Real-time Software Research Articles

Demonstration of an End-To-End Workflow Using Atmospheric Solids Analysis Probe-Mass Spectrometry (ASAP-MS) With Real-Time Sample Recognition Software for the Identification of Falsified and Substandard Pharmaceutical Tablets.

Counterfeit pharmaceuticals are a subclass of falsified and substandard medicines. They are illicit products, purporting to be genuine medicines, that are made and sold by criminal organisations. They represent a significant risk to patient safety, as well as a financial and reputational threat to the companies who make the genuine medicines. It is essential to have analytical methods to determine if suspect samples seized by law enforcement agencies are counterfeit, with mass spectrometry (MS) being a commonly used technique in forensic cases. Speed-to-answer is vital to enable law enforcement agencies to progress investigations, as well as for pharmaceutical companies so that they can notify health authorities of the circulation of counterfeit medicines. In this work, an atmospheric solids analysis probe (ASAP)-MS was assessed as a fast and simple-to-use approach to analyse tablets on a commercially available instrument. Complementing the analytics with real-time sample recognition software demonstrated that the classification of tablets as authentic or counterfeit could be achieved quickly (< 2 min) and without the need for MS interpretation skills. Authentication of five tablets (two authentic pharmaceuticals, one placebo and two counterfeits containing the correct active pharmaceutical ingredient [API] but at lower quantities than in the genuine medicine and with different excipient contents) of unknown origin was achieved with 100% success. This creates the opportunity to deploy the end-to-end workflow as a tool for non-scientists, such as law enforcement officers and border control staff, for use in-territory to obtain fast answers and make data-led decisions to control the illegal trading of medicines.

ExaFEL: extreme-scale real-time data processing for X-ray free electron laser science

ExaFEL is an HPC-capable X-ray Free Electron Laser (XFEL) data analysis software suite for both Serial Femtosecond Crystallography (SFX) and Single Particle Imaging (SPI) developed in collaboration with the Linac Coherent Lightsource (LCLS), Lawrence Berkeley National Laboratory (LBNL) and Los Alamos National Laboratory. ExaFEL supports real-time data analysis via a cross-facility workflow spanning LCLS and HPC centers such as NERSC and OLCF. Our work therefore constitutes initial path-finding for the US Department of Energy's (DOE) Integrated Research Infrastructure (IRI) program. We present the ExaFEL team's 7 years of experience in developing real-time XFEL data analysis software for the DOE's exascale supercomputers. We present our experiences and lessons learned with the Perlmutter and Frontier supercomputers. Furthermore we outline essential data center services (and the implications for institutional policy) required for real-time data analysis. Finally we summarize our software and performance engineering approaches and our experiences with NERSC's Perlmutter and OLCF's Frontier systems. This work is intended to be a practical blueprint for similar efforts in integrating exascale compute resources into other cross-facility workflows.

A-193 Optimizing Emergency Laboratory Operations Through Six-Sigma Strategies for Enhanced Efficiency and Effectiveness: A Pilot Study

