Reduce Hydrogen Consumption Research Articles

Hydrodeoxygenation of guaiacol over modified coconut carbon supported Ni nanoparticles catalysts under alkaline condition

The efficient utilization of lignin from the papermaking black liquor has attracted interest due to its carbon-neutral value and industrial application. This work illustrates a simple support-phosphate-pretreatment strategy to develop superdispersed Ni species in activated carbon (AC) to enhance the hydrodeoxygenation (HDO) reactions of the lignin model compound guaiacol into biohydrocarbons under alkaline conditions. For the first time, sodium polyphosphate was applied as a pretreatment agent for coconut carbon in the synthesis of carbon-supported Ni catalysts. Superdispersed Ni nanoparticles were achieved on Ni/NaPnOAC with a particle size of 2.5 nm, which was much smaller than that on unmodified Ni/C (13.1 nm). The characterization results and reactions revealed that the P−O that formed served as anchoring sites for the Ni species and strengthened the interaction between the Ni species and support (SMSI), which resulted in significantly improved dispersion of the Ni metal sites and, thus, a nearly 6-fold greater yield of hydrocarbons was obtained on Ni/NaPnOAC (58.1%) than on Ni/C (10.1%). In addition, compared with Ni2P/NaPnOAC and NiS/NaPnOAC, Ni/NaPnOAC exhibited higher HDO activity, especially higher direct deoxygenation (DDO) activity and higher benzene yield, reducing hydrogen consumption during the HDO reaction.

Fuzzy Logic-Based Energy Management Strategy for Hybrid Fuel Cell Electric Ship Power and Propulsion System

The growing use of proton-exchange membrane fuel cells (PEMFCs) in hybrid propulsion systems is aimed at replacing traditional internal combustion engines and reducing greenhouse gas emissions. Effective power distribution between the fuel cell and the energy storage system (ESS) is crucial and has led to a growing emphasis on developing energy management systems (EMSs) to efficiently implement this integration. To address this goal, this study examines the performance of a fuzzy logic rule-based strategy for a hybrid fuel cell propulsion system in a small hydrogen-powered passenger vessel. The primary objective is to optimize fuel efficiency, with particular attention on reducing hydrogen consumption. The analysis is carried out under typical operating conditions encountered during a river trip. Comparisons between the proposed strategy with other approaches—control based, optimization based, and deterministic rule based—are conducted to verify the effectiveness of the proposed strategy. Simulation results indicated that the EMS based on fuzzy logic mechanisms was the most successful in reducing fuel consumption. The superior performance of this method stems from its ability to adaptively manage power distribution between the fuel cell and energy storage systems.

Dynamic Programming-Based ANFIS Energy Management System for Fuel Cell Hybrid Electric Vehicles

Reducing reliance on fossil fuels has driven the development of innovative technologies in recent years due to the increasing levels of greenhouse gases in the atmosphere. Since the automotive industry is one of the main contributors of high CO2 emissions, the introduction of more sustainable solutions in this sector is fundamental. This paper presents a novel energy management system for fuel cell hybrid electric vehicles based on dynamic programming and adaptive neuro fuzzy inference system methodologies to optimize energy distribution between battery and fuel cell, therefore enhancing powertrain efficiency and reducing hydrogen consumption. Three different approaches have been considered for performance assessment through a simulation platform developed in MATLAB/Simulink 2023a. Further validation has been conducted via a rapid control prototyping device, showcasing significant improvements in hydrogen usage and operational efficiency across different drive cycles. Results manifest that the developed controllers successfully replicate the optimal control trajectory, providing a robust and computationally feasible solution for real-world applications. This research highlights the potential of combining advanced control strategies to meet performance and environmental demands of modern heavy-duty vehicles.

Performance analysis and optimization of a vehicular LT-PEMFC for maximum power density and efficiency based on NSGA-II algorithm

Based on finite temporal thermodynamics (FTT), a model for a low-temperature proton exchange membrane fuel cell (LT-PEMFC) is created. The link between the LT-PEMFC’s output power density and efficiency was determined using the aforementioned model. The experimental results confirm the algorithm model’s accuracy. Investigations are also conducted into how the design and operating parameters affect the LT-PEMFC’s output outcomes. Moreover, the NSGA-II technique is used to maximize the power density and efficiency of the LT-PEMFC for multi-objective optimization. The experiment outcomes demonstrate that the optimized LT-PEMFC model performs better at output. The related powertrain design scheme for the various output performances of Fuel cell vehicles (FCVs) is provided by LT-PEMFC optimization. It is demonstrated by the study of simulation data that the optimized LT-PEMFC achieves reduced hydrogen consumption and higher FCV efficiency.

