Steady-state Model Research Articles

Interstellar Neutral Hydrogen in the Heliosphere: New Horizons Observations in the Context of Models

Interstellar neutral (ISN) hydrogen is the most abundant species in the outer heliosheath and the very local interstellar medium (VLISM). Charge-exchange collisions in the outer heliosheath result in filtration, reducing the ISN hydrogen density inside the heliosphere. Additionally, these atoms are intensively ionized close to the Sun, resulting in a substantial reduction of their density within a few astronomical units from the Sun. The products of this ionization—pickup ions (PUIs)—are detected by charged particle detectors. The Solar Wind Around Pluto instrument on New Horizons provides, for the first time, PUI observations from the distant heliosphere. We analyze the observations collected between 22 and 52 au from the Sun to find the ISN hydrogen density profile and compare the results with predictions from global heliosphere models. We conclude that the density profile derived from the observations is inconsistent with steady-state model predictions. This discrepancy is not explained by time variations close to the Sun and thus may be related to the temporal evolution of the outer boundaries or VLISM conditions. Furthermore, we show that the cold and hot models of ISN hydrogen distribution are not a good approximation closer to the termination shock. Therefore, we recommend a new fiduciary point based on the available New Horizons observations at 40 au from the Sun, at ecliptic direction (285.°62, 1.°94), where the ISN hydrogen density is 0.11 cm−3. The continued operation of New Horizons should give better insight into the source of the discussed discrepancy.

An effective parametric sensitivity analysis to improve electrical and thermal energy/exergy efficiency of PVT system using nanofluid

A 3D steady-state model of a photovoltaic/Thermal system with nanofluid is studied to investigate the electrical and thermal performance of PVT by utilizing a combination of statistical modeling and numerical simulation. The 3D CAD (Computer Aided Design) modeling of the photovoltaic thermal (PV/T) is performed using Ansys Space Claim. The current study is based on using a 2-way coupling FSI approach for modeling nanofluid-based PVT systems. Five output responses electrical power, thermal power, electrical exergy, thermal exergy, and entropy generation are chosen against four input parameters, solar radiation (G_sun), ambient temperature (Tamb), mass flow rate (ṁ), and nanoparticles concentration % (φ) to analyze system performance. A statistical prediction model is developed using response surface methodology (RSM) that starts from the design of experiment (DOE). The predicted outcomes obtained from the statistical model and the numerical simulation of the heat transfer model exhibit the existence of good fitness. Furthermore, optimization is performed using RSM for various optimization objectives to suggest the ideal design parameter values for optimal system performance. Sensitivity analysis illustrates that a rise in ambient temperature causes a drop in electrical power production and leads to an increase in thermal power. However, the increase in solar radiation boosts electrical power and causes a drop in thermal exergy, entropy generation increases with an increase in solar irradiation. Similarly, an increase in mass flow rate leads to an increase in thermal power. Multi-objective optimization with the highest composite desirability values depicts the influence of input design parameters and has a favorable effect on the objective of concurrently maximizing electrical and thermal power.

A computational study for simulating MHD duct flows at high Hartmann numbers using a stabilized finite element formulation with shock-capturing

The concern of this manuscript is stabilized finite element simulations of two-dimensional incompressible and viscous magnetohydrodynamic (MHD) duct flows governed by a coupled system of partial differential equations of the convection–diffusion type. In such flows, the high values of the Hartmann numbers (Ha) indicate that the convection process dominates the flow field. As is typical trouble encountered in convection-dominated flow simulations, classical discretization methods fail to function properly for MHD flows at high Hartmann numbers, yielding several numerical instabilities. In this study, the core computational tool we employ in order to overcome such challenges is the streamline-upwind/Petrov–Galerkin (SUPG) formulation. Beyond that, since the SUPG-stabilized formulation requires additional treatment where solutions experience rapid changes, we also enhance the SUPG-stabilized finite element formulation with a shock-capturing operator for achieving better solution profiles around sharp layers. The formulations are derived for both steady-state and time-dependent models. The proposed method, the so-called SUPG-YZβ formulation, techniques used, and in-house-developed finite element solvers are evaluated on a comprehensive set of numerical experiments. The test computations reveal that the SUPG-YZβ formulation yields quite good solution profiles near strong gradients without any significant numerical instabilities, even for quite challenging values of Ha, whereas the SUPG usually fails alone. Furthermore, by using only linear elements on relatively coarser meshes, the proposed formulation achieves this without the need for any adaptive mesh strategy, in contrast to most previously reported studies.

