anomaly Detection, TILDA, AITEX and DAGM). Additionally, our approach improves inference times, ensuring expedited and efficient anomaly detection during production. The proposed method offers a practical and efficient solution for real-time industrial quality control.

A Deep Learning Approach to Distance Map Generation Applied to Automatic Fiber Diameter Computation from Digital Micrographs.

Precise measurement of fiber diameter in animal and synthetic textiles is crucial for quality assessment and pricing; however, traditional methods often struggle with accuracy, particularly when fibers are densely packed or overlapping. Current computer vision techniques, while useful, have limitations in addressing these challenges. This paper introduces a novel deep-learning-based method to automatically generate distance maps of fiber micrographs, enabling more accurate fiber segmentation and diameter calculation. Our approach utilizes a modified U-Net architecture, trained on both real and simulated micrographs, to regress distance maps. This allows for the effective separation of individual fibers, even in complex scenarios. The model achieves a mean absolute error (MAE) of 0.1094 and a mean square error (MSE) of 0.0711, demonstrating its effectiveness in accurately measuring fiber diameters. This research highlights the potential of deep learning to revolutionize fiber analysis in the textile industry, offering a more precise and automated solution for quality control and pricing.

Automated Quality Control Solution for Radiographic Imaging of Lung Diseases.

Background/Objectives: Radiography is an essential and low-cost diagnostic method in pulmonary medicine that is used for the early detection and monitoring of lung diseases. An adequate and consistent image quality (IQ) is crucial to ensure accurate diagnosis and effective patient management. This pilot study evaluates the feasibility and effectiveness of the International Atomic Energy Agency (IAEA)'s remote and automated quality control (QC) methodology, which has been tested in multiple imaging centers. Methods: The data, collected between April and December 2022, included 47 longitudinal data sets from 22 digital radiographic units. Participants submitted metadata on the radiography setup, exposure parameters, and imaging modes. The database comprised 968 exposures, each representing multiple image quality parameters and metadata of image acquisition parameters. Python scripts were developed to collate, analyze, and visualize image quality data. Results: The pilot survey identified several critical issues affecting the future implementation of the IAEA method, as follows: (1) difficulty in accessing raw images due to manufacturer restrictions, (2) variability in IQ parameters even among identical X-ray systems and image acquisitions, (3) inconsistencies in phantom construction affecting IQ values, (4) vendor-dependent DICOM tag reporting, and (5) large variability in SNR values compared to other IQ metrics, making SNR less reliable for image quality assessment. Conclusions: Cross-comparisons among radiography systems must be taken with cautious because of the dependence on phantom construction and acquisition mode variations. Awareness of these factors will generate reliable and standardized quality control programs, which are crucial for accurate and fair evaluations, especially in high-frequency chest imaging.

Deep Learning-Based Defects Detection in Keyhole TIG Welding with Enhanced Vision.

Keyhole tungsten inert gas (keyhole TIG) welding is renowned for its advanced efficiency, necessitating a real-time defect detection method that integrates deep learning and enhanced vision techniques. This study employs a multi-layer deep neural network trained on an extensive welding image dataset. Neural networks can capture complex nonlinear relationships through multi-layer transformations without manual feature selection. Conversely, the nonlinear modeling ability of support vector machines (SVM) is limited by manually selected kernel functions and parameters, resulting in poor performance for recognizing burn-through and good welds images. SVMs handle only lower-level features such as porosity and excel only in detecting simple edges and shapes. However, neural networks excel in processing deep feature maps of "molten pools" and can encode deep defects that are often confused in keyhole TIG. Applying a four-class classification task to weld pool images, the neural network adeptly distinguishes various weld states, including good welds, burn-through, partial penetration, and undercut. Experimental results demonstrate high accuracy and real-time performance. A comprehensive dataset, prepared through meticulous preprocessing and augmentation, ensures reliable results. This method provides an effective solution for quality control and defect prevention in keyhole TIG welding process.

