Optimal Operation Research Articles

System simulation and parametric analysis of low-concentration coal mine gas fed SOFC-CHP with deoxygenation and enrichment pretreatment

Solid oxide fuel cell (SOFC) combined heat and power (CHP) can efficiently convert low-concentration coal mine gas (LC-CMG) into clean energy, avoiding the greenhouse effect of LC-CMG. Deoxygenation and methane enrichment pretreatment of LC-CMG can prevent explosion and anode oxidation risk. Herein, a chemical process model of LC-CMG fed SOFC-CHP system with the pretreatment was established to predict the performances under different operating conditions. Performance evaluation and exergy analysis were performed considering the use of oxygen-rich gas from the pretreatment at SOFC cathode. The effects of key operation parameters on system performances were investigated in terms of residual oxygen concentration, inlet methane concentration, fuel flow and fuel utilization. The results indicated that net electrical efficiency and heat supply efficiency can respectively reach 49.13 % and 23.90 % using LC-CMG of 15 % CH4 with enhancing effect of oxygen-rich gas at cathode. The decrease of methane concentration and the increase of residual oxygen concentration and flow rate caused the decreased electrical efficiency but the increased heat supply efficiency due to the enlargement of concentration polarization and heat supply. The optimal operation parameters were recognized by comprehensively considering diverse performance indicators. This work can guide system integration and operation optimization of LC-CMG fed SOFC-CHP.

Machine learning enables electrical resistivity modeling of printed lines in aerosol jet 3D printing.

Among various non-contact direct ink writing techniques, aerosol jet printing (AJP) stands out due to its distinct advantages, including a more adaptable working distance (2-5mm) and higher resolution (~ 10μm). These characteristics make AJP a promising technology for the precise customization of intricate electrical functional devices. However, complex interactions among the machine, process, and materials result in low controllability over the electrical performance of printed lines. This significantly affects the functionality of printed components, thereby limiting the broad applications of AJP. Therefore, a systematic machine learning approach that integrates experimental design, geometrical features extraction, and non-parametric modeling is proposed to achieve printing quality optimization and electrical resistivity prediction for the printed lines in AJP. Specifically, three classical convolutional neural networks (CNNs) architectures are compared for extracting representative features of printed lines, and an optimal operating window is identified to effectively discriminate better line morphology from inferior printed line patterns within the design space. Subsequently, three representative non-parametric machine learning techniques are employed for resistivity modeling. Following that, the modeling performances of the adopted machine learning methods were systematically compared based on four conventional evaluation metrics. Together, these aspects contribute to optimizing the printed line morphology, while simultaneously identifying the optimal resistivity model for accurate predictions in AJP.

Stochastic modeling for the aggregated flexibility of distributed energy resources

This paper proposes an uncertainty modeling method for the aggregated power flexibility of DERs. Basically, both outer and inner approximated power-energy boundary models are utilized to describe the aggregated flexibility of controllable DERs. These power and energy boundary parameters are uncertain because the availability of controllable devices, such as electric vehicles and thermostatically controlled loads, cannot be precisely predicted. The optimal operation problem of the aggregator is thus formulated as chance-constrained programming (CCP). Then, a flexibility envelope searching algorithm based on the ALSO-X+ method is proposed to solve the CCP, the result of which is a conservative approximation of the original CCP but not as conservative as the Conditional Value-at-Risk approximation. After optimizing the aggregated power of the group of DERs, the decision at the aggregator level is disaggregated into the flexibility regions of individual DERs. Finally, the numerical test demonstrates the effectiveness and robustness of the proposed method.

Comprehensive comparative study of experimental and simulated critical heat flux in special high-flux cooling devices: The Short Helical Minichannel-Evaporators

Short Helical Minichannel Evaporators are compact heat exchangers characterized by small, helical channels that allow high heat flux dissipation by means of direct cooling. It is a promising cooling solution in applications requiring high heat transfer rates in a limited space, such as power electronics, machining processes, injection molding among others. Optimal and safe operation of these devices is critically dependent on accurate determination of the critical heat flux (CHF) when nucleate boiling changes to filmwise regime. Misestimation of the CHF could lead to system burnout or suboptimal performance, highlighting the need for reliable theoretical models. In this study, the performance of a theoretical model was compared with a series of experiments carried out on a custom-built test rig. The results show a strong correlation between the experimental data and the adapted Groeneveld model, with a Mean Absolute Error (MAE) of 8.51kWm−2, a Mean Error (ME) of −4.71kWm−2, and a Deviation (Dev) of 8.77kWm−2. Notably, 96.8% of the simulated values were within a 15% error band. The validated model is also applied to increase knowledge about design parameters beyond the experimental data. The study emphasizes the practicality of the Groeneveld model in accurately calculating the CHF for these specific heat exchangers, encouraging improvements for performance and operational safety.

