Actual Operation Research Articles

RUL Prediction for Lithium Battery Systems in Fuel Cell Ships Based on Adaptive Modal Enhancement Networks

With the widespread application of fuel cell technology in the fields of transportation and energy, Battery Management Systems (BMSs) have become one of the key technologies for ensuring system stability and extending battery lifespan. As an auxiliary power source in fuel cell systems, the prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for enhancing the reliability and efficiency of fuel cell ships. However, due to the complex degradation mechanisms of lithium batteries and the actual noisy operating conditions, particularly capacity regeneration noise, accurate RUL prediction remains a challenge. To address this issue, this paper proposes a lithium battery RUL prediction method based on an Adaptive Modal Enhancement Network (RIME-VMD-SEInformer). By incorporating an improved Variational Mode Decomposition (VMD) technique, the RIME algorithm is used to optimize decomposition parameters for the adaptive extraction of key modes from the signal. The Squeeze-and-Excitation Networks (SEAttention) module is employed to enhance the accuracy of feature extraction, and the sparse attention mechanism of Informer is utilized to efficiently model long-term dependencies in time series. This results in a comprehensive prediction framework that spans signal decomposition, feature enhancement, and time-series modeling. The method is validated on several public datasets, and the results demonstrate that each component of the RIME-VMD-SEInformer framework is both necessary and justifiable, leading to improved performance. The model outperforms the state-of-the-art models, with a MAPE of only 0.00837 on the B0005 dataset, representing a 59.96% reduction compared to other algorithms, showcasing outstanding prediction performance.

Dynamic Response Simulation for a Novel Single-Point Mooring Gravity-Type Deep-Water Net Cage Under Irregular Wave and Current

This study investigated the structural response characteristics of a novel single-point mooring gravity-type deep-water (SPM-GDW) net cage under irregular waves and currents. A hydrodynamic numerical model of the cage was created and validated through model experiments. Based on the validated cage model, the structural response characteristics such as cage motion response, mooring line forces, and floating collar stress were studied, considering the actual operating conditions in the target sea area. The response time history curves, wave height time history, and spectral density statistics were studied and compared. The results showed that the heave motion of the cage was consistent with wave elevation in the vertical direction and mainly influenced by wave conditions. The surge motion of the cage was closely related to the current, with a significant lag effect compared to wave elevation motion. Low-frequency loads under the combined action of waves and currents had a significant impact on the surge motion of the cage. In addition, the mooring line tension and pontoon stress were closely related to the wave elevation, with peak values of tension and stress occurring almost simultaneously with the peak wave elevation. However, the pontoon stress exhibited high-frequency response characteristics while satisfying the wave frequency response trend. It was found that the flow velocity had a significant impact on the spectral density of mooring line tension and pontoon stress in the low-frequency range, with an increase in spectral density values as the flow velocity increased. The structural response characteristics identified in this study provide a computational basis for the optimized design and analysis of single-point mooring gravity-type deep-water cages.

Fault Diagnosis of Wire Disconnection in Heater Control System Using One-Dimensional Convolutional Neural Network

Heaters are critical components in various heating control systems, and their faults are often a primary cause of system failure, drawing significant attention from engineers and researchers. Early and accurate fault diagnosis is crucial to prevent cascading failures. Many diagnostic methods target faults under generally stable and simple operating conditions, such as constant load or steady-state temperature. However, real-world scenarios are often complex and variable, involving dynamic loads, nonlinear temperature rises, and other challenges, which limit diagnostic accuracy. To address this issue, this paper proposes an intelligent fault diagnosis model based on a one-dimensional convolutional neural network (CNN), using the heater’s current and voltage as the input to the neural network. The effectiveness and accuracy of the proposed model were validated through experimental data under two different conditions, achieving an average accuracy rate of 98%. The disconnection faults were generated during actual operation and occurred in the early stages, differing significantly from artificially simulated faults, thereby increasing the difficulty of accurate diagnosis. Analysis and comparison of the experimental results demonstrate the feasibility of the intelligent diagnostic model and its high diagnostic accuracy.

