Hydrogen has the physical and chemical characteristics of being flammable, explosive and prone to leakage, and its safety is the main issue faced by the promotion of hydrogen as an energy source. The most common scene in vehicle application is the longitudinal wind generated by driving, and the original position of hydrogen concentration sensors (HCSs) did not consider the influence of longitudinal wind on the hydrogen leakage trajectory. In this paper, the computational fluid dynamics (CFD) software STAR CCM 2021.1 is used to simulate the hydrogen leakage and diffusion trajectories of fuel cell vehicles (FCVs) at five different leakage locations the longitudinal wind speeds of 0 km/h, 37.18 km/h and 114 km/h, and it is concluded that longitudinal wind prolongs the diffusion time of hydrogen to the headspace and reduces the coverage area of hydrogen in the headspace with a decrease of 81.35%. In order to achieve a good detection effect of fuel cell vehicles within the longitudinal wind scene, based on the simulated hydrogen concentration–time matrix, the scene clustering method based on vector similarity evaluation was used to reduce the leakage scene set by 33%. Then, the layout position of HCSs was optimized according to the proposed multi-scene full coverage response time minimization model, and the response time was reduced from 5 s to 1 s.

In the vehicle-to-everything scenario, the fuel cell bus can accurately obtain the surrounding traffic information, and quickly optimize the energy management problem while controlling its own safe and efficient driving. This paper proposes an energy management strategy (EMS) that considers speed control based on deep reinforcement learning (DRL) in complex traffic scenarios. Using SUMO simulation software (Version 1.15.0), a two-lane urban expressway is designed as a traffic scenario, and a hydrogen fuel cell bus speed control and energy management system is designed through the soft actor–critic (SAC) algorithm to effectively reduce the equivalent hydrogen consumption and fuel cell output power fluctuation while ensuring the safe, efficient and smooth driving of the vehicle. Compared with the SUMO–IDM car-following model, the average speed of vehicles is kept the same, and the average acceleration and acceleration change value decrease by 10.22% and 11.57% respectively. Compared with deep deterministic policy gradient (DDPG), the average speed is increased by 1.18%, and the average acceleration and acceleration change value are decreased by 4.82% and 5.31% respectively. In terms of energy management, the hydrogen consumption of SAC–OPT-based energy management strategy reaches 95.52% of that of the DP algorithm, and the fluctuation range is reduced by 32.65%. Compared with SAC strategy, the fluctuation amplitude is reduced by 15.29%, which effectively improves the durability of fuel cells.

In the context of intelligent and connected transportation, obtaining the real-time vehicle status and comprehensive traffic data is crucial for addressing challenges related to speed optimization and energy regulation in intricate transportation situations. This paper introduces a control method for the speed optimization and energy management of a fuel cell hybrid bus (FCHB) based on the Deep Deterministic Policy Gradient (DDPG) algorithm. The strategy framework is built on a dual-objective optimization deep reinforcement learning (D-DRL) architecture, which integrates traffic signal information into the energy management framework, in addition to conventional state spaces to guide control decisions. The aim is to achieve “green wave” traffic while minimizing hydrogen consumption. To validate the effectiveness of the proposed strategy, simulation tests were conducted using the SUMO platform. The results show that in terms of speed planning, the difference between the maximum and minimum speeds of the FCHB was reduced by 21.66% compared with the traditional Intelligent Driver Model (IDM), while the acceleration and its variation were reduced by 8.89% and 13.21%, respectively. In terms of the hydrogen fuel efficiency, the proposed strategy achieved 95.71% of the performance level of the dynamic programming (DP) algorithm. The solution proposed in this paper is of great significance for improving passenger comfort and FCHB economy.

The traditional methods of treating and disposing waste drilling fluid are encountering severe challenges with the tightening of environmental regulations in China. Commonly used chemical destabilization technology needs to be improved, and treating processes are required to be optimized and updated. This paper details a laboratory investigation on two waste water-based drilling fluids (a high-density drilling fluid and a reservoir drilling fluid) from the rig sites, as well as the corresponding treatment and disposal process design. The laboratory work focused on a solid-liquid separation technique on the basis of chemical flocculation and centrifugation. According to the properties of each waste drilling fluid, different flocculants were added to initiate chemical flocculation. The effects of type and concentration of flocculants as well as treating conditions (including sulfuric acid, dilution ratio, centrifugal speed and agent adding sequence) on the degree of separation were comprehensively investigated. To apply the separation technology in various fields, two integrated processes for collective and in-site treatments were designed and discussed respectively. The results showed that the correct combination of inorganic and polymeric flocculants can lead to a great degree of separation. In particular, a novel flocculant based on modified starch exhibited excellent performance. The molecular weight of polymeric flocculant substantially influences treatment efficiency, which can be explained by the combination of adsorption-bridging and sweep-flocculation mechanisms. Adding sulfuric acid to the waste drilling fluid in treatment is beneficial to solid-liquid separation due to the reduction of electrostatic repulsion between solid particles. Molecular weight of flocculant is also a crucial factor influencing the separation effectiveness and rate. For the two waste fluids studied, the optimal treating conditions were proposed. The weight fraction of separated liquid phase reached higher than 80% and the liquid content of the solid phase was lower than 30%. Detailed analysis and discussion of the advantages of this process were presented. Preliminary field practice of this technique exhibited its satisfactory performance. This highly-integrated technique based on novel chemical agents, rational design and high-efficiency equipment is helpful to deal with the increasing technical and environmental challenges. The information provided in this work offers a reference to relevant researchers, designers and engineers.

Summary form only given. A commercial Langmuir probe system, which can be operated in a boxcar averaging mode, has been used to measure time resolved electron and ion densities and electron temperatures on a repetitively-pulsed inductively coupled plasma (ICP) in argon. The probe is positioned inside the ICP source region, near the RF window, in order to follow closely the temporal variation of the plasma parameters with time resolution of approximately 7 /spl mu/s. The RF power at 13.56 MHz is pulsed by applying 100% square wave modulation to the output of a RF generator which can supply up to 5 kW cw output. Modulation frequencies can be varied between 100 Hz and 30 kHz with duty cycles between 10% and 90%. Along with the Langmuir probe data, current and voltage measurements at the coil are used to measure time resolved RF power and complex impedance of the coil-plasma system with a time resolution of 0.5 /spl mu/s. A photomultiplier tube (PMT) with narrow band filter is used to monitor the time dependence of the plasma emission. At lower rep rates and/or duty cycles the ICP changes from capacitively coupled to inductively coupled during the RF pulse. During this transition a large transient is observed in the RF power, plasma emission, the electron and ion densities. Results will be presented for a variety of rep rates, duty cycles and RF powers.

Urban bus operations under signalized traffic conditions are characterized by frequent stop-and-start behaviors which significantly degrade fuel economy, especially for fuel cell buses (FCB). In this paper, a collaborative optimization method is proposed that combines speed planning and energy management for FCB in this situation. The method calculates the target speed of FCB using traffic light phase information and the remaining signal time. With an intelligent driving model, the vehicle can adjust its speed in advance when approaching intersections so it can pass through intersections without stopping. At the same time, a learning-based energy management strategy is used to reasonably share power between the fuel cell and the battery. The results indicate that the method proposed in this paper reduces hydrogen consumption by approximately 11.3% compared to the standard method.