Abstract Background Quality assurance is vital in laboratory diagnostics to ensure patient safety and accurate results. Laboratories employ internal and external quality control measures, particularly focusing on the analytical phase. Sigma metrics, representing process variation, play a crucial role in assessing accuracy, with Six Sigma setting high standards for operational excellence. This study aims to enhance laboratory testing procedures by analyzing the analytical phase. In 2022, the Emergency laboratory at KFAFH, Jeddah, achieved optimal sigma performance, marking a significant improvement in analytical testing. http://www.westgard.com/sigma-vp-kfafh.htm. In 2023, The study also demonstrated a 30% productivity increase, driven by sustained efforts and Sigma metrics implementation, leading to cost reductions and improved quality. Real-time patient moving averages enhance quality assurance, extending coverage to the total testing process. The primary objectives include redesigning IQC procedures and optimizing resource utilization. This will be accomplished by reducing the frequency of controls, minimizing unnecessary reruns, optimizing resource allocation, and managing total costs, all while ensuring compliance and averting violations. Methods A retrospective-prospective study was conducted at the Emergency Laboratory of King Fahad Armed Forces Hospital in Jeddah, Saudi Arabia, from May 2021 to October 2023. The study assessed 841,558 tests for 131,785 emergency patients. Forty-seven analytes were evaluated for Internal Quality Control and External Quality Control (CAP), with monthly data collection. Chemistry and Hematology parameters were analyzed using Unity Real-Time software on Alinity-ci and Alinity-hq analyzers. Data was collected in three stages: baseline, pre-Sigma verification, post-Sigma implementation, and a sustainability phase. The methodology involved DMAIC model tools like flowcharts, cause &amp; effect analysis, and sigma metric scales. Six Sigma process tools, including DPM and process capability analysis, were used. Interventions included Six Sigma metrics for QC performance, DPM for TAT process improvement, and patient moving average Real-time for proactive risk management in reducing analytical testing variation. Results Excellent performance (&amp;gt; 6 sigma) was achieved for 70% of chemistry test parameters for three control levels.: This indicates a high level of process capability and reliability in laboratory operations, ensuring accuracy and consistency in test results. The study successfully reduced process variation in Emergency Room (ER) laboratory Turnaround times (TATs), leading to an increase in process capability as measured on the Sigma scale, which improved from 1.35 to 3.1, indicating a more robust and efficient laboratory operation. Reducing the cost and efficiency of internal failure controls by 29% and at the same time saving in labor cost/workload marked a decrease in QC frequency from 7.0% to 0.18%, reaching the target goal of TATs and satisfaction. Conclusions The adoption of Six Sigma methodologies resulted in a notable decrease in QC runs, signaling cost savings. These high-quality methods streamline laboratory operations, improve cost-effectiveness for the healthcare system, and ultimately benefit patients and clinicians. Six Sigma methodologies play a crucial role in selecting appropriate QC designs for each parameter, ensuring efficient control while maximizing cost-effectiveness. Emphasizing the importance of suitable QC strategies and continuous technologist training is vital for mitigating analytical errors and enhancing turnaround time through comprehensive automation of the analytical phase.

Real-Time Software Implementation of Filter-Separated MIMO Adaptive Equalizer for Fully Virtualized Access Networks

Real-Time Software Implementation of Filter-Separated MIMO Adaptive Equalizer for Fully Virtualized Access Networks

Perencanaan Ulang Bangunan Gedung Dormitory Kawasan Sains dan Teknologi (KST) Nuklir Yogyakarta Berbasis Building Information Modelling (BIM) 5D

Building Information Modeling (BIM) is a method of processing data during the life cycle of a construction project that uses three-dimensional, real-time, and dynamic building modeling software, thereby creating a complete model related to project data from the planning to the construction phase of the project. In the re-planning of the Yogyakarta Nuclear Science and Technology (KST) Dormitory Building based on Building Information Modeling (BIM) 5D, a special moment resisting frame system (SRPMK) structure was used. The planned building consists of 7 floors, including the lower structure consisting of a 50x50 square pile foundation and five types of pile cap, the upper structure consisting of columns, beams, and floor plates. The quality of concrete in the structure used is fc' 40 Mpa and 35 Mpa with reinforcing steel quality fy 420B. In this planning, 2D and 3D modeling was carried out. Structural analysis was carried out using the SAP2000 application, which was then continued with 2D 3D modeling and Quantity take-off results using Revit and Scheduling using Ms. Projects. The Draft Budget (RAB) results for this planning are Rp. 37,691,368,000.00. The planned duration of work is 26 weeks. The results of the scheduling and RAB are then integrated using the Naviswork software so that the simulation results of the work to be planned can be seen.

Real-time energy management simulation for enhanced integration of renewable energy resources in DC microgrids

The presented work addresses the growing need for efficient and reliable DC microgrids integrating renewable energy sources. However, for the sake of practicality, implementing complex control strategies can increase system complexity. Thus, efficient methodologies are required to provide efficient energy management of microgrids while increasing the integration of renewable energy sources. The primary contribution of this work is to investigate the issues related to operating a DC microgrid with conventional control designed to power DC motors using readily available, non-advanced control strategies with the objective of achieving stable and reliable grid performance without resorting to complex control schemes. The proposed microgrid integrates a combination of uncontrollable renewable distributed generators (DGs) alongside controllable DGs and energy storage systems, including batteries and supercapacitors, connected via DC links. The Incremental Conductance (InCond) algorithm is employed for maximum power point tracking to maximize power output from the PV system. The energy management strategy prioritizes the solar system as the primary source, with the battery and supercapacitor acting as backup power sources to ensure overall system reliability and sustainability. The effectiveness of the microgrid under various operating conditions is evaluated through extensive simulations conducted using MATLAB. These simulations explore different power generation scenarios, including normal operation with varying load levels and operation under Standard Test Conditions (STC). Moreover, fault analysis of the DC microgrid is performed to examine system reliability. The system performance is evaluated using real-time simulation software (OPAL-RT) to validate the effectiveness of the approach under real-time conditions. This comprehensive approach demonstrates the efficacy of operating a DC microgrid with conventional controllers, ensuring grid stability and reliability across various operating conditions and fault scenarios while prioritizing the use of renewable energy sources. The results illustrated that system efficiency increases with load, but fault tolerance measures, can introduce trade-offs between reliability and peak efficiency.