Development of temperature-controlled batch and 3-column counter-current protein A system for improved therapeutic purification

Maximizing product quality attributes by optimizing process parameters and performance attributes is a crucial aspect of bioprocess chromatography process design. Process parameters include but are not limited to bed height, eluate cut points, and elution pH. An under-characterized chromatography process parameter for protein A chromatography is process temperature. Here, we present a mechanistic understanding of the effects of temperature on the protein A purification of a monoclonal antibody (mAb) using a commercial chromatography resin for batch and continuous counter-current systems. A self-designed 3D-printed heating jacket controlled the 1 mL chromatography process temperature during the loading, wash, elution, and cleaning-in-place (CIP) steps. Batch loading experiments at 10, 20, and 30 °C demonstrated increased dynamic binding capacity (DBC) with temperature. The experimental data were fit to mechanistic and correlation-based models that predicted the optimal operating conditions over a range of temperatures. These model-based predictions optimized the development of a 3-column temperature-controlled periodic counter-current chromatography (TCPCC) and were validated experimentally. Operating a 3-column TCPCC at 30 °C led to a 47% increase in DBC relative to 20 °C batch chromatography. The DBC increase resulted in a two-fold increase in productivity relative to 20 °C batch. Increasing the number of columns to the TCPCC to optimize for increasing feed concentration resulted in further improvements to productivity. The feed-optimized TCPCC showed a respective two, three, and four-fold increase in productivity at feed concentrations of 1, 5, and 15 mg/mL mAb, respectively. The derived and experimentally validated temperature-dependent models offer a valuable tool for optimizing both batch and continuous chromatography systems under various operating conditions.

Distributed Cooperative Optimal Operation of Multiple Virtual Power Plants Based on Multi-Stage Robust Optimization

This paper develops a distributed cooperative optimization model for multiple virtual power plant (VPP) operations based on multi-stage robust optimization and proposes a distributed solution methodology based on the combination of the alternating direction method of multipliers (ADMMs) and column-and-constraint generation (CCG) algorithm to solve the corresponding optimization problem. Firstly, considering the peer-to-peer (P2P) electricity transactions among multiple VPPs, a deterministic cooperative optimal operation model of multiple VPPs based on Nash bargaining is constructed. Secondly, considering the uncertainties of photovoltaic generation and load demand, as well as the non-anticipativity of real-time scheduling of VPPs in engineering, a cooperative optimal operation model of multiple VPPs based on multi-stage robust optimization is then constructed. Thirdly, the constructed model is solved using a distributed solution methodology based on the combination of the ADMM and CCG algorithms. Finally, a case study is solved. The case study results show that the proposed method can realize the optimal scheduling of renewable energy in a more extensive range, which contributes to the promotion of the local consumption of renewable energy and the improvement of the renewable energy utilization efficiency of VPPs. Compared with the traditional deterministic cooperative optimal operation method of multiple VPPs, the proposed method is more resistant to the risk of the uncertainties of renewable energy and load demand and conforms to the non-anticipativity of real-time scheduling of VPPs in engineering. In summary, the presented works strike a balance between the operational robustness and operational economy of VPPs. In addition, under the presented works, there is no need for each VPP to divulge personal private data such as photovoltaic generation and load demand to other VPPs, so the security privacy protection of each VPP can be achieved.

Performance analysis of a battery thermal management system combining thermoelectric, composite phase change material, and liquid cooling under extreme operating conditions

To maintain optimal operating temperatures for lithium-ion batteries under extreme operating conditions, a battery thermal management system (BTMS) integrating the thermoelectric cooler (TEC), liquid cooling, and composite phase change material (CPCM) is developed. Moreover, a transient numerical model has been established, considering thermal, electrical, and fluid multiphysics fields, to precise evaluation the system's performance. The outcomes indicate a decrease in both the maximum battery temperature and CPCM liquid fraction as the expanded graphite (EG) mass fraction, TEC cooling input current, and coolant flow speed increase. Furthermore, the temperature difference among batteries exhibits a decrease as the EG mass fraction increases, but experiences an elevation with an increase in both current and coolant flow speed. The BTMS features the lowest power consumption and optimal cooling performance at the EG mass fraction of 12 %, the TEC cooling input current of 3 A, and the coolant flow speed of 0.05 m/s. In preheating situations, the battery pack can achieve a temperature of 293.15 K starting from 263.15 K with the assistance of TEC preheating input currents of 4 A and 5 A, taking 5600 s and 2240 s, respectively, to complete the entire preheating procedure. This study will offer new insights into the advancement of the BTMS, allowing for the control of battery temperatures in high-temperature and high-discharge rate conditions, coupled with preheating at low-temperature.

