Performance Uncertainty Research Articles

Regulatory Fragmentation and Operational Efficiency

ABSTRACTThis study employs a fragmentation index derived from Federal Register data to explore the relationship between regulatory fragmentation and operational efficiency. I find a positive association between regulatory fragmentation and operational efficiency, validated through various robustness checks, including entropy balancing, instrumental variable methods, fixed effects at different levels, change specification models, and alternative measures of operational efficiency. Additional analyses indicate that this positive association is more pronounced in firms exhibiting internal control weaknesses and facing higher operational uncertainty. Overall, my results suggest that although regulatory fragmentation poses challenges, it also creates opportunities for firms to enhance their operation efficiency. This research sheds light on the multifaceted effects of regulatory environments, providing valuable insights for policymakers and corporate strategists in managing regulatory complexities.

A blockchain-based self-adaptive collaboration mechanism for service-oriented manufacturing system

ABSTRACT The service-oriented manufacturing system, based on the cloud manufacturing platform, is adversely affected by factors such as diverse entities, geographical dispersion, and distributed collaboration. The system faces challenges like a lack of trust among entities and operational process uncertainty, thereby significantly reducing service collaboration efficiency. Blockchain, a promising technology in the information era, can address these issues by enhancing the authenticity of service data and trust among entities. In this paper, a blockchain-based operational mode is proposed to establish a completely trustworthy manufacturing environment. The potential abnormal events during collaborative service processes and their impacts are illustrated. According to these analyses, a self-adaptive service collaboration process is designed utilizing the blockchain, and a trust-based service collaboration model involving multiple entities is developed to enhance the system adjustment efficiency. This model, integrated with the solving algorithm, is deployed in smart contracts for automatic adjustment of service entities. An experimental study is conducted to verify the effectiveness of the proposed self-adaptive collaboration process and adjustment model. The results show that the proposed method can effectively boost the efficiency of the service-oriented manufacturing system.

Robust optimization of a gas turbine performance under geometrical and operational uncertainties using a novel robustness criterion

Robust optimization of a gas turbine performance under geometrical and operational uncertainties using a novel robustness criterion

A successful case study of using HCl and viscoelastic diverting acid systems for carbonate matrix acidizing in an oil well with optimized predictive model

The oil and gas industry is continuously seeking advanced methods to optimize reservoir productivity. Among the techniques employed, matrix acidizing, particularly using Viscoelastic Diverting Acid (VDA), has shown significant promise in enhancing permeability and improving well performance. However, the absence of standardized formulations for VDA requires a careful evaluation of commercial additives to ensure their reliability and effectiveness in diverse reservoir conditions. This article presents a comprehensive case study that examining the application of hydrochloric acid (HCl) and VDA systems in a low-permeability carbonate reservoir situated in southern Iran. The study integrates design of experiments (DOE) and multi-objective optimization techniques to assess the impact of various operational parameters on acidizing performance. The research focused on high-temperature formations, employing multi-stage acid injections to evaluate the effectiveness of the acid treatments. The use of DoE and response surface methodology (RSM) provided a structured approach for optimizing key parameters to enhance treatment outcomes. The results of this study demonstrated a substantial increase in both oil production and well pressure following the acidizing process. Specifically, oil production improved by 80%, and well pressure increased by 73%, underscoring the effectiveness of the acid treatment in stimulating the reservoir. The VDA system, combined with the bullheading injection technique, facilitated superior acid placement and distribution within the reservoir, enhancing the overall acidizing efficiency. Furthermore, the optimization of operational parameters such as skin factor and maximum invasion depth through the use of RSM allowed for a significant refinement of the acidizing process. The developed models, with correlation coefficients exceeding 0.96, further validated the accuracy of the optimization process and provided valuable insights into the critical factors influencing acidizing performance. Additionally, the refined acidizing program minimized operational uncertainties, leading to improved treatment efficiency and more consistent results in challenging high-temperature formations.

