Combined Power Research Articles

Artificial intelligence application for assessment/optimization of a cost-efficient energy system: Double-flash geothermal scheme tailored combined heat/power plant

Utilizing the capabilities of artificial intelligence can lead to the development of energy systems and power supply chain that are more efficient, sustainable, and resilient. The integration of machine learning techniques within these systems provides substantial benefits and is essential for enhancing overall performance. As the global community confronts challenges like climate change and rising energy demands, machine learning will play an increasingly vital role in defining the future of energy systems. This research examines how effective regression-based machine learning techniques are for analyzing and optimizing the performance of a geothermal combined heat and power system. It focuses on creating both linear and quadratic models to assess electricity generation, heat production, and the efficiency of the entire system. The evaluation of these models is performed through residual analysis and R-squared statistics. Results indicate that quadratic models surpass linear ones, with linear model achieving an R-squared value of 88.56 % for power generation, while the quadratic model reaches an impressive R-squared level of 99.88 %. Furthermore, the study demonstrates that quadratic machine learning models hold significant promise for optimizing system performance, shown by desirability metrics exceeding 0.99. This research highlights the importance of regression-based machine learning methods in analyzing and improving geothermal combined heat and power systems.

Fuel Cell-Based Distributed Robust Optimal Scheduling for Combined Heat and Power Supply

At present, the safe operation of integrated energy systems is significantly affected by the considerable uncertainty inherent to wind and photovoltaic power generation. Based on this, this paper proposes an optimal scheduling model for integrated electricity, heat, and hydrogen-based energy systems on distributed robust optimization (DRO). Firstly, a combined heat and power microgrid system considering hydrogen energy systems was constructed based on the thermoelectric cogeneration characteristics of fuel cells and electrolyzers. Then, a data-driven two-stage distribution robust optimization scheduling model is built by combining typical historical data of wind power output, photovoltaic power output, and load. The results show that the distributed robust method reduces the running cost by 6% compared to the deterministic method. The proposed method and model are capable of meeting the demand for thermoelectric loads within the microgrid in a more cost-effective manner, thereby achieving stable and independent operation of the system.

Heat energy utilization of a double-flash geothermal source efficiently for heating/electricity supply through particle swarm optimization method

The principal aim of this article is to optimize the thermal and electrical efficiency of a geothermal combined heat and power system through metaheuristic particle swarm optimization (PSO) method. The objective of this research is to conduct a thorough analysis of the incorporation of metaheuristic PSO technique, with a specific emphasis on the potential advantages and obstacles associated with the utilization of metaheuristic approaches in improving the effectiveness of geothermal energy systems. The utilization of a double-flash geothermal system in conjunction with a transcritical carbon dioxide Rankine cycle is utilized for the co-generation of electricity and thermal energy. The research utilized a PSO method to enhance power generation, heating capacity, and overall system efficiency. The PSO algorithm was employed to determine the optimum operational parameters for a pressure level of 820 kPa and a pressure ratio of 1.59, leading to the maximization of power output to 2591.4 kW The PSO algorithm effectively identified the optimal operational parameters as a pressure of 820 kPa and a pressure ratio of 1.59, resulting in the achievement of a peak power output of 2591.4 kW. The methodology has determined that a pressure of 916.4 kPa and a pressure ratio of 1.5 represent the optimal parameters for achieving a maximum heating capacity of 12329.1 kW.

Data-driven stochastic dynamic economic dispatch for combined heat and power systems using particle swarm optimization

In modern power systems, the uncertain behavior of renewable energy sources (RESs) may result in deviations from the optimal operating dispatches for the various power sources. The electric power generation from Combined Heat and Power (CHP) units is characterized by high efficiency with less pollution. To use CHP units more efficiently, the dynamic economic dispatch problem is applied to obtain the optimal power and heat sources’ outputs to satisfy heat and power demands while meeting the different operational constraints. In this paper, data-driven stochastic optimization is used to model these uncertainties utilizing the Generative Adversarial Networks in scenario generation that are accurate and do not require fitting models or modeling probability distributions. Then, the Fast Forward Selection technique is used to decrease the number of scenarios to improve system tractability. The results obtained indicate that the variability in electrical load, photovoltaic (PV), and wind turbine (WT) output significantly impacts the overall operational costs of the power system. Therefore, it is essential to incorporate the uncertainties associated with electrical load, PV, and WT when aiming for accurate results in economic dispatch. the study found that the overall operation cost of the dynamic economic dispatch of the electrical system for 24 hours after integrating RES achieved a daily savings of 2.5 % in the first scenario. Furthermore, the findings demonstrate that RESs positively contribute to reducing the total operational costs of the power system. The economic dispatch of CHP systems is influenced by the integration of RESs and the uncertainties associated with electrical parameters.

