Optimisation of piping network design for district cooling system

A district cooling system (DeS) is a.scheme for centralised cooling energy distribution which takes advantage of economies of scale and load diversity. . A cooling medium (chilled water) is generated at a central refrigeration plant and then supplied to a district area, comprising multiple buildings, through a closed-loop piping circuit. Because of the substantial capital investment involved, an optimal design of the distribution piping . configuration is one of the crucial factors for successful implementation of a district 1'. cooling scheme. Since there. exists an enormous number of different combinations of the piping configuration, it is not feasible to evaluate each individual case using an exhaustive approach. This thesis exammes the problem of determining an optimal distribution piping configuration using a genetic algorithm (GA). In order to estimate the spatial and temporal distribution of cooling loads; the climatic conditions of Hong Kong were investigated and a weather database in the form of a typical meteorological year (TMY) was developed. Detailed thermal modelling of a number of prototypical buildings was carried out to determine benchmark cooling loads. A novel Local Search/Looped Local Search algorithm was developed for finding optimal/near-optimal distribution piping configurations. By means of computational . experiments, it was demonstrated that there is a promising improvement to GA performance by including the Local Search/Looped Local Search algorithm, in terms of both solution quality and computational efficiency. The effects on the search performance of a number of parameters were systematically investigated to establish the most effective settings. In order to illustrate the effectiveness of the Local Search/Looped Local Search algorithm, a benchmark problem - the optimal communication,spanning tree (OCST) was used for comparison. The results showed that the Looped Local Search method developed in this work was an effective tool for optimal network design of the distribution piping system in DCS, as well as for optimising the OCST problem.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Review of Optimization Problems in Wireless Sensor Networks

International audienc

On the mathematical modeling for optimal selecting of calibers of conductors in DC radial distribution networks: An MINLP approach

This paper addresses the problem of optimal conductor selection in direct current (DC) distribution networks with radial topology. A nonlinear mixed-integer programming model (MINLP) is developed through a branch-to-node incidence matrix. An important contribution is that the proposed MINLP model integrates a set of constraints related to the telescopic structure of the network, which allows reducing installation costs. The proposed model also includes a time-domain dependency that helps analyze the DC network under different load conditions, including renewable generation and battery energy storage systems, and different voltage regulation operative consigns. The objective function of the proposed model is made up of the total investment in conductors and the total cost of energy losses in one year of operation. These components of the objective function show multi-objective behavior. For this reason, different simulation scenarios are performed to identify their effects on the final grid configuration. An illustrative 10-nodes medium-voltage DC grid with 9 lines is used to carry out all the simulations through the General Algebraic Modeling System known as GAMS

The commuter rail circulator network design problem: formulation, solution methods, and applications

Commuter rail is increasingly popular as a means to introduce rail transportation to metropolitan transportation systems. The long-term benefits of commuter rail include the addition of capacity to the transportation system, providing a quality commute alternative, and shifting land use toward transit-oriented development patterns. The success of a commuter rail system depends upon cultivating a ridership base upon which to expand and improve the system. Cultivating this ridership is dependent upon offering a quality transportation option to commuters. Characteristics of commuter rail systems in the United States present challenges to offering quality service that must be overcome. Commuter rail has been implemented only on existing rail right-of-way (ROW) and infrastructure (depending upon condition) in the United States. Existing rail ROW does not often coincide with current commercial and residential demand centers and necessitates the use of a circulator system to expand the service boundary of commuter rail to reach these demand centers. The commuter rail circulator network design problem (CRCNDP) addresses a particular aspect of the commuter rail trip, seeking to improve the performance of the entire system through accurately modeling the portion of the trip from rail station to the final destination. This final leg includes both the trip on the circulator vehicle and the walking trip from the circulator stop to the final destination. This dissertation seeks to provide an innovative mathematical programming formulation and solution methodology for the CRCNDP and apply this method to a case study.Civil, Architectural, and Environmental Engineerin

A Polyhedral Study of Mixed 0-1 Set

We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set

OPTIMIZATION OF RAILWAY TRANSPORTATION HAZMATS AND REGULAR COMMODITIES

Transportation of dangerous goods has been receiving more attention in the realm of academic and scientific research during the last few decades as countries have been increasingly becoming industrialized throughout the world, thereby making Hazmats an integral part of our life style. However, the number of scholarly articles in this field is not as many as those of other areas in SCM. Considering the low-probability-and-high-consequence (LPHC) essence of transportation of Hazmats, on the one hand, and immense volume of shipments accounting for more than hundred tons in North America and Europe, on the other, we can safely state that the number of scholarly articles and dissertations have not been proportional to the significance of the subject of interest. On this ground, we conducted our research to contribute towards further developing the domain of Hazmats transportation, and sustainable supply chain management (SSCM), in general terms. Transportation of Hazmats, from logistical standpoint, may include all modes of transport via air, marine, road and rail, as well as intermodal transportation systems. Although road shipment is predominant in most of the literature, railway transportation of Hazmats has proven to be a potentially significant means of transporting dangerous goods with respect to both economies of scale and risk of transportation; these factors, have not just given rise to more thoroughly investigation of intermodal transportation of Hazmats using road and rail networks, but has encouraged the competition between rail and road companies which may indeed have some inherent advantages compared to the other medium due to their infrastructural and technological backgrounds. Truck shipment has ostensibly proven to be providing more flexibility; trains, per contra, provide more reliability in terms of transport risk for conveying Hazmats in bulks. In this thesis, in consonance with the aforementioned motivation, we provide an introduction into the hazardous commodities shipment through rail network in the first chapter of the thesis. Providing relevant statistics on the volume of Hazmat goods, number of accidents, rate of incidents, and rate of fatalities and injuries due to the incidents involving Hazmats, will shed light onto the significance of the topic under study. As well, we review the most pertinent articles while putting more emphasis on the state-of-the-art papers, in chapter two. Following the discussion in chapter 3 and looking at the problem from carrier company’s perspective, a mixed integer quadratically constraint problem (MIQCP) is developed which seeks for the minimization of transportation cost under a set of constraints including those associating with Hazmats. Due to the complexity of the problem, the risk function has been piecewise linearized using a set of auxiliary variables, thereby resulting in an MIP problem. Further, considering the interests of both carrier companies and regulatory agencies, which are minimization of cost and risk, respectively, a multiobjective MINLP model is developed, which has been reduced to an MILP through piecewise linearization of the risk term in the objective function. For both single-objective and multiobjective formulations, model variants with bifurcated and nonbifurcated flows have been presented. Then, in chapter 4, we carry out experiments considering two main cases where the first case presents smaller instances of the problem and the second case focuses on a larger instance of the problem. Eventually, in chapter five, we conclude the dissertation with a summary of the overall discussion as well as presenting some comments on avenues of future work

Open problems in causal structure learning: A case study of COVID-19 in the UK

Causal machine learning (ML) algorithms recover graphical structures that tell us something about cause-and-effect relationships. The causal representation praovided by these algorithms enables transparency and explainability, which is necessary for decision making in critical real-world problems. Yet, causal ML has had limited impact in practice compared to associational ML. This paper investigates the challenges of causal ML with application to COVID-19 UK pandemic data. We collate data from various public sources and investigate what the various structure learning algorithms learn from these data. We explore the impact of different data formats on algorithms spanning different classes of learning, and assess the results produced by each algorithm, and groups of algorithms, in terms of graphical structure, model dimensionality, sensitivity analysis, confounding variables, predictive and interventional inference. We use these results to highlight open problems in causal structure learning and directions for future research. To facilitate future work, we make all graphs, models, data sets, and source code publicly available online