Leveraging Deep Learning for Robust Structural Damage Detection and Classification: A Transfer Learning Approach via CNN

Transfer learning techniques for structural health monitoring in bridge-type structures are investigated, focusing on model generalizability and domain adaptation challenges. Finite element models of bridge-type structures with varying geometry were simulated using the OpenSeesPy platform. Different levels of damage states were introduced at the midspans of these models, and Gaussian-based load time histories were applied at mid-span for dynamic time-history analysis to calculate acceleration data. Then, this acceleration time-history series was transformed into grayscale images, serving as inputs for a Convolutional Neural Network developed to detect and classify structural damage states. Initially, it was trained and tested on datasets derived from a Single-Source Domain structure, achieving perfect accuracy (1.0) in a ten-label multi-class classification task. However, this accuracy significantly decreased when the model was sequentially tested on structures with different geometry without retraining. To address this challenge, it is proposed that transfer learning be employed via feature extraction and joint training. The model showed a reduction in accuracy percentage when adapting from a Single-Source Domain to Multiple-Target Domains, revealing potential issues with non-homogeneous data distribution and catastrophic forgetting. Conversely, joint training, which involves training on all datasets except the specific Target Domain, generated a generalized network that effectively mitigated these issues and maintained high accuracy in predicting unseen class labels. This study highlights the integration of simulation data into the Deep Learning-based SHM framework, demonstrating that a generalized model created via Joint Learning utilizing FEM can potentially reduce the consequences of modeling errors and operational uncertainties unavoidable in real-world applications.

Application of Dual-Stage Attention Temporal Convolutional Networks in Gas Well Production Prediction

Effective production prediction is vital for optimizing energy resource management, designing efficient extraction strategies, minimizing operational risks, and informing strategic investment decisions within the energy sector. This paper introduces a Dual-Stage Attention Temporal Convolutional Network (DA-TCN) model to enhance the accuracy and efficiency of gas production forecasting, particularly for wells in tight sandstone reservoirs. The DA-TCN architecture integrates feature and temporal attention mechanisms within the Temporal Convolutional Network (TCN) framework, improving the model’s ability to capture complex temporal dependencies and emphasize significant features, resulting in robust forecasting performance across multiple time horizons. Application of the DA-TCN model to gas production data from two wells in Block T of the Sulige gas field in China demonstrated a 19% improvement in RMSE and a 21% improvement in MAPE compared to traditional TCN methods for long-term forecasts. These findings confirm that dual-stage attention not only increases predictive accuracy but also enhances forecast stability over short-, medium-, and long-term horizons. By enabling more reliable production forecasting, the DA-TCN model reduces operational uncertainties, optimizes resource allocation, and supports cost-effective management of unconventional gas resources. Leveraging existing knowledge, this scalable and data-efficient approach represents a significant advancement in gas production forecasting, delivering tangible economic benefits for the energy industry.

Reducing Uncertainty in Energy Hub Operation: A Demand Response Approach for Improved Cost-Efficiency

Abstract In this paper, a daily programming was performed for an Energy Hub (EH). This EH has a typical electricity generation unit, a boiler, a Combined Heat and Power (CHP), two hectares of solar farm, three wind turbines with different capacities, a battery and a heat storage unit. This hub can purchase electricity from the upstream grid and is also responsible for fully supplying the electrical and thermal loads. Herein, power generation uncertainty by Renewable Energy Sources (RESs), the consumer's electrical and thermal demands as well as the upstream grid electricity market price are modeled by appropriate probability functions. In addition, the combined thermal and electrical energies to consumer are optimized. Moreover, the effect of uncertainty of uncertain parameters is reduced using a Demand Response Program (DRP) by load shifting method. The DRP is applied to both energy forms (electrical and thermal), which reduces hub costs by shifting the load from the hours when energy is expensive and unavailable to the hours when energy is cheap and available. Energy storage devices also shift energy from expensive to cheap hours. The integration of a Demand Response Program effectively minimizes operational costs and enhances the efficiency of the Energy Hub by optimizing energy usage in response to fluctuating prices and availability.

