A methodology for evaluating and selecting processes for natural gas conditioning

The aim of this work was to develop a generic methodology for evaluating and selecting, at the conceptual design phase of a project, the best process technology for Natural Gas conditioning. A generic approach would be simple and require less time and would give a better understanding of why one process is to be preferred over another. This will lead to a better understanding of the problem. Such a methodology would be useful in evaluating existing, novel and hybrid technologies. However, to date no information is available in the published literature on such a generic approach to gas processing. It is believed that the generic methodology presented here is the first available for choosing the best or cheapest method of separation for natural gas dew-point control. Process cost data are derived from evaluations carried out by the vendors. These evaluations are then modelled using a steady-state simulation package. From the results of the modelling the cost data received are correlated and defined with respect to the design or sizing parameters. This allows comparisons between different process systems to be made in terms of the overall process. The generic methodology is based on the concept of a Comparative Separation Cost. This takes into account the efficiency of each process, the value of its products, and the associated costs. To illustrate the general applicability of the methodology, three different cases suggested by BP Exploration are evaluated. This work has shown that it is possible to identify the most competitive process operations at the conceptual design phase and illustrate why one process has an advantage over another. Furthermore, the same methodology has been used to identify and evaluate hybrid processes. It has been determined here that in some cases they offer substantial advantages over the separate process techniques.

The application of knowledge based expert systems to mechanical engineering design

Investigation of the different approaches used by Expert Systems researchers to solve problems in the domain of Mechanical Design and Expert Systems was carried out. The techniques used for conventional formal logic programming were compared with those used when applying Expert Systems concepts. A literature survey of design processes was also conducted with a view to adopting a suitable model of the design process. A model, comprising a variation on two established ones, was developed and applied to a problem within what are described as class 3 design tasks. The research explored the application of these concepts to Mechanical Engineering Design problems and their implementation on a microcomputer using an Expert System building tool. It was necessary to explore the use of Expert Systems in this manner so as to bridge the gap between their use as a control structure and for detailed analytical design. The former application is well researched into and this thesis discusses the latter. Some Expert System building tools available to the author at the beginning of his work were evaluated specifically for their suitability for Mechanical Engineering design problems. Microsynics was found to be the most suitable on which to implement a design problem because of its simple but powerful Semantic Net Knowledge Representation structure and the ability to use other types of representation schemes. Two major implementations were carried out. The first involved a design program for a Helical compression spring and the second a gearpair system design. Two concepts were proposed in the thesis for the modelling and implementation of design systems involving many equations. The method proposed enables equation manipulation and analysis using a combination of frames, semantic nets and production rules. The use of semantic nets for purposes other than for psychology and natural language interpretation, is quite new and represents one of the major contributions to knowledge by the author. The development of a purpose built shell program for this type of design problems was recommended as an extension of the research. Microsynics may usefully be used as a platform for this development.

Goal detection from natural language queries

This paper aims to identify the communication goal(s) of a user's information-seeking query out of a finite set of within-domain goals in natural language queries. It proposes using Tree-Augmented Naive Bayes networks (TANs) for goal detection. The problem is formulated as N binary decisions, and each is performed by a TAN. Comparative study has been carried out to compare the performance with Naive Bayes, fully-connected TANs, and multi-layer neural networks. Experimental results show that TANs consistently give better results when tested on the ATIS and DARPA Communicator corpora.

Goal-based modeling of dynamically adaptive system requirements

Self-adaptation is emerging as an increasingly important capability for many applications, particularly those deployed in dynamically changing environments, such as ecosystem monitoring and disaster management. One key challenge posed by Dynamically Adaptive Systems (DASs) is the need to handle changes to the requirements and corresponding behavior of a DAS in response to varying environmental conditions. Berry et al. previously identified four levels of RE that should be performed for a DAS. In this paper, we propose the Levels of RE for Modeling that reify the original levels to describe RE modeling work done by DAS developers. Specifically, we identify four types of developers: the system developer, the adaptation scenario developer, the adaptation infrastructure developer, and the DAS research community. Each level corresponds to the work of a different type of developer to construct goal model(s) specifying their requirements. We then leverage the Levels of RE for Modeling to propose two complementary processes for performing RE for a DAS. We describe our experiences with applying this approach to GridStix, an adaptive flood warning system, deployed to monitor the River Ribble in Yorkshire, England.