Oxidative conversion of methane: maximizing the carbon yield in natural gas valorization via the C123 process

The Oxidative Conversion of Methane (OCoM) reactor, within the C123 process, is a novel concept for methane valorization into a product stream with a molar C2H4/CO/H2 composition of 1/1/1, i.e., suitable for further hydroformylation. This overcomes the limitations traditionally encountered during the Oxidative Coupling of Methane in the pursuit of a maximum ethylene yield. An experimental assessment was performed using a MnNaW/SiO2 catalyst under industrially relevant conditions (T=850°C, ptot=100 kPa, Wcat/FCH4,in = 2–2.5 kgcat s mol-1, CH4/O2 inlet molar ratio = 3 – 5 mol mol-1 and inlet CO2 molar fraction <20%) as a basis for kinetic model development. Simultaneously pursuing a C2H4/CO/H2 molar ratio of 1/1/1 and a maximum C2 yield, the impact of the operating conditions on the OCoM performance was simulated. Three different real feedstocks were assessed: natural gas, shale gas and biogas. Shale gas exhibited the highest OCoM potential, with as optimal operating conditions T=850°C, Wcat/FCH4,in = 6.5 kgcat s mol-1, CH4/O2 inlet molar ratio = 10 mol mol-1. The achieved C2H4/CO/H2 molar ratio amounted to 1/1.1/3.7 with a carbon yield (ethylene + CO) of 28% mol mol-1 and ethylene yield of 19% mol mol-1. Last, the considerations to validate the C123 process viability are given.

Multi-mode monitoring and energy management for photovoltaic-storage systems

The integration of photovoltaic generation systems and variable demand can cause instability in the distribution network, due to power fluctuations and the increase in reactants, particularly in the industrial sector. In response, photovoltaic units have been equipped with local storage systems, which eventually absorb power fluctuations and improve installation performance. However, during this procedure other functionalities that energy storage could provide are neglected. Consequently, this study provides a multi-mode energy monitoring and management model that enables voltage regulation, frequency regulation and reactive power compensation through the optimal operation of energy storage systems. With this objective, a smoothing control algorithm is developed that interacts with parameters of the electrical grid at the common connection point and also allows the compensation of reactive power based on an industrial demand profile. This strategy uses the Long short-term memory neural network of historical demand data prior to energy consumption with a relatively low RMSE of 1.2e-09. The results are previously validated in a development environment using a real-time OPAL-RT simulator and tests in the electrical Microgrid laboratory at the University of Cuenca. This configuration allows establishing a demand forecasting model that improves the supervision, automation and analysis of daily energy production. A series of results are provided and analyzed that demonstrate that the new tool allows taking advantage of the provision of multimode functionalities, achieving optimal voltage regulation and improving power quality by reducing the total harmonic distortion THD (V) and THD (I) indices by 0.5. % and 2 % respectively.

Optimal energy management based on real-time performance analysis for the solid oxide fuel cell-combined heat and power system

In order to realize the efficient and stable operation of the solid oxide fuel cell (SOFC)-combined heat and power (CHP) system in the typical applied scenario, three different energy management schemes are proposed in this study. First, a coupled calculation model of the solid oxide fuel cell-gas turbine-combined heat and power (SOFC-GT-CHP) system with a rated power of 30 kW for the SOFC stack is constructed. The inlet temperature and inlet pressure of the SOFC stack are optimized. The recommended design for the proposed system is thence obtained. Second, three operation schemes of SOFC-GT-CHP are proposed. They consist of the operation scheme of single-stack, multi-stack and optimal operation scheme coupled with vanadium redox flow battery (VRB). Finally, four types of typical applied scenarios in North China are selected. The real-time performance of the SOFC-GT-CHP system when applying three different operation schemes is obtained and comparatively analyzed. The results are presented as follows. First, the SOFC-GT-CHP system achieves a rated power of 44.50 kW and a rated system efficiency of 47.20 % when the inlet temperature and pressure of the stack are preferably 505 °C and 709.1 kPa. Second, the application of the multi-stack SOFC-GT-CHP system has the advantage of high efficiency in the low-load operation domain. With a net output power at 20 kW, the time average efficiency of SOFC-GT-CHP is increased from 38.37 % to 46.96 %. Finally, the SOFC-GT-CHP system achieves the highest time average generation efficiency of 46.13 % in the hotel applied scenario when applying the optimal operation scheme coupled with VRB. This meets the practical operation requirement of the SOFC stack. The reduction of the net output power range of SOFC-GT-CHP is 67.87 %. It shows that the optimal operation scheme is efficient and stable. The study can provide a reference for the energy management of the SOFC-GT-CHP system in typical applied scenarios.

