Interactive plant simulation modeling for developing an operator training system in a natural gas pressure-regulating station

This study proposes a method of interactive plant simulation modeling which delivers the online simulated results to the field operators and induces them to take proper actions in the case of pre-identified accident scenarios in a chemical plant. The developed model integrates the real-time process dynamic simulation with 3DCFD accident simulation in a designed interface using object linking and embedding technology so that it can convey to trainees the online information of the accident which is not available in existing operator training systems.The model encompasses the whole process of data transfer till the end of the training at which a trainee operates an emergency shutdown system in a programmed model. In this work, an overall scenario is simulated which is from an abnormal increase in the main valve discharge（second）pressure due to valve malfunction to accidental gas release through the crack of a pressure recorder, and the magnitude of the accident with respect to the lead time of each trainee＇s emergency response is analyzed. The model can improve the effectiveness of the operator training system through interactively linking the trainee actions with the simulation model resulting in different accident scenarios with respect to each trainee＇s competence when facing an accident.

Research on the cooperative train control method in the metro system for energy saving

Purpose–To address the problem that the current train operation mode that train selects one of several offline pre-generated control schemes before the departure and operates following the scheme after the departure,energy-saving performance of the whole metro system cannot be guaranteed.Design/methodology/approach–A cooperative train control framework is formulated to regulate a novel train operation mode.The classic train four-phase control strategy is improved for generating specific energy-efficient control schemes for each train.An improved brute force(BF)algorithm with a two-layer searching idea is designed to solve the optimisation model of energy-efficient train control schemes.Findings–Case studies on the actual metro line in Guangzhou,China verify the effectiveness of the proposed train control methods compared with four-phase control strategy under different kinds of train operation scenarios and calculation parameters.The verification on the computation efficiency as well as accuracy of the proposed algorithm indicates that it meets the requirement of online optimisation.Originality/value–Most existing studies optimised energy-efficient train timetable or train control strategies through an offline process,which has a defect in coping with the disturbance or delays effectively and promptly during real-time train operation.This paper studies an online optimisation of cooperative train control based on the rolling optimisation idea,where energy-efficient train operation can be realised once train running time is determined,thus mitigating the impact of unpredictable operation situations on the energy-saving performance of trains.

Braking distance prediction for vehicle consist in low-speed on-sight operation:a Monte Carlo approach

The first and last mile of a railway journey, in both freight and transit applications, constitutes a high effort and is either non-productive(e.g. in the case of depot operations) or highly inefficient(e.g. in industrial railways). These parts are typically managed on-sight, i.e. with no signalling and train protection systems ensuring the freedom of movement. This is possible due to the rather short braking distances of individual vehicles and shunting consists. The present article analyses the braking behaviour of such shunting units. For this purpose, a dedicated model is developed. It is calibrated on published results of brake tests and validated against a high-definition model for lowspeed applications. Based on this model, multiple simulations are executed to obtain a Monte Carlo simulation of the resulting braking distances. Based on the distribution properties and established safety levels, the risk of exceeding certain braking distances is evaluated and maximum braking distances are derived. Together with certain parameters of the system, these can serve in the design and safety assessment of driver assistance systems and automation of these processes.

Dynamic train dwell time forecasting:a hybrid approach to address the influence of passenger flow fluctuations

Train timetables and operations are defined by the train running time in sections,dwell time at stations,and headways between trains.Accurate estimation of these factors is essential to decision-making for train delay reduction,train dispatching,and station capacity estimation.In the present study,we aim to propose a train dwell time model based on an averaging mechanism and dynamic updating to address the challenges in the train dwell time prediction problem(e.g.,dynamics over time,heavy-tailed distribution of data,and spatiotemporal relationships of factors)for real-time train dispatching.The averaging mechanism in the present study is based on multiple state-of-the-art base predictors,enabling the proposed model to integrate the advantages of the base predictors in addressing the challenges in terms of data attributes and data distributions.Then,considering the influence of passenger flow on train dwell time,we use a dynamic updating method based on exponential smoothing to improve the performance of the proposed method by considering the real-time passenger amount fluctuations(e.g.,passenger soars in peak hours or passenger plunges during regular periods).We conduct experiments with the train operation data and passenger flow data from the Chinese high-speed railway line.The results show that due to the advantages over the base predictors,the averaging mechanism can more accurately predict the dwell time at stations than its counterparts for different prediction horizons regarding predictive errors and variances.Further,the experimental results show that dynamic smoothing can significantly improve the accuracy of the proposed model during passenger amount changes,i.e.,15.4%and 15.5%corresponding to the mean absolute error and root mean square error,respectively.Based on the proposed predictor,a feature importance analysis shows that the planned dwell time and arrival delay are the two most important factors to dwell time.However,planned time has positive influences,whereas arrival delay has negative influences.

