Asset Performance Research Articles

Briefing: How long-term monitoring of SuDS will inform next-generation guidance and modelling tools

Sustainable drainage systems (SuDS) are becoming more commonplace in urban environments due to the additional benefits they can provide alongside their core stormwater management function. In Mansfield, UK, Severn Trent Water is investing £76 million in retrofitting hundreds of SuDS to provide flood risk reduction to 90 000 people while simultaneously creating 5 ha of biodiversity-rich habitat. However, there is continued uncertainty about the long-term performance of these nature-based assets in comparison to traditional grey infrastructure. Through a new industrial–academic partnership, a long-term evaluation of SuDS performance across Mansfield will be used to inform the next generation of design guidance and modelling tools.

Small business, big footprint: the digital carbon footprint dilemma in small and medium-sized enterprises

A major contributor to climate change is the emissions from Information and Communication Technology (ICT) devices and digitalisation. Energy use, heat production, and the operation of assets all contribute to the production of harmful emissions. However, indirect emissions, such as production and disposal, also play a role. We rely mainly on the output of small and medium-sized enterprises (SMEs). This paper focuses on the emissions of SMEs. Is it certain that cloud services (remote data storage and management) leave a much smaller carbon footprint than ICT devices for own use? These two solutions lead to a paradox: using more modern devices to produce less emissions requires more energy and generates more heat. This article analyses how to resolve this paradox for SMEs.

Optimizing Maintenance of Energy Supply Systems in City Logistics with Heuristics and Reinforcement Learning

In urban logistics, effective maintenance is crucial for maintaining the reliability and efficiency of energy supply systems, impacting both asset performance and operational stability. This paper addresses the scheduling and routing plans for maintenance of power generation assets over a multi-period horizon. We model this problem as a multi-period team orienteering problem. To address this multi-period challenge, we propose a dual approach: a novel reinforcement learning (RL) framework and a biased-randomized heuristic algorithm. The RL-based method dynamically learns from real-time operational data and evolving asset conditions, adapting to changes in asset health and failure probabilities to optimize decision making. In addition, we develop and apply a biased-randomized heuristic algorithm designed to provide effective solutions within practical computational limits. Our approach is validated through a series of computational experiments comparing the RL model and the heuristic algorithm. The results demonstrate that, when properly trained, the RL-based model is able to offer equivalent or even superior performance compared to the heuristic algorithm.

CHALLENGES IN THE OPERATION AND MAINTENANCE OF ASSETS AND FACILITIES IN ELDERLY CARE CENTRES IN MALAYSIA

Abreast with the rapid growth of older people every year, the number of elderly care centres has increased tremendously to cater to the demands of older people to spend their time after retirement in Malaysia. Effective operation and maintenance activities in an elderly care centre can optimise the service life of assets that indirectly lead to well-maintained facilities and provide the occupants with a safe, comfortable, and efficient living environment. Throughout the asset life cycle, operation and maintenance are the most extended phases, with various core activities that determine the financial health of an organisation. Nevertheless, this area remains relatively underexplored in the existing literature, particularly concerning the challenges posed by Malaysia's ageing population by 2030. Accordingly, this study seeks to investigate the issues of operation and maintenance of assets and facilities in elderly care centres. The method used to collect the data is through site observations and interviews in three elderly care centres, one in Perak and two in Selangor. These findings reveal three significant challenges the operators face: safety inside the accommodation, high operation and maintenance costs, and lack of a structured approach to managing the assets and facilities. Finally, the paper ends with implications of the findings that provide directions for future research.

Analysis of the Development Strategy of Urban Investment Companies Under the Perspective of Financial Generosity

This paper analyzes the current situation of the city investment company in terms of capital, funds and operation from the perspective of financial prosperity, based on the huge financing scale and potential risk situation of the city investment company, and takes A County City Investment Company as an example, and provides a new development mode and direction for the revitalization of the economy of the city investment company. From the perspective of "financial prosperity", it provides a new development mode and direction for revitalizing the economy of city investment company, and puts forward corresponding development strategies from the aspects of asset management, capital management and operation management.