Optimal power management for hybrid electrical vehicle

This paper introduces an innovative optimal energy management strategy tailored for a hybrid electric-powered hydraulic excavator system. The aim is to bolster power performance, extend the lifespan of power sources, and optimize hydrogen usage. In this system, the fuel cell serves as the primary power source, while integrating supercapacitors and batteries offers benefits such as enhancing power performance and storing regenerative energy for future use. To efficiently distribute power among these three sources and maximize the utilization of regenerative energy, we propose an energy management strategy. This strategy combines fuzzy logic control with a rule-based algorithm. Moreover, we optimize the parameters of the fuzzy logic system using a combination of the backtracking search algorithm and sequential dynamic programming. This optimization aims to reduce hydrogen consumption and prolong the lifespan of the power sources. Simulation results demonstrate that our proposed energy management strategy significantly enhances vehicle performance, improves fuel economy of the hybrid electric-powered hydraulic excavator system, and enhances the durability and efficiency of the battery and supercapacitor systems within the fuel cell system.

Energy management strategy of fuel cell electric vehicle based on work condition recognition

To reduce hydrogen consumption in fuel cell electric vehicles, mitigate power fluctuations, and maintain the battery system's state of charge, a novel transition process recognition method based on work condition recognition framework of energy management strategy is proposed. This method offers the advantages such as higher recognition rates and faster recognition speed comparing with the traditional condition recognition methods. A comparison is made with the commonly used LVQ recognition method, and the simulations are conducted to demonstrate the superiority of this condition recognition algorithm and the improved performance of the energy management strategy based on the present recognition method under mixed work conditions.

A temperature fluctuation suppression control method of fuel cell vehicles to reduce hydrogen consumption

During the driving of the fuel cell vehicles, temperature fluctuations occur in the Proton Exchange Membrane Fuel Cell (PEMFC), whose output efficiency decreases and hydrogen consumption increases. Rule-based (RB) and proportional-integral-derivative (PID) controllers can roughly suppress the temperature fluctuation, but they cannot perform multi-objective optimization, which cannot reduce hydrogen consumption at the same time. To solve this problem, in the first step of this paper, the hydrogen consumption factor and the temperature fluctuation factor in the operating temperature control are introduced to design the DDQN controller. In the second step of this paper, the DDQN controller is trained offline and verified under simulation conditions. The simulation results show that compared with the RB controller and PID controller, the average temperature fluctuation of the DDQN controller is reduced by 27.27 % and 28.50 %, and the hydrogen consumption is saved by 1.78 % and 2.58 %, respectively. The research in this paper is innovative in that it reduces the hydrogen consumption of PEMFC and improves the driving range of fuel cell vehicles from a specific point of view.

Hierarchical intelligent energy-saving control strategy for fuel cell hybrid electric buses based on traffic flow predictions

Vehicles' perception capability of environmental situations has been enhanced in the connected environment. However, the utilization of abundant and diverse traffic information poses a challenge in the formulation of energy-efficient strategies for current connected vehicles. To address this challenge, a hierarchical intelligent energy-saving control strategy for fuel cell hybrid buses based on traffic flow prediction is proposed. Diverging from traditional approaches that rely on surrounding vehicles status information, a more macroscopic perspective is introduced by incorporating traffic flow prediction information into the formulation of energy-saving control strategies for the first time, which enhances the adaptability of strategies. At the upper layer, a multi-objective intelligent eco-driving control strategy is designed, encompassing driving safety, energy consumption costs, traffic efficiency, and ride comfort. At the lower layer, an intelligent energy management strategy is developed to reduce hydrogen consumption and maintain stable battery state of charge. Simultaneously, action variables serve as the information bridge between the upper and lower layer strategies, and comprehensive analyses and validations of strategic performance are conducted from various perspectives. The research findings indicate that the introduction of traffic flow information enhances the cognitive capabilities of the intelligent system towards the traffic environment with little impact on the convergence process. The model excels in energy efficiency, driving smoothness, and passenger comfort compared to the baseline model, while also having the opportunity to surpass the traffic efficiency of the IDM model. The developed energy management strategy demonstrates an energy-saving benefit of 92.07 % of the offline optimal benchmark, and closely approaches the theoretical optimal performance of MPC with a prediction horizon of 5s. Additionally, the proposed strategy demonstrates high computational efficiency and effectively mitigates the degradation of the vehicle lifespan, making it conducive to real-time online applications.