Automatic Driving Operation Strategy of Urban Rail Train Based on Improved DQN Algorithm

To realize a better automatic train driving operation control strategy for urban rail trains,an automatic train driving method with improved DQN algorithm(classical deep reinforcement learning algorithm)is proposed as a research object.Firstly,the train control model is established by considering the train operation requirements.Secondly,the dueling network and DDQN ideas are introduced to prevent the value function overestimation problem.Finally,the priority experience playback and“restricted speed arrival time”are used to reduce the useless experience utilization.The experiments are carried out to verify the train operation strategy method by simulating the actual line conditions.From the experimental results,the train operation meets the ATO requirements,the energy consumption is 15.75%more energy-efficient than the actual operation,and the algorithm convergence speed is improved by about 37%.The improved DQN method not only enhances the efficiency of the algorithm but also forms a more effective operation strategy than the actual operation,thereby contributing meaningfully to the advancement of automatic train operation intelligence.

A train timetable rescheduling approach based on multi-train tracking optimization of high-speed railways

Purpose–This paper aims to propose a train timetable rescheduling(TTR)approach from the perspective of multi-train tracking optimization based on the mutual spatiotemporal information in the high-speed railway signaling system.Design/methodology/approach–Firstly,a single-train trajectory optimization(STTO)model is constructed based on train dynamics and operating conditions.The train kinematics parameters,including acceleration,speed and time at each position,are calculated to predict the arrival times in the train timetable.A STTO algorithm is developed to optimize a single-train time-efficient driving strategy.Then,a TTR approach based on multi-train tracking optimization(TTR-MTTO)is proposed with mutual information.The constraints of temporary speed restriction(TSR)and end of authority are decoupled to calculate the tracking trajectory of the backward tracking train.The multi-train trajectories at each position are optimized to generate a timeefficient train timetable.Findings–The numerical experiment is performed on the Beijing-Tianjin high-speed railway line and CR400AF.The STTO algorithm predicts the train’s planned arrival time to calculate the total train delay(TTD).As for the TSR scenario,the proposed TTR-MTTO can reduce TTD by 60.60%compared with the traditional TTR approach with dispatchers’experience.Moreover,TTR-MTTO can optimize a time-efficient train timetable to help dispatchers reschedule trains more reasonably.Originality/value–With the cooperative relationship and mutual information between train rescheduling and control,the proposed TTR-MTTO approach can automatically generate a time-efficient train timetable to reduce the total train delay and the work intensity of dispatchers.

Delay recovery model for high-speed trains with compressed train dwell time and running time

Modeling the application of train operation adjustment actions to recover from delays is of great importance to supporting the decision-making of dispatchers.In this study,the effects of two train operation adjustment actions on train delay recovery were explored using train operation records from scheduled and actual train timetables.First,the modeling data were sorted to extract the possible influencing factors under two typical train operation adjustment actions,namely the compression of the train dwell time at stations and the compression of the train running time in sections.Stepwise regression methods were then employed to determine the importance of the influencing factors corresponding to the train delay recovery time,namely the delay time,the scheduled supplement time,the running interval,the occurrence time,and the place where the delay occurred,under the two train operation adjustment actions.Finally,the gradient-boosted regression tree(GBRT)algorithm was applied to construct a delay recovery model to predict the delay recovery effects of the train operation adjustment actions.A comparison of the prediction results of the GBRT model with those of a random forest model confirmed the better performance of the GBRT prediction model.

Research on Optimization of Freight Train ATO Based on Elite Competition Multi-Objective Particle Swarm Optimization

In view of the complex problems that freight train ATO (automatic train operation) needs to comprehensively consider punctuality, energy saving and safety, a dynamics model of the freight train operation process is established based on the safety and the freight train dynamics model in the process of its operation. The algorithm of combining elite competition strategy with multi-objective particle swarm optimization technology is introduced, and the winning particles are obtained through the competition between two elite particles to guide the update of other particles, so as to balance the convergence and distribution of multi-objective particle swarm optimization. The performance comparison experimental results verify the superiority of the proposed algorithm. The simulation experiments of the actual line verify the feasibility of the model and the effectiveness of the proposed algorithm.