Human Resource Empowerment Regarding the Operation of TPS 3R Assets

This article provides an overview of the empowerment of Human Resources that have TPS 3R Assets. Berat Kulon, Kemlagi District is one of the villages that has the potential for the best TPS 3R asset management in Mojokerto Regency, which was originally able to rise with the large amount of waste in the operation of Village Assets, namely TPS 3R. Precisely since the Corona pandemic in early 2020, there has been a spike in the amount of waste in this village which then resulted in piles of garbage everywhere, however, there are also small groups of residents who take advantage of this momentum to increase the economic income of residents around Berat Kulo Village, one of which is by managing waste into handicrafts in the form of bags, but this business did not have time to develop, after the pandemic, residents returned to sugar cane farming which has indeed been very much pursued by residents. The purpose of this service is to emphasize building awareness of empowering Human Resources (HR) through the operation of TPS 3R Assets. Utilizing the Asset Based Community Development (ABCD) approach, the results of the community service are 1) Implementation of human resource empowerment for the operation of TPS 3R Assets and 2) Assistance in the operation of TPS 3R Assets through, a) Waste Management Education Seminar through TPS 3R, b) Implementation of the August 17th competition in the context of environmental cleanliness creativity, c) Procurement of garbage signs, and d) Procurement of garbage bins.

Air vehicle system health and asset management: modeling, simulation, and decision support

AbstractEffective Asset Management (AM) is essential to achieve operational excellence in the context of large complex Cyber-Physical Systems (CPSs). It involves coordinated activities across all life-cycle stages, including information and service exchange within and between adjacent air operations domains, for effective air operations and collaboration. For enterprises and military air operations, AM presuppose information and service exchange between the adjacent domains of air operations. Important aspects of aviation AM are that CPSs include Integrated Vehicle Health Management (IVHM), CPS models, and Digital Twin (DT), as central concepts used to predict and optimize asset performance. This article provides an overview of the aviation AM phenomenology, environment, and challenges, and how they may impact envisioned realisations of effective AM solutions for support to enterprise air vehicle operations. The article serves as an extension of the paper Cyber-physical Asset Management of Air Vehicle Systems presented at the International Congress and Workshop of Industrial AI and Maintenance (Candell et al., in Proceedings of the IAI2023–7th international congress and workshop on industrial AI and eMaintenance, Luleå, Sweden, 2023). A comprehensive approach is presented, comprising interdependent dimensions of the problem domain, and how they may be integrated into a framework and a platform concept addressing aviation AM needs.

Developing and evaluating a risk-informed decision support system for earthquake early warning at a railway bridge

Earthquake early warning (EEW) systems provide timely information on the arrival of strong seismic waves at a site. Such information can help mitigate the consequences of earthquakes on the operation of infrastructure assets. EEW-informed mitigation actions should stem from risk-based decision-making protocols, and EEW benefits should be evaluated for the range of possible rupture scenarios that affect an asset of interest. This paper addresses these challenges specifically for railway bridges by: (1) developing a risk-informed EEW decision support system (DSS) for these assets; and (2) quantifying the effectiveness of the proposed EEW-DSS in mitigating seismic risks for railway bridges across relevant rupture scenarios. The proposed EEW-DSS combines information on site-specific seismic hazard, time-dependent EEW algorithm outputs, probabilistic seismic demand modeling, damage/derailment fragilities, and seismic loss models. These modeling components compose a multi-criteria decision-making framework. Value of information theory is then proposed to estimate the loss-mitigation benefits of the EEW-DSS, accounting for varied stakeholder risk priorities as well as dynamic lead-time/accuracy tradeoffs related to EEW performance. A railway bridge with multiple piers is adopted for the case study, which demonstrates the importance of risk-based, uncertainty-informed decision-making to the overall effectiveness of EEW in potentially reducing seismic losses.

Operational status effect on the seismic risk assessment of oil refineries

The operational status of an oil refinery (type and scale of operations that take place at any time instance) largely determines the amount of fuel produced, circulated within the facility, and stored in tanks. This status is affected by seasonality, periods of peak or low demand, as well as periods of routine maintenance. However, it is an aspect that is typically neglected even though it stands out among the factors that determine the seismic performance of several critical industrial assets, such as the storage tanks, as well as the consequences of any potential failure. An open-source refinery testbed is employed herein to demonstrate the effect of the refinery's operational status on the seismic risk estimates. Alternative realistic operational scenarios are developed following typical industry practices and are arranged over a time period between two refinery major maintenance shutdown events. The most probable damage state is selected for each asset to identify the most vulnerable ones. Based on the type and importance of the impacted assets, the potential consequences are determined at the facility level. Resulting estimates are very different if an earthquake strikes during a regular/high/low-demand period, or a maintenance period. The framework can be utilized to identify the locations within the refinery that may trigger cascading failures and secondary damages, should their assets be damaged by a seismic event. The outcomes can be exploited by stakeholders, risk engineers, and emergency action planners for developing customized and businesslike procedures to enhance the seismic resilience of the facility.