Insights into the effect of operating parameters and hydrodynamics on hydrolysis reaction in Copper–Chlorine thermochemical water splitting cycle for hydrogen production: Modelling and experimental validation

Copper–Chlorine (Cu–Cl) thermochemical cycle is being explored as one of the promising technologies for large scale production of clean hydrogen from water using nuclear or renewable energy. In this method, water splitting is achieved through a series of reactions in a loop producing only hydrogen and oxygen with complete recycle of all the intermediate copper and chlorine compounds. The first step of the cycle is the hydrolysis reaction wherein steam reacts with solid CuCl2 to form solid Cu2OCl2 (copper oxychloride) and HCl gas. The products enter the subsequent steps of the cycle and are recycled back to complete the loop. Multiple side reactions in the hydrolysis step result in reduced yield and loss of chemicals from the cycle. Efficient reactor design and optimal operating conditions of hydrolysis reactor is imperative towards achieving sustained closed loop operation and reducing the energy demand of Cu–Cl cycle. This paper presents a mathematical model for the simulation of hydrolysis reaction in a bubbling fluidized bed reactor. The one-dimensional model is developed based on modified two-phase theory of fluidization and is coupled with kinetics of multiple side reactions to investigate the effect of operating parameters and hydrodynamics on the conversion and yield of Cu2OCl2. The model results are validated with experimental data in 50 mm and 100 mm semi-batch fluidized bed reactors. The developed model clearly brings out the effect of different process parameters for process optimization and scale-up of the hydrolysis reactor for efficient integration in the cycle.

The Influence of the Frequency of Ultrasound on the Exhaust Gas Purification Process in a Diesel Car Muffler

This research aimed to analyze the possibility of installing an ultrasonic emitter in an already manufactured car and to prove the possibility of cleaning the exhaust gases of an internal combustion engine through the action of an ultrasonic wave due to coagulation and examining the optimal regimes of its work. The existing theoretical solution to describe the proposed process was analyzed. A Mercedes-Benz M-Class ML 270 CDI MT car with the OM 612 DE 27 LA Diesel engine was used for the experiment. An ultrasound generator and an ultrasound emitter were connected to the muffler. The stand was connected to the car via the inlet with a rubber hose that directs the exhaust gases out of the car. The crankshaft speed of the engine was changed in the range of 750 to 1250 rpm, which corresponds to urban conditions when cars are moving in heavy traffic jams. The content of CH, CO, CO2, and O2 in the exhaust gas of the vehicle was determined as a function of the crankshaft speed without ultrasonic exposure and with ultrasonic exposure at an ultrasound frequency of 25, 28, and 40 kHz. The results of the experiment showed that the introduction of an ultrasonic emitter into the muffler reduced the smoke content of the gas, increased the oxygen content, and reduced the amount of carbon dioxide in the exhaust gases. With an increase in the ratio between the ultrasonic frequency and the angular velocity of the engine crankshaft (f/ω), the smoke content of the gas also decreased. At the maximum values of ultrasonic frequency and angular velocity of the engine crankshaft selected in the experimental studies, the minimum value of the ratio of gas smoke indicators was achieved, and the degree of purification was 10–13%. Such results correspond to the condition of optimal operation of the ultrasonic muffler, where the ratio of gas to smoke values should tend to a minimum. These results confirm the potential of using ultrasound as a method for cleaning exhaust gases and underline the need for further research in this area.