Effects of joint tolerances on thermal bridging in precast concrete shear walls: Field tests and numerical simulations

Prefabricated buildings are gaining momentum due to their ecological benefits, speedy construction, and precision. However, unlike conventional cast-in-place structures, precast concrete shear walls are more susceptible to thermal and quality defects due to inherent tolerances. Four precast concrete shear wall connections prone to thermal bridges were identified through case studies of factories and real projects. Steady-state and dynamic modeling was conducted using determinant and stochastic matrices to assess the impact of joint tolerances on thermal distribution. The effects of joint tolerance magnitude and insulation material properties on linear thermal transmittance were investigated, complemented by a polynomial-based sensitivity analysis. A versatile prediction model was developed, integrating physical modeling and data-driven approaches to quickly quantify the thermal bridges of precast concrete shear wall connections. The results revealed a significant positive correlation between thermal bridges and joint tolerances, leading to extensive low-temperature regions within precast concrete shear walls. In dynamic modeling, joint tolerances contributed to additional heat dissipation, with a peak heat flux of 20.07 W/m. The sensitivity analysis demonstrated that joint tolerances greatly influence the correlation between linear thermal transmittance and insulation layer properties. Leveraging Categorical Boosting and Extreme Gradient Boosting, the prediction of linear thermal transmittance for precast concrete shear wall connections exhibited exceptional accuracy while significantly reducing computational costs. This research aids designers and policymakers by providing rapid and precise linear thermal transmittance calculations, offering valuable guidance for manufacturing prefabricated components to ensure enhanced energy efficiency and sustainability.

Modeling of a heat-integrated biomass downdraft gasifier: Influence of feed moisture and air flow

A model for a heat-integrated biomass downdraft gasifier is developed and used to study the influence of changes in biomass moisture content and gasifier air flow. This one-dimensional steady-state model accounts for pyrolysis, combustion and gasification reaction kinetics as well as transport phenomena occurring within the gasifier and heat integration system. The gasifier is divided into four zones for solving the ordinary differential equations (ODEs), because each zone has different geometry for the reactor or heating system. The material and energy balance ODEs are solved as a boundary value problem (BVP), ensuring that conditions for the producer gas at the bottom of the reactor match the conditions of the countercurrent annulus gas, which is used for heating. The model also accounts for the preheating of the biomass using exhaust gas from an associated engine used to generate electricity from the producer gas. The model predicts the process gas temperature, flow rate and composition and was validated using two experimental runs with different control inputs. The model predictions show good agreement with the data. Simulations with the highest feed moisture result in lower reactor temperatures and simulations with the highest air flow result in the highest reactor temperatures.

Engine & vehicle modeling for fuel assessment under local driving conditions

Automotive biofuels offer a promising alternative to traditional fossil fuels. Accurate evaluation of combustion and emissions in IC engines and vehicles is crucial. This research aimed at developing and validating an engine and vehicle simulation methodology to assess the fuel effect on vehicle consumption and emissions considering different driving cycles and the road slope (barely evaluated for fuels widely used in emerging markets). Two blends were tested: 20 % biodiesel (B20) and 20 % hydrotreated vegetable oil (HVO20) with Ultra-Low-Sulfur Diesel (ULSD). A light-duty diesel vehicle model was developed in GTSuite®, using emission maps from a calibrated steady-state engine model. Good agreement with experiments was found. Road slope in local DC significantly increased fuel consumption and CO, CO2, NOx, and PN emissions, reducing HC. Compared to ULSD, B20 reduced PN and HC by 27–35 % and 12–22.5 %, respectively. HVO20 had a smaller effect on PN but reduced HC emissions by up to 19.5 %. Neither blend significantly affected CO and CO2. B20 slightly increased NOx and fuel consumption, while HVO20 had no significant impact on these.