Simulation-Based Optimization of Crane Lifting Position and Capacity Using a Construction Digital Twin for Prefabricated Bridge Deck Assembly

The growing adoption of off-site construction methods has increased the critical role of mobile cranes within the construction sector. This study develops a Construction Digital Twin (CDT) framework to optimize crane lifting positions and capacities for the installation of prefabricated bridge decks. By integrating 3D site modeling, Building Information Modeling (BIM), and crane simulations within the Unity game engine, the CDT overcomes the limitations of conventional 2D-based planning by providing a three-dimensional representation of site conditions. An exhaustive search method identifies optimal crane configurations, enhancing precision and efficiency. Simulation calibration using video analysis of real bridge deck installations aligns crane speed and cycle times with actual operations, improving reliability. Case studies demonstrate the CDT’s ability to reduce crane operation costs by 27% when employing a smaller capacity crane while maintaining operational efficiency. Additional DFA-focused simulations with varying deck dimensions revealed a potential 10% cost reduction by optimizing crane operations and deck design strategies. The CDT framework supports early-stage planning, reduces operational risks, and contributes to cost-effective and safer construction practices, offering a scalable solution adaptable to various construction scenarios.

Scheduling and Evaluation of a Power-Concentrated EMU on a Conventional Intercity Railway Based on the Minimum Connection Time

Power-concentrated EMU trains have the advantages of being fast and comfortable, having a flexible formation and a short turn-back time, and so on. They can effectively release the transportation capacity of tense lines and hubs (the replacement of conventional trains with power-concentrated EMUs can reduce the time it takes to enter and exit locomotive yards by 40 min per train), optimize operating structures, improve the quality and efficiency of passenger products for conventional railways, and enhance the travel experience of passengers. Moreover, they have certain cost advantages and practical operational value for improving the market competitiveness of conventional railways. In this study, a two-stage, two-layer cycle method is adopted to solve the application plan of an EMU with the minimum total connection time. Through the decomposition of optimization objectives, the search space and the solution scale in each stage are reduced. In the first stage, the feasible number of routes and the number division plan of internal running lines are listed. In the second stage, an improved ant colony algorithm is designed to arrange and combine the internal running lines in each plan to improve the search quality and convergence speed, which changes the pheromone volatilization coefficient with iteration. The optimal number of routes, the number of internal routes, and the optimal sequence between routes are obtained. The study also puts forward a method of route division according to the passenger load factor, which can help railway bureaus adjust the capacity according to fluctuations in demand. A running diagram of six pairs of power-concentrated EMUs on an intercity railway is used as the background to solve the problem. The optimal connection plan with 14 groups of different route division plans was evaluated by using the entropy weight–TOPSIS method, and the optimal plan was obtained in the form of a route division method with two groups of routes with three pairs of trains in each group. Compared with the actual operation plan, the number of routes and the number of first-level repairs are reduced by 50%, respectively, which can effectively reduce the operation and maintenance costs of EMUs. Compared with the actual plan, the average operation mileage is increased by 100%, the average mileage loss is decreased by 54.6%, and the minimum distance traveled by EMUs is increased by 200%, which indicates that the mileage maintenance cycle of the actual operation plan is not fully used. The average number of tasks of EMUs is increased by 100%, indicating that the efficiency of EMUs in the actual operation plan needs to be improved. The traffic mileage balance is improved by 100%, indicating that the EMUs in different routes are more balanced.

Modification and Performance Evaluation of Radio Frequency Identification Technology in High-Temperature and High-Pressure Environments