Part-load performance analysis of a modular biomass boiler with a combined heat and power industrial Rankine cycle and supplementary sCO2 Brayton cycle

Abstract The addition of a supplementary high efficiency cycle integrated with an existing steam power cycle may increase energy efficiency and net generation. In this paper, part load performance and operation of a modular biomass boiler with an existing industrial Rankine steam heat and power cycle and supplementary supercritical CO2 (sCO2) Brayton cycle is analysed. The aim is to leverage the high efficiency of the sCO2 cycle by retrofitting sCO2 heaters in the existing biomass boiler, increasing net power output and thermal efficiency. With nominal load performance previously investigated, understanding part-load performance and operation is vital to determine cycle feasibility. A quasi-steady-state one dimensional thermofluid network model was used to simulate the integrated cycle performance for loads ranging from 100% to 60%. The model solves the mass, energy, momentum, and species balance equations, capturing detailed component characteristics. Two control methodologies are explored for the sCO2 Brayton cycle, namely inventory control and inventory control combined with throttling valve control. Inventory control is selected as the better performing control strategy for load following, maintaining high thermal efficiency across partial loads. At 60% load, the sCO2 compressor operates near the pseudo-critical point, leading to a sharp decrease in sCO2 cycle capacity, which requires careful management of inventory control. Two sCO2 heater configurations are investigated, namely a single convective-dominant heater, and a dual heater configuration with a radiative and a convective heater. The single heater configuration is preferred to minimise adverse impacts on the Rankine cycle superheaters.

Botnets Unveiled: A Comprehensive Survey on Evolving Threats and Defense Strategies

ABSTRACTBotnets have emerged as a significant internet security threat, comprising networks of compromised computers under the control of command and control (C&C) servers. These malevolent entities enable a range of malicious activities, from denial of service (DoS) attacks to spam distribution and phishing. Each bot operates as a malicious binary code on vulnerable hosts, granting remote control to attackers who can harness the combined processing power of these compromised hosts for synchronized, highly destructive attacks while maintaining anonymity. This survey explores botnets and their evolution, covering aspects such as their life cycles, C&C models, botnet communication protocols, detection methods, the unique environments botnets operate in, and strategies to evade detection tools. It analyzes research challenges and future directions related to botnets, with a particular focus on evasion and detection techniques, including methods like encryption and the use of covert channels for detection and the reinforcement of botnets. By reviewing existing research, the survey provides a comprehensive overview of botnets, from their origins to their evolving tactics, and evaluates how botnets evade detection and how to counteract their activities. Its primary goal is to inform the research community about the changing landscape of botnets and the challenges in combating these threats, offering guidance on addressing security concerns effectively through the highlighting of evasion and detection methods. The survey concludes by presenting future research directions, including using encryption and covert channels for detection and strategies to strengthen botnets. This aims to guide researchers in developing more robust security measures to combat botnets effectively.

Hourly electricity CO[formula omitted] intensity profiles based on the real operation of large-scale natural gas combined cycle cogeneration plants

The decarbonization of energy systems is a major challenge that requires complex cross-sectoral strategies that need to be supported by energy modeling. As many technologies rely on electricity, an accurate estimation of its CO2 intensity is of utmost importance for the reliability of modeling results. When electricity is generated from fossil sources, its CO2 intensity depends on several parameters. This research paper presents an hourly calculation of the CO2 intensity of power generation from natural gas combined cycles, based on real data from several years of operation of three plants. As these plants are also operated in combined heat and power mode, two alternative allocation methods are compared. The results confirm the variability of CO2 intensity based on the different operation strategies of the plants and the share of heat and electricity generated. The hourly analysis shows average values in the range of 230–250 gCO2/kWh in winter, rising to around 330–370 gCO2/kWh in summer. The real CO2 intensity profiles presented in this paper can be integrated into energy planning models to improve their ability to estimate the potential benefits of different decarbonization solutions by including the effect of the operational profiles over the day and the year.