Enhancing Asset Performance: Maintenance Strategies for the GBC Pumping System at PT Freeport Indonesia

Focusing on Grasberg Block Caving (GBC) pumping system PT Freeport Indonesia, this study examines the factors of not achieving target and analyze the recommended solution. The research aims to identify the root causes of performance gaps and propose strategies to improve asset performance ultimately to achieve the company targets. The study employs a mixed method (quantitative approach as primary and qualitative as secondary) and analysis conducted with Pareto Chart identifies key factors contributing to performance gaps, Root Cause Analysis using Current Reality Tree to gather potential causes, as well as generating alternative solution and using Kepner-Tregoe Decision Analysis Evaluates and selects the recommended alternative solutions. The results are expected to provide actionable insights for the company. Implementing recommended alternative solution estimated saving 83% of the costs associated with component replacements and reduce overall unscheduled downtime 63.65%.

Strategic maintenance planning in the digital era: a hybrid approach merging Reliability-Centered Maintenance with digitalization opportunities

AbstractIn the age of digital transformation, maintenance operations are crucial for leveraging the potential of Industry 4.0 and 5.0. Yet, this domain remains significantly under-optimized in terms of strategic maintenance planning and enhancing asset performance. The advent of smart technologies offers a myriad of innovative avenues; however, harnessing these effectively requires systematic planning that incorporates these new, various and quite diversified, smart practices. Thus, this paper proposes a new methodological approach to maintenance planning, based on the Reliability-Centered Maintenance method, aimed at providing an operative tool for organizations to foster the evolution of their maintenance plans towards the paradigm of digitalization. This novel method enables the identification of hidden opportunities of improvement not identifiable through the use of the traditional approach through the proposal of an Opportunity Index, to use together with the Criticality Index in asset selection, and a Digitalization Score to use during Failure Mode, Effects, and Criticality Analysis. The proposed method is applied to transform the maintenance planning of a production line, thus identifying the opportunities of the approach and testing its feasibility.

Parcel-Locker-Sharing Model for E-Commerce Logistics Service Providers

In the e-commerce last-mile delivery process, the asset operators (logistics service providers who own parcel locker facilities) support their delivery service with parcel lockers, while the non-asset operators (logistics service providers without parcel lockers) perform door-to-door delivery. Due to demand fluctuation, asset operators’ parcel-locker slots may be left vacant, while non-asset operators are stuck with the high-cost door-to-door service. The exclusiveness of parcel-locker usage reduces resource utilization and service efficiency in last-mile delivery. Therefore, this paper proposes a parcel-locker-sharing model in which these two parties share the parcel-locker capacity in last-mile delivery. The asset operator rents the unused parcel lockers to the non-asset operator by charging a rental fee,while the non-asset operator rents the parcel lockers for delivery to save logistics costs. The motivation of this alliance is to increase the profits of both parties and that of the total supply chain. This study establishes the supply-chain profit model for the parcel-locker-sharing framework and finds that the profit or loss depends on the comparison of the operation cost savings and delivery-cost savings. A numerical analysis is conducted to validate the final result. The research further suggests the optimal rental quantity and price interval. This paper is the first to study the operational mechanism of sharing the parcel locker between two distinct types of logistics service providers and to offer recommendations for industrial application.

Analisis Kinerja Perusahaan Asuransi Umum Nasional dan Perusahaan Asuransi Umum Patungan di Indonesia