Summary As oil and gas exploration progresses into deeper and ultradeep territories, the radio frequency identification (RFID) tags used in drillpipes are required to demonstrate enhanced resilience to elevated temperatures and pressures. Current research on tags for ultrahigh temperature and ultrahigh pressure environments is relatively scarce. This study focuses on the application of RFID in deeper drilling operations, aiming to further enhance the tags’ temperature and pressure resistance to meet the application requirements. Numerical simulations were conducted to assess the mechanical properties of RFID tags installed on drillpipes of varying dimensions under conditions of elevated temperatures and pressures. This process enabled the optimization of the dimensions of the RFID tags and installation holes. The optimal dimensions for the RFID tag were determined to be φ24.3×7 mm, with the corresponding installation hole being φ24.3×11 mm. To evaluate the high-temperature endurance of the RFID tags, they were subjected to a temperature of 200℃ for a period of time, after which a maximum temperature of 230℃ was applied. Furthermore, a bespoke high-temperature and high-pressure apparatus was used to conduct a 7-day endurance test at 200℃ and 200 MPa. The findings demonstrated that the RFID tags exhibited reliable performance even when subjected to an extreme temperature of 230℃. Furthermore, the RFID tags exhibited consistent performance when subjected to prolonged periods of high temperature and pressure. The efficacy of the RFID tags was further validated through an 11-well field experiment, wherein the embedded tags exhibited no indications of detachment or deterioration. Once removed from the apparatus, the tags were able to be read automatically in real time, thus enabling the automated collection of data from the drillpipe in actual operating conditions. The findings of this study provide valuable insights and establish a foundation for the design and application of RFID tags in the challenging environments of deep and ultradeep oil and gas exploration.

Multi-resource constrained elective surgical scheduling with Nash equilibrium toward smart hospitals

This paper focuses on the elective surgical scheduling problem with multi-resource constraints, including material resources, such as operating rooms (ORs) and non-operating room (NOR) beds, and human resources (i.e., surgeons, anesthesiologists, and nurses). The objective of multi-resource constrained elective surgical scheduling (MESS) is to simultaneously minimize the average recovery completion time for all patients, the average overtime for medical staffs, and the total medical cost. This problem can be formulated as a mixed integer linear multi-objective optimization model, and the honey badger algorithm based on the Nash equilibrium (HBA-NE) is developed for the MESS. Experimental studies were carried out to test the performance of the proposed approach, and the performance of the proposed surgical scheduling scheme was validated. Finally, to narrow the gap between the optimal surgical scheduling solution and actual hospital operations, digital twin (DT) technology is adopted to build a physical-virtual hospital surgery simulation model. The experimental results show that by introducing a digital twin, the physical and virtual spaces of the smart hospital can be integrated to visually simulate and verify surgical processes.

Aptamer-Antibody Birecognized Sandwich SERRS Biosensor in Accurate and Rapid Identification of Intraoperative Parathyroid Hormone.

With the increasing incidence of thyroid cancer worldwide and the increasing demand for surgery, the risk of parathyroid injury is also increasing, which will lead to postoperative hypoparathyroidism (HP) and hypocalcemia. In order to improve the quality of life of patients after surgery, there is an urgent need to develop a novel platform that can identify the parathyroid gland immediately during surgery. The parathyroid gland promotes the increase of blood calcium concentration by secreting parathyroid hormone (PTH). Therefore, in this study, we used the sensitive surface-enhanced Raman spectroscopy (SERS) technique for rapid detection of PTH, while the near-infrared dye IR808 was used for resonance with the incident excitation wavelength of 785 nm to achieve the surface enhanced resonance Raman spectroscopy (SERRS) effect and provide a higher signal. The signal is enhanced by up to 200-fold compared to nonresonant sulfhydryl molecules. In addition, we used an aptamer-antibody mode with low Gibbs free energy (ΔG) and low dissociation constant (Kd), using the sandwich method, to achieve specific recognition of PTH. By optimizing the incubation temperature, aptamer dosage, particle concentration, and other key factors, the optimal detection conditions of the system were obtained. The sensitivity of PTH detection was as high as 9.75 pg/mL at a 5 min incubation time. In this work, we achieved an extension from standard products to rats to clinically practical samples. Finally, the eluate samples obtained from the actual clinical operations were compared with the lateral flow biosensor (LFB), chemiluminescence microparticle immunoassay (CMIA), and SERRS, respectively. Finally, using the CMIA results as the reference standard, the SERRS assay achieved a sensitivity of 94% and a specificity of 100%. This study provides new ideas and methods for the refined development of thyroid cancer surgery, improving postoperative safety and quality of life of patients, and promoting the new mode of intraoperative diagnosis and treatment.