Genie:supporting the model driven development of reflective, component-based adaptive systems

Engineering adaptive software is an increasingly complex task. Here, we demonstrate Genie, a tool that supports the modelling, generation, and operation of highly reconfigurable, component-based systems. We showcase how Genie is used in two case-studies: i) the development and operation of an adaptive flood warning system, and ii) a service discovery application. In this context, adaptation is enabled by the Gridkit reflective middleware platform.

Emergence of equilibriumlike domains within nonequilibrium Ising spin systems

Many natural, technological and social systems are inherently not in equilibrium. We show, by detailed analysis of exemplar models, the emergence of equilibriumlike behavior in localized or nonlocalized domains within nonequilibrium Ising spin systems. Equilibrium domains are shown to emerge either abruptly or gradually depending on the system parameters and disappear, becoming indistinguishable from the remainder of the system for other parameter values. © 2013 American Physical Society.

Knowledge management for more sustainable water systems

The management and sharing of complex data, information and knowledge is a fundamental and growing concern in the Water and other Industries for a variety of reasons. For example, risks and uncertainties associated with climate, and other changes require knowledge to prepare for a range of future scenarios and potential extreme events. Formal ways in which knowledge can be established and managed can help deliver efficiencies on acquisition, structuring and filtering to provide only the essential aspects of the knowledge really needed. Ontologies are a key technology for this knowledge management. The construction of ontologies is a considerable overhead on any knowledge management programme. Hence current computer science research is investigating generating ontologies automatically from documents using text mining and natural language techniques. As an example of this, results from application of the Text2Onto tool to stakeholder documents for a project on sustainable water cycle management in new developments are presented. It is concluded that by adopting ontological representations sooner, rather than later in an analytical process, decision makers will be able to make better use of highly knowledgeable systems containing automated services to ensure that sustainability considerations are included.

Disseminating a warning message to evacuate:a simulation study of the behaviour of neighbours

Large-scale evacuations are a recurring theme on news channels, whether in response to major natural or manmade disasters. The role of warning dissemination is a key part in the success of such large-scale evacuations and its inadequacy in certain cases has been a 'primary contribution to deaths and injuries' (Hayden et al.; 2007). Along with technology-driven 'official warning channels' (e.g. sirens, mass media), the role of unofficial channel (e.g. neighbours, personal contacts, volunteer wardens) has proven to be significant in warning the public of the need to evacuate. Although post-evacuation studies identify the behaviours of evacuees as disseminators of the warning message, there has not been a detailed study that quantifies the effects of such behaviour on the warning message dissemination. This paper develops an Agent-Based Simulation (ABS) model of multiple agents (evacuee households) in a hypothetical community to investigate the impact of behaviour as an unofficial channel on the overall warning dissemination. Parameters studied include the percentage of people who warn their neighbours, the efficiency of different official warning channels, and delay time to warn neighbours. Even with a low proportion of people willing to warn their neighbour, the results showed considerable impact on the overall warning dissemination. © 2012 Elsevier B.V. All rights reserved.