Toward mending the marine mass balance model for nickel: Experimentally determined isotope fractionation during Ni sorption to birnessite

In fewer than fifteen years, the study of Ni stable isotopes has advanced from early method development to application of a powerful tool for resolving a long-standing question: why does it appear that output fluxes of Ni from the global oceans far exceed input fluxes? The seawater concentration of Ni, a bioessential trace metal, is almost certainly at steady state on timescales comparable to its residence time of ∼20 kyr, so some of the current flux estimates must be inaccurate. Just as the input and output fluxes should balance, so should the flux-weighted isotopic compositions of the inputs and outputs. Thus, isotopic characterization of inputs and outputs provide an additional constraint on a balanced model of the marine Ni budget. Here, we report on experiments designed to elucidate fractionation mechanisms and magnitudes for sorption of Ni to Mn oxyhydroxide (birnessite), because Mn-rich sediments accumulating on abyssal plains represent the largest sink flux of Ni from seawater to marine sediments. Our results show remarkably large fractionations at low ionic strength (average Δ60/58Nidissolved-sorbed = +1.38 ‰). Neither closed-system equilibrium trends nor Rayleigh curves fit the data well. Fractionations are even larger at high ionic strength (Δ60/58Nidissolved-sorbed ranging from +2.0 to +4.0 ‰), and they decrease with experimental duration from 2 d (49 h) to 27 d. The high ionic strength data fit Rayleigh trends well. Here, we use X-ray absorption fine-structure spectroscopy (EXAFS) and results from previous studies to support interpretation of our data as combinations of kinetic and equilibrium isotope effects that vary in their proportional contributions to the total fractionation with time and with surface loading. One important consequence of this study is that none of the experimental results reported thus far, including ours, are directly applicable to building steady-state models of the Ni cycle. Even our longest duration experiments did not achieve equilibrium, which is likely to be manifest in the very slowly accumulating sediments on abyssal plains. Our work constrains further the mechanisms of Ni sorption to birnessite and clearly indicates that determination of equilibrium fractionation in this system, although challenging, will be a crucial step toward resolving the apparent marine Ni imbalance.

Magnetic field dragging in filamentary molecular clouds

Context. Maps of polarized dust emission of molecular clouds reveal the morphology of the magnetic field associated with star-forming regions. In particular, polarization maps of hub-filament systems show the distortion of magnetic field lines induced by gas flows onto and inside filaments. Aims. We aim to understand the relation between the curvature of magnetic field lines associated with filaments in hub-filament systems and the properties of the underlying gas flows. Methods. We consider steady-state models of gas with finite electrical resistivity flowing across a transverse magnetic field. We derive the relation between the bending of the field lines and the flow parameters represented by the Alfvén Mach number and the magnetic Reynolds number. Results. We find that, on the scale of the filaments, the relevant parameter for a gas of finite electrical resistivity is the magnetic Reynolds number, and we derive the relation between the deflection angle of the field from the initial direction (assumed perpendicular to the filament) and the value of the electrical resistivity, due to either Ohmic dissipation or ambipolar diffusion. Conclusions. Application of this model to specific observations of polarized dust emission in filamentary clouds shows that magnetic Reynolds numbers of a few tens are required to reproduce the data. Despite significant uncertainties in the observations (the flow speed, the geometry and orientation of the filament), and the idealization of the model, the specific cases considered show that ambipolar diffusion can provide the resistivity needed to maintain a steady state flow across magnetic fields of significant strength over realistic time scales.

Prediction of geothermal temperature field by multi-attribute neural network

Hot dry rock (HDR) resources are gaining increasing attention as a significant renewable resource due to their low carbon footprint and stable nature. When assessing the potential of a conventional geothermal resource, a temperature field distribution is a crucial factor. However, the available geostatistical and numerical simulations methods are often influenced by data coverage and human factors. In this study, the Convolution Block Attention Module (CBAM) and Bottleneck Architecture were integrated into UNet (CBAM-B-UNet) for simulating the geothermal temperature field. The proposed CBAM-B-UNet takes in a geological model containing parameters such as density, thermal conductivity, and specific heat capacity as input, and it simulates the temperature field by dynamically blending these multiple parameters through the neural network. The bottleneck architectures and CBAM can reduce the computational cost while ensuring accuracy in the simulation. The CBAM-B-UNet was trained using thousands of geological models with various real structures and their corresponding temperature fields. The method’s applicability was verified by employing a complex geological model of hot dry rock. In the final analysis, the simulated temperature field results are compared with the theoretical steady-state crustal ground temperature model of Gonghe Basin. The results indicated a small error between them, further validating the method's superiority. During the temperature field simulation, the thermal evolution law of a symmetrical cooling front formed by low thermal conductivity and high specific heat capacity in the center of the fault zone and on both sides of granite was revealed. The temperature gradually decreases from the center towards the edges.