Research and implementation of a large-bandwidth real-time radar jamming simulator.

The electromagnetic environment faced by modern radar is becoming increasingly complex. One effective means to improve the performance of radar systems is testing in an anti-jamming ability test chamber, where the increased complexity has also led to higher performance requirements for radar jamming simulators. Based on the requirements for modern radar system testing, this paper presents a study of a large-bandwidth real-time radar jamming simulator and describes its overall design architecture; the simulator covers the L-Ku and Ka frequency bands and the instantaneous bandwidth is ≥2GHz, which means that the system is able to simulate 11 interference patterns. Synchronous control of the system is realized in 1ms through use of the reflection memory interrupt mechanism, the synchronous pulse signal mechanism, synchronous timing design, and a real-time control software architecture. An overall design scheme for real-time simulation of a radar target jamming echo is given and baseband signal processing resources are saved through information preprocessing, a large-capacity high-speed storage board is designed to improve the data reading speed, a multiphase filtering structure is used to achieve high sampling rates and save hardware resources, and a high-speed parallel computing method is used to improve computing efficiency; the actual measured baseband signal processing time is less than 500ns. Finally, a measurement platform is built, and the main interference patterns are verified through experimental measurements.

Fiber-integrated quantum microscopy system for cells.

Quantum entanglement serves as an essential resource across various fields, including quantum communication, quantum computing, and quantum precision measurement. Quantum microscope, as one of the significant applications in quantum precision measurement, could bring revolutionary advancements in both signal-to-noise ratio (SNR) and spatial resolution of imaging. Here, we present a quantum microscopy system that relies on a fully fiber-integrated high-performance energy-time entangled light source operating within the near-infrared II (NIR-II) window. Complemented by tailored real-time data acquisition and processing software, we successfully demonstrate the quantum imaging of a standard target, achieving a SNR of 131.51 ± 6.74 and a spatial resolution of 4.75 ± 0.27 µm. Furthermore, we showcase quantum imaging of cancer cells, unveiling the potential of quantum entanglement in biomedical applications. Our fiber-integrated quantum microscope, characterized by high imaging SNR, instantaneous image capture, and analysis capabilities, marks an important step toward the practical application in life sciences.

Field-domain rapid-scan EPR at 240 GHz for studies of protein functional dynamics at room temperature

We present field-domain rapid-scan (RS) electron paramagnetic resonance (EPR) at 8.6T and 240GHz. To enable this technique, we upgraded a home-built EPR spectrometer with an FPGA-enabled digitizer and real-time processing software. The software leverages the Hilbert transform to recover the in-phase (I) and quadrature (Q) channels, and therefore the raw absorptive and dispersive signals, χ′ and χ′′, from their combined magnitude (I2+Q2). Averaging a magnitude is simpler than real-time coherent averaging and has the added benefit of permitting long-timescale signal averaging (up to at least 2.5×106 scans) because it eliminates the effects of source-receiver phase drift. Our rapid-scan (RS) EPR provides a signal-to-noise ratio that is approximately twice that of continuous wave (CW) EPR under the same experimental conditions, after scaling by the square root of acquisition time. We apply our RS EPR as an extension of the recently reported time-resolved Gd-Gd EPR (TiGGER) [Maity et al., 2023], which is able to monitor inter-residue distance changes during the photocycle of a photoresponsive protein through changes in the Gd-Gd dipolar couplings. RS, opposed to CW, returns field-swept spectra as a function of time with 10ms time resolution, and thus, adds a second dimension to the static field transients recorded by TiGGER. We were able to use RS TiGGER to track time-dependent and temperature-dependent kinetics of AsLOV2, a light-activated phototropin domain found in oats. The results presented here combine the benefits of RS EPR with the improved spectral resolution and sensitivity of Gd chelates at high magnetic fields. In the future, field-domain RS EPR at high magnetic fields may enable studies of other real-time kinetic processes with time resolutions that are otherwise difficult to access in the solution state.