Optimization and Modeling of a Dual-Chamber Microbial Fuel Cell (DCMFC) for Industrial Wastewater Treatment: A Box–Behnken Design Approach

Microbial fuel cells (MFCs) have garnered significant attention due to their capacity to generate electricity using renewable and carbon-neutral energy sources such as wastewater. Extensive experimental work and modeling techniques have been employed to dissect these processes and understand their respective impacts on electricity generation. The driving force is to enhance MFC performance for practical applications commercially. Among the various statistical modeling approaches, one particularly robust tool is the Design of Experiments (DoE). It serves to establish the relationships between different variables that influence MFC performance and allows for the optimization of the MFC configuration and operation for scaled-up performances in terms of bioelectricity generation. This study focused on optimizing microbial fuel cells (MFCs) for bioelectricity production using industrial wastewater treatment, employing the Box–Behnken design (BBD) methodology. Through an analysis of response surface models and ANOVA tests, it was found that a combined approach of reduced quadratic, reduced two-factor interaction, and linear models yielded sound results, particularly in voltage yield, COD removal, and current density. Second-order regression models predicted optimal conditions for various parameters, with surface area, temperature, and catholyte dosage identified as critical input variables for optimization. Under these conditions, conducted by the four-factor and three-level Box–Behnken design methodology in a double-chamber MFC unit considering eight output variables—CCV yield, % COD removal, current density, power density, % TSS removal, % CE, and % PO43−—the optimum values were 700 mV, 54.4%, 54.4 mA/m2, 73.7 mW/m2, 99%, 21.2%, and 100%, respectively. At optimum operating conditions, the results revealed a desirability of 76.6% out of a total of 92 iterations. The paper highlights the effectiveness of statistical ANOVA fit-statistics modeling and optimization in enhancing DCMFC performance, recommending its use as a sustainable bioenergy source. Furthermore, validation results supported the above optimization output response findings and confirmed the viability of biorefinery wastewater as an anolyte for scaling up DCMFC bioelectricity generation.

Inducing a tunable skyrmion-antiskyrmion system through ion beam modification of FeGe films

Skyrmions and antiskyrmions are nanoscale swirling textures of magnetic moments formed by chiral interactions between atomic spins in magnetic noncentrosymmetric materials and multilayer films with broken inversion symmetry. These quasiparticles are of interest for use as information carriers in next-generation, low-energy spintronic applications. To develop skyrmion-based memory and logic, we must understand skyrmion-defect interactions with two main goals—determining how skyrmions navigate intrinsic material defects and determining how to engineer disorder for optimal device operation. Here, we introduce a tunable means of creating a skyrmion-antiskyrmion system by engineering the disorder landscape in FeGe using ion irradiation. Specifically, we irradiate epitaxial B20-phase FeGe films with 2.8 MeV Au4+ ions at varying fluences, inducing amorphous regions within the crystalline matrix. Using low-temperature electrical transport and magnetization measurements, we observe a strong topological Hall effect with a double-peak feature that serves as a signature of skyrmions and antiskyrmions. These results are a step towards the development of information storage devices that use skyrmions and antiskyrmions as storage bits, and our system may serve as a testbed for theoretically predicted phenomena in skyrmion-antiskyrmion crystals.

Dry Reforming of CH4 Using a Microreactor

In the present study, a comparison of the dry reforming of a gas mixture containing methane, carbon dioxide and nitrogen without contaminants to a ruthenium-based Ru/Al2O3 catalyst was carried out in a microreactor for the first time. The influence of the contact time, temperature and composition of the feed on the conversion was exhaustively investigated. The optimal operating conditions were found to be a contact time of 80 milliseconds, a temperature of 700 °C and a CH4:CO2 ratio of 1. The assessment of diffusional limitations reveals that there is no resistance to mass transfer, which reveals the potential benefit of the determination of intrinsic reaction kinetics within a microreactor.

Deep Fuzzy Min-Max Neural Network: Analysis and Design.

Fuzzy min-max neural network (FMNN) is one kind of three-layer models based on hyperboxes that are constructed in a sequential way. Such a sequential mechanism inevitably leads to the input order and overlap region problem. In this study, we propose a deep FMNN (DFMNN) based on initialization and optimization operation to overcome these limitations. Initialization operation that can solve the input order problem is to design hyperboxes in a simultaneous way, and side parameters have been proposed to control the size of hyperboxes. Optimization operation that can eliminate overlap region problem is realized by means of deep layers, where the number of layers is immediately determined when the overlap among hyperboxes is eliminated. In the optimization process, each layer consists of three sections, namely, the partition section, combination section, and union section. The partition section aims to divide the hyperboxes into a nonoverlapping hyperbox set and an overlapping hyperbox set. The combination section eliminates the overlap problem of overlapping hyperbox set. The union section obtains the optimized hyperbox set in the current layer. DFMNN is evaluated based on a series of benchmark datasets. A comparative analysis illustrates that the proposed DFMNN model outperforms several models previously reported in the literature.

