Towards the design of resilient waste-to-energy systems using Bayesian networks

The concept of resilience has emerged from various domains to address how systems, people and organizations can handle uncertainty. This paper presents a method to improve the resilience of an engineering system by maximizing the system economic lifecycle value, as measured by Net Present Value, under uncertainty. The method is applied to a Waste-to-Energy system based in Singapore and the impact of combining robust and flexible design strategies to improve resilience are discussed. Robust strategies involve optimizing the initial capacity of the system while Bayesian Networks are implemented to choose the flexible expansion strategy that should be deployed given the current observations of demand uncertainties. The Bayesian Network shows promise and should be considered further where decisions are more complex. Resilience is further assessed by varying the volatility of the stochastic demand in the simulation. Increasing volatility generally made the system perform worse since not all demand could be converted to revenue due to capacity constraints. Flexibility shows increased value compared to a fixed design. However, when the system is allowed to upgrade too often, the costs of implementation negates the revenue increase. The better design is to have a high initial capacity, such that there is less restriction on the demand with two or three expansions.</jats:p

The evaluation of redundancy for road traffic networks.

This paper presents two redundancy indices for road traffic network junctions and also an aggregated network redundancy index. The proposed redundancy indices could be implemented to identify optimal design alternatives during the planning stage of the network junctions whereas the aggregated network redundancy index could assess the best control and management policies under disruptive events. Furthermore, effective measures of network redundancy are important to policy makers in understanding the current resilience and future planning to mitigate the impacts of greenhouse gases. The proposed junction indices cover the static aspect of redundancy, i.e. alternative paths, and the dynamic feature of redundancy reflected by the availability of spare capacity under different network loading and service level. The proposed redundancy indices are based on the entropy concept, due to its ability to measure the system configuration in addition to being able to model the inherent uncertainty in road transport network conditions. Various system parameters based on different combinations of link flow, relative link spare capacity and relative link speed were examined. However, the two redundancy indices developed from the combined relative link speed and relative link spare capacity showed strong correlation with junction delay and volume capacity ratio of a synthetic road transport network of Delft city. Furthermore, the developed redundancy indices responded well to demand variation under the same network conditions and supply variations. Another case study on Junction 3A in M42 motorway near Birmingham demonstrated that the developed redundancy index is able to reflect the impact of the Active Traffic Management scheme introduced in 2006

Resilience Assignment Framework using System Dynamics and Fuzzy Logic.

This paper is concerned with the development of a conceptual framework that measures the resilience of the transport network under climate change related events. However, the conceptual framework could be adapted and quantified to suit each disruption’s unique impacts. The proposed resilience framework evaluates the changes in transport network performance in multi-stage processes; pre, during and after the disruption. The framework will be of use to decision makers in understanding the dynamic nature of resilience under various events. Furthermore, it could be used as an evaluation tool to gauge transport network performance and highlight weaknesses in the network. In this paper, the system dynamics approach and fuzzy logic theory are integrated and employed to study three characteristics of network resilience. The proposed methodology has been selected to overcome two dominant problems in transport modelling, namely complexity and uncertainty. The system dynamics approach is intended to overcome the double counting effect of extreme events on various resilience characteristics because of its ability to model the feedback process and time delay. On the other hand, fuzzy logic is used to model the relationships among different variables that are difficult to express in numerical form such as redundancy and mobility

Close-To-Nature Heuristic Design Principles for Future Urban Green Infrastructure

The global nature-climate crisis along with a fundamental shift in world population towards cities and towns has sharpened the focus on the role of urban green infrastructure. Green infrastructure has the potential to deliver cost-effective, nature-based solutions to help mitigate problems of climate change as well as provide improved human well-being through the ecosystem services inherent in landscapes rich in biodiversity. The absence of under-pinning science, specifically complex systems science and ecosystem theory in the design and planning of urban green infrastructure, has limited the capacity of these landscapes to deliver ecosystem services and to effectively demonstrate natural resilience to the impacts of climate change. To meet future challenges of environmental uncertainty and social change, the design of urban green space should embrace an adaptive ecosystem-based approach that includes fully integrated participatory planning and implementation strategies founded on principles of close to nature science. Our article offers two models to inform green space planning: urban green space framework and sustainable urban community network. Both concepts provide the foundation for six ecosystem-based design principles. In a case study on Essex green infrastructure, UK, recommendations made by the Essex Climate Action Commission to transform land management practices are presented as examples of adopting principles of the ecosystem approach and nature-based science. Our article concludes by emphasising the importance of reconnecting society with nature in cities through close-to-nature design of urban green space to secure essential ecosystem services and to build resilience to the impacts of climate change