O-161 #Infertility: a systematic review of information available on social media platforms

Abstract Study question How accurate, credible, reliable and readable is online information obtained from social media content in relation to the diagnosis and management of fertility problems? Summary answer Information available on social media regarding infertility often uses language that is inappropriate for a lay audience and lacks sufficient accuracy, credibility and reliability. What is known already Social media has become a popular source of information for people seeking guidance, advice and educational resources related to their own fertility journey. Social media can provide free and easily accessible information and can act as a space for community support to share real-world experiences. Many of the platforms delivering this information have not been verified and there is a growing concern regarding the accuracy of the content on these posts. A variation in expertise among content creators and the rapid spread of anecdotal experiences may contribute to the unreliability of information. Study design, size, duration Using real-time analytics software, the #infertility was tracked prospectively during National Infertility Awareness Week, an annual campaign organised by the National Infertility Association (USA) to raise awareness around infertility, on Twitter and Instagram platforms. During this one-week period, a total of 28,612 posts containing the #infertility was posted on Twitter and Instagram. Posts containing non-English language, commercial advertising and content related solely to the lived experience of infertility, for example, a patient’s blog, were excluded. Participants/materials, setting, methods Once screening was completed, 673 posts (310 on Twitter and 363 on Instagram) were identified as containing information pertaining to the diagnosis and management of fertility problems. Two independent researchers screened the included posts. Posts were evaluated using validated instruments across four domains, including assessments of 1) credibility (White Paper instrument) 2) quality (adapted DISCERN instrument) 3) accuracy (assessed by prioritized criteria developed in consultation with healthcare professionals and researchers) and 4) Readability (Flesch-Kincaid instrument). Main results and the role of chance A total of 28,612 posts, with a reach of 285,826,087 people were identified. Once screening had been completed, there were 310 posts on Twitter containing information pertaining to infertility, with 20,136 engagements and an overall reach of 75,184,476. For Instagram, 363 posts were identified as containing information pertaining to infertility, with 46,443 engagements. The most discussed topics included: 1) lifestyle interventions including nutrition and supplements, 2) polycystic ovarian syndrome, 3) endometriosis and 4) infertility in relation to COVID and the COVID vaccination programme. Only 87 Twitter posts (28%) had clear demonstration of credibility and of those posts the information contained within them was accurate 92% of the time. Of the remaining 72% of Twitter posts, no clear credentials were displayed, and the accuracy of those posts dropped to 34%. 139 Twitter posts (45%) appeared to have an undeclared Trial registration number Not applicable

Research on the Application of Embedded Real-time Software in Computer Software Design

With the continuous development of computer technology, embedded real-time software is also developing and improving. In the design of computer software, embedded real-time software is the core part, and its reasonable application can effectively improve the function and performance of computer software and ensure its stable and reliable operation. At the same time, embedded real-time software can effectively realize the real-time operation of computers and bring convenience to people's daily life and work. This paper deeply studies the application of embedded real-time software in computer software design. The key technologies and methods such as real-time operating system, real-time task scheduling algorithm, embedded software design mode and embedded software testing and verification method are discussed in detail. Combined with specific cases, the application practice of embedded real-time software in computer software design is analyzed, including real-time requirements analysis, performance optimization, resource management and application in specific fields. This paper analyzes the specific application of embedded real-time software in computer software design, and provides some reference for related workers.

A Toolset for Predicting Performance of Legacy Real-Time Software Based on the RAST Approach

A Toolset for Predicting Performance of Legacy Real-Time Software Based on the RAST Approach

Non-Destructive Inspection of Physicochemical Indicators of Lettuce at Rosette Stage Based on Visible/Near-Infrared Spectroscopy.

Lettuce is a globally important cash crop, valued by consumers for its nutritional content and pleasant taste. However, there is limited research on the changes in the growth indicators of lettuce during its growth period in domestic settings. Quality assessment primarily relies on subjective evaluations, resulting in significant variability. This study focused on hydroponically grown lettuce during the rosette stage and investigated the patterns of changes in the indicators and spectral curves over time. By employing spectral preprocessing and selecting characteristic wavelengths, three models were developed to predict the indicators. The results showed that the optimal model structures were S_G-UVE-PLSR (SSC and vitamin C) and Nor-CARS-PLSR (moisture content). The PLSR models achieved prediction set correlation coefficients of 0.8648, 0.8578, and 0.8047, with residual prediction deviations of 1.9685, 1.9568, and 1.6689, respectively. The optimal models were integrated into a portable device, using real-time analysis software written in Matlab2021a, for the prediction of the physicochemical indicators of lettuce during the rosette stage. The results demonstrated prediction set correlation coefficients of 0.8215, 0.8472, and 0.7671, with root mean square errors of prediction of 0.5348, 1.5813, and 2.3347 for a sample size of 180. The small discrepancies between the predicted and actual values indicate that the developed device can meet the requirements for real-time detection.