Hierarchical Energy Management Strategy Based On M-DQN for Fuel Cell Hybrid Electric Vehicle

For fuel cell hybrid electric vehicles equipped with a fuel cell (FC), battery (BAT), and supercapacitor (UC), this paper proposes a hierarchical energy management strategy (EMS) based on the improved M-DQN algorithm to reduce hydrogen consumption, enhance the efficiency of the FC, and maintain the state of charge (SoC) of the BAT. Firstly, an adaptive fuzzy low-pass filter is employed to achieve the frequency decoupling of power demand, enabling the UC to provide/absorb the peak power demand. Secondly, an energy management framework based on M-DQN is designed, and the concept of an equivalent consumption minimization strategy is applied to construct the reward function, which includes penalty factors related to the efficiency of the FC and SoC deviation of the BAT, aiming at optimizing the power allocation between the FC and BAT. Simulations and platform tests were conducted under various typical driving cycles. The results indicate that, compared with the traditional M-DQN-based EMS, the proposed EMS can significantly maintain the SoC of the BAT, improve the efficiency of the FC, reduce hydrogen consumption, and even achieve an average improvement of 5.2% in fuel economy.

Sensitivity study of integrated carbon capture and methanation process using dual function materials

Integrated Carbon Capture and Methanation (ICCM) is a promising technology to produce Synthetic Natural Gas (SNG) via captured CO2 and renewable hydrogen. This work performed a sensitivity study on how operation pressure, the types of DFM and point source, and the catalytic metal loading in DFMs affect the performance of ICCM process. As Na-Ru/Al2O3 (Na-DFM), (Li-Na-K)NO3-MgO-Ni/CeO2-phy (Mg-DFM), and Ni5-CeLi (Li-DFM) were adopted as DFMs, the performances of ICCM process were investigated in the range of 0.1 Mpa-5.0 Mpa, showing elevated pressure is favorable to improving capture efficiency and the quality of SNG products, and reducing hydrogen consumption. Under the optimized operation conditions, Na-DFM based ICCM system shows the better overall performance than ICCM system using Mg-DFM or Li-DFM, which energy conversion efficiency, comprehensive energy consumption, and net CO2 emission are 82.74 %, –14.61 MJ/kg CO2, and –1.18 kg CO2/kg CO2, respectively. Circulation rate is the amount of DFMs necessary to be transferred between reactors to ensure the desired CO2 capture efficiency or SNG productivity; it heavily depends on the type of adsorbent component, capture/hydronation reactions, and the fraction of active adsorbent component in DFMs for CO2 transport. Na-DFM based ICCM systems using different point sources (Power Plant, Steel Plan, and Cement Plant) possess the approximate performances in terms of energy conversion efficiency, comprehensive energy consumption, net CO2 emission, and SNG composition; but when Steel Plan is the point source, Na-DFM based ICCM process features of low electricity and hydrogen consumptions. Cyclic redox reactions of catalytic metal and the cycled capture/hydrogenation proceed in parallel in ICCM process. The increased hydrogen consumption and strong exothermic effects arising from the redox reactions of catalytic metal leads to profound performance changes of ICCM system. Overall, the present work is providing comprehensive information and necessary data to guide the design and operation of an ICCM plant.

Effects of Fuel Cell Size and Dynamic Limitations on the Durability and Efficiency of Fuel Cell Hybrid Electric Vehicles under Driving Conditions

In order to enhance the durability of fuel cell systems in fuel cell hybrid electric vehicles (FCHEVs), researchers have been dedicated to studying the degradation monitoring models of fuel cells under driving conditions. To predict the actual degradation factors and lifespan of fuel cell systems, a semi-empirical and semi-physical degradation model suitable for automotive was proposed and developed. This degradation model is based on reference degradation rates obtained from experiments under known conditions, which are then adjusted using coefficients based on the electrochemical model. By integrating the degradation model into the vehicle simulation model of FCHEVs, the impact of different fuel cell sizes and dynamic limitations on the efficiency and durability of FCHEVs was analyzed. The results indicate that increasing the fuel cell stack power improves durability while reducing hydrogen consumption, but this effect plateaus after a certain point. Increasing the dynamic limitations of the fuel cell leads to higher hydrogen consumption but also improves durability. When considering only the rated power of the fuel cell, a comparison between 160 kW and 100 kW resulted in a 6% reduction in hydrogen consumption and a 10% increase in durability. However, when considering dynamic limitation factors, comparing the maximum and minimum limitations of a 160 kW fuel cell, hydrogen consumption increased by 10%, while durability increased by 83%.