U-Net Semantic Segmentation-Based Calorific Value Estimation of Straw Multifuels for Combined Heat and Power Generation Processes

This paper proposes a system for real-time estimation of the calorific value of mixed straw fuels based on an improved U-Net semantic segmentation model. This system aims to address the uncertainty in heat and power generation per unit time in combined heat and power generation (CHPG) systems caused by fluctuations in the calorific value of straw fuels. The system integrates an industrial camera, moisture detector, and quality sensors to capture images of the multi-fuel straw. It applies the improved U-Net segmentation network for semantic segmentation of the images, accurately calculating the proportion of each type of straw. The improved U-Net network introduces a self-attention mechanism in the skip connections of the final layer of the encoder, replacing traditional convolutions by depthwise separable convolutions, as well as replacing the traditional convolutional bottleneck layers with Transformer encoder. These changes ensure that the model achieves high segmentation accuracy and strong generalization capability while maintaining good real-time performance. The semantic segmentation results of the straw images are used to calculate the proportions of different types of straw and, combined with moisture content and quality data, the calorific value of the mixed fuel is estimated in real time based on the elemental composition of each straw type. Validation using images captured from an actual thermal power plant shows that, under the same conditions, the proposed model has only a 0.2% decrease in accuracy compared to the traditional U-Net segmentation network, while the number of parameters is significantly reduced by 74%, and inference speed is improved 23%.

Comparison of Aiming Point Strategy Algorithms and Their Assessment for Volumetric Solar Receivers

The present work summarizes the work done within the scope of CATION, CHLOE, and HECTOR projects related to the aiming point strategies optimization algorithms applied to volumetric solar receivers. Several optimization methods have been applied in the literature for optimizing the aiming strategy of solar power tower plants. However, the use of these algorithms for the requirements of volumetric solar receivers and solar fuel applications has not been accomplished. Herein, the problem is formulated as a constrained optimization problem whose objective function is a combination of the total power on the receiver surface and the flux homogeneity. A comparison of the results is presented in this work. It will be seen that TABU search and SA algorithms are the most efficient in terms of computation time and, in addition, the first one shows very good results in power and spillage. The rest of the mentioned algorithms are much slower and, in general, the results are slightly worse.

Modeling of calculations on the performance optimization of a double-flash geothermal renewable energy-based combined heat/power plant coupled by transcritical carbon dioxide rankine cycle

This study investigates the integration of a double-flash geothermal system with a transcritical CO2 Rankine cycle, presenting a groundbreaking approach for enhancing efficient and sustainable energy generation. By harnessing renewable geothermal resources, this combined heat and power system aims to minimize greenhouse gas emissions, contributing to a more sustainable future. The research emphasizes multi-objective optimization to maximize net power output, net heating capacity, and overall system efficiency. The expanded stream pressures from the expansion valves, denoted as P2 and P6, are systematically varied to evaluate their impact on system performance. Significant findings reveal that as P6 increases from 260 kPa to 640 kPa, net power output rises dramatically from 2688 kW to 3163 kW. The optimal conditions for peak performance are identified at P2 of 820 kPa and P6 of 640 kPa, resulting in impressive outcomes: a net power output of 3392 kW, a net heating capacity of 14,586 kW, and an overall efficiency of 52.65 %. By delineating these relationships and establishing benchmarks for performance, this research offers valuable insights for the scientific community, policymakers, and industry stakeholders aiming to advance geothermal energy technologies.

Bi-Level Operation Optimization and Performance Analysis for a Distributed Energy System with Energy Network

The increasing energy crisis and environmental problems promote the development of distributed energy systems (DESs) that utilize combined heat and power technology, renewable energy technology, and waste heat recovery technology to meet various load requirements. Although there is existing work and research on a variety of DESs, most previous studies tend to focus on energy production with little consideration of a distributed energy network and its energy loss, which results in large errors in energy-efficiency calculations and performance analyses. In this paper, a new DES model is proposed with full consideration of an energy network and the full use of solar energy, terrestrial heat, and exhaust gases. At the same time, an effective bi-level optimization method is also proposed for the daily operation of the DES in order to improve the system performance and benefits in the energy, the economy, and the environment. Specifically, the co-generation energy station and water heating network in the DES are optimized separately with two different optimization models. The first-level optimization model is used to seek the optimal values of water mass flow rate and water temperature of the water heating network, and the second-level optimization model is built to determine the optimal energy purchasing strategy and the optimal energy outputs of the co-generation energy station. In order to verify the advantages and effectiveness of the proposed system and method, a contrastive simulation study is undertaken to provide a comparison of the global optimization method. Simulation results show that energy loss, energy cost, and energy consumption of the DES using the bi-level optimization operation strategy only account for 22.51%, 33.42%, and 51.31% of the quantities of the global optimization method, respectively. The hourly curves of the optimal operating variables also demonstrate that the proposed bi-level optimization method can improve the operating stability of the DES better than the global optimization method.