Rice Transplanting Mechanization in Bangladesh: Way to Make It Sustainable

“Rice transplanter” has brought a radical change in the method of rice seedlings transplanting in Bangladesh. The machine is gaining popularity with the increase of labor crisis during the peak period of transplanting, reduction time of in-between crops, rapid urbanization and industrialization, and changing socio-economic status of the farmers. Alternatively, the traditional way of rice transplanting is labor-intensive and involves high drudgery. Mechanical rice transplanter is cost-effective and operation-friendly. It is suitable to plant young seedlings maintaining plant to plant and line to line distance which plays an important role in increasing yield. Moreover, it is easy to take care of the crops planted in rows. It helps in maintaining soil physical properties and is considered to be better from a crop management and productivity point of view. In malignancy of having an edge over the traditional broadcasting, the adoption rate of mechanical transplanters is low due to high primary investment, lack of knowledge in growing mat type nursery, lack of repair and maintenance facility to the end-users, and unavailability of spare parts. Imparting these, available supply, entrepreneurship development, and encouraging custom hire services may be some of the practical results for adding the rice area under mechanical transplanting. This review summarizes the problems and prospects associated with seedling rice transplanting, technological gaps in the adoption of mechanical transplanters in a sustainable manner, and future ways to make transplanters more user-friendly and cost-effective.

Research on the mechanics of high speed rails

In the preceding theme issue on＂Current research progress on mechanics of high speed rails＂（Acta Mechanica Sinica,30：846–909（2014））,we invited several authors in the fiel to present their research on high speed rails（HSR）,including work on dynamic derailment analysis（Ling et al.[1]）,

Study on Development of Train Running Diagram Optimization

This article from a whole new perspective on existing studies was classified, from the specific issues addressed by starting on two key issues of recent train scheduling plan: train running diagram and train operation adjustment. Solutions, basic ideas and researches are discussed in detail. Among them, the train working diagram is divided into single and double lane sections and Passenger Train Running Diagram. And the train operation adjustment includes the real-time adjustment, the service of train insert and the conflict prediction. This paper introduces automatic train operation dispatching system in scheduling applications, and discusses the train scheduling problems and the future direction of development.

Study on Optimization of Urban Rail Train Operation Control Curve Based on Improved Multi-Objective Genetic Algorithm

A multi-objective improved genetic algorithm is constructed to solve the train operation simulation model of urban rail train and find the optimal operation curve.In the train control system,the conversion point of operating mode is the basic of gene encoding and the chromosome composed of multiple genes represents a control scheme,and the initial population can be formed by the way.The fitness function can be designed by the design requirements of the train control stop error,time error and energy consumption.the effectiveness of new individual can be ensured by checking the validity of the original individual when its in the process of selection,crossover and mutation,and the optimal algorithm will be joined all the operators to make the new group not eliminate on the best individual of the last generation.The simulation result shows that the proposed genetic algorithm comparing with the optimized multi-particle simulation model can reduce more than 10%energy consumption,it can provide a large amount of sub-optimal solution and has obvious optimization effect.

Optimization method of urban rail train operational plan based on O-D time-varying demand

Purpose–Under the constraints of given passenger service level and coupling travel demand with train departure time,this study optimizes the train operational plan in an urban rail corridor to minimize the numbers of train trips and rolling stocks considering the time-varying demand of urban rail passenger flow.Design/methodology/approach–The authors optimize the train operational plan in a special network layout,i.e.an urban rail corridor with dead-end terminal yard,by decomposing it into two sub-problems:train timetable optimization and rolling stock circulation optimization.As for train timetable optimization,the authors propose a schedule-based passenger flow assignment method,construct the corresponding timetabling optimization model and design the bi-directional coordinated sequential optimization algorithm.For the optimization of rolling stock circulation,the authors construct the corresponding optimization assignment model and adopt the Hungary algorithm for solving the model.Findings–The case study shows that the train operational plan developed by the study’s approach meets requirements on the passenger service quality and reduces the operational cost to the maximum by minimizing the numbers of train trips and rolling stocks.Originality/value–The example verifies the efficiency of the model and algorithm.

Assessment and Analysis of Carrying Capacity of 350 km/h EMUs Operating on Beijing-Shanghai Highspeed Railway

This paper makes an analysis of the impact on the structure of the corridortype train working diagram by increasing the 350 km/h train pairs of Beijing-Shanghai High-speed Railway(HSR).According to the requirements of operation speed diversity,the multi-station receiving characteristics of cross-line trains and different train stopping schemes,this paper proposes a scheme of adding 350 km/h train pairs via computer analysis of carrying capability mode.The scheme is divided into three stages:(1)Initial stage:It is planned to increase trains running at 350 km/h,with 15~26 pairs of trains put into operation;(2)Rapid increase stage:27~142 pairs of 350 km/h trains are put into operation;(3)Full replacement stage:143~195 pairs of 350 km/h trains are put into operation,during which the number of cross-line trains under operation is controlled,the number of cross-line trains in each section is determined,the operation and connection scheme of cross-line trains is adjusted and the train stopping scheme is optimized.The results of this study were used for the adjustment of the train working diagram in the third quarter of June 25,2021 to increase the number of 350 km/h train pairs from 19 to 30 on Beijing-Shanghai HSR.350 km/h trains are evenly arranged during 7:00—19:00 on the train working diagram and 300 km/h trains are arranged by making full use of every time and space,to improve the travelling speed on Beijing-Shanghai HSR as a whole.