This analysis reflects the performance of National General Insurance Companies and Joint Venture General Insurance Companies in Indonesia. This allows this mapping to be used as a tool to analyze and control the performance of National General Insurance Companies and Joint Venture General Insurance Companies in Indonesia. So based on the above we will raise the research title "Performance Analysis of National General Insurance Companies and Joint Venture General Insurance Companies in Indonesia," This research aims; To find out the performance of National General Insurance Companies, to find out the performance of general insurance companies and joint venture general insurance companies in Indonesia, and to find out the differences in the performance of national general insurance companies and joint venture general insurance companies in Indonesia. This research method is quantitative descriptive. The conclusions are 1) Asset performance, equity performance, debt performance, revenue performance, cost performance, profit and loss performance, investment performance, technical reserve performance of joint venture general insurance companies have a tendency to increase from 2018 - 2022, while investment performance / technical reserves (%) Joint General Insurance has a downward trend from year to year from 2018 - 2022, 2.a) Asset Performance, Equity Performance, Debt Performance, Income Performance, Investment Performance, and Technical Reserve Performance of national general insurance has an upward trend from year to year 2018 - 2022, 2.b) but the performance of national general insurance costs has a tendency to fall from year to year from 2018 - 2022 then rises and then falls at the end of 2022, while the profit and loss performance of national general insurance has a tendency to be unstable from year to year from 2018 - 2022 with an upward trend, and Investment Performance / Technical Reserves (%) of national general insurance has a downward trend from 2018 - 2022, 3) The differences in the performance of National General Insurance Companies and Joint Venture General Insurance Companies in Indonesia are as follows; 1). Joint Venture General Insurance has better performance than national general insurance companies in; Asset performance, debt performance, cost performance, and investment performance / technical reserves (%), and 2) National General Insurance has better performance than joint venture general insurance companies from 2018 - 2022 in; Equity performance, income performance, profit and loss performance, investment performance, and technical reserve performance.

ONLINE TASK SEGREGATION AND RESOURCE UTILIZATION IN A UNIVERSITY

The Online Task Management System (OTMS) is built to organize an organization resource and therefore enhance assignment’s performance by assigning, the required tasks, identify users, their full details, and even their working progress while estimating their working performance. The proposed system also compliments the interaction between the administrators, users by providing an efficient interface to accomplish goals on the web hassle freely. OTMS covers all activities related to the search for, finding, distribution, monitoring, and forecasting of the performance of every organizational asset. It is the responsibility of administrators to ensure that the school has a proper data base system in which the performance of the peer can be determined from. Thanks to the machine learning solution, this system can even predict such organizational performance based on individual peer analysis, which in turn is helpful in the organization’s growth. KEYWORDS: Allocation, Resources, Scheduling and Tracking.

Technology Focus: Reservoir Surveillance (September 2024)

In today’s oil and gas operations, surveillance technologies have undergone a revolution, reshaping how facilities, wells, and reservoirs are monitored. These advancements not only have increased the scale at which these technologies are deployed but also have led to an unprecedented influx of data. The sheer volume of data, however, poses a significant challenge to traditional analytical methods, overwhelming their capacity to derive actionable insights effectively. To address this challenge, the industry is rapidly advancing toward automated solutions powered by artificial-intelligence (AI) -driven analytics. These systems automate data ingestion and use machine-learning algorithms to sift through massive data sets, identifying anomalies and prioritizing actionable insights. By automating routine surveillance tasks, engineers can focus on critical actions that deliver substantial operational benefits. For example, in paper IPTC 23912, operators successfully optimized production operations by harnessing real-time field data through smart systems, effectively managing operations with complex near-critical fluids. Similarly, in paper SPE 218470, researchers proposed a novel workflow integrating virtual flowmetering and permanent downhole gauge data for pattern recognition to enhance real-time monitoring and decision-making in petroleum and geothermal industries. Nevertheless, ensuring effective surveillance of operational assets requires a strategic approach. A valuable tool in crafting such strategies is the value of information (VOI) assessment. This method systematically evaluates how acquiring specific information can influence decision-making and operational outcomes. For instance, paper SPE 215318 highlights a field operator’s systematic approach to VOI assessment, aiming to optimize daily operations and guide future development activities. In essence, while surveillance technologies have inundated operators with unprecedented data flows, advancements in automation and AI-driven analytics offer the promise of unlocking this data’s true potential. By embracing these technologies, the oil and gas industry can navigate the complexities of the modern energy landscape with greater agility, precision, and cost-effectiveness. Recommended additional reading at OnePetro: www.onepetro.org. SPE 215119 Surveillance, Analysis, and Optimization During Active Drilling Campaign by Yanfen Zhang, Chevron, et al. OTC 35413 New Opportunities in Well and Reservoir Surveillance Using Multiple Downhole Pressure Gauges in Deepwater Injector Wells by Piyush Pankaj, ExxonMobil, et al. OTC 34863 Digital Twin for Oil-Rim Management Using Early Warning System and Exception-Based Surveillance, Offshore Malaysia by M. Mahamad Amir, Petronas, et al.