Microgrid based on ICA and improved cuckoo search algorithm optimizing scheduling methods

Abstract The optimal operation of microgrids can provide a strong guarantee for the efficient utilization of distributed power and renewable energy. To solve the important problem of optimized operation of microgrids with a large proportion of distributed power sources, a microgrid optimal scheduling and operation method is proposed in the paper. This method first establishes a microgrid model containing distributed power sources and uses the ICA algorithm to screen out the key factors in the economy of the grid system. Then, taking the construction and operation cost of the microgrid as the objective function, it proposes to solve the model based on the ICA and the optimized cuckoo search algorithm. The method is able to effectively schedule the microgrid objective function based on the conditions, with the ability to quickly solve the scheduling problem and greatly reduce the system’s electricity cost. It can greatly increase the utilization rate of photovoltaic power generation. The method has a small amount of computation, small storage, and simple operation. An empirical study is carried out on the designed microgrid optimal scheduling scheme.

Optimal Synergetic Operation and Experimental Evaluation of an Ultracompact GaN-Based Three-Phase 10-kW EV Charger

Optimal Synergetic Operation and Experimental Evaluation of an Ultracompact GaN-Based Three-Phase 10-kW EV Charger

Techno-economic analysis and dynamic power simulation of a hybrid solar-wind-battery system for power supply in rural areas in Pakistan

This study presents the optimal design and operation of a proposed hybrid renewable energy system (HRES) for the electrification of a residential building in rural areas in Pakistan. The main contributions of this study are twofold. Firstly, it develops a size optimization model based on the particle swarm optimization (PSO) technique to determine the optimal configuration for two hybrid renewable energy systems (HRES), including both grid-tied and off-grid modes, integrating wind and photovoltaic (PV) systems with battery storage. The optimal configuration is determined by minimizing the levelized cost of electricity, using local meteorological and electricity load data, along with technical specifications of the main HRES components. Secondly, dynamic simulations of two HRES configurations are conducted, using MATLAB Simulink, ensuring the optimal energy balance between multiple energy sources and the load at each operation hour. To meet an annual electrical demand of 131.035 MWh, the grid-tied HRES yields 146.081 MWh annually, with solar contributing 68.85 MWh and wind 77.272 MWh. Conversely, the off-grid system generates 133.533 MWh annually, with solar and wind output power at 43.932 MWh and 89.601 MWh, respectively. The grid-tied system achieves an LCOE of approximately 0.29 $/kWh, with optimal wind turbine and PV capacities of 11 kW and 29 kW, respectively. While in off-grid configuration, the off-grid scenario exhibits an LCOE of 0.91 $/kWh, with optimal capacities of 10 kW for wind turbine, 20 kW for PV, and 2437.5 AH for batteries. The findings provide insights relevant to diverse locations, emphasizing the importance of local meteorological and geographical data. Multiple case studies ensure the robustness and applicability of the proposed system under varying conditions.

Stability Analysis of “321” Prefabricated Highway Steel Truss Bridge

The “321” prefabricated highway steel truss bridge is widely used for highway rescue, disaster relief, and emergency traffic. This paper uses a 33 m double-row monolayer “321” prefabricated highway steel truss bridge to analyze its mechanical properties and component stability. The actual traffic flow capacity of a total weight of 53.32 tons is used in this study. The results show that the maximum internal force in the truss chord (including the stiffening chord) occurs in the middle span section when a centrally distributed load is applied. Meanwhile, the maximum internal force of truss diagonal members and truss vertical bars appears at the fulcrum section. Under the eccentrically distributed load, the maximum internal forces of truss chords (including stiffening chords) appear in the middle span section, which is closest to the vehicle load, while the maximum internal forces of truss diagonal members and truss vertical bars appear in the fulcrum section, which is closest to the vehicle load. While the maximum internal forces under the eccentrically distributed load are greater than the maximum internal forces under the centered-layout load, under the vehicle load, truss chords (including stiffening chords) are prone to buckling instability, and the buckling mode is mainly reverse out-of-plane buckling. The inclined members of the truss are prone to buckling instability, and the buckling mode is mainly the combination of out-of-plane bending and two-way out-of-plane bending. Truss vertical bars have good stability and are not easy to buckle. The main conclusions of this paper can provide references for the optimal design and operation safety of prefabricated highway steel truss bridges.