Paul Amyotte

Bio: Paul Amyotte is an academic researcher from Dalhousie University. The author has contributed to research in topics: Dust explosion & Poison control. The author has an hindex of 51, co-authored 215 publications receiving 7955 citations. Previous affiliations of Paul Amyotte include St. John's University & Technical University of Nova Scotia.

Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches

Abstract: Safety analysis in gas process facilities is necessary to prevent unwanted events that may cause catastrophic accidents. Accident scenario analysis with probability updating is the key to dynamic safety analysis. Although conventional failure assessment techniques such as fault tree (FT) have been used effectively for this purpose, they suffer severe limitations of static structure and uncertainty handling, which are of great significance in process safety analysis. Bayesian network (BN) is an alternative technique with ample potential for application in safety analysis. BNs have a strong similarity to FTs in many respects; however, the distinct advantages making them more suitable than FTs are their ability in explicitly representing the dependencies of events, updating probabilities, and coping with uncertainties. The objective of this paper is to demonstrate the application of BNs in safety analysis of process systems. The first part of the paper shows those modeling aspects that are common between FT and BN, giving preference to BN due to its ability to update probabilities. The second part is devoted to various modeling features of BN, helping to incorporate multi-state variables, dependent failures, functional uncertainty, and expert opinion which are frequently encountered in safety analysis, but cannot be considered by FT. The paper concludes that BN is a superior technique in safety analysis because of its flexible structure, allowing it to fit a wide variety of accident scenarios.

Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network

Abstract: Among the various techniques used for safety analysis of process systems, bow-tie (BT) analysis is becoming a popular technique as it represents an accident scenario from causes to effects. However, the BT application in the dynamic safety analysis is limited due to the static nature of its components, i.e. fault tree and event tree. It is therefore difficult in BT to take accident precursors into account to update the probability of events and the consequent risk. Also, BT is unable to represent conditional dependency. Event dependency is common among primary events and safety barriers. The current paper illustrates how Bayesian network (BN) helps to overcome these limitations. It has also been shown that BN can be used in dynamic safety analysis of a wide range of accident scenarios due to its flexible structure. This paper also introduces the application of probability adapting in dynamic safety analysis rather than probability updating. A case study from the U.S. Chemical Safety Board has been used to illustrate the application of both BT and BN techniques, with a comparison of the results from each technique.

Quantitative risk analysis of offshore drilling operations: A Bayesian approach

Abstract: Blowouts are among the most undesired and feared accidents during drilling operations. The dynamic nature of blowout accidents, resulting from both rapidly changing physical parameters and time-dependent failure of barriers, necessitates techniques capable of considering time dependencies and changes during the lifetime of a well. The present work is aimed at demonstrating the application of bow-tie and Bayesian network methods in conducting quantitative risk analysis of drilling operations. Considering the former method, fault trees and an event tree are developed for potential accident scenarios, and then combined to build a bow-tie model. In the latter method, first, individual Bayesian networks are developed for the accident scenarios and finally, an object-oriented Bayesian network is constructed by connecting these individual networks. The Bayesian network method provides greater value than the bow-tie model since it can consider common cause failures and conditional dependencies along with performing probability updating and sequential learning using accident precursors.

Dynamic risk analysis using bow-tie approach

Abstract: Accident probability estimation is a common and central step to all quantitative risk assessment methods. Among many techniques available, bow-tie model (BT) is very popular because it represent the accident scenario altogether including causes and consequences. However, it suffers a static structure limiting its application in real-time monitoring and probability updating which are key factors in dynamic risk analysis. The present work is focused on using BT approach in a dynamic environment in which the occurrence probability of accident consequences changes. In this method, on one hand, failure probability of primary events of BT, leading to the top event, are developed using physical reliability models, and constantly revised as physical parameters (e.g., pressure, velocity, dimension, etc) change. And, on the other hand, the failure probability of safety barriers of the BT are periodically updated using Bayes’ theorem as new information becomes available over time. Finally, the resulting, updated BT is used to estimate the posterior probability of the consequences which in turn results in an updated risk profile.

Domino effect analysis using Bayesian networks.

Abstract: A new methodology is introduced based on Bayesian network both to model domino effect propagation patterns and to estimate the domino effect probability at different levels. The flexible structure and the unique modeling techniques offered by Bayesian network make it possible to analyze domino effects through a probabilistic framework, considering synergistic effects, noisy probabilities, and common cause failures. Further, the uncertainties and the complex interactions among the domino effect components are captured using Bayesian network. The probabilities of events are updated in the light of new information, and the most probable path of the domino effect is determined on the basis of the new data gathered. This study shows how probability updating helps to update the domino effect model either qualitatively or quantitatively. The methodology is applied to a hypothetical example and also to an earlier-studied case study. These examples accentuate the effectiveness of Bayesian network in modeling domino effects in processing facility.