Tsunami disaster recovery experience:a case study on micro, small and medium scale enterprises in Southern Sri Lanka

Post-disaster recovery of Micro, Small and Medium-Scale Enterprises (SMEs) remains an issue of interest for policy and practice given the wide scale occurrences of natural disasters around the globe and their significant impacts on local economies and SMEs. Asian Tsunami of December 2004 affected many SMEs in southern Sri Lanka. The study was developed to identify the main issues encountered by the Tsunami affected SMEs in Southern Sri Lanka in the process of their post-tsunami recovery. The study: a) identifies tsunami damage and loss in micro and SMEs in the Galle district; b) ascertains the type of benefits received from various parties by the affected micro and SMEs; c) evaluates the problems and difficulties faced by the beneficiary organizations in the benefit distribution process; and d) recommends strategies and policies for the tsunami-affected micro and SMEs for them to become self-sustaining within a reasonable time frame. Fifty randomly selected tsunami-affected micro and SMEs were surveyed for this study. Interviews were conducted in person with the business owners in order to identify the damages, recovery, rehabilitation, re-establishment and difficulties faced in the benefit distribution process. The analysis identifies that the benefits were given the wrong priorities and that they were not sufficient for the recovery process. In addition, the many governance-related problems that arose while distributing benefits are discussed. Overall, the business recovery rate was approximately 65%, and approximately 88% of business organizations were sole proprietorships. Therefore, the policies of the tsunami relief agencies should adequately address the needs of sole proprietorship business requirements. Consideration should also be given to strengthen the capacity and skills of the entrepreneurs by improving operational, technological, management and marketing skills and capabilities.

Knowledge management for more sustainable water systems

The management and sharing of complex data, information and knowledge is a fundamental and growing concern in the Water and other Industries for a variety of reasons. For example, risks and uncertainties associated with climate, and other changes require knowledge to prepare for a range of future scenarios and potential extreme events. Formal ways in which knowledge can be established and managed can help deliver efficiencies on acquisition, structuring and filtering to provide only the essential aspects of the knowledge really needed. Ontologies are a key technology for this knowledge management. The construction of ontologies is a considerable overhead on any knowledge management programme. Hence current computer science research is investigating generating ontologies automatically from documents using text mining and natural language techniques. As an example of this, results from application of the Text2Onto tool to stakeholder documents for a project on sustainable water cycle management in new developments are presented. It is concluded that by adopting ontological representations sooner, rather than later in an analytical process, decision makers will be able to make better use of highly knowledgeable systems containing automated services to ensure that sustainability considerations are included. © 2010 The authors.

The evolution of learning systems:to Bayes or not to be

Bayesian algorithms pose a limit to the performance learning algorithms can achieve. Natural selection should guide the evolution of information processing systems towards those limits. What can we learn from this evolution and what properties do the intermediate stages have? While this question is too general to permit any answer, progress can be made by restricting the class of information processing systems under study. We present analytical and numerical results for the evolution of on-line algorithms for learning from examples for neural network classifiers, which might include or not a hidden layer. The analytical results are obtained by solving a variational problem to determine the learning algorithm that leads to maximum generalization ability. Simulations using evolutionary programming, for programs that implement learning algorithms, confirm and expand the results. The principal result is not just that the evolution is towards a Bayesian limit. Indeed it is essentially reached. In addition we find that evolution is driven by the discovery of useful structures or combinations of variables and operators. In different runs the temporal order of the discovery of such combinations is unique. The main result is that combinations that signal the surprise brought by an example arise always before combinations that serve to gauge the performance of the learning algorithm. This latter structures can be used to implement annealing schedules. The temporal ordering can be understood analytically as well by doing the functional optimization in restricted functional spaces. We also show that there is data suggesting that the appearance of these traits also follows the same temporal ordering in biological systems. © 2006 American Institute of Physics.

Modelling sustainability performance to achieve absolute reductions in socio-ecological systems

As the world’s natural resources dwindle and critical levels of environmental pollution are approached, sustainability becomes a key issue for governments, organisations and individuals. With the consequences of such an issue in mind, this paper introduces a unifying approach to measure the sustainability performance of socio-economic systems based on the interplay between two key variables: essentiality of consumption and environmental impact. This measure attributes to every system a ‘fitness’ value i.e. a quantity that reflects its ability to remain resilient/healthy by avoiding ecological, social and economic collapse as it consumes the available resources. This new measure is tested on a system where there is a limited supply of resources and four basic consumption types. The analysis has theoretical implications as well as practical importance as it can help countries, organisations or even individuals, in finding better ways to measure sustainability performance.

