Performance modelling of fuel cell systems through Petri nets

This paper introduces a model based on the Petri net method for the performance evaluation of fuel cell systems during operation. The model simulates the operation of the fuel cell stack and its supporting systems by taking into account the causal relationships between the operation of the balance of plant and the fuel cell stack performance. Failures of the supporting system affect the operating parameters such as the stack temperature and humidity, the reactants’ flow and pressure, and, in turn, the stack performance in terms of output voltage. Voltage degradation rates are needed in order to evaluate the system lifetime. The voltage degradation is related to the important operating parameters by means of empirical relationships. In order to demonstrate the capability of the model, numerical simulations are performed using data for voltage degradation rates collected from the literature. The voltage decay rate is modelled as a random variable within the aforementioned ranges. Time to failure and time to repair of components are generated from stochastic distributions. The use of a stochastic approach allows taking into account data uncertainty and variability. The modelling process produces distributions of the output parameters rather than point estimates delivered by alternative methods. This enables an appreciation of the best and worst possible output lifetime as well as the expected system performance. The model can be used to support the design, operation and maintenance of fuel cell systems

A mathematical programming approach to railway network asset management

A main challenge in railway asset management is selecting the maintenance strategies to apply to each asset on the network in order to effectively manage the railway infrastructure given that some performance and safety targets have to be met under budget constraints. Due to economic, functional and operational dependencies between different assets and different sections of the network,# optimal solutions at network level not always include the best strategies available for each asset group. This paper presents a modelling approach to support decisions on how to effectively maintain a railway infrastructure system. For each railway asset, asset state models combining degradation and maintenance are used to assess the impact of any maintenance strategy on the future asset performance. The asset state models inform a network-level optimisation model aimed at selecting the best combination of maintenance strategies to manage each section of a given railway network in order to minimise the impact of the assets conditions on service, given budget constraints and performance targets. The optimisation problem is formulated as an integer-programming model. By varying the model parameters, scenario analysis can be performed so that the infrastructure manager is provided with a range of solutions for different combination of budget available and performance targets

Intervention Grouping Strategy for Multi-component Interconnected Systems:A Scalable Optimization Approach

The well-being of modern societies depends on the functioning of their infrastructure networks. During their service lives, infrastructure networks are subject to different stresses (e.g., deterioration, hazards, etc.). Interventions are performed to ensure the continuous fulfillment of the infrastructure's functional goals. To guarantee a high level of infrastructure availability and serviceability with minimal intervention costs, preventive intervention planning is essential.Finding the optimal grouping strategy of intervention activities is an NP-hard problem that is well studied in the literature and for which various economic models and optimization approaches are proposed. This research focuses on a new efficient optimization model to cope with the intervention grouping problem of interconnected multi-component systems. We propose a scalable two-step intervention grouping model based on a clustering technique. The clustering technique is formulated using Integer Linear Programing, which guarantees the convergence to global optimal solutions of the considered problem. The proposed optimization model can account for the interactions between multiple infrastructure networks and the impact on multiple stakeholders (e.g., society and infrastructure operators). The model can also accommodate different types of intervention, such as maintenance, removal, and upgrading.We show the performance of the proposed model using a demonstrative example. Results reveal a substantial reduction in net costs. In addition, the optimal intervention plan obtained in the analysis shows repetitive patterns, which indicates that a rolling horizon strategy could be adopted so that the analysis is only performed for a short time horizon

Modelling resilient railway systems

The complexity of the railway asset management process motivates the need for bespoke tools to enable optimal asset management decisions. To address such a need, a Railway Asset Management Modelling Framework is presented to support a structured and systematic decision making process on asset interventions. The framework describes the structure and requirements of the railway asset management system for delivering a safe and reliable railway, whilst minimising the life-cycle costs. It specifies the models and data needed to predict assets’ and network performance indicators on a whole-life, whole-system basis. Depending on the level of abstraction, the models support decisions at asset/route/network level to ultimately meet service and safety targets for the minimum cost. Two main types of models are described: (i) predictive models to forecast the performance of the system of interest under a variety of circumstances, and (ii) optimisation models that use real and predicted data to achieve optimal decisions on the asset interventions. To the first group belong the asset state models aimed at assessing the assets’ response to a range of maintenance strategies. To demonstrate the capabilities of such models, a track asset management model is presented. It combines the description of the degradation and intervention processes involved in the maintenance of the overall track geometry. The model is built for a line section to account for dependencies due to opportunistic maintenance and renewals. The technique adopted to develop the model is based on Coloured Petri nets with the Monte Carlo simulation method used for its analysis. The asset state models provide statistics on the asset's behaviour which inform a network-level optimisation model for the selection of the optimal combination of intervention strategies for all assets along a given route. A nonlinear integer model is presented along with the ad hoc solution approaches developed to address the nonlinearities. Relaxation tools offered by the mathematical programming formulation enable the percentage error to be estimated thus giving a measure of the quality of the approximate solutions. Effective asset management strategies result in higher reliability and availability of the assets. However failures and possessions of the infrastructure cannot be completely avoided, and a capability is needed to tolerate disruptions. Crossovers enable trains to switch track, and thus are essential to provide a flexible and connected network. If their number and distribution on the network is optimised, then they unlock the potential for a fault tolerant network. A nonlinear bi-objective mixed-integer optimisation model is developed to this purpose along with a solution approach. The aim is to find the number and distribution of crossovers for the minimum costs, which also minimises the loss of train flow and enables availability targets to be achieved for each line. Both optimisation models are applied to analyse a variety of scenarios for different values of the system parameters. The analysis of the results enables an evaluation of the robustness of the solutions towards the system parameters