Application of Advanced Deep Learning Models for Efficient Apple Defect Detection and Quality Grading in Agricultural Production

In this study, a deep learning-based system for apple defect detection and quality grading was developed, integrating various advanced image-processing technologies and machine learning algorithms to enhance the automation and accuracy of apple quality monitoring. Experimental validation demonstrated the superior performance of the proposed model in handling complex image tasks. In the defect-segmentation experiments, the method achieved a precision of 93%, a recall of 90%, an accuracy of 91% and a mean Intersection over Union (mIoU) of 92%, significantly surpassing traditional deep learning models such as U-Net, SegNet, PSPNet, UNet++, DeepLabv3+ and HRNet. Similarly, in the quality-grading experiments, the method exhibited high efficiency with a precision of 91%, and both recall and accuracy reaching 90%. Additionally, ablation experiments with different loss functions confirmed the significant advantages of the Jump Loss in enhancing model performance, particularly in addressing class imbalance and improving feature learning. These results not only validate the effectiveness and reliability of the system in practical applications but also highlight its potential in automating the detection and grading processes in the apple industry. This integration of advanced technologies provides a new automated solution for quality control of agricultural products like apples, facilitating the modernization of agricultural production.

Enhancing mango quality control: A novel approach to spongy tissue inspection through image clustering and machine learning models via X‐ray imaging

AbstractThe globally renowned Alphonso mango is esteemed for its exceptional flavor, fragrance, texture, and extended shelf life. However, it faces a substantial challenge with spongy tissue (ST) disorder, affecting up to 30% of a single lot. This issue leads to the rejection of entire batches during export due to delayed detection, causing significant produce loss. Additionally, the current destructive assessment method is inadequate, as physically inspecting only a few mangoes within a batch does not ensure overall quality, emphasizing the need for a non‐destructive approach. To address these challenges, this study proposes a non‐destructive approach utilizing X‐ray imaging to identify ST in Alphonso mangoes, employing machine learning (ML) for binary classification. We captured and augmented 216 X‐ray images, utilizing various augmentation techniques. K‐means clustering effectively distinguished healthy and ST‐affected areas in mango fruits. For automated classification, ML models were fine‐tuned through grid search hyperparameter optimization within a 5‐fold cross‐validation framework. The CNN model achieved impressive accuracies of 99.20% and 96.82% at threshold values of .5 and .95, respectively, on the test dataset, while the SVM model attained overall accuracy rates of 95.23% and 87.35% for the same thresholds. Our study demonstrates the potential of machine learning in X‐ray imaging for detecting internal fruit issues, improving inspection for disease and damage identification, and enhancing quality control across the food industry. Practical implications include the implementation of non‐destructive inspection methods to reduce produce loss and ensure consistent quality in mango lots. Future research could focus on validating the model's efficacy with external datasets, evaluating its robustness in detecting various internal irregularities, and exploring advancements in X‐ray imaging technology for broader applications in agriculture.Practical applicationsThe suggested non‐destructive approach provides a practical solution for quality control, especially in export scenarios. The versatility of the ML‐powered X‐ray imaging technique to examine internal irregularities in various fruits beyond mangoes broadens its potential application in the fruit industry for sorting and grading purposes. This innovation contributes to heightened consumer satisfaction, providing a cost‐effective and scalable solution for widespread implementation. In summary, the findings of the study show promise for revolutionizing mango quality control, particularly for the esteemed Alphonso variety, and propelling advancements in fruit inspection methodologies across the industry.

Active Learning in Feature Extraction for Glass-in-Glass Detection

In the food industry, ensuring product quality is crucial due to potential hazards to consumers. Though metallic contaminants are easily detected, identifying non-metallic ones like wood, plastic, or glass remains challenging and poses health risks. X-ray-based quality control systems offer deeper product inspection than RGB cameras, making them suitable for detecting various contaminants. However, acquiring sufficient defective samples for classification is costly and time-consuming. To address this, we propose an anomaly detection system requiring only non-defective samples, automatically classifying anything not recognized as good as defective. Our system, employing active learning on X-ray images, efficiently detects defects like glass fragments in food products. By fine tuning a feature extractor and autoencoder based on non-defective samples, our method improves classification accuracy while minimizing the need for manual intervention over time. The system achieves a 97.4% detection rate for foreign glass bodies in glass jars, offering a fast and effective solution for real-time quality control on production lines.

How to verify the analytical and clinical performance of ELISA immunoanalysis in the real laboratory practice. PCSK9 as an example