Closing the Loop between Plastic Waste Management and Energy Cogeneration: An Innovative Design for a Flexible Pyrolysis Small-Scale Unit

This study proposes a simplified unit that can be employed in an industrial facility for the utilization of its own abundant plastic waste, primarily from discarded packaging, to achieve full or partial energy autonomy. By converting this waste into synthetic pyrolysis oil equivalent to 91,500 L, the industry can power a combined heat and power generation unit. The proposed unit was designed with a focus on maintaining high temperatures efficiently while minimizing oxygen exposure to protect the integrity of hydrocarbons until they transform into new compounds. Pyrolysis stands as a foundational procedure, paving the way for subsequent thermochemical transformations such as combustion and gasification. This study delves into the factors affecting pyrolysis and presents analytically the mathematical formulations and relevant calculations in order to effectively design and apply a real-life system. On this basis, fuels from plastic waste can be produced, suitable for utilization in typical equipment meant to produce heat, estimated for six months’ operation and 800 MWh of electricity. This study enhances the transition towards a more circular and resource-efficient economy with technologies that unlock the latent energy contained within the discarded matter. Additionally, it demonstrates the feasibility of a moderate investment in a co-generation system for industries utilizing 568 tonnes of plastic waste per year. The design and accurate calculations of this study highlight the theoretical potential of this technology, promoting environmental sustainability and resource conservation.

Experimental and theoretical assessment of the effects of electrical load variation on the operability of a small-scale Organic Rankine Cycle (ORC)-based unit equipped with a hermetic scroll expander

Scroll expanders are widely adopted in small-scale Organic-Rankine-Cycles-(ORCs)-units integrated with renewable energy sources for low power residential Combined Heat and Power Production (CHP). These expanders are often connected to variable direct loads without the possibility to impose its speed externally. Despite the effect of electric load variation on expander speed is known, a minor attention is focused on its impact on the plant operability and regulation. To fill this gap, a wide experimental analysis is carried out on a solar micro-cogenerative ORC-based power unit equipped with a 1 kW hermetic scroll expander connected with a variable electric load. The experimental behaviour of the ORC-unit was assessed for different values of load resistance (from 20 Ω up to 80 Ω). Results show that properly acting on the load resistance the ORC-power unit can be improved up to 50 % in terms of efficiency and power production. Moreover, if the expander is properly designed its self-regulatory capability allows to achieve similar performance of an equivalent speed-controlled expander tested on the same plant. Finally, a new theoretical model reproducing the impact of main operating quantities on expander behaviour was developed and experimentally validated paving the way to a novel model-based control approach.

Home energy management system for residential fuel cell-based combined heat and power system

The goal of this paper is to propose an optimization scheme for enhancing power dispatch and load scheduling for residential fuel cell-based combined heat and power systems (FC-CHPS) using mixed integer linear programming (MILP), considering the lifetime degradation of both the fuel cell system (FCS) and battery energy storage systems (BESS). The scheme is applied in the home energy management system that oversees the electric and thermal power of residential FC-CHPS. First, the nonlinearity in the relationship between the natural gas consumption and output electric power, and between the residual thermal power and output electric power of the fuel cell system (FCS) in the FC-CHPS is approximated using piecewise linearization. Then, the piecewise linearization is integrated with MILP to derive optimal power dispatch and load scheduling solutions, while accounting for the nonlinearity of the FCS. Next, cost functions representing the lifetime degradation of FCS and BESS are formulated. These cost functions result in nonlinear optimization constraints. Therefore, innovative methods are proposed to linearize these constraints, allowing the continued use of MILP as the optimization method and factoring in the lifetime degradation of FCS and BESS. Compared to the mixed-integer non-linear programming (MINLP) method that does not linearize the non-linearity in the FCS or the lifetime degradation of the FCS and BESS, the proposed MILP scheme achieves the identical objective function value. Furthermore, the computation time for the proposed MILP scheme is 99.94 % lower than that required by the MINLP method. The proposed scheme provides accurate results in short computation times.