A New Impedance Measurement Method for Wind Farms Considering the Influence of Background Harmonics

The impedance of renewable energy stations is crucial for determining system stability. Typically, the impedance is obtained using a measurement method. However, in actual operation, background harmonics at the point of common coupling (PCC) affect the accuracy of the impedance measurement results, which could cause unstable analysis results. Therefore, this paper first analyzes the impact of background harmonics at the PCC on impedance measurement. Then, a novel impedance measurement method, based on the adaptive adjustment coefficient, is proposed. In addition, the design process for the coefficient is presented. By adaptively adjusting the amplitude of the injected perturbation using this coefficient, the proposed measurement method mitigates the effect of background harmonics on the impedance measurement. Finally, simulation results are given to validate the effectiveness of the proposed method.

Numerical study on contact characteristics and sealing performance of rubber-bellows mechanical seals in ESPs

Purpose This study aims to evaluate the effects of the downhole environment and auxiliary rubber bellows on the contact mechanical characteristics and sealing performance of rubber-bellows mechanical seals (RBMS) in electric submersible pumps (ESPs), considering the elastic support of the rubber bellows, multi-field coupling effect and actual operating conditions. Design/methodology/approach A thermal-fluid-solid multi-field coupling numerical model for RBMS in ESPs is developed using the finite element analysis and influence coefficient method. Based on the contact mechanical characteristics of RBMS, the interactions of multiple physical fields between the sealing rings and lubricating oil are accounted for to assess the liquid lubrication state and sealing performance of RBMS in ESPs. Findings The findings indicate the anti-leakage effects of rubber bellows, the transition of lubrication state of the sealing end face and the evolution law of sealing performance with environmental pressure, axial compression amount and contact widths of rubber bellows. Originality/value This study innovatively proposes a multi-field numerical research method to reveal the impact of the downhole environment and rubber bellows on RBMS in ESPs. These findings contribute to a more comprehensive understanding of the sealing mechanism of RBMS and optimize the sealing design for ESPs in high-pressure environments. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2024-0369/

Deep reinforcement learning for multiple reservoir operation planning in the Chao Phraya River Basin

This study demonstrates application of Deep Deterministic Policy Gradient (DDPG)-based algorithm to provide comprehensive and flexible plans for reservoir operation planning of the multiple reservoir system in the Chao Phraya River Basin (CPYRB), Thailand aiming to mitigate flood and drought risks in the region. The multi-agent-based Deep Reinforcement Learning (DRL) model is accordingly constructed considering 7-D predicted inflow, reservoir water released from adjacent reservoir, downstream flow condition, and changes in reservoir water storage, as state variables. The desired goal is to increase water storage levels in all reservoirs by 10–15% to ensure higher potential in supplying water for crop cultivation over the dry seasons and preventing flood occurrences during wet season. Simulation results from 2009 to 2022 indicate that DRL–DDPG-based algorithm can perform well in solving sequential decision problems for optimal operation of multiple reservoir system to achieve the desired water storage goal. It can offer realistic simulation results of seasonal and annual release schemes and reservoir release ratios among reservoirs in the system compared to actual operation and Fmincon and ANFIS optimizations. Importantly, DRL model demonstrates a significant advantage in view of increasing the long-term water storage levels in all reservoirs as targeted in the modelling process while maintaining the similar and consistent release schemes in the reservoir system. For the multipurpose multiple reservoir system operation, adjusting the dynamic desired goals within multi-agent-based RL model is advisable to attain the specific desired outcomes and address various water scenarios.

Development of low-cost conventional milling fixture for micro-friction stir welding process

Fixtures are robust mechanical clamping devices used for precise machining. It is used to securely locate, hold and support the workpiece in a specific position during the machining operation. Fixtures for micro-friction stir welding (µFSW) are basically used to hold workpieces during welding operations. Using a conventional milling machine for µFSW operation with a simplified milling fixture is less expensive. This paper presents a simplified design of an adjustable milling fixture for µFSW, in which plates of different thicknesses can be welded. The low-cost fixture is designed using AutoCAD software, considering the transverse and lateral movement of tools and workpieces. Later, the developed milling fixture setup was tested in actual µFSW operations using the pinless tool of H13 material and 8 mm diameter. During operation, 0.8 mm thick Al6061-T6 plates were placed in lap and butt configuration and successful welding joints were obtained at a constant tool spinning speed of 2000 rpm and traverse speed of 100 mm/min. The maximum strength of the joint found 274.40 MPa for butt configuration and 201.25 MPa in lap configuration, which is about 87.34% and 64.10% of the parent material’s strength (314.19 MPa).