Design and Verification of Spacecraft Pose Estimation Algorithm using Deep Learning

This study developed a real-time spacecraft pose estimation algorithm that combined a deep learning model and the least-squares method. Pose estimation in space is crucial for automatic rendezvous docking and inter-spacecraft communication. Owing to the difficulty in training deep learning models in space, we showed that actual experimental results could be predicted through software simulations on the ground. We integrated deep learning with nonlinear least squares (NLS) to predict the pose from a single spacecraft image in real time. We constructed a virtual environment capable of mass-producing synthetic images to train a deep learning model. This study proposed a method for training a deep learning model using pure synthetic images. Further, a visual-based real-time estimation system suitable for use in a flight testbed was constructed. Consequently, it was verified that the hardware experimental results could be predicted from software simulations with the same environment and relative distance. This study showed that a deep learning model trained using only synthetic images can be sufficiently applied to real images. Thus, this study proposed a real-time pose estimation software for automatic docking and demonstrated that the method constructed with only synthetic data was applicable in space.

Synthesized method for estimating a power system regime parameters state for real time hardware and software packages

Synthesized method for estimating a power system regime parameters state for real time hardware and software packages

Adaptive extended kalman filter for PEMFC membrane water content estimation

Proton Exchange Membrane Fuel Cells are a favorite technology for decarbonizing the transportation sector. However, their large-scale democratization is hampered by their high cost compounded by their unsatisfactory lifespan. To anticipate potential degradation while keeping improving performance, it is essential to maintain an acceptable humidity range inside the cells, especially at the membrane level. However, membrane humidity level is not directly measurable, alternative techniques must be considered to recover this key variable. Here, we develop a real-time software sensor of the membrane water content at the fuel cell’s heart. We build a model describing the membrane water balance, electrochemical behavior, and species mass balance. We then reduce the model and perform an Adaptive Extended Kalman Filter. We perform sensitivity analyses in both steady-state and transient conditions. We validate the filter on a “Worldwide Harmonized Light Vehicles Test Cycles” test procedure. Finally, we obtain a fast and accurate model-based software sensor.

Towards digital twin trains: implementing a cloud-based framework for railway vehicle dynamics simulation

ABSTRACT The digital twin technology holds great promise in driving the railway industry into a new era of digital intelligence. By creating a dynamic, personalized digital replica of a physical train, it has the potential to enable a range of advanced applications, including predictive maintenance, adaptive control, and early fault detection. However, substantial challenges exist that hinder the effective implementation of the digital twin concept for trains. To resolve this bottleneck issue, in this paper, these challenges are first identified and thoroughly examined, with a specific focus on those within the realm of railway vehicle dynamics simulation. A novel cloud-based simulation framework for the development of railway vehicle dynamics simulation software is then proposed to tackle the identified challenges. The framework makes use of cloud computing and cloud-based visualization technology to offer a scalable and flexible solution for railway vehicle dynamics simulation. Using the framework as its foundation, a cloud platform for constructing digital twin trains is also proposed and presented, with a detailed explanation of its architecture and functionalities. In addition, CTTSIM, a cloud computing-aided train-track system dynamics real-time simulation software, is developed as an illustrative application case of the proposed framework. An evaluation is undertaken to demonstrate the feasibility and utility of both CTTSIM and the proposed framework. This work provides a practical and effective solution for using vehicle dynamics simulation for constructing digital twin trains, representing a step towards the full implementation of the digital twin concept in the railway industry.