A theoretical thermodynamic investigation on solar-operated combined electric power, heating, and ejector cooling cycle driven by an ORC turbine waste heat, tri-generation cycle system

The current study investigated a solar thermal power plant to simultaneously produce multiple energy outputs, including electric power, process heating, and cooling. This integrated approach aligns with the concept of a tri-generation system, where three different forms of energy are produced from a single source. The study involves a parametric analysis, where the impact of each influencing parameter including, turbine inlet temperature, and turbine inlet pressure is systematically varied with DNI ranging from 600 W/m2 to 1000 W/m2 to understand its effects on both first-law and second-law efficiencies. Further, the exergy analysis shows the irreversibility's occur within the system. Notably, the central receiver and heliostat are identified as major sources of exergy loss. The comparison to a combined power and heating (CPH) cycle without an ORC-ejector indicates that the additional technologies significantly contribute to improving the system's performance, approximately 3.97 % and 2.3 % for both efficiencies, respectively.

Sky localization of space-based detectors with time-delay interferometry

The accurate sky localization of gravitational wave (GW) sources is an important scientific goal for space-based GW detectors. The main differences between future space-based GW detectors, such as Laser Interferometer Space Antenna (LISA), Taiji, and TianQin, include the time-changing orientation of the detector plane, the arm length, the orbital period of the spacecraft and the noise curve. Because of the effects of gravity on three spacecraft, it is impossible to maintain the equality of the arm length, so the time-delay interferometry (TDI) method is needed to cancel out the laser frequency noise for space-based GW detectors. Extending previous work based on equal-arm Michelson interferometer, we explore the impacts of different first-generation TDI combinations and detector's constellations on the sky localization for monochromatic sources. We find that the sky localization power is almost unaffected by the inclusion of the TDI Michelson (X, Y, Z) combination in the analysis. We also find that the variation in the sky localization power for different TDI combinations is entirely driven by the variation in the sensitivities of these combinations. For the six particular TDI combinations studied, the Michelson (X, Y, Z) combination is the best for source localization.

A novel configuration optimization approach for IES considering exergy-degradation and non-energy costs of equipment

Improving the exergy utilization and economic performance is the core goal for the configuration optimization of the integrated energy system (IES). Achieving such a goal requires accurate evaluation of the exergy efficiency and economy. However, IES is a multisource heterogeneous and strongly coupled system. The development of reasonable exergy efficiency-economy characteristic evaluation indicators is a complex issue that limits its application in IES configuration. This article proposes a novel IES configuration optimization approach based on the concept of the equipment exergyeconomic cost coefficient. The exergyeconomic cost coefficient consists of the exergy-degradation and non-energy costs, which can quantitatively characterize the exergy efficiency and economy of the IES under a unified benchmark. A mechanism model for the equipment exergyeconomic cost coefficient, output of various equipment and total exergyeconomic cost is established. Furthermore, exergy flow attribute classification method is proposed to obtain the equipment exergyeconomic cost coefficient in the IES. The proposed method is verified in a real-world study case located in Yancheng, China. Compared to the conventional configuration method, the capacity of the renewable energy and combined heat and power generation (CHP) are enhanced, while the capacity of purchased electricity and electric boiler (EB) are reduced. It can obtain the minimum total exergyeconomic cost (TEC) for the IES. This study offers a new direction for the optimal configuration of IES.

Probing the physics of star formation (ProPStar)

Context. Turbulence is a key component of molecular cloud structure. It is usually described by a cascade of energy down to the dissipation scale. The power spectrum for subsonic incompressible turbulence is ∝k−5/3, while for supersonic turbulence it is ∝k−2. Aims. We determine the power spectrum in an actively star-forming molecular cloud, from parsec scales down to the expected magnetohydrodynamic (MHD) wave cutoff (dissipation scale). Methods. We analyzed observations of the nearby NGC 1333 star-forming region in three different tracers to cover the different scales from ∼10 pc down to 20 mpc. The largest scales are covered with the low-density gas tracer 13CO (1–0) obtained with a single dish, the intermediate scales are covered with single-dish observations of the C18O (3–2) line, while the smallest scales are covered in H13CO+ (1–0) and HNC (1–0) with a combination of NOEMA interferometer and IRAM 30m single-dish observations. The complementarity of these observations enables us to generate a combined power spectrum covering more than two orders of magnitude in spatial scale. Results. We derive the power spectrum in an active star-forming region spanning more than 2 decades of spatial scales. The power spectrum of the intensity maps shows a single power-law behavior, with an exponent of 2.9 ± 0.1 and no evidence of dissipation. Moreover, there is evidence that the power spectrum of the ions to have more power at smaller scales than the neutrals, which is opposite to the theoretical expectations. Conclusions. We show new possibilities for studying the dissipation of energy at small scales in star-forming regions provided by interferometric observations.