Simulation Supported Bayesian Network Approach for Performance Assessment of Infrastructure Systems

We present a simulation supported Bayesian Network modeling approach to evaluate the performance of bridge networks with respect to both infrastructure owner's cost and users' travel time based on bridge level maintenance decisions. By combining system decomposition, simulation and Bayesian Networkm (BN) modelling, our approach enables the construction of a BN model of bridge networks where probabilistic information resulting from simulation are used to populate the conditional probability tables. Our approach is therefore useful when access to actual conditions of bridges and their monitoring is difficult, and the conditional dependencies accross different networks elements are not easily quantifiable. Once built, the BN can be used by infrastructure managers as a scenario analysis tool to assess how maintenance decisions on individual bridges affect maintenance costs and travel time for the whole network. The approach is presented on a small-scale bridge network for demonstration purposes

A Petri net approach for performance modelling of polymer electrolyte membrane fuel cell systems

Fuel cells are promising technologies for zero-emission energy conversion and power generation. However, durability and reliability are among the main barriers to their commercialisation. Clearly the system performance depends on the reliability of the overall system including both the stack and the balance of plant. This paper seeks to introduce a modelling approach based on the Petri net method for the performance analysis of fuel cell systems. The proposed Petri net model intends to simulate the operation of the fuel cell stack and its supporting system to predict the system performance based on the system structure, along with the components deterioration process. The model considers the causal relationship between the operation of the balance of plant and the fuel cell stack performance. Purging is performed periodically in order to restore some of the voltage loss due to water accumulation or impurities within the cell. Failures of single components of the supporting systems are considered, which will have an immediate effect on the output voltage as well as long term effects on the stack performance

A Petri net approach to assess the effects of railway maintenance on track availability

The railway infrastructure includes a portfolio of assets that are subjected to degradation and failure processes due to both usage and ageing. As a consequence of degradation and failures, speed restrictions and line closures may be imposed to control the risk of derailment. Such actions have a direct impact on service, as they lead to delays and journey cancellations. Maintenance is implemented to control the state of the assets. Different maintenance strategies determine different asset conditions and performance profiles and, consequently, a different impact on service. This paper presents a simulation tool based on Petri nets, which combines degradation and maintenance processes to predict future track geometry conditions, including the probability of those failure modes leading to speed restrictions and line closures. Such a model is a valuable feature of an effective infrastructure-asset-management system that intends to support cost-effective informed decisions on railway maintenance

Toward more realistic viscosity measurements of tyre rubber–bitumen blends

The measurement of rheological properties of the tyre rubber bitumen blends is often challenging due to presence of suspended tyre rubber’s crumbs. Furthermore, the phase separation during the course of measurements makes the viscosity of these non-homogeneous blends difficult to ascertain. In this study, a new dual helical impeller was designed and manufactured to be used with a rotational viscometer in order to have a real-time control of the viscosity while performing a laboratory mixing of the blends. Layer based manufacturing techniques showed to be a convenient method to produce complex shaped impeller prototypes before manufacturing the more expensive stainless steel assembly. Impeller geometry was optimised to create a convective like flow within the sample and so minimise phase separation. Shear rate constant is geometry dependent and a calibration exercise was carried out to ascertain this. Results of both calibration and validation phases showed that the new impeller provides reliable viscosity measurements of homogenous fluids such as neat bitumen. With regards to complex fluids the new impeller showed a more stable and realistic trend than that obtained by using a standard spindle. In fact, it was demonstrated that the new impeller significantly decreases phase separation within the blend and in turns provides a more realistic measurement of the viscosity. This system represents a feasible and improved solution for optimising the laboratory modification process of tyre rubber bitumen blends by adapting the rotational viscometer as a low-shear mixer

A Petri net model for railway bridge maintenance

This article describes the application of the Petri net modelling approach to managing the maintenance process of railway bridges. The Petri net model accounts for the degradation, inspection and repair processes of individual bridge elements in investigating the effectiveness of alternative maintenance strategies. The times governing the degradation and repair processes considered are stochastic and defined by the appropriate Weibull distribution. The model offers a capability for modelling the bridge asset which overcomes the limitations in the currently used modelling techniques reported in the literature. The bridge model also provides a means of predicting the future asset condition as a result of adopting different maintenance strategies. The solution of the Petri net model is performed using a Monte Carlo simulation routine. The application of the model to a typical metal railway bridge is also presented in the article

System design and maintenance modelling for safety in extended life operation

It is frequently the most cost effective option to operate systems and infrastructure over an extended life period rather than enter a new build programme. The condition and performance of existing systems operated beyond their originally intended design life are controlled through maintenance. For new systems there is the option to simultaneously develop the design and the maintenance processes for best effect when a longer life expectancy is planned. This paper reports a combined Petri net and Bayesian network approach to investigate the effects of design and maintenance features on the system performance. The method has a number of features which overcome limitations in traditionally used system performance modelling techniques, such as fault tree analysis, and also enhances the modelling capabilities. Significantly, for the assessment of aging systems, the new method avoids the need to assume a constant failure rate over the lifetime duration. In addition the assumption of independence between component failures events is no longer required. In comparison with the commonly applied system modelling techniques, this new methodology also has the capability to represent the maintenance process in far greater detail and as such options for: inspection and testing, servicing, reactive repair and component replacement based on condition, age or use can all be included. In considering system design options, levels of redundancy and diversity along with the component types selected can be investigated. All of the options for the design and maintenance can be incorporated into a single integrated Petri net and Bayesian network model and turned on and off as required to predict the effects of any combination of options selected. In addition this model has the ability to evaluate different system failure modes. The integrated Petri-net and Bayesian network approach is demonstrated through application to a remote un-manned wellhead platform from the oil and gas industry