Recent advances in removing phosphorus from wastewater and its future use as fertilizer (1997-2003).

Abstract: Large quantities of phosphate present in wastewater is one of the main causes of eutrophication that negatively affects many natural water bodies, both fresh water and marine. It is desirable that water treatment facilities remove phosphorus from the wastewater before they are returned to the environment. Total removal or at least a significant reduction of phosphorus is obligatory, if not always fulfilled, in most countries. This comprehensive review summarizes the current status in phosphorus-removal technologies from the most common approaches, like metal precipitation, constructed wetland systems, adsorption by various microorganisms either in a free state or immobilized in polysaccharide gels, to enhanced biological phosphorus removal using activated sludge systems, and several innovative engineering solutions. As chemical precipitation renders the precipitates difficult, if not impossible, to recycle in an economical industrial manner, biological removal opens opportunities for recovering most of the phosphorus and beneficial applications of the product. This review includes the options of struvite (ammonium-magnesium-phosphate) and hydroxyapatite formation and other feasible options using, the now largely regarded contaminant, phosphorus in wastewater, as a raw material for the fertilizer industry. Besides updating our knowledge, this review critically evaluates the advantage and difficulties behind each treatment and indicates some of the most relevant open questions for future research.

Immobilized microalgae for removing pollutants: review of practical aspects.

Abstract: This review analyzes the state-of-the-art of a specific niche in biological wastewater treatment that uses immobilized eukaryotic microalgae (and several prokaryotic photosynthetic cyanobacteria), with emphasis on removing nutrients with the support of microalgae growth-promoting bacteria. Removal of other pollutants by this technology, such as heavy metals and industrial pollutants, and technical aspects related to this specific subfield of wastewater treatment are also presented. We present a general perspective of the field with most known examples from common literature, emphasizing a practical point of view in this technologically oriented topic. The potential venues of future research in this field are outlined and a critical assessment of the failures, limitations, and future of immobilized microalgae for removal of pollutants is presented.

Introduction to quality engineering. designing quality into products a

Abstract: Contents: Variety and Quality. Variability loss and tolerance. Determining tolerances. Tolerance design and experimental design. Offline and online quality control. Parameter design and tolerance design: case study. Experimental design for smaller is better characteristics. Experimental design for larger is better characteristics. Bypassing the S/N ratio: spring experiment. Experimental design for dynamic characteristics.

Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches

Abstract: Safety analysis in gas process facilities is necessary to prevent unwanted events that may cause catastrophic accidents. Accident scenario analysis with probability updating is the key to dynamic safety analysis. Although conventional failure assessment techniques such as fault tree (FT) have been used effectively for this purpose, they suffer severe limitations of static structure and uncertainty handling, which are of great significance in process safety analysis. Bayesian network (BN) is an alternative technique with ample potential for application in safety analysis. BNs have a strong similarity to FTs in many respects; however, the distinct advantages making them more suitable than FTs are their ability in explicitly representing the dependencies of events, updating probabilities, and coping with uncertainties. The objective of this paper is to demonstrate the application of BNs in safety analysis of process systems. The first part of the paper shows those modeling aspects that are common between FT and BN, giving preference to BN due to its ability to update probabilities. The second part is devoted to various modeling features of BN, helping to incorporate multi-state variables, dependent failures, functional uncertainty, and expert opinion which are frequently encountered in safety analysis, but cannot be considered by FT. The paper concludes that BN is a superior technique in safety analysis because of its flexible structure, allowing it to fit a wide variety of accident scenarios.

Hydrogen storage and delivery: Review of the state of the art technologies and risk and reliability analysis

Abstract: Among all introduced green alternatives, hydrogen, due to its abundance and diverse production sources is becoming an increasingly viable clean and green option for transportation and energy storage. Governments are considerably funding relevant researches and the public is beginning to talk about hydrogen as a possible future fuel. Hydrogen production, storage, delivery, and utilization are the key parts of the Hydrogen Economy (HE). In this paper, hydrogen storage and delivery options are discussed thoroughly. Then, since safety and reliability of hydrogen infrastructure is a necessary enabling condition for public acceptance of these technologies and any major accident involving hydrogen can be difficult to neutralize, we review the main existing safety and reliability challenges in hydrogen systems. The current state of the art in safety and reliability analysis for hydrogen storage and delivery technologies is discussed, and recommendations are mentioned to help providing a foundation for future risk and reliability analysis to support safe, reliable operation.