Research on Working out Pasenger and Goods Train Diagram with Computer Simultaneously

According to the characteristics of the railway traffic organization in China,we have studied the use of computer in making train working graph.In this paper,by analysing influences on line carrying capacities when making train working graph with two steps,we put forward a new method to work out passenger and goods train working graph simultaneously and state its principle and solution.

Model-free adaptive robust control method for high-speed trains

Aiming at the robustness issue in high-speed trains(HSTs)operation control,this article proposes a model-free adaptive control(MFAC)scheme to suppress disturbance.Firstly,the dynamic linearization data model of train system under the action of measurement disturbance is given,and the Kalman filter(KF)based on this model is derived under the minimum variance estimation criterion.Then,according to the KF,an anti-interference MFAC scheme is designed.This scheme only needs the input and output data of the controlled system to realize the MFAC of the train under strong disturbance.Finally,the simulation experiment of CRH380A HSTs is carried out and compared with the traditional MFAC and the MFAC with attenuation factor.The proposed control algorithm can effectively suppress the measurement disturbance,and obtain smaller tracking error and larger signal to noise ratio with better applicability.

Integrated Distribution Management System:Architecture,Functions,and Application in China

As massive distributed energy resources(DERs)are integrated into distribution networks(DNs)and the distribution automation facilities are widely deployed,the DNs are evolving to active distribution networks(ADNs).This paper introduces the architecture and main function modules of an integrated distribution management system(IDMS)and its applica-tions in China.This system consists of three subsystems,including the real-time operation and control system(OCS),outage management system(OMS),and operator training simulator(OTS).The OCS has a hierarchical architecture with three levels,including the local controller for DER clusters,the optimization of DNs incorporated with multi-clusters,and the coordina-tion operation of integrated transmission&distribution(T&D)networks.The OMS is developed based on the geographical information system(GIS)and coordinated with OCS.While in the OTS,both the ADN and its host transmission network(TN)are simulated to make the simulation results more credible.The main functions of the three subsystems and their interaction data flows are described and some typical application scenarios are also presented.

Research on Multi-Objective Real-Time Optimization of Automatic Train Operation(ATO) in Urban Rail Transit

The determination and optimization of Automatic Train Operation(ATO) control strategy is one of the most critical technologies for urban rail train operation. The practical ATO optimal control strategy must consider many goals of the train operation, such as safety, accuracy, comfort, energy saving and so on. This paper designs a set of efficient and universal multi-objective control strategy. Firstly, based on the analysis of urban rail transit and its operating environment, the multi-objective optimization model considering all the indexes of train operation is established by using multi-objective optimization theory. Secondly, Non-dominated Sorting Genetic Algorithm II(NSGA-II) is used to solve the model, and the optimal speed curve of train running is generated.Finally, the intelligent controller is designed by the combination of fuzzy controller algorithm and the predictive control algorithm, which can control and optimize the train operation in real time. Then the robustness of the control system can ensure and the requirements for multi-objective in train operation can be satisfied.

Research on emergency rescue technology and equipment for train operation accidents on a heavy-haul railway network

Bridges,tunnels,cuttings and high subgrade account for a relatively large proportion in China’s heavy-haul railway system,where 10000 t of unit trains and 20000 t of combined trains are in operation.When a train operation accident occurs,it can easily cause vehicle intrusions,slant-span lines,tipping and stacking.Based on the viewpoint of system engineering,rescue methods such as hoisting,lifting,pulling and overturning are integrated,according to the characteristics of heavy-haul transport and the construction practice of train accident rescue system.A scheme of technical research and equipment configuration relating to heavy-haul railway rescue in China is put forward based on the situation—embankment,bridge,tunnel(including cuttings),ramp and curve rescue,and so on—and three-dimensional finite-element modelling and calculation checks on the key components are carried out.

Automatic Train Operation Based on Adaptive Terminal Sliding Mode Control

This paper presents an adaptive terminal sliding mode control(ATSMC) method for automatic train operation. The criterion for the design is keeping high-precision tracking with relatively less adjustment of the control input. The ATSMC structure is designed by considering the nonlinear characteristics of the dynamic model and the parametric uncertainties of the train operation in real time. A nonsingular terminal sliding mode control is employed to make the system quickly reach a stable state within a finite time, which makes the control input less adjust to guarantee the riding comfort. An adaptive mechanism is used to estimate controller parameters to get rid of the need of the prior knowledge about the bounds of system uncertainty. Simulations are presented to demonstrate the effectiveness of the proposed controller, which has robust performance to deal with the external disturbance and system parametric uncertainties. Thereby, the system guarantees the train operation to be accurate and comfortable.