K-CONTENT PRODUCTION AND GLOBAL DISTRIBUTION STRATEGIES

The purpose of this study is to analyze the production characteristics of K-content and find ways to make it sustainably competitive for global distribution. The research questions of this study are as follows: First, what are the characteristics of K-content production? Second, what are the global distribution strategies for the sustainable development of K-content? The results of the study show that PDs of K-content, which has recently become popular in OTT, have developed their production capabilities in an organizational culture that tolerates failure and provides many opportunities. The fierce environment of the Korean content industry has made K-content more competitive, and the combination with global platforms has also played an important role in its growth into global content. In the current OTT-centered video content ecosystem, the domestic video industry is losing its competitiveness. To strengthen the competitiveness of K-content in the future, new creators must enter the digital content ecosystem to realize a virtuous cycle of continuous creation.

Simulation Study of Thermal Performance on the Inner and Outer Surfaces of Semi-Transparent Photovoltaic (STPV) Glass with Different Transparency

This paper used the experimentally verified one-dimensional steady-state heat transfer numerical model of the semi-transparent photovoltaic (STPV) glass to simulate the influence of wind speed, ambient temperature and air gap thickness on the surface temperature of PV modules, and analyzed the thermal performance of the inner and outer surfaces of STPV glazing with different transparency. Results shows that for every 1m/s increase in wind speed, the outer surface temperatures of SL-STPV glass with transparency of 10%, DL-STPV glass with transparency of 20% and 40% decreased by 3 °C, 3.02 °C and 2.22 °C, respectively. For every 10 °C increase in ambient temperature, the outer surface temperatures of SL-STPV glass with transparency of 10%, DL-STPV glass with transparency of 20% and 40% increase by 8.45 °C, 7.6 °C, and 7.79 °C, respectively. For the DL-STPV glass with transparency of 20% and 40%, for every 2 mm increase in the thickness of the air gap, the outer surface temperature increases by 0.13 °C and 0.12 °C, respectively. The heat transfer between the inner surface of the glass and the indoor environment increases the heat gain through the glass into the room in summer, which will lead to an increase in refrigeration energy consumption. However, in winter, the heat exchange between the inner surface and the in-door air to heat the indoor air reduces the heating energy consumption to a certain extent, and achieves the effect of building energy conservation to a certain extent.

A multiscale interphase heat transfer model for fluidized beds based on the steady-state EMMS approach

Fluidized beds are characterized by heterogeneous structures, which significantly influence the interphase drag and heat transfer. To account for the effects of sub-grid structures on heat transfer in coarse-grid simulations, this study proposes a multiscale heat transfer model based on the steady-state energy-minimization multi-scale (EMMS) approach. This model introduces four structure-dependent internal energy balance equations into the steady-state EMMS model. The heat source and sink terms are included in the internal energy balance equation to maintain a steady-state condition for heat transfer. Solving the model yields the functions of both multiscale drag and heat transfer coefficients. These functions are integrated into coarse-grid simulations under the two-fluid model framework. The simulation results are fairly consistent with the experimental data in terms of both flow and temperature fields, with flow regimes covering bubbling fluidization, fast fluidization and dilute transport.

Full year performance analysis and steady state operation model for a stationary Shadeless solar thermal collector with a horizontal aperture for steam generation

This work documents a full-year performance of a new design of a non-tracking zero-latitude-tilt external compound parabolic concentrator (XCPC) solar thermal system called Non-tracking Asymmetric Shadeless (NASH) concentrator. The system has a horizontal-aperture design that offers several advantages in terms of land use efficiency because of zero row-to-row spacing, reduced capital costs, and improved heat management. The horizontal aperture (no tilt) design enables it to be scaled to a large area easily without lost area from row-to-row shading as experienced by a tilted design. The system was tested at the University of California Merced, Castle test facility for a full year. The data are analyzed to investigate the system efficiency and thermal energy generated during the year. The system generated 766 kWh/m2 during 2022 with annual efficiency of 41 %. A steady-state model is developed to predict the system performance based on the direct- and diffuse-light optical efficiencies, radiative and manifold heat losses, and observed soiling rate. The system efficiency decreased by up to 14 % over a month due to soiling in this test location. The model gives a good estimation of the steady-state operation during July and predicts the general annual trend of the generated thermal energy.