BackgroundManufacturers and diagnostic companies often recommend on-site verification of analytical performance in the clinical laboratory. The validation process used by manufacturers is rarely described in detail, and certain information on analytical performance is missing from the product sheet, especially for immunoanalytical methods. We describe an approach to the detailed validation of an ELISA method for the measurement of proprotein convertase subtilisin/kexin type 9 (PCSK9) plasma concentrations. We compared manufacturers' claims of analytical performance with data obtained in the field laboratory using several approaches. MethodsWe used the Human Proprotein Convertase 9/PCSK9 Quantikine ELISA diagnostic kit (R&D systems, Bio-Techne Ltd., Abingdon Science Park, Abingdon, UK) and three levels of quality control solution Quantikine Immunoassay Control Group 235 (R&D systems, Bio-Techne Ltd., Abingdon Science Park, Abingdon, UK) to verify precision. We measured the concentration of PCSK9 using the DS2 ELISA Reader (Dynex Technologies GmbH, Denkendorf, Germany). We used analysis of variance (ANOVA) and the R statistical package (R core team, version 1.4.5). Statistical analysis and terminology were performed according to protocol CLSI EP15-A3, and the reference interval was checked according to CLSI/IFCC C28-A3c. ResultsWe found a significant difference between the manufacturer's claims of analytical performance and real data measured in the routine clinical laboratory. The calculated CV (%) for repeatability (calculated by simple estimation of the mean and SD, as used by the manufacturer) was between 5.5% and 7.4%, but the manufacturer's claim was between 4.1% and 6.5%. Using ANOVA, the true repeatability was between 5.0% and 6.9%. Similarly, ANOVA revealed values of CV (%) for within-laboratory imprecision between 6.5% and 9.1%, while the manufacturer's claims were between 4.1% and 5.9%. The recovery ranged from 105.5% to 121.8%. The manufacturer's recommended reference interval was checked and we didn't find any significant difference between men and women. ConclusionsWe describe a comprehensive approach to verify the analytical performance of an ELISA method using the measurement of PCSK9 plasma concentration as an example. We found differences between the results of this approach based on the CLSI EP15-A3 protocol and data provided by the manufacturer. We recommend the verification of analytical performance by more complex statistical tools in laboratory practice.

Q3D: a complete solution for quality control and inspection in additive manufacturing processes

PurposeThis paper aims to present an integrated system designed for quality control and inspection in additive manufacturing (AM) technologies.Design/methodology/approachThe study undertakes a comprehensive examination of the process in three distinct stages. First, the quality of the feedstock material is inspected during the preprocessing step. Subsequently, the main research topic of the study is directed toward the 3D printing process itself with real-time monitoring procedures using computer vision methods. Finally, an evaluation of the 3D printed parts is conducted, using measuring methods and mechanical experiments.FindingsThe main results of this technical paper are the development and presentation of an integrated solution for quality control and inspection in AM processes.Originality/valueThe proposed solution entails the development of a promising tool for the optimization of the quality in 3D prints based on machine learning algorithms.

Effect of Dispensing Type On Void Formation Using Convolutional Neural Network

Abstract Underfilling in flip chip packages is a critical component of reliability. This study utilized I-type, L-type, and U-type dispensing methods to address the issue namely voiding that creates empty spaces which compromises reliability. An automated solution using Convolutional Neural Network (CNN) is propose for void detection in chip images to replace the conventional manual inspection approach. The CNN model built on MobileNetV2, attains a mean average precision of 0.533. This method calculates void percentage, adhering to IPC standards, to determine product acceptance or rejection, offering an efficient solution for quality control in flip-chip package manufacturing.

Development of an integrated approach to the analysis and forecast of hydrographic and bathymetric data of water bodies and tailings ponds

There is a need for an effective monitoring solution for water quality control in tailings dumps and adjacent water bodies in order to prevent environmental pollution. This article highlights the importance of water quality monitoring and surveillance to prevent pollution. It is proposed to develop a mobile robotic complex equipped with sensors for monitoring water bodies and tailings, which is also capable of measuring underwater topographic data. The objects of study were a tailings pond and water bodies. The authors analyzed existing technical monitoring solutions, designed and developed a robotic complex, echolocation device, tested them on specific sites (the tailings dump of the Zhayrem Mining and Processing Plant and the Ishim River), conducted laboratory analysis of water samples, classified the results. Additionally, they obtained 2D and 3D maps of the bottom, and entered all collected data into a developed database and software. The developed complex demonstrated high accuracy of movement (an error of about 0.2 m on the x axis and 0.1 m on the y axis) and the ability to register environmental parameters such as temperature, humidity, PH. Data analysis for 2021–2023 showed a significant excess of recycled water discharged into the evaporator pond, which emphasizes the importance of monitoring and management of water resources. The research applies ARIMA models, neural networks to predict water body parameters. The results obtained indicate the high efficiency of the developed robotic complex and methods for analyzing data on water resources. These methods can be used in industry, scientific research and environmental projects to regularly monitor water quality and take measures to protect it

On standardization of controls in lifespan studies.