Deep mining of megawatt large wind turbine actual operating data: exploration of Bayesian parameter fusion modeling

Abstract Real-time operation data analysis and condition monitoring of large wind turbines are crucial for efficient and safe operation of wind farms. In response to this, an accurate prediction model architecture based on the multivariate linear regression algorithm is proposed for a deeper understanding of the actual operation of large wind turbines. Comparing different prediction variables, we have confirmed that the average wind direction and average wind speed play a core role in predicting active power. The display has significantly better approximation than the traditional multiple linear regression (MLR), especially in the peak region of period-like variation. The quasi-periodic peak variation characteristics of the active power near the observed series 2000, 2500 and 3000 are realized, and the peak prediction results of B-MLR are significantly better than those of traditional MLR. Meanwhile, for RMSE and R-squared, B-MLR is significantly better than the traditional MLR algorithm in the active power prediction of wind turbines. However, the data complexity in actual applications poses a challenge to the effectiveness of the Bayesian fusion algorithm. Further optimization of the algorithm is needed to cope with the complex and variable real data environment. This study provides scientific evidence for the efficient operation, precise maintenance, and environmentally friendly design of wind turbines, promoting the continuous progress and development of wind power generation technology.

The Application of Intelligent Agent-Based Model Method in the Operation Process of the Catering Industry

The Intelligent Agent-Based Model method, by modeling many interacting individuals and simulating the behavior of individual agents, demonstrates the operation of complex multi-agent systems from a micro to a macro level. It can be effectively utilized to study the operational process of the catering industry surrounding rural scenic spots. This study takes the catering industry surrounding a tourism scenic spot in the southern countryside of Guizhou, China as an example. The emergence and development process of the catering industry is analyzed and the interaction process between tourists and restaurants is modeled. The consistency between the simulation results of restaurant customer flow and the field survey findings indicates that this model could be used to simulate the actual operation of the restaurant. The simulations shows that when the tourist traffic is high, the Matthew Effect on the distribution of customer flow among restaurants is not significant, and the impact of restaurant location is limited. However, when the tourist traffic at the scenic spot is low such as during the off-peak season, the location of restaurants has great influence on the customer flow. Furthermore, establishing partnerships with travel agencies represents an alternative solution for restaurants.

Cooperative Active Disturbance Rejection Control for Heavy-Haul Trains

Cooperative control of multiple heavy-haul trains can improve the safety and efficiency of heavy-haul railway transportation. However, the influence of internal and external unknown disturbances for multiple heavy-haul trains is a serious obstacle, which will lead to imprecise train operation control. To address this issue, a cooperative active disturbance rejection control for heavy-haul trains is proposed. First, a multi-mass point longitudinal dynamic model of heavy-haul trains is established to meet the actual operation. Second, a cooperative active disturbance rejection controller is designed to estimate and compensate for the disturbance caused by the interaction between the trains and environment. Moreover, the extended state observer is leverage to estimate the nonlinear disturbance online, which enhances the resistance of multiple heavy-haul trains to nonlinear time-varying disturbance and suppresses the overshoots of train velocities and inter-train distance. Finally, the performance of the proposed method is verified in two different simulation scenarios: acceleration and deceleration conditions. The simulation results show that the proposed method reduces the maximum relative displacement by 38.9% and the velocity error by 54.5%.