Performance Assessment Analyzes of Research Nuclear Reactors Based on Steady-state and Kinetic Models

The mainstays of nuclear substance radiation and isotopic synthesis are nuclear-powered power plants, however effective safety evaluation is made tougher by the complicated construction topologies and physical connection effects. This work proposes a multiphysics-linked technique for evaluating both the kinetic and steady-state behaviors of the MPRR and LVR-15 laboratory reactors. To represent complicated member geometries, homogenized assembling sections are generated using two-dimensional whole-core computational simulations. It is discovered that the steady-state findings and the so-called Monte Carl solution comparisons correspond quite nicely. The greatest assemble power mistakes for LVR-15 and MPRR are 6.49%/10%, and the highest command rod value mistakes are 31 pcm/136 pcm, and the mistakes are 377 pcm/383 pcm, accordingly. Meanwhile, the study is done on transitory procedures, such as reactivity-initiated disasters and exposed loss-of-flow mishaps. Both units' modeling findings show plausible adverse feedback events. Furthermore, it is shown that the two reactors' accident-related behaviors are comparable though having different core architectures since they employ the exact same kinds of water as a fluid. The technique for studying nuclear power plant kinetics known as Multi-Physics Simulation (MPM) is explained. Drawing on many research and verification efforts conducted at Politecnico di Milan, Italy, MPM is shown to be a valuable instrument for managing reactors' security and oversight. It may be viewed as a holistic analytical tool that is implemented during the reactor architecture design phase. The capacity to concurrently answer the interrelated equations that control the many physical processes taking place in a nuclear plant inside the same simulated setting is a core characteristic of MPM.

Control-oriented modeling of direct-heat co-current rotary dryers for energy demand flexibility

The contribution presents a new control-oriented model for direct-heat co-current rotary dryers with a focus on demand-side flexibility considering the variation of material throughput and heat source. The dryer model is based on partial differential-algebraic equations covering time- and location-dependent gas and particle velocities taking phase interactions into account. In addition, a steady-state model of a heat supply unit covering electricity-based and chemical heat sources is presented. The model is qualitatively compared with measured data and models from literature showing good overall agreement. Furthermore, simulations of the flexibility mechanisms are performed and their effect on temperature and humidity profiles as well as input and output streams of the dryer are analyzed. On this basis, requirements to process control are discussed. The simulations show that the variation of the throughput is associated with significant changes to the gas input stream, while only small changes are observed for the variation of the energy carrier.

Management of tree density to sustain Kirtland’s warbler breeding habitat and timber production from jack pine plantations

Maintaining the recovery of the formerly federally endangered Kirtland’s warbler (Setophaga kirtlandii) requires continual creation of early-successional jack pine (Pinus banksiana) habitat every year, which, in turn, requires a steady supply of commercial timber sales. However, dedicated Kirtland’s warbler habitat plantations are established at high stem densities (ca. 3600 trees ha−1) which can delay the production of marketable timber compared to a conventional forestry planting density (ca. 1900 trees ha−1). We used a retrospective approach to assess the impacts of tree density on Kirtland’s warbler occupancy, and an experimental approach to understand benefits and costs of pre-commercial thinning of Kirtland’s warbler habitat plantations. We observed an unexpected negative relationship between realized plantation density and maximum occupancy by Kirtland’s warbler singing males. We found that thinning of high-density jack pine planted for Kirtland’s warbler habitat resulted in increased diameter growth, increased live crown ratio, and a trend towards increased intrinsic water-use efficiency in the older age classes. These findings provide strong support for the importance of using adaptive management approaches to more rigorously evaluate the impacts of plantation spacing on Kirtland’s warbler productivity. Although increases in volume growth and timber prices following thinning are unlikely to ever recoup the costs of implementation, we argue that thinning could still be an important tool to accelerate the rate at which these stands attain marketability for the timber sales that are necessary to create a continuous supply of Kirtland’s warbler breeding habitat.

Analytic discrete state-space model of LLC resonant converter

The LLC resonant converter has widely been used for having high-frequency soft-switching capability which could lead to high power density power electronic solutions, and higher power conversion efficiency at lower costs. However, the presence of excessive non-linearity resulting from power conversion through the resonant tank has led to a lack of accurate and reliable large-signal and small-signal models which limits the performance optimization. This paper presents a theoretical analysis of the LLC converter using the sampled-data modeling approach to accurately convert the continuous-time-variant state equations to discrete-time-invariant state equations and solve them using switching time-segmented boundary conditions to obtain the discrete large-signal model. The small-signal model is obtained by perturbation and linearization of the steady-state model in the vicinity of the cyclic equilibrium states of the converter. Theoretical analysis results are validated by simulating a 650W LLC converter in Matlab Simulink. Based on the provided results, the proposed steady-state and small-signal models can accurately predict the dynamic behavior of the LLC converter in different operational conditions.