Stochastic Plantwide Optimizing Control for an Acrylic Acid Plant

This work addresses the design of an optimized control system for an acrylic acid plant through the lens of the Stochastic Plant-Wide Optimizing Control (S-PWOC) framework. The S-PWOC employs stochastic optimization methods and advanced computer modeling to optimize plant performance by dynamically adjusting operational parameters under varying uncertainties. A comparison between the proposed S-PWOC model and two conventional approaches, the two-level identification method and the typical plant-wide decentralized control structure, highlights the advantages of S-PWOC despite its higher computational demands. Experimental results demonstrate significant improvements, including a 15% increase in process efficiency, a 10% reduction in energy consumption, enhanced product quality consistency, and greater economic viability. Additionally, S-PWOC proves effective in reducing safety risks and improving control efficiency, making it a robust solution for handling uncertainties in real-world plant operations.

Extracting Bridge Modal Frequencies Using Stationary Versus Drive-By Modes of Smartphone Measurements

In this research, we harmonize the two mobility approaches, stationary and mobile measurements, within the same framework to generate comparison opportunities, particularly in terms of identified bridge modal frequencies. Vibration tests were conducted to determine the natural frequency of a pedestrian bridge located in University College Dublin using smartphones. Both stationary and mobile smartphone measurements were collected, a novel use of two levels of mobility. Stationary measurements involved leaving the smartphone on the bridge deck at different positions along the bridge for a period of time, and mobile measurements were carried out using an electric scooter to ride across the bridge with the smartphone attached to the scooter deck. Single-output identification results were then compared to visualize the differences at two mobility levels. The tests showed that it is possible to extract the first natural frequency of the bridge using both stationary and mobile smartphone measurement techniques, although operational uncertainties seemed to alter the latter one. A first natural frequency of 5.45 Hz from a reference data acquisition system confirmed the accuracy of stationary smartphone data. On the other hand, the mobile data require consideration of the driving frequency, a function of the speed of the test vehicle and length of the bridge. These results show that smartphone sensors can be regarded as an alternative to industrial accelerometers with certain barriers to account for the multi-modality of the mobile sensing and identification process.

Hierarchical reconciliation of convolutional gated recurrent units for unified forecasting of branched and aggregated district heating loads

Independent hierarchical estimations for the branched and aggregated streams of district heating loads often conflict with each other, leading to significant uncertainties in the daily operations of heating plants. To address this issue, a hierarchical reconciliation of convolutional gated recurrent units (HR-CGRU) is proposed to simultaneously forecast both branched and aggregated heating loads. Specifically, the trend, seasonal, and residual components of each branch are decomposed using singular spectrum analysis. For each time series within the hierarchical mainline-branch-component system, a temporal convolutional network is employed for feature extraction, followed by a bidirectional gated recurrent unit with a multi-head attention mechanism for forecast modeling. To integrate time series information across the hierarchy, a novel hierarchical reconciler is developed to unify the branched and aggregated forecasts using a joint top-down mapping and bottom-up fusion strategy. The proposed HR-CGRU is evaluated using industrial heating load data collected from a combined heat and power plant in Quzhou City of China. Comparative methods are also applied to test various forecasting scenarios. The results demonstrate the superiority of the present HR-CGRU over existing methods, highlighting its effectiveness in forecasting district heating loads.

Predicting Rolling Element Bearings’ Deterioration Vibration Trend based on Limited Historical Data for a Desired Confidence Level using Machine Learning Algorithms