Quantitative study on the environmental impact of Beijing’s urban rail transit based on carbon emission reduction

Urban rail transit, as an efficient and eco-friendly mode of transportation, plays a pivotal role in mitigating traffic congestion and lowering urban carbon emissions. Despite the significant contributions by scholars in this area, debates surrounding the quantification of carbon emissions during the operational phase of urban rail transit persist, particularly in assessing its impact on reducing ground traffic congestion. This study examines the passenger flow during Beijing’s morning and evening peak hours, assuming that all passengers initially using urban rail transit switch to buses and taxis during these periods. A traffic congestion prediction model is developed based on the analysis of actual traffic operation data under this assumption. Through this model, the study calculates the potential congestion times across various scenarios, employing a bottom-up approach to carbon emission estimation to analyze the impact on carbon emissions. Results spanning 2015 to 2021 suggest that substituting urban rail transit with buses could increase congestion by 37–92 min and 46–59 min during morning and evening peaks, respectively, leading to a 24-82% and 27-56% surge in carbon emissions. The conversion of all these vehicles to taxis would result in a direct paralysis of Beijing’s road transport network, with a corresponding increase in carbon emissions of between 289% and 556% and 333% and 614%, respectively.These outcomes emphasize the substantial efficacy of urban rail transit in curbing traffic congestion and carbon emissions.

The “prostate-pelvic syndrome” theory used in patients with type-III prostatitis and its correlation with prostate volume

BackgroundType-III prostatitis is the most common prostate disease in adult males below 40 years old. The actual operation of its diagnosis process is cumbersome. Recently, a group of top Chinese urologists have proposed the theory of “prostate-pelvic syndrome (PPS)” and suggested using it to replace the traditional term for type-III prostatitis. However, the practical application effectiveness of PPS theory in clinical practice is still unclear.ObjectiveThe aims of this study were to verify the clinical outcome of PPS theory in diagnosing the adult patients with type-III prostatitis below 40 years old and analyze the related factors for the main symptoms of PPS in adult males below 40 years old, providing references for the prevention and treatment of PPS in young adult males.MethodsThe clinical medical records of 548 adult outpatients with type-III prostatitis under 40 years old between August 2018 and May 2023 were retrospectively analyzed. The patients were diagnosed retrospectively again by using PPS diagnostic criteria in this retrospective cohort study. Subsequently, the age, disease duration, prostate volume (PV), PV ≥ 20 mL detection rate and other related indicators among different symptom groups were analyzed by univariate analysis. The correlation between different symptoms of PPS patients and PV as well as disease duration was analyzed by correlation analysis. Additionally, the related factors for different main symptoms of PPS patients were analyzed by multivariate analysis.ResultsOf the 548 patients, 229 patients had lower urinary tract symptoms, 159 patients had pelvic pain symptoms, and 160 patients had lower urinary tract and pelvic pain symptoms, respectively corresponding to those with voiding symptoms (VS), pain symptoms (PS), and voiding + pain symptoms (VS + PS) defined according to the concept of PPS. There were significant differences in PV and disease duration among the three main symptoms groups of PPS. PV in the VS group was larger than that in the PS group. Spearman correlation analysis showed that VS was positively correlated with PV and disease duration, while four secondary symptoms (including sexual dysfunction, psychosocial symptoms, reproductive dysfunction and other symptoms) were not related to PV. The proportion of VS patients in the PV ≥ 20 mL group was higher than that in the PV < 20 mL group. Multivariate logistic analysis showed that PV and disease duration were independent related factors for VS in adult PPS patients below 40 years old.ConclusionsType-III prostatitis in Chinese adult males below 40 years old can be diagnosed and treated with PPS. PV and disease duration were independent related factors for VS in Chinese adult PPS patients below 40 years old. The risk of VS in PPS patients with PV ≥ 20 mL was 5.348 times as long as that in PPS patients with PV < 20 mL.

Application of CRISPR/Cas9 system in type 2 diabetes mellitus

With the development of the social economy, the quality of people's living standards has improved. The risk of Type 2 diabetes mellitus (T2DM) is greatly increased. CRISPR/Cas9 system can provide more accurate and effective treatment for T2DM. Nowadays, the CRISPR/Cas9 system is still in the scientific research stage in the field of treatment of T2DM, and still has many uncertainties. For example, the off-target problem, the lack of clinical trials and ethical issues. This paper mainly analyzes and summarizes the research of CRISPR/Cas9 system on the treatment of T2DM, so as to provide a more systematic reference for the treatment method for future research. However, this treatment method also has the problem of Off-target. Although CRISPR/Cas9 system has high accuracy, it still has the problem of off-target, which may lead to the accidental injury of normal genes, thus causing unpredictable consequences. Technical problems: Although CRISPR/Cas9 system is relatively simple, it still needs superb technology and complex conditions in actual operation, which increases the treatment cost and time, and the sequelae of later treatment is still unclear. Both need further research by researchers.