Multi-Objective Optimization Strategy for Fuel Cell Hybrid Electric Trucks Based on Driving Patern Recognition

Fuel cell hybrid electric trucks have become a cutting-edge field in understanding urban traffic emissions due to their enormous potential in low-carbon areas. In order to improve the economy of fuel cell hybrid electric trucks and reduce the decline of fuel cell lifespan, this paper proposes a multi-objective energy management strategy that optimizes weight coefficients. On the basis of establishing a fuel cell battery hybrid system model, three modes of uniform speed, acceleration, and deceleration were identified through clustering analysis of vehicle speed. Reinforcement learning algorithms were used to learn the corresponding weights for different modes, which reduced the decline in fuel cell life while improving the economic efficiency. The simulation results indicate that, under the conditions of no load, half load, and full load, the truck only sacrificed 0.9–5.6%, 1.7–2.6%, and 1.2–1.6% SOC, saving 5.7–6.45%, 5.9–6.67%, and 6.1–6.67% in lifespan loss, and reducing hydrogen consumption by 3.0–7.1%, 2.8–4.4%, and 1.0–3.0%, respectively.

Energy management of an eVTOL aircraft with optimization based on the Equivalent Consumption Minimization Strategy (ECMS)

This article introduces an optimization-based energy management strategy developed and validated for electric vertical take-off and landing (eVTOL) aircraft. The primary objective of this strategy is to minimize hydrogen consumption while accounting for resource constraints, such as the battery and fuel cell, to enhance energy efficiency and sustainability while adhering to performance and safety requirements. The eVTOL employed in this research is an essential component of our “Viable” research project, featuring a hybrid propulsion system that combines batteries and fuel cells.To accomplish this, mathematical and computational techniques were employed using the equivalent consumption minimization strategy method to determine the optimal operational parameters for the eVTOL aircraft, considering the available energy resources. These techniques were implemented for the first time in MATLAB, enabling simulations of the aircraft’s performance under various conditions. The results demonstrate the efficacy of the energy management strategy in significantly reducing hydrogen consumption while maintaining optimal performance and safety. Graphs and comparative analyses are presented to highlight the evolution of hydrogen consumption compared to different parameters and to compare the approach with alternative methods.Furthermore, the article explores an alternative solution that offers related results and performance as MATLAB, utilizing the open-source software OpenModelica (OM). Energy management experiments using ECMS were conducted on OM, yielding highly satisfactory outcomes in terms of simulation accuracy and cost-effectiveness. The method was also evaluated in simulations of propulsive system resources, developed on OM, validating the results concerning energy distribution, compliance with constraints, and real-time feedback.In summary, this article presents a comprehensive strategy for effectively managing energy consumption in eVTOL aircraft. By leveraging simulations conducted in MATLAB and OM, the strategy’s effectiveness was assessed, with potential implications for advancing the sustainability of electric eVTOL aircraft.

Efficiency improvement strategy of fuel cell system based on oxygen excess ratio and cathode pressure two-dimensional optimization

Improving the commercial fuel cell system efficiency is critical to reducing hydrogen consumption and improving the operational economy. This paper studies the power consumption pattern of the air supply subsystem, the most power-consuming auxiliary components, aiming to improve the system’s efficiency. A method was proposed to get the extremum of the system net power by optimizing the oxygen excess ratio and cathode pressure. Mathematical models describing the fuel cell stack and air supply subsystem power characteristics were developed based on the mechanism analysis and estimation of unknown parameters. Then, a system net power model for energy efficiency optimization was developed. The operating conditions for optimal efficiency at a specific current density can be obtained by numerical calculation. To verify the method’s effectiveness, a series of experiments were conducted, and the results show that the proposed method can maximize system net power output at various current density conditions. Compared with the conventional oxygen excess rate based one-dimensional optimization, the two-dimensional method can improve system efficiency by 2.06%, offering a good application prospect.