Predicting the deterioration trend of bearings and determining the optimal replacement time is crucial for maintenance teams to effectively manage consumable parts, enhance equipment reliability, and prevent potential breakdowns. Given the common practice of periodic condition monitoring based on vibrations in large industries, utilizing this data to forecast future vibration trends is a key focus of this research. Considering the limited availability of data in industrial settings, it is essential for the model used to be capable of working with minimal data. To address these challenges, a run-to-failure test has been conducted on an industrial bearing sample, with its vibration data recorded. Acknowledging the impact of operational variations and uncertainties in vibration behavior even among identical bearings, leveraging the bearing’s vibration history for predictive modeling has been deemed critical. This study proceeds in three distinct phases: selecting the key characteristic for predicting its deterioration trend, identifying the feature to pinpoint the failure onset, and determining the most suitable model for forecasting future bearing vibration states. RMS has emerged as the optimal characteristic for trend prediction, while Peak and Kurtosis have been identified as effective indicators for failure onset detection. The selection of the deterioration estimation model has involved comparing the outcomes of SVR + Bootstrapping and RVR models under various limited data scenarios. Ultimately, the RVR method has proved superior, requiring just three data points for estimation and delivering results with a confidence level leading to an accuracy of approximately 94% for the analyzed bearing.

Division Managers’ Private Information and Capital Investment Efficiency: Exploiting External Social Connections as an Information Source

ABSTRACT We examine the relation between division-level information and capital investment in conglomerates, exploiting the external social connections of division managers (DMs) as an information source. We find that investment efficiency, proxied by investment sensitivity to relative divisional investment opportunities, is higher for divisions whose DMs are socially connected with the CEOs of industry peers than divisions without such connections. This effect is stronger when headquarters are more likely to delegate decisions (proxied by larger information distances between headquarters and the divisions and by greater division operational uncertainties), when the DMs’ information source is more useful (proxied by the closeness of DMs’ external social connections and the information quality from the connected external CEOs), and when the DM has greater influence in the conglomerate. Further, connected divisions exhibit higher profitability in the subsequent years than unconnected divisions. Our study sheds light on how division-level information influences internal capital allocation and performance. Data Availability: Data are available from the sources cited in the text. JEL Classifications: G30; G34; M40.

A novel multi-fidelity Gaussian process regression approach for defect characterization in motion-induced eddy current testing

This study introduces a novel framework aimed at addressing the challenge of surface defect characterization in lab-scale tests. It utilizes a high-speed rotational disc setup to simulate the dynamics of rolling contact fatigue found in railway inspections through Motion-Induced Eddy Current Testing (MIECT). A key component of our approach was the integration of experimental data and finite element modeling, aimed at interpreting the relationship between defect dimensions, velocity, and their impact on magnetic sensor outputs. Our research focused on two main objectives: developing a forward model to predict the differential peak-to-peak amplitude (ΔVpp) of sensor readings from defect size and velocity, and to perform inverse estimation of defect sizes from ΔVpp across continuous velocity ranges. The key findings reveal that for the forward problem, the Radial Basis Function Multi-Fidelity Scaling (RBF-MFS) method outperforms other multi-fidelity and single-fidelity approaches. Moreover, the proposed Gaussian Process Regression with Multi-Fidelity Scaling and Feature Discretization (GPR-MFS-FD) method outperformed the state-of-the-art multi-fidelity method in the inverse estimation of defect geometries. This innovative method leverages high-fidelity experimental data together with low-fidelity physics simulations via multi-fidelity scaling and feature discretization to effectively manage velocity range inputs, reflecting real-world operational uncertainties in high-speed transport vehicles and infrastructures. Our integrated and novel data-driven approaches advance defect characterization, enhancing MIECT's application in surface defect detection and analysis, with potential extensions to other NDE applications.

Steady-State Response Analysis of an Uncertain Rotor Based on Chebyshev Orthogonal Polynomials

The performance of a rotor system is influenced by various design parameters that are neither precise nor constant. Uncertainties in rotor operation arise from factors such as assembly errors, material defects, and wear. To obtain more reliable analytical results, it is essential to consider these uncertainties when evaluating rotor performance. In this paper, the Chebyshev interval method is employed to quantify the uncertainty in the steady-state response of the rotor system. To address the challenges of high-dimensional integration, an innovative sparse-grid integration method is introduced and demonstrated using a rotor tester. The effects of support stiffness, mass imbalance, and uncertainties in the installation phase angle on the steady-state response of the rotor system are analyzed individually, along with a comprehensive assessment of their combined effects. When compared to the Monte Carlo simulation (MCS) method and the full tensor product grid (FTG) method, the proposed method requires only 68% of the computational cost associated with MCS, while maintaining calculation accuracy. Additionally, sparse-grid integration reduces the computational cost by approximately 95.87% compared to the FTG method.