An ML-Enhanced Laser-Based Methane Slip Sensor Using Wavelength Modulation Spectroscopy.

Natural gas (NG) is a promising alternative to diesel for sustainable transport, potentially reducing GHG and air quality emissions significantly. However, the GHG benefits hinge on managing methane slip, the unburned methane in the exhaust of NG engines, which carries a significant global warming potential. The CH4 slip from NG engines is highly dependent on engine type and operation, and effective greenhouse gas emission mitigation requires that the actual operation of real-world engines is monitored. This requires suitable instrumentation for online robust CH4 measurement in engine exhaust. Traditional methane slip measurement methods need frequent calibration, may not be suited to dynamic operational conditions, carry significant costs, or require expert users. Furthermore, the significant computational demands associated with calibration-free spectroscopic methods and the prevalent noise uncertainty underscore the urgent requirement for innovative sensors. These sensors must not only respond rapidly but also have low uncertainty in their readings. This paper presents a machine learning (ML)-enhanced, laser-based methane slip sensor using wavelength modulation spectroscopy (WMS) for rapid, accurate, and calibration-free CH4 measurements for application in the exhaust of NG engines. The sensor utilizes a distributed feedback (DFB) laser diode emitting around 1.65 μm propagated through a multipass optical cell. An ML-based approach is used to invert the recorded WMS signal, which reduces computational cost and uncertainty due to noise vulnerabilities inherent in traditional measurement inversion approaches. A Gaussian process regression (GPR) model, trained on measured and simulated WMS signals, was selected for its high predictive accuracy, where it achieved a mean absolute percent error (MAPE) of 0.24%. For exhaust measurement on an in-use natural gas marine vessel, a mean absolute difference of 3.95% was observed, relative to simultaneous reference Fourier transform infrared spectroscopy measurements. The ML-based WMS inversion system marks a significant advancement in methane slip measurement, offering real-time monitoring capabilities with reduced computational demands. Its development supports the realization of NG environmental benefits for transport by providing accurate CH4 slip data, which are essential for engine performance optimization, regulatory adherence, and sustainable policy decisions.

Design of Power Economic Dispatch Method Integrating Differential Evolution and PSO

As energy demand continues to grow and environmental problems become increasingly serious, optimizing the economic dispatch of the power system is crucial to ensuring the sustainability and economic benefits of energy supply. To ensure the safe operation of the power system, reduce power generation costs as much as possible, and develop a method that can adapt to the needs of different power systems, the experiment combines the differential evolution algorithm with the power economic dispatch problem and proposes a method based on improved differential evolution. Electric power economic dispatch method with particle swarm optimization algorithm. The experiment first introduces a moderate interference strategy to appropriately adjust the position of the particles; then combines the local mutation strategy to enhance the searchability of the differential evolution algorithm in the solution space and achieve good economic dispatch. The results show that when running on the F11 test set and F21 test set, when the system iterates to the 26th and 32nd times respectively, the loss function of the method constructed in the experiment begins to have a minimum value and remains stable thereafter. In addition, on the F11 test set, when the number of iterations is 150, this method has a minimum time of 0.153s. While running the loop for the first time on System 1, the total cost of this approach was only $1.01× 104. Through the actual operation of power generation equipment, under the operation of this method, the power system can ensure optimal operating power of each power generation equipment unit based on ensuring optimal cost. It can be seen from the above results that this method provides power system operators and decision-makers with a new tool to help them maximize cost-effectiveness while ensuring system stability and meeting power demand. In addition, the method's superior convergence and stability can effectively improve the solution's accuracy and speed and has strong practicality and promotion value.