Bimetallic nickel molybdenum nitride catalyst with low pressure and reduced hydrogen consumption for hydrogenation of dimethyl oxalate to ethanol: the impact of reduction temperature on catalytic performance

The bimetallic nickel molybdenum nitride (Ni3Mo3N) catalyst exhibits exceptional performance with a high EtOH yield (>97.5%) under low pressure (0.5 MPa) and minimal hydrogen consumption (H2/DMO (mol)) conditions.

Techno-economic analysis of a hydrogen fuel cell hybrid system and corresponding optimum matching design for hydrogen fuel cell forklifts

Conventional forklifts face serious issues, with internal combustion engine models causing indoor pollution, and lead-acid battery variants having slow charging and reduced power output as the charge diminishes. To address these drawbacks, this paper introduces a 2.5-tonne fuel cell forklift designed for Hong Kong's bustling logistics, warehousing, and transportation needs. It presents the development of dynamic simulation and cycle condition models, incorporating life cycle cost and average efficiency functions. Simulations reveal that selecting a 50-cell stack (rated at 11.8 kW) is the most cost-effective option, reducing hydrogen consumption by 2.3% using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) optimisation. Cycle conditions do not alter the stack's optimal working voltage. However, the stack's voltage is influenced by stack and hydrogen prices, requiring an optimal design based on Hong Kong's actual costs. This study provides a theoretical foundation for future fuel cell forklift design through techno-economic analysis.

Speedy Hierarchical Eco-Planning for Connected Multi-Stack Fuel Cell Vehicles via Health-Conscious Decentralized Convex Optimization

<div>Connected fuel cell vehicles (C-FCVs) have gained increasing attention for solving traffic congestion and environmental pollution issues. To reduce operational costs, increase driving range, and improve driver comfort, simultaneously optimizing C-FCV speed trajectories and powertrain operation is a promising approach. Nevertheless, this remains difficult due to heavy computational demands and the complexity of real-time traffic scenarios. To resolve these issues, this article proposes a two-level eco-driving strategy consisting of speed planning and energy management layers. In the top layer, the speed planning predictor first predicts dynamic traffic constraints using the long short-term memory (LSTM) model. Second, a model predictive control (MPC) framework optimizes speed trajectories under dynamic traffic constraints, considering hydrogen consumption, ride comfort, and traffic flow efficiency. A multivariable polynomial hydrogen consumption model is also introduced to reduce computational time. In the bottom layer, the decentralized MPC framework uses the calculated speed trajectory to figure out how to allocate the power optimally between the fuel cell modules and the battery pack. The objective of the optimization problem is to reduce hydrogen consumption and mitigate component degradation by focusing on targets such as the operating range of state of charge (SoC), as well as battery and fuel cell degradation. Simulation results show that the proposed decentralized eco-planning strategy can optimize the speed trajectory to make the ride much more comfortable with a small amount of jerkiness (−0.18 to 0.18 m/s<sup>3</sup>) and reduce the amount of hydrogen used per unit distance by 7.28% and the amount of degradation by 5.33%.</div>

Effect of Zirconium modified Y zeolite via in situ synthesis and its regulation on the formation of excellent NiW catalyst for ultra-deep hydrodesulfurization of 4,6-DMDBT

Alternative support materials, such as Y zeolite, have been introduced to overcome the challenges associated with industrial alumina-supported hydrodesulfurization (HDS) catalysts. However, to improve catalytic activity while avoiding excessive hydrocarbon cracking, the properties of Y zeolite, including acidity, should be modulated. Herein, Zr-modified Y zeolite was prepared via in-situ synthesis, and the corresponding NiW catalyst was obtained by impregnating hybrid zeolite-alumina supports with metal precursors. The catalytic performance was tested using 4,6-dimethyldibenzothiophene (4,6-DMDBT) as an HDS probe. The position of the Zr species in the zeolites depended on the modification methods. The ZrHY zeolite prepared by impregnation mainly formed non-framework ZrO2 on the surface of zeolite, whereas the HZrY zeolite via in-situ synthesis exhibited Zr species dispersed in the zeolitic framework. Zr species in the zeolitic framework not only enhanced the acidities of the zeolite and hybrid-support (HZrY-Al2O3), but also regulated the interaction of active metals and hybrid support, which significantly enhancing the catalytic activity of the catalyst. Among the four investigated catalysts, the turnover frequency (TOF) and reaction rate constant (k) of 4,6-DMDBT HDS over NiW/HZrYA catalyst were higher efficient than those of other catalysts, and their isomerization selectivity was improved, reducing hydrogen consumption. This enhanced efficiency was attributed to the NiW/HZrYA catalyst having more suitable acid sites, a greater degree of active metals sulfidation, more NiWS phase, superior isomerization performance, and a higher matching degree between hydrogenation and hydrolysis activity. These findings are great meaning for design the ultra-deep low hydrogen consumption HDS catalysts.