The effects of real-time waveform analysis software on patient ventilator synchronization during pressure support ventilation: a randomized crossover physiological study

BackgroundPatient-ventilator asynchrony commonly occurs during pressure support ventilation (PSV). IntelliSync + software (Hamilton Medical AG, Bonaduz, Switzerland) is a new ventilation technology that continuously analyzes ventilator waveforms to detect the beginning and end of patient inspiration in real time. This study aimed to evaluate the physiological effect of IntelliSync + software on inspiratory trigger delay time, delta airway (Paw) and esophageal (Pes) pressure drop during the trigger phase, airway occlusion pressure at 0.1 s (P0.1), and hemodynamic variables.MethodsA randomized crossover physiologic study was conducted in 14 mechanically ventilated patients under PSV. Patients were randomly assigned to receive conventional flow trigger and cycling, inspiratory trigger synchronization (I-sync), cycle synchronization (C-sync), and inspiratory trigger and cycle synchronization (I/C-sync) for 15 min at each step. Other ventilator settings were kept constant. Paw, Pes, airflow, P0.1, respiratory rate, SpO2, and hemodynamic variables were recorded. The primary outcome was inspiratory trigger and cycle delay time between each intervention. Secondary outcomes were delta Paw and Pes drop during the trigger phase, P0.1, SpO2, and hemodynamic variables.ResultsThe time to initiate the trigger was significantly shorter with I-sync compared to baseline (208.9±91.7 vs. 301.4±131.7 msec; P = 0.002) and I/C-sync compared to baseline (222.8±94.0 vs. 301.4±131.7 msec; P = 0.005). The I/C-sync group had significantly lower delta Paw and Pes drop during the trigger phase compared to C-sync group (-0.7±0.4 vs. -1.2±0.8 cmH2O; P = 0.028 and − 1.8±2.2 vs. -2.8±3.2 cmH2O; P = 0.011, respectively). No statistically significant differences were found in cycle delay time, P0.1 and other physiological variables between the groups.ConclusionsIntelliSync + software reduced inspiratory trigger delay time compared to the conventional flow trigger system during PSV mode. However, no significant improvements in cycle delay time and other physiological variables were observed with IntelliSync + software.Trial registrationThis study was registered in the Thai Clinical Trial Registry (TCTR20200528003; date of registration 28/05/2020).

A real-time signal processing software package for the electrophysiology laboratory.

Real-time signal processing has to date been difficult to implement in the clinical electrophysiology laboratory. To date, no open access software solutions are available in electrophysiology (EP) laboratories to facilitate real-time intraprocedural signal analysis. We aimed to develop an open access, scalable Python plug-in to allow real-time signal processing during human EP procedures. A Python-based plug in for the widely available EnsiteX mapping system was developed. This plug-in utilized the LiveSync feature of the system to allow real-time signal analysis. An open access library was developed to allow end-users to implement real-time signal analysis using this platform, implemented in the Python programming language https://github.com/anand9176/WaveWatch5000Public. We have developed and demonstrated the feasibility of a readily scalable and open-access Python-based plug in to an electroanatomic mapping system (EnSiteX) to allow real-time processing and display of electrogram (EGM) based information for the procedural electrophysiologist to view intraprocedurally in the electrophysiology laboratory. The availability, to the clinician, of traditional and novel EGM-based metrics at the time of intervention, such as atrial fibrillation ablation, allows for key mechanistic insights into critical unresolved questions regarding arrhythmia mechanism.

Development of near-infrared spectroscopy calibration model and monitoring software: For monitoring hexamethylenetetramine concentration in hexamethylenetetramine–acetic acid solution

In recent years, near-infrared spectroscopy has been increasingly used in chemical engineering processes. However, accurately and in real-time monitoring concentrations remains highly challenging. This study presents a method for accurately monitoring the hexamethylenetetramine concentration in hexamethylenetetramine-acetic acid solution by integrating near-infrared spectroscopy with software techniques. Compared to the methods of zero mean standardization (Z-score), standard normal variate (SNV), first derivative (D1), second derivative (D2), and vector normalization (VN), the Savitzky-Golay first derivative (SGFD) method effectively addresses issues related to baseline drift, noise, and other interferences. This results in clearer identification of characteristic absorption peaks of different components and a significant improvement in the accuracy of the analysis. The study combined the uninformative variable elimination (UVE), differential evolution (DE), and moth flame optimization (MFO) algorithms to optimize a feature wavenumber selection method, which reduced the number of variables by 78.74 %, and the values of root mean square error of calibration set (RMSEC) and root mean square error of prediction (RMSEP) by 18.86 % and 30.38 %, respectively. Additionally, the analytical method was integrated with real-time monitoring software, enabling the monitoring of hexamethylenetetramine concentration. This integration supports the improvement of the quality and safety of the manufacturing process. The results demonstrate the potential application of near-infrared spectroscopy in real-time monitoring and automated control of chemical processes in the chemical industry.