Availability estimation and management for complex processing systems

“Availability” is the terminology used in asset intensive industries such as petrochemical and hydrocarbons processing to describe the readiness of equipment, systems or plants to perform their designed functions. It is a measure to suggest a facility’s capability of meeting targeted production in a safe working environment. Availability is also vital as it encompasses reliability and maintainability, allowing engineers to manage and operate facilities by focusing on one performance indicator. These benefits make availability a very demanding and highly desired area of interest and research for both industry and academia. In this dissertation, new models, approaches and algorithms have been explored to estimate and manage the availability of complex hydrocarbon processing systems. The risk of equipment failure and its effect on availability is vital in the hydrocarbon industry, and is also explored in this research. The importance of availability encouraged companies to invest in this domain by putting efforts and resources to develop novel techniques for system availability enhancement. Most of the work in this area is focused on individual equipment compared to facility or system level availability assessment and management. This research is focused on developing an new systematic methods to estimate system availability. The main focus areas in this research are to address availability estimation and management through physical asset management, risk-based availability estimation strategies, availability and safety using a failure assessment framework, and availability enhancement using early equipment fault detection and maintenance scheduling optimization.

Application of battery energy storage in the Québec interconnection

Le Système Stockage de l’Énergie par Batterie ou Batterie de Stockage d’Énergie (BSE) offre de formidables atouts dans les domaines de la production, du transport, de la distribution et de la consommation d’énergie électrique. Cette technologie est notamment considérée par plusieurs opérateurs à travers le monde entier, comme un nouveau dispositif permettant d’injecter d’importantes quantités d’énergie renouvelable d’une part et d’autre part, en tant que composante essentielle aux grands réseaux électriques. De plus, d’énormes avantages peuvent être associés au déploiement de la technologie du BSE aussi bien dans les réseaux intelligents que pour la réduction de l’émission des gaz à effet de serre, la réduction des pertes marginales, l’alimentation de certains consommateurs en source d’énergie d’urgence, l’amélioration de la gestion de l’énergie, et l’accroissement de l’efficacité énergétique dans les réseaux. Cette présente thèse comprend trois étapes à savoir : l’Étape 1 - est relative à l’utilisation de la BSE en guise de réduction des pertes électriques ; l’Étape 2 - utilise la BSE comme élément de réserve tournante en vue de l’atténuation de la vulnérabilité du réseau ; et l’Étape 3 - introduit une nouvelle méthode d’amélioration des oscillations de fréquence par modulation de la puissance réactive, et l’utilisation de la BSE pour satisfaire la réserve primaire de fréquence. La première Étape, relative à l’utilisation de la BSE en vue de la réduction des pertes, est elle-même subdivisée en deux sous-étapes dont la première est consacrée à l’allocation optimale et le seconde, à l’utilisation optimale. Dans la première sous-étape, l’Algorithme génétique NSGA-II (Non-dominated Sorting Genetic Algorithm II) a été programmé dans CASIR, le Super-Ordinateur de l’IREQ, en tant qu’algorithme évolutionniste multiobjectifs, permettant d’extraire un ensemble de solutions pour un dimensionnement optimal et un emplacement adéquat des multiple unités de BSE, tout en minimisant les pertes de puissance, et en considérant en même temps la capacité totale des puissances des unités de BSE installées comme des fonctions objectives. La première sous-étape donne une réponse satisfaisante à l’allocation et résout aussi la question de la programmation/scheduling dans l’interconnexion du Québec. Dans le but de réaliser l’objectif de la seconde sous-étape, un certain nombre de solutions ont été retenues et développées/implantées durant un intervalle de temps d’une année, tout en tenant compte des paramètres (heure, capacité, rendement/efficacité, facteur de puissance) associés aux cycles de charge et de décharge de la BSE, alors que la réduction des pertes marginales et l’efficacité énergétique constituent les principaux objectifs. Quant à la seconde Étape, un nouvel indice de vulnérabilité a été introduit, formalisé et étudié ; indice qui est bien adapté aux réseaux modernes équipés de BES. L’algorithme génétique NSGA-II est de nouveau exécuté (ré-exécuté) alors que la minimisation de l’indice de vulnérabilité proposé et l’efficacité énergétique représentent les principaux objectifs. Les résultats obtenus prouvent que l’utilisation de la BSE peut, dans certains cas, éviter des pannes majeures du réseau. La troisième Étape expose un nouveau concept d’ajout d’une inertie virtuelle aux réseaux électriques, par le procédé de modulation de la puissance réactive. Il a ensuite été présenté l’utilisation de la BSE en guise de réserve primaire de fréquence. Un modèle générique de BSE, associé à l’interconnexion du Québec, a enfin été proposé dans un environnement MATLAB. Les résultats de simulations confirment la possibilité de l’utilisation des puissances active et réactive du système de la BSE en vue de la régulation de fréquence.