An integrated system based on liquid air energy storage, closed Brayton cycle and solar power: Energy, exergy and economic (3E) analysis

In pursuing net zero emissions amid increasing energy consumption, renewable energy sources offer a path towards environmental sustainability. However, they are plagued by instability and intermittency. Energy storage systems have emerged as a solution to address these challenges, ensuring a stable power supply despite the fluctuations of renewables. Liquid air energy storage (LAES) has advantages over compressed air energy storage (CAES) and Pumped Hydro Storage (PHS) in geographical flexibility and lower environmental impact for large-scale energy storage, making it a versatile and sustainable large-scale energy storage option. However, research on integrated closed Brayton cycle (CBC) systems with LAES is still in infancy. A novel integrated system is proposed, incorporating LAES, CBC and solar power. Steady-state models for LAES and CBC were developed and validated in Aspen Plus® V12. A comprehensive and systematic evaluation of the proposed LAES-CBC system was performed. The optimal round-trip efficiency (RTE) reaches up to 68.82 %, improving 11.70 % compared to the base LAES-CBC system. Parametric analysis reveals that higher compressor outlet pressures enhance both exergy efficiency and RTE. Compressors contribute significantly to exergy destruction, particularly in the charging process. The optimal outlet pressure for PUMP1 is found to be 80 bar. Economic analysis shows a reasonable payback period of 8.60 years and 0.307 $/kWh of LCOS, confirming the system's financial viability.

Energy Cost Optimization for Incorporating Energy Hubs into a Smart Microgrid with RESs, CHP, and EVs

The energy carrier infrastructure, including both electricity and natural gas sources, has evolved and begun functioning independently over recent years. Nevertheless, recent studies are pivoting toward the exploration of a unified architecture for energy systems that combines Multiple-Energy Carriers into a single network, hence moving away from treating these carriers separately. As an outcome, a new methodology has emerged, integrating electrical, chemical, and heating carriers and centered around the concept of Energy Hubs (EHs). EHs are complex systems that handle the input and output of different energy types, including their conversion and storage. Furthermore, EHs include Combined Heat and Power (CHP) units, which offer greater efficiency and are more environmentally benign than traditional thermal units. Additionally, CHP units provide greater flexibility in the use of natural gas and electricity, thereby offering significant advantages over traditional methods of energy supply. This article introduces a new approach for exploring the steady-state model of EHs and addresses all related optimization issues. These issues encompass the optimal dispatch across multiple carriers, the optimal hub interconnection, and the ideal hub configuration within an energy system. Consequently, this article targets the reduction in the overall system energy costs, while maintaining compliance with all the necessary system constraints. The method is applied in an existing Smart Microgrid (SM) of a typical Greek 17-bus low-voltage distribution network into which EHs are introduced along with Renewable Energy Sources (RESs) and Electric Vehicles (EVs). The SM experiments focus on the optimization of the operational cost using different operational scenarios with distributed generation (DG) and CHP units as well as EVs. A sensitivity analysis is also performed under variations in electricity costs to identify the optimal scenario for handling increased demand.

Removal of Carbon Dioxide and Hydrogen Sulfide from Natural Gas Using a Hybrid Solvent of Monoethanolamine and N-Methyl 2-Pyrrolidone.

The main goal of traditional methods for sweetening natural gas (NG) is to remove hydrogen sulfide (H2S) and significantly lower carbon dioxide (CO2). However, when NG processes are integrated into the carbon capture and storage (CCS) framework, there is potential for synergy between these two technologies. A steady-state model utilizing a hybrid solvent consisting of N-methyl-2-pyrrolidone (NMP) and monoethanolamine (MEA) has been developed to successfully anticipate the CO2 and H2S capture process from NG. The model was tested against important variables affecting process performance. This article specifically explores the impact of operational parameters such as lean amine temperature, absorber pressure, and amine flow rate on the concentrations of CO2 and H2S in the sweet gas and reboiler duty. The result shows that hybrid solvents (MEA + NMP) perform better in removing acid gases and reducing reboiler duty than conventional chemical solvent MEA. The primary purpose is to meet product requirements while consuming the least energy possible, which is in line with any process plant's efficiency goals.