Static Voltage Stability Analysis with the Integration of Distributed Generation: An Albanian Case Study

Modern power networks face significant operational uncertainties that can threaten the stability of the power grid, due to the rising integration of distributed generation (DG), particularly from renewable energy sources, such as photovoltaic plants. The integration of distributed generation (DG) into power systems has become a pivotal factor in enhancing the efficiency and sustainability of electricity supply. This paper presents a detailed static voltage stability analysis of the Albanian power grid, focused on the effects of the Karavasta Photovoltaic Park, one of Albania’s largest renewable energy projects. Using a combination of P-V and Q-V curves, V-Q sensitivity analysis, and modal analysis, the study reveals significant improvements in the system’s voltage stability following the integration of the PV plant. The NEPLAN software is used for voltage stability analysis. Particularly, the reactive power margin at the critical node Nst. Babice increased considerably from -82.009 MVAr to -1061.2 MVAr, while the system’s loading margin improved from 99.08% to 101.97%, demonstrating that the integration of large-scale PV generation enhances voltage stability by providing additional reactive power support and improving the grid’s resilience to voltage collapse. Furthermore, the modal analysis identified the Bistrice-Delvine-Sarande buses as the system’s weakest points, highlighting areas where additional reinforcement is required. As more renewable energy has to be integrated into the grid, the study also emphasizes the importance of making targeted interventions at critical buses and branches to ensure long-term stability. The results of this research are essential for Albania’s energy transition as the country seeks to diversify its energy sources, especially solar power. More generally, the methodologies and results in this paper provide a suggestion for maintaining power system voltage stability with increased renewable energy penetration.

Uncertainty in underground mining operations: a bibliometric and systematic literature review analysis

Purpose This study aims to provide a comprehensive review of the existing literature on uncertainty in underground mining operations, using a bibliometric and systematic analysis covering the period from 1975 to 2024. Design/methodology/approach To achieve this, the following questions were addressed using a mixed-method approach involving bibliometrics, text mining and content analysis: How has the field of uncertainty research in underground mining operations evolved? What are the most prominent research topics and trends in uncertainty in underground mining operations? and What are the possible directions for future research on uncertainty in underground mining operations? Findings As a result, bibliometric networks of 327 journal articles from the Scopus database were created and examined, the main research topics were underground mining management; rock mechanics; operational optimization; and stochastic systems. Finally, the inclusive investigation of uncertainty in underground mining operations and its prominent patterns can serve as a basis for real-time direction for new research and as a tool to improve underground mining activities by implementing advanced technology for innovative practices and optimizing operational efficiency. This is fundamental to identify unknown variables that impair the planning, operation, safety and economic viability of underground mines. Originality/value This research is 100% original because there is no review research on the uncertainty present in underground mining operations.

Exploring the interaction of design variability and stochastic operational uncertainties in software-intensive systems through the lens of modeling

AbstractIn software-intensive systems, navigating the complexities that emerge from the interaction of design variability and stochastic operational uncertainties presents a daunting challenge. This paper delves into the dynamics between these two dimensions of uncertainty, offering novel insights about how modeling can contribute to the analysis of their combined impact upon system properties. By elevating the abstraction level at which probabilistic models are conceptualized, our approach enables an integrated analysis framework that considers both structural and quantitative dimensions of design spaces. Through the introduction of novel language constructs, our methodology facilitates the direct referencing of structural relationships within probabilistic behavioral specifications. Furthermore, the adoption of novel quantifiers in probabilistic temporal logic enables evaluating complex properties across diverse design variants, thereby streamlining the assessment of guarantees within the solution space. We demonstrate the feasibility of this approach on four case studies, showcasing its potential to offer comprehensive insights into the trade-offs and decision-making processes inherent in managing different types of structural design variability and operational uncertainties in software-intensive systems.