Host and Network Optimizations for Performance Enhancement and Energy Efficiency in Data Center Networks

Modern data centers host hundreds of thousands of servers to achieve economies of scale. Such a huge number of servers create challenges for the data center network (DCN) to provide proportionally large bandwidth. In addition, the deployment of virtual machines (VMs) in data centers raises the requirements for efficient resource allocation and find-grained resource sharing. Further, the large number of servers and switches in the data center consume significant amounts of energy. Even though servers become more energy efficient with various energy saving techniques, DCN still accounts for 20% to 50% of the energy consumed by the entire data center. The objective of this dissertation is to enhance DCN performance as well as its energy efficiency by conducting optimizations on both host and network sides. First, as the DCN demands huge bisection bandwidth to interconnect all the servers, we propose a parallel packet switch (PPS) architecture that directly processes variable length packets without segmentation-and-reassembly (SAR). The proposed PPS achieves large bandwidth by combining switching capacities of multiple fabrics, and it further improves the switch throughput by avoiding padding bits in SAR. Second, since certain resource demands of the VM are bursty and demonstrate stochastic nature, to satisfy both deterministic and stochastic demands in VM placement, we propose the Max-Min Multidimensional Stochastic Bin Packing (M3SBP) algorithm. M3SBP calculates an equivalent deterministic value for the stochastic demands, and maximizes the minimum resource utilization ratio of each server. Third, to provide necessary traffic isolation for VMs that share the same physical network adapter, we propose the Flow-level Bandwidth Provisioning (FBP) algorithm. By reducing the flow scheduling problem to multiple stages of packet queuing problems, FBP guarantees the provisioned bandwidth and delay performance for each flow. Finally, while DCNs are typically provisioned with full bisection bandwidth, DCN traffic demonstrates fluctuating patterns, we propose a joint host-network optimization scheme to enhance the energy efficiency of DCNs during off-peak traffic hours. The proposed scheme utilizes a unified representation method that converts the VM placement problem to a routing problem and employs depth-first and best-fit search to find efficient paths for flows.

Novel efficient technologies in Europe for axle bearing condition monitoring – the MAXBE project

Axle bearing damage with possible catastrophic failures can cause severe disruptions or even dangerous derailments, potentially causing loss of human life and leading to significant costs for railway infrastructure managers and rolling stock operators. Consequently the axle bearing damage process has safety and economic implications on the exploitation of railways systems. Therefore it has been the object of intense attention by railway authorities as proved by the selection of this topic by the European Commission in calls for research proposals. The MAXBE Project (http://www.maxbeproject.eu/), an EU-funded project, appears in this context and its main goal is to develop and to demonstrate innovative and efficient technologies which can be used for the onboard and wayside condition monitoring of axle bearings. The MAXBE (interoperable monitoring, diagnosis and maintenance strategies for axle bearings) project focuses on detecting axle bearing failure modes at an early stage by combining new and existing monitoring techniques and on characterizing the axle bearing degradation process. The consortium for the MAXBE project comprises 18 partners from 8 member states, representing operators, railway administrations, axle bearing manufactures, key players in the railway community and experts in the field of monitoring, maintenance and rolling stock. The University of Porto is coordinating this research project that kicked-off in November 2012 and it is completed on October 2015. Both on-board and wayside systems are explored in the project since there is a need for defining the requirement for the onboard equipment and the range of working temperatures of the axle bearing for the wayside systems. The developed monitoring systems consider strain gauges, high frequency accelerometers, temperature sensors and acoustic emission. To get a robust technology to support the decision making of the responsible stakeholders synchronized measurements from onboard and wayside monitoring systems are integrated into a platform. Also extensive laboratory tests were performed to correlate the in situ measurements to the status of the axle bearing life. With the MAXBE project concept it will be possible: to contribute to detect at an early stage axle bearing failures; to create conditions for the operational and technical integration of axle bearing monitoring and maintenance in different European railway networks; to contribute to the standardization of the requirements for the axle bearing monitoring, diagnosis and maintenance. Demonstration of the developed condition monitoring systems was performed in Portugal in the Northern Railway Line with freight and passenger traffic with a maximum speed of 220 km/h, in Belgium in a tram line and in the UK. Still within the project, a tool for optimal maintenance scheduling and a smart diagnostic tool were developed. This paper presents a synthesis of the most relevant results attained in the project. The successful of the project and the developed solutions have positive impact on the reliability, availability, maintainability and safety of rolling stock and infrastructure with main focus on the axle bearing health.

Bidding Decision of Wind-Thermal GenCo in Day-Ahead Market

This paper deals with the self-scheduling problem of a price-taker having wind and thermal power production and assisted by a cyber-physical system for supporting management decisions in a day-ahead electric energy market. The self-scheduling is regarded as a stochastic mixed-integer linear programming problem. Uncertainties on electricity price and wind power are considered through a set of scenarios. Thermal units are modelled by start-up and variable costs, furthermore constraints are considered, such as: ramp up/down and minimum up/down time limits. The stochastic mixed-integer linear programming problem allows a decision support for strategies advantaging from an effective wind and thermal mixed bidding. A case study is presented using data from the Iberian electricity market.