A Representation of Tactical and Strategic Precursors of Supply Network Resilience Using Simulation Based Experiments

Modern supply chains are becoming increasingly complex and are exposed to higher levels of risk. Globalization, market uncertainty, mass customization, technological and innovation forces, among other factors, make supply networks more susceptible to disruptions (both those that are man-made and/or ones associated with natural events) that leave suppliers unavailable, shut-down facilities and entail lost capacity. Whereas several models for disruption management exist, there is a need for operational representations of concepts such as resilience that expand the practitioners’ understanding of the behavior of their supply chains. These representations must include not only specific characteristics of the firm’s supply network but also its tactical and strategic decisions (such as sourcing and product design). Furthermore, the representations should capture the impact those characteristics have on the performance of the network facing disruptions, thus providing operations managers with insights on what tactical and strategic decisions are most suitable for their specific supply networks (and product types) in the event of a disruption. This research uses Agent-Based Modeling and Simulation (ABMS) and an experimental set-up to develop a representation of the relationships between tactical and strategic decisions and their impact on the performance of multi-echelon networks under supply uncertainty. Two main questions are answered: 1) How do different tactical and strategic decisions give rise to resilience in a multi-echelon system?, and 2) What is the nature of the interactions between those factors, the network’s structure and its performance in the event of a disruption? Product design was found to have the most significant impact on the reliability (Perfect Order Fulfillment) for products with high degrees of componentization when dual sourcing is the chosen strategy. However, when it comes to network responsiveness (Order Fulfillment Cycle Time), this effect was attenuated. Generally, it was found that the expected individual impact these factors have on the network performance is affected by the interactions between them

Scaling Success: Lessons from Adaptation Pilots in the Rainfed Regions of India

"Scaling Success" examines how agricultural communities are adapting to the challenges posed by climate change through the lens of India's rainfed agriculture regions. Rainfed agriculture currently occupies 58 percent of India's cultivated land and accounts for up to 40 percent of its total food production. However, these regions face potential production losses of more than $200 billion USD in rice, wheat, and maize by 2050 due to the effects of climate change. Unless action is taken soon at a large scale, farmers will see sharp decreases in revenue and yields.Rainfed regions across the globe have been an important focus for the first generation of adaptation projects, but to date, few have achieved a scale that can be truly transformational. Drawing on lessons learnt from 21 case studies of rainfed agriculture interventions, the report provides guidance on how to design, fund and support adaptation projects that can achieve scale

Enhancing Infrastructure Resilience Under Conditions of Incomplete Knowledge of Interdependencies

Today’s infrastructures — such as road, rail, gas, electricity and ICT — are highly interdependent, and may best be viewed as multi-infrastructure systems. A key challenge in seeking to enhance the resilience of multi-infrastructure systems in practice relates to the fact that many interdependencies may be unknown to the operators of these infrastructures. How can we foster infrastructure resilience lacking complete knowledge of interdependencies? In addressing this question, we conceptualize the situation of a hypothetical infrastructure operator faced with incomplete knowledge of the interdependencies to which his infrastructure is exposed. Using a computer model which explicitly represents failure propagations and cascades within a multi-infrastructure system, we seek to identify robust investment strategies on the part of the operator to enhance infrastructure resilience. Our results show that a strategy of constructing redundant interdependencies may be the most robust option for a financially constrained infrastructure operator. These results are specific to the infrastructure configuration tested. However, the developed model may be tailored to the conditions of real-world infrastructure operators faced with a similar dilemma, ultimately helping to foster resilient infrastructures in an uncertain world

Reliability assessment of water distribution systems with statistical entropy and other surrogate measures

There is ever increasing commercial and regulatory pressure to minimise the cost of water distribution networks even as the demand for them keeps on growing. But cost minimizing is only one of the demands placed on network design. Satisfactory networks are required to operate above a minimum level even if they experience failure of components. Reliable hydraulic performance can be achieved if sufficient redundancy is built in the network. This has given rise to various water distribution system optimization methods including genetic algorithms and other evolutionary computing methods. Evolutionary computing approaches frequently assess the suitability of enormous numbers of potential solutions for which the calculation of accurate reliability measures could be computationally prohibitive. Therefore, surrogate reliability measures are frequently used to ease the computational burden. The aim of this paper is to assess the correlation of surrogate reliability measures in relation to more accurate measures. The surrogate measures studied are statistical entropy, network resilience, resilience index and modified resilience index. The networks were simulated with the prototype software PRAAWDS that produces more realistic results for pressure-deficient water distribution systems. Statistical entropy outperformed resilience index in this study. The results also demonstrate there is a strong correlation between entropy and failure tolerance