The Value of Biomarkers in Major Cardiovascular Surgery Necessitating Cardiopulmonary Bypass.

The use of biomarkers in cardiovascular surgery is an evolving field with promising potential; however, current research remains largely limited, requiring further validation for routine clinical application. This review explores the application of biomarkers in cardiovascular surgery, focusing on heart failure, cardiac ischemia, and organ dysfunction, including renal, cerebral, pulmonary, and splanchnic impairments. Additionally, it examines the significance of biomarkers in assessing the inflammatory state and oxidative stress during the perioperative period, particularly in the context of major surgical trauma and cardiopulmonary bypass (CPB). From January 2018 to June 2024, we reviewed 133 studies and four systematic reviews and meta-analyses using the Medline, Embase, and Central databases, screening for pre- or postoperative biomarker levels in patients undergoing cardiac surgery. Outcomes of interest were postoperative mortality, nonfatal myocardial infarction, stroke, congestive heart failure, and major adverse cardiovascular events (MACEs). Studies reporting multivariable-adjusted risk estimates were included. The findings revealed that cardiac troponins (cTns) and creatine kinase isoenzyme MB (CK-MB) remain the most widely utilized biomarkers for assessing myocardial injury post-surgery. These elevated biomarker levels were consistently associated with an increased risk of postoperative complications, including low cardiac output syndrome, prolonged ventilation, and mortality. Emerging biomarkers, such as heart-type fatty acid-binding protein (h-FABP) and high-sensitivity C-reactive protein (hs-CRP), demonstrated promising early detection and risk stratification results. In particular, h-FABP increased rapidly within one hour of myocardial injury, peaking at 4-6 hours and returning to baseline within 24 hours. This rapid clearance makes h-FABP a valuable tool for early myocardial injury detection, potentially allowing for timely interventions. Inflammatory biomarkers, including hs-CRP and pentraxin 3 (PTX3), were found to be associated with poor outcomes, such as increased morbidity and mortality. Elevated preoperative levels of these markers were indicative of a heightened inflammatory response, correlating with worse postoperative recovery and higher rates of complications. Furthermore, the neutrophil-to-lymphocyte ratio (NLR) emerged as a cost-effective and easily accessible predictor of postoperative outcomes. Elevated NLR values were linked to an increased risk of adverse events, including prolonged ventilation, low cardiac output syndrome, and overall mortality. Further, the practicality of measuring NLR through routine blood tests makes it viable for widespread clinical use. In conclusion, integrating biomarkers in cardiovascular surgery significantly advances predicting postoperative outcomes for cardiac surgery patients. Therefore, it is essential to categorize these biomarkers into two distinct groups in the future, inflammatory and non-inflammatory (related to organ damage), to improve understanding and enhance their clinical applicability. Future research should focus on standardizing the use of these biomarkers and exploring their combined predictive power to enhance risk stratification and improve patient prognosis.

ARCHITECTURAL AND CIRCUIT MODEL OF NAVIGATION DEVICE FOR ULTRA-LIGHT LAUNCH VEHICLE

The paper considered the architectural and circuitry model of a navigation device for an ultra-lightweight launch vehicle designed to improve the accuracy and reliability of positioning and orientation in flight conditions. The study utilized modern approaches to the integration of global navigation satellite system (GNSS) and inertial navigation system (INS) to achieve high accuracy and stability of the system. The key components of the device including UM980 module, LSM6DSV32X inertial measurement module, STM32 F7 series microcontroller, E220-400T30S radio module, LMR50410XDBVR converter based power supply and RS422 interface were described in detail. The UM980 module, including SAW and LNA filter blocks, demonstrated the ability to efficiently process incoming RF signals. The NebulasIV main function block combined GNSS BB, GNSS RF, interfaces and power management unit (PMU) modules, supporting multiple communication interfaces such as UART, SPI and I²C, as well as RTK and PPS signals. The study analyzed the integration and accuracy aspects of the module in navigation systems. The signal processing scheme of the inertial navigation system module was also presented, including digital filters, free-fall and activity/inactivity detection functions, and interfaces for data communication. User bias functions and filter modes are considered to ensure the accuracy and stability of the navigation device. The work lays the groundwork for further improvements in navigation technology, especially in the context of increasing availability and accuracy for small scale space launches.