Bidding strategy of wind-thermal energy producers

This paper presents a stochastic mixed-integer linear programming approach for solving the self-scheduling problem of a price-taker thermal and wind power producer taking part in a pool-based electricity market. Uncertainty on electricity price and wind power is considered through a set of scenarios. Thermal units are modelled by variable costs, start-up costs and technical operating constraints, such as: forbidden operating zones, ramp up/down limits and minimum up/down time limits. An efficient mixed-integer linear program is presented to develop the offering strategies of the coordinated production of thermal and wind energy generation, having as a goal the maximization of profit. A case study with data from the Iberian Electricity Market is presented and results are discussed to show the effectiveness of the proposed approach.

A regulatory framework for short-term stochastic maintenance outage scheduling in smart grids

The current regulatory framework for maintenance outage scheduling in distribution systems needs revision to face the challenges of future smart grids. In the smart grid context, generation units and the system operator perform new roles with different objectives, and an efficient coordination between them becomes necessary. In this paper, the distribution system operator (DSO) of a microgrid receives the proposals for shortterm (ST) planned outages from the generation and transmission side, and has to decide the final outage plans, which is mandatory for the members to follow. The framework is based on a coordination procedure between the DSO and other market players. This paper undertakes the challenge of optimization problem in a smart grid where the operator faces with uncertainty. The results show the effectiveness and applicability of the proposed regulatory framework in the modified IEEE 34- bus test system.

Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming

In the energy management of the isolated operation of small power system, the economic scheduling of the generation units is a crucial problem. Applying right timing can maximize the performance of the supply. The optimal operation of a wind turbine, a solar unit, a fuel cell and a storage battery is searched by a mixed-integer linear programming implemented in General Algebraic Modeling Systems (GAMS). A Virtual Power Producer (VPP) can optimal operate the generation units, assured the good functioning of equipment, including the maintenance, operation cost and the generation measurement and control. A central control at system allows a VPP to manage the optimal generation and their load control. The application of methodology to a real case study in Budapest Tech, demonstrates the effectiveness of this method to solve the optimal isolated dispatch of the DC micro-grid renewable energy park. The problem has been converged in 0.09 s and 30 iterations.

Scheduling forest road maintenance using the analytic hierarchy process and heuristics

Abstract

A priori reformulations for joint rolling-horizon scheduling of materials processing and lot-sizing problem

In many production processes, a key material is prepared and then transformed into different final products. The lot sizing decisions concern not only the production of final products, but also that of material preparation in order to take account of their sequence-dependent setup costs and times. The amount of research in recent years indicates the relevance of this problem in various industrial settings. In this paper, facility location reformulation and strengthening constraints are newly applied to a previous lot-sizing model in order to improve solution quality and computing time. Three alternative metaheuristics are used to fix the setup variables, resulting in much improved performance over previous research, especially regarding the use of the metaheuristics for larger instances. © 2013 Elsevier Ltd. All rights reserved.

Geographic base file system--uses, maintenance, problem solving : conference proceedings, November 16-17, 1971, Arlington, Tex.

Issued May 1972; revised November 1976.

Knowledge maintenance and the frame problem

Knowledge maintenance is a major challenge for both knowledge management and the Semantic Web. Operating over the Semantic Web, there will be a network of collaborating agents, each with their own ontologies or knowledge bases. Change in the knowledge state of one agent may need to be propagated across a number of agents and their associated ontologies. The challenge is to decide how to propagate a change of knowledge state. The effects of a change in knowledge state cannot be known in advance, and so an agent cannot know who should be informed unless it adopts a simple ‘tell everyone – everything’ strategy. This situation is highly reminiscent of the classic Frame Problem in AI. We argue that for agent-based technologies to succeed, far greater attention must be given to creating an appropriate model for knowledge update. In a closed system, simple strategies are possible (e.g. ‘sleeping dog’ or ‘cheap test’ or even complete checking). However, in an open system where cause and effect are unpredictable, a coherent cost-benefit based model of agent interaction is essential. Otherwise, the effectiveness of every act of knowledge update/maintenance is brought into question.

Digraphs Definition for an Array Maintenance Problem

In this paper we present a data structure which improves the average complexity of the operations of updating and a certain type of retrieving information on an array. The data structure is devised from a particular family of digraphs verifying conditions so that they represent solutions for this problem.

An Effective Method for Constructing Data Structures Solving an Array Maintenance Problem

In this paper a constructive method of data structures solving an array maintenance problem is offered. These data structures are defined in terms of a family of digraphs which have previously been defined, representing solutions for this problem. We present as well a prototype of the method in Haskell.