Thermodynamic Insights into Sustainable Aviation Fuel Synthesis via CO/CO2 Hydrogenation

The transformation of CO/CO2 hydrogenation into high-density sustainable aviation fuel (SAF) represents a promising pathway for carbon emission reduction in the aviation industry but also serves as a method for renewable energy assimilation. However, current hydrocarbon products synthesized through CO/CO2 often focus on various catalytic paths with high selectivity and high conversion rates rather than the synthesis of SAFs with complex components. This study undertakes a thermodynamic investigation into the direct or indirect synthesis of SAFs from CO/CO2 hydrogenation. By analyzing the synthesis of seven aviation fuels defined by the American Society for Testing and Materials (ASTM) D7566 standard, our study reveals a temperature-dependent reduction in the reaction driving force for all products. Specifically, for CO, ΔG transitions from approximately −88.6 J/(mol·K) at 50 °C to 26.7 J/(mol·K) at 500 °C, with the switch from negative to positive values occurring around 390 °C. Similarly, for CO2, ΔG values change from approximately −66.7 J/(mol·K) at 50 °C to 37.3 J/(mol·K) at 500 °C, with the transition point around 330 °C. The thermodynamic favorability for various hydrocarbon products synthesized is also examined, highlighting a transition at temperatures of around 250 °C, beyond which the thermodynamic drive for the synthesis of aromatic compounds increasingly surpasses that of cycloparaffin synthesis. Our findings also underscore that the products with a higher aromatic content yield a lower H2/CO2 ratio, thus reducing hydrogen consumption. The influence of cycloparaffin and aromatic proportions in the typical SAF products on the ΔG is also explored, revealing that an increase in cycloparaffin content in SAFs slightly elevates the ΔG, whereas an increase in aromatic content significantly reduces ΔG, thereby markedly enhancing the thermodynamic drive of the CO/CO2 hydrogenation reaction. These findings underscore the thermodynamic preference for synthesizing SAF with a higher proportion of aromatic compounds, shedding light on potential pathways for optimizing fuel synthesis to improve efficiency. Finally, the thermodynamic challenges and potential solutions involved in synthesizing SAFs via specific intermediate compounds are discussed, presenting opportunities for more strategic process schemes in industrial scenarios.

Effective reduction of hydrogen consumption in ultra-deep hydrodesulfurization of diesel: Deep insights into the effect of thermodynamic limitations during hydrotreating

Reducing hydrogen (H2) consumption massively while ensuring the ultra-strict product quality is the most critical and extremely challenging issue in diesel hydrotreating process. In this work, the effect of operating parameters (including reaction temperature, reaction pressure, hydrogen/oil (H2/oil) volume ratio and liquid hourly space velocity (LHSV)) on H2 consumption were systematically studied first. Most importantly, the reduction of H2 consumption caused by temperature elevation was calculated upon satisfying the requirements of content of sulfur (S) and polycyclic aromatic hydrocarbons (PAHs) in the diesel quality standards (S≯10 mg/kg, PAHs≯7%) using feedstock with different properties. Results showed that H2 consumption was reduced by 23.1% when the temperature reached 401 °C (compared with temperature being 365 °C) using a diesel feed whose S content, N content and density were 9759.1 mg/kg, 147.1 mg/kg and 0.8626 g/cm3, respectively, while the impact on the catalysts activity, structure and coke deposition behavior was relatively little which was proven by the XPS, TEM, CAT-CS and TG-MS studies on the fresh and spent sulfide catalysts. The significant reduction of H2 consumption was attributed to the different thermodynamic behavior of S and aromatic compounds at relatively high temperature which led to a slightly decrease in hydrodesulfurization (HDS) conversion while a significantly drop in hydrodearomatization (HDA) conversion. This is a promising method to effectively reduce H2 consumption which can greatly benefit the future industrial applications in producing ultra clean diesel products with ultra-low H2 consumption.