The performance of renewable-rich wholesale electricity markets with significant energy storage and flexibility

This work investigates the technical and financial performance of deeply decarbonised wholesale electricity markets with uncertain variable renewable generation (VRG), uncertain demand and different types of flexibility. It first finds that different combinations of power reserves (i.e. power generation capacity) and energy reserves (i.e. energy storage capacity), can achieve adequate system reliability at similar total costs. However, use of only energy reserves never achieved adequate system reliability, and this looks to be a fundamental challenge to the reliability of systems with only energy reserves but no power reserves. Two independent aspects of wholesale market operations are then found to be the main determinants of system reliability: the length of the unit commitment (UC) schedule prior to dispatch and the degree of operational uncertainty of VRG during dispatch.It is then argued that reliable, least cost and efficient wholesale markets with dominant renewables need acceptably low levels of operational uncertainty of VRG, sufficient flexibility in generation and/or demand, and UC schedules that are significantly longer than the longest energy storage durations present. Rather than suggesting that innovation in market design is required, these results therefore suggest that different technical approaches should have important, future roles. These include mitigating the operational uncertainty of VRG via several potential means as well as flexible generation, battery storage of a few hours duration and flexible electrolysis. Long duration energy storage, however, may challenge market performance and all participants’ returns even if it supports system reliability.

Flexible design and optimization of electronic-grade propylene glycol monomethyl ether acetate production via integrated reactive and pressure-swing distillation

Propylene glycol monomethyl ether acetate (PMA) is a critical solvent in industrial applications, recognized for its high solvency, low corrosiveness, minimal toxicity, and thermal stability. It is produced via the transesterification of propylene glycol methyl ether (PM) and methyl acetate (MEAC) using sodium methoxide as a catalyst. Since the reaction is reversible and limited by equilibrium, reactive distillation (RD) is used to improve the conversion. However, methanol (MEOH), a by-product, forms an azeotrope with methyl acetate, requiring separation by pressure-swing distillation (PSD). The conventional reactive distillation system is typically optimized using sequential iterative optimization (SIO), but this method may not fully maximize optimization potential for systems with complex internals. This study used a genetic algorithm (GA) to optimize the production system by adjusting multiple design variables simultaneously. The process was automated by integrating Aspen Plus® with MATLAB®, aiming to minimize the total annual cost (TAC). The genetic algorithm’s elimination mechanism ensured efficient steady-state designs, while the flexibility index (FI) accounted for operational uncertainties. By combining the objectives of minimizing total annual cost and maximizing flexibility index, the study aimed to identify the most cost-effective and safest design parameters for the production system, resulting in TACFI. The results showed a 48.62 % reduction in TAC as the evolutionary generations progressed. For the bi-objective optimization, TAC was reduced by 41.073 %, TACFI by 97.719 %, and the flexibility index improved from 0.027 to 1.

Navigating the legal uncertainty around the governance of an integrated energy system using the values of the energy transition and energy justice

AbstractIn order to realise the European Union's (EU) transition to clean and carbon‐neutral energy systems, various individual infrastructure systems and energy networks will have to coordinate and operate as an integrated energy system. Traditionally, these different infrastructure systems operate within their own sectors and are governed by separate legal regimes. The intersection of these different legal rules and interests creates regulatory uncertainty and risk for infrastructure operators and can dissuade operators from participating in system integration. This paper explores how infrastructure operators can overcome the legal uncertainty and regulatory risks of operating as an integrated system, whilst also ensuring that the policy objectives of the energy transition are realised. This paper argues that, by pursuing public values and energy justice principles, infrastructure operators will overcome the legal uncertainty surrounding system integration. By doing so, infrastructure operators will facilitate the realisation of an energy transition that is just, inclusive and in the public interest.