A multi-agency perspective to disaster preparedness

The increasing number of victims from disasters in recent years results in several challenges for authorities aiming to protect and provide support to affected people. Humanitarian logistics represents one of the most important fields during preparedness and response in cases of disaster, seeking to provide relief, information and services to disaster victims. However, on top of the challenges of logistical activities, the successful completion of operations depends to a large extent on coordination. This is particularly important for developing countries, where disasters occur very often and resources are even scarcer. This paper assumes a multi-agency approach to disaster preparedness that combines geographical information systems (GIS) and multi-objective optimization. The purpose of the tool is to determine the location of emergency facilities, stock prepositioning and distribution allocation for floods. We illustrate the application and the results using a case study centred on Acapulco, México.

Disaster risk reduction infrastructure requirements for South-Western Bangladesh:perspectives of local communities

Purpose: Development of effective disaster risk reduction (DRR) strategies for communities at risk of being affected by natural disasters is considered essential, especially in the wake of devastating disaster events reported worldwide. As part of a wider research study investigating community perspectives on existing and potential strategies for enhancing resilience to natural disasters, community perspectives on infrastructure and structural protection requirements were investigated. The paper aims to discuss these issues. Design/methodology/approach: Patuakhali region in South-Western Bangladesh is a region significantly at risk of multiple natural hazards. In order to engage local communities and obtain their perspectives, focus group discussions were held with local community leaders and policy makers of at-risk communities in Patuakhali region, South-Western Bangladesh. Findings: Infrastructure and structural protection requirements highlighted included multi-purpose cyclone shelters, permanent embankments and improved transport infrastructure. Much of the discussions of focus group interviews were focused on cyclone shelters and embankments, suggesting their critical importance in reducing disaster risk and also dependence of coastal communities on those two measures. Originality/value: The research design adopted sought to answer the research questions raised and also to inform local policy makers on community perspectives. Local policy makers involved in DRR initiatives in the region were informed of community perspectives and requirements, thus contributing to community engagement in implementing DRR activities.

A multi-organisational approach for disaster preparedness and response:the use of optimisation and GIS for facility location, stock pre-positioning, resource allocation and relief distribution

From 1992 to 2012 4.4 billion people were affected by disasters with almost 2 trillion USD in damages and 1.3 million people killed worldwide. The increasing threat of disasters stresses the need to provide solutions for the challenges faced by disaster managers, such as the logistical deployment of resources required to provide relief to victims. The location of emergency facilities, stock prepositioning, evacuation, inventory management, resource allocation, and relief distribution have been identified to directly impact the relief provided to victims during the disaster. Managing appropriately these factors is critical to reduce suffering. Disaster management commonly attracts several organisations working alongside each other and sharing resources to cope with the emergency. Coordinating these agencies is a complex task but there is little research considering multiple organisations, and none actually optimising the number of actors required to avoid shortages and convergence. The aim of the this research is to develop a system for disaster management based on a combination of optimisation techniques and geographical information systems (GIS) to aid multi-organisational decision-making. An integrated decision system was created comprising a cartographic model implemented in GIS to discard floodable facilities, combined with two models focused on optimising the decisions regarding location of emergency facilities, stock prepositioning, the allocation of resources and relief distribution, along with the number of actors required to perform these activities. Three in-depth case studies in Mexico were studied gathering information from different organisations. The cartographic model proved to reduce the risk to select unsuitable facilities. The preparedness and response models showed the capacity to optimise the decisions and the number of organisations required for logistical activities, pointing towards an excess of actors involved in all cases. The system as a whole demonstrated its capacity to provide integrated support for disaster preparedness and response, along with the existence of room for improvement for Mexican organisations in flood management.