Analysis of Light Asset Operation Mode and Effect

Analysis of Light Asset Operation Mode and Effect

Stochastic Convex Cone Programming for Joint Optimal BESS Operation and Q-Placement in Net-Zero Microgrids

Microgrids have emerged as a pivotal solution in the quest for efficient, resilient, and sustainable energy systems. Comprising diverse distributed energy resources, microgrids present a compelling opportunity to revolutionize how we generate, store, and distribute electricity, while simultaneously reducing carbon footprints. This paper proposes an optimal battery energy storage system (BESS) management scheme, along with capacitor placement for reactive power (Q)-compensation, and scheduling for the purpose of a renewable-based microgrid’s loss minimization. The proposed model evaluates the impact of BESS management on energy efficiency and analyzes how optimal scheduling of BESS influences system losses. Furthermore, it investigates the coordinated planning and operation of active assets within the microgrid, such as controllable capacitor banks, in enhancing overall efficiency. The model is formulated as a mixed-integer second-order cone programming (MISOCP) problem which is solved for both deterministic and stochastic generation and consumption data. The proposed model is tested on a 21-bus microgrid comprising photovoltaic and hydropower energy resources, and the efficacy of the model is approved by several case studies. The simulation results show that the proposed method can reduce microgrid energy losses by approximately 12 percent using the deterministic approach and around 14 percent with the stochastic approach.

Optimizing Asset Management: Comprehensive Analysis and Innovative Strategy Design to Increase the Effectiveness of Life Cycle Delivery at PT Pembangkitan Jawa-Bali Services

Asset management is the art and science of making the right decisions and optimizing the delivery of value from organizational assets. ( Institute of Asset Management , 2010). PT PLN Pembangkitan Jawa Bali Services, which is often abbreviated as PT PJB Services, is a subsidiary of PT Pembangkitan Jawa-Bali (PT PJB) . This company operates in the field of power plant operation and maintenance services, as well as supporting services related to power plants. The company has a strong focus on asset management, given its role in managing and maintaining critical power generation assets. This research examines the implementation of multidimensional strategies in optimizing asset management at PT Pembangkitan Jawa-Bali Services (PJB Services). The main focus of the analysis is to test the variables that influence the success of asset performance at PT Pembangkitan Jawa Bali Services based on group 3 ( Life Cycle Delivery ) consist of 11 key dimensions of asset management: Technical Standards & Legislation , Asset Creation & Acquisition , Systems Engineering , Configuration Management , Maintenance Delivery. Reliability Engineering, Asset Operation, Resource Management, Shutdown & Outage Management, Fault & Incident Response, Asset Decommissioning & Disposal. The main aim of the research is to design an integrated approach to improve the effectiveness of corporate asset management. The method used in this research is the Structural Equation Model with the Partial Least Square approach. The research results revealed that the accepted hypothesis is H1, H2, H3, H4, H9, H10, H12, H16, H17, H18, and H19 are acceptable because they have t-statistic values > 1.96 and p-values < 0.05. Meanwhile, the hypotheses H5, H6, H7, H8, H11, H13, H14, and H15 were rejected because they had t-statistic values > 1.96 and p-values > 0.05.

Identifying climate-related failures in railway infrastructure using machine learning

Climate change impacts pose challenges to a dependable operation of railway infrastructure assets, thus necessitating understanding and mitigating its effects. This study proposes a machine learning framework to distinguish between climatic and non-climatic failures in railway infrastructure. The maintenance data of turnout assets from Sweden’s railway were collected and integrated with asset design, geographical and meteorological parameters. Various machine learning algorithms were employed to classify failures across multiple time horizons. The Random Forest model demonstrated a high accuracy of 0.827 and stable F1-scores across all time horizons. The study identified minimum-temperature and quantity of snow and rain prior to the event as the most influential factors. The 24-hour time horizon prior to failure emerged as the most effective time window for the classification. The practical implications and applications include enhancement of maintenance and renewal process, supporting more effective resource allocation, and implementing climate adaptation measures towards resilience railway infrastructure management.

The Impact of Geopolitical Conflicts on the Volatility of the International Financial Markets

This paper aims to explore the specific impact of geopolitical conflicts on the international financial market. By analyzing the volatility of financial markets, investor sentiment, commodity prices, and the performance of various assets, the short-term and long-term financial impacts of conflict are revealed. Studies show that geopolitical conflicts have a significant impact on global markets, especially in energy and commodity markets, with different volatility effects on different regions and asset classes.