Integrating uncertainty quantification in reliability, availability, and maintainability (RAM) analysis in the conceptual and preliminary stages of chemical process design

Al‐Douri, Ahmad; Kazantzi, Vasiliki; Currie-Gregg, Nancy J.; El‐Halwagi, Mahmoud M.

doi:10.1016/j.cherd.2021.01.014

Cited by 15 publications

(3 citation statements)

References 30 publications

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“…Along with the technical dimension, RAM analysis has been largely applied to study the economic sustainability of maintenance decision making throughout the different phases of the asset life cycle. For instance, Al-Douri et al [42] utilized this tool during the conceptual design of a chemical process plant to assess the impacts of component RAM characteristics on technical and economic performance indicators such as system availability, annual net profit, and return on investment. Regattieri et al [43] tested and simulated different preventive maintenance policies for an aircraft, demonstrating a reduction in the yearly maintenance cost without a significant change in availability with the adoption of the new policies.…”

Section: Systemic Approachesmentioning

confidence: 99%

Sustainability Indices and RAM Analysis for Maintenance Decision Making Considering Environmental Sustainability

Barbieri,

Hernandez

2024

Sustainability

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Maintenance is becoming an increasingly green process, aiming to simultaneously minimize the impact of assets on the economy, society, and the surrounding environment while also mitigating any adverse consequences arising from maintenance activities. To manage maintenance from a sustainable perspective, appropriate indices able to represent environmental assessment results in an aggregated and synthetic way are necessary, along with rational decision-making tools able to simulate different maintenance alternatives considering various sustainability dimensions. In response to this challenge, a methodology rooted in sustainability indices and RAM analysis is proposed to support decision making with respect to sustainable maintenance practices. By applying the proposed methodology to a proof of concept we demonstrate that the approach enables the evaluation of both the environmental and economic impacts of different maintenance activities. Furthermore, the methodology facilitates the identification of specific environmental dimensions that need improvement, in addition to discerning the distinct contributions of individual components to the environmental and economic impacts at the system level. These insights offer valuable information to support informed decision making regarding the allocation of internal resources for impact mitigation. The obtained results underscore the potential applicability of this approach to industrial systems, signifying its contribution to the goal of achieving sustainable maintenance practices.

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Section: Systemic Approachesmentioning

confidence: 99%

Sustainability Indices and RAM Analysis for Maintenance Decision Making Considering Environmental Sustainability

Barbieri,

Hernandez

2024

Sustainability

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“…Such an analysis aims at quantifying the impact of uncertainty on the results, thus obtaining probability distributions of key performance indicators and decision variables. Uncertainty analyses have found application in a variety of process design tasks, − as well as in techno-economic assessments and reaction pathway screening methods for renewable transportation fuels from biomass. Frutiger et al further analyzed uncertainty influences on the integrated product and process design for working fluids in organic Rankine cycles.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Quantification in Integrated Fuel and Process Design

Panofen,

Ackermann,

Viell

et al. 2024

Energy Fuels

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Integrated fuel and process design aims to identify production-efficient renewable fuels that exhibit tailored combustion characteristics. In recent years, we have developed an optimizationbased integrated fuel and process design framework that combines production pathway screening methods and predictive fuel property models. Inherently, the design of multicomponent fuels at an early stage of process development involves various sources of uncertainty that may affect the predicted economic and environmental performance, the optimal fuel composition, and the corresponding pathway selection. We present a systematic uncertainty analysis for the integrated design of multicomponent fuels and their production processes. To this end, we identify data and model uncertainties and quantify their influences on the integrated design of renewable drop-in fuels with Monte Carlo-based sampling methods. In addition, we evaluate the influence of structural uncertainty stemming from submodel choice within the optimization-based design approach. We show that the optimal fuel components and production pathways are quite robust to parameter variations and submodel choices. In contrast, the fuel component mass fractions and production costs are highly sensitive to uncertainties, underlining the importance of considering uncertainty effects in integrated fuel and process design. Such uncertainty-aware integrated fuel and process design can provide crucial guidance for future modeling and allocating research expenditures in fuel and process development.

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“…For example, medical systems [1], video systems [2], traffic systems [3], industrial intelligence systems [4], network systems [5][6][7][8][9], wireless sensor networks [10,11], power transmission networks [12], social networks [13], and Internet of Things [14,15]. The evaluation and enhancement of system reliability must be optimized in the early design stage because the enhancement of reliability is the most efficient at this stage [16,17].…”

Section: Introductionmentioning

confidence: 99%

A Novel Constraints Model of Credibility-Fuzzy for Reliability Redundancy Allocation Problem by Simplified Swarm Optimization

2021

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A novel constraints model of credibility-fuzzy for the reliability redundancy allocation problem (RRAP) is studied in this work. The RRAP that must simultaneously decide reliability and redundancy of components is an effective approach in improving the system reliability. In practice various systems, the uncertainty condition of components used in the systems, which few studies have noticed this state over the years, is a concrete fact due to several reasons such as production conditions, different batches of raw materials, time reasons, and climatic factors. Therefore, this study adopts the fuzzy theory and credibility theory to solve the components uncertainty in the constraints of RRAP including cost, weight, and volume. Moreover, the simplified swarm optimization (SSO) algorithm has been adopted to solve the fuzzy constraints of RRAP. The effectiveness and performance of SSO algorithm have been experimented by four famous benchmarks of RRAP.

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Integrating uncertainty quantification in reliability, availability, and maintainability (RAM) analysis in the conceptual and preliminary stages of chemical process design

Cited by 15 publications

References 30 publications

Sustainability Indices and RAM Analysis for Maintenance Decision Making Considering Environmental Sustainability

Sustainability Indices and RAM Analysis for Maintenance Decision Making Considering Environmental Sustainability

Uncertainty Quantification in Integrated Fuel and Process Design

A Novel Constraints Model of Credibility-Fuzzy for Reliability Redundancy Allocation Problem by Simplified Swarm Optimization

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