The search for interventions to slow down and even reverse aging is a burgeoning field. The literature cites hundreds of supposedly beneficial pharmacological and genetic interventions in model organisms: mice, rats, flies and worms, where research into physiology is routinely accompanied by lifespan data. However, when experimental animals from one article live as long as controls from another article, comparing the results of interventions across studies can yield misleading outcomes. Theoretically, all lifespan data are ripe for re-analysis: we could contrast the molecular targets and pathways across studies and help focus the further search for interventions. Alas, the results of most longevity studies are difficult to compare. This is in part because there are no clear, universally accepted standards for conducting such experiments or even for reporting such data. The situation is worsened by the fact that the authors often do not describe experimental conditions completely. As a result, works on longevity make up a set of precedents, each of which might be interesting in its own right, yet incoherent and incomparable at least for the reason that in a general context, it may indicate, for example, not prolonging the life of an average organism, but compensating for any genetic abnormalities of a particular sample or inappropriate living conditions. Here we point out specific issues and propose solutions for quality control by checking both inter- and intra-study consistency of lifespan data.

Portable solutions for quality control of beers: a review

Brazil is one of the top five market leaders in beer production. The quantity of establishments legally registered is expected to increase in the coming years. The quality of beer is measured by a complex set of sensory characteristics that include appearance, aroma, taste, and texture. It is also composed of more than 800 chemical compounds originating from different raw materials. The evaluation of the product quality of this process is extremely necessary in order to guarantee customer security and satisfaction. Although human and chemical analyses can be considered complex, there is a tendency to use artificial intelligence technology for precise and cheaper evaluation. Sensory training technologies for evaluators and electronic nose technologies have the purpose of facilitating the recognition of on-and-off characteristics of beer. There were found and discussed one review article, four patents, seven commercial products concerning aroma sensory training kits, and the top 10 technology areas screened with the Orbit® platform. Eight research articles were also highlighted about electronic nose technology, one commercially available, and the top 10 technology areas based on Orbit® platform data. The advent of such technologies represents a step forward in improving quality assurance, but electronic nose technologies do not replace human evaluators yet, because human recognition is a decision factor in releasing a product to market shelves.

Designing a computer-vision-based artifact for automated quality control: a case study in the food industry

AbstractReducing waste through automated quality control (AQC) has both positive economical and ecological effects. In order to incorporate AQC in packaging, multiple quality factor types (visual, informational, etc.) of a packaged artifact need to be evaluated. Thus, this work proposes an end-to-end quality control framework evaluating multiple quality control factors of packaged artifacts (visual, informational, etc.) to enable future industrial and scientific use cases. The framework includes an AQC architecture blueprint as well as a computer vision-based model training pipeline. The framework is designed generically, and then implemented based on a real use case from the packaging industry. As an innovate approach to quality control solution development, the data-centric artificial-intelligence (DCAI) paradigm is incorporated in the framework. The implemented use case solution is finally tested on actual data. As a result, it is shown that the framework’s implementation through a real industry use case works seamlessly and achieves superior results. The majority of packaged artifacts are correctly classified with rapid prediction speed. Deep-learning-based and traditional computer vision approaches are both integrated and benchmarked against each other. Through the measurement of a variety of performance metrics, valuable insights and key learnings for future adoptions of the framework are derived.

A Microalgae Photobioreactor System for Indoor Air Remediation: Empirical Examination of the CO2 Absorption Performance of Spirulina maxima in a NaHCO3-Reduced Medium

Microalgae-based photobioreactors (PBRs) have gained attention as a sustainable solution for indoor air quality (IAQ) control. This study investigates indoor CO2 absorption performance of Spirulina maxima (S. maxima) in NaHCO3-limited cultivation (standard: NaHCO3-free medium = 1:1 v/v%) of a lab-scale PBR system. Cultivation performance of three medium amendments (standard, 50% NaHCO3, and NaHCO3-free) was compared by observing cell growth for 30 days in a controlled environment. Empirical examinations were conducted to evaluate the algal CO2 uptake, and overall system performance in the culture volumes of 2, 4, and 7 L and natural indoor CO2 concentration of ~1100 ppm. We found CO2 was reduced by ~55%, in an air chamber of 0.064 m3, showing the greatest mitigation rate (~20%) on Day 4. Under a high concentration of CO2 (10,000 ppm), the CO2 levels were decreased up to ~90% before saturation. This research provides valuable insights into the development of S. maxima-activated IAQ control systems for airtight buildings.