This page has only limited features, please log in for full access.

Dr. Faisal Khan
Faculty of Engineering & Applied Science, Centre for Risk, Integrity and Safety Engineering (C‐RISE), Memorial University, St. John's, Canada

Basic Info


Research Keywords & Expertise

0 Operational Risk Management
0 Safety Management
0 Risk assessment under uncertainty
0 Dynamic risk assessment
0 Safety challenges in harsh environment

Fingerprints

Risk assessment under uncertainty
Dynamic risk assessment
Safety Management
Operational safety

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 31 July 2021 in Process Safety and Environmental Protection
Reads 0
Downloads 0

Corrosion is a threat to asset integrity, with engineering challenges and economic burdens. Since the last decade, microbiologically influenced corrosion (MIC) began to be recognized among corrosion professionals as a severe corrosion form. It is challenging to detect and predict MIC due to the complex behaviour of microorganisms. The current MIC risk assessment models define the dependencies of parameters with their synergic interactions. A data-driven approach is needed to utilize available operational and microbiological data and learn as the data changes. The model proposed in this study is used to strengthen the variables' correlation and their features to assess MIC likelihood. It can integrate available field and laboratory data into a Learning-based Bayesian network (LBN) model. The model minimizes current research gap and has the advantage of adapting to changes in process operation. It is based on an advanced Bayesian learning algorithm, which develops topology of the Bayesian network (BN) from the input data and its parameters. This paper focuses on the development of the LBNmodel that utilizes available MIC data for likelihood estimation. The model is tested and validated using data reported in the public domain. The application of the model is demonstrated on the processing facility on a Floating, Production, Storage and Offloading (FPSO). The topology and parameter estimation will update as data changes/improve to capture the system behaviour to assess MIC likelihood, which helps in decision-making to control and mitigate MIC threats.

ACS Style

Mohammad Zaid Kamil; Mohammed Taleb-Berrouane; Faisal Khan; Paul Amyotte. Data-driven operational failure likelihood model for microbiologically influenced corrosion. Process Safety and Environmental Protection 2021, 153, 472 -485.

AMA Style

Mohammad Zaid Kamil, Mohammed Taleb-Berrouane, Faisal Khan, Paul Amyotte. Data-driven operational failure likelihood model for microbiologically influenced corrosion. Process Safety and Environmental Protection. 2021; 153 ():472-485.

Chicago/Turabian Style

Mohammad Zaid Kamil; Mohammed Taleb-Berrouane; Faisal Khan; Paul Amyotte. 2021. "Data-driven operational failure likelihood model for microbiologically influenced corrosion." Process Safety and Environmental Protection 153, no. : 472-485.

Journal article
Published: 30 July 2021 in International Journal of Hydrogen Energy
Reads 0
Downloads 0

Hydrogen is a carbon-free alternative energy source for use in future energy frameworks with the advantages of environment-friendliness and high energy density. Among the numerous hydrogen production techniques, sustainable and high purity of hydrogen can be achieved by water electrolysis. Therefore, developing electrocatalysts for water electrolysis is an emerging field with great importance to the scientific community. On one hand, precious metals are typically used to study the two-half cell reactions, i.e., hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). However, precious metals (i.e., Pt, Au, Ru, Ag, etc.) as electrocatalysts are expensive and with low availability, which inhibits their practical application. Non-precious metal-based electrocatalysts on the other hand are abundant with low-cost and eco-friendliness and exhibit high electrical conductivity and electrocatalytic performance equivalent to those for noble metals. Thus, these electrocatalysts can replace precious materials in the water electrolysis process. However, considerable research effort must be devoted to the development of these cost-effective and efficient non-precious electrocatalysts. In this review article, we provide key fundamental knowledge of water electrolysis, progress, and challenges of the development of most-studied electrocatalysts in the most desirable electrolytic solutions: alkaline water electrolysis (AWE), solid-oxide electrolysis (SOE), and proton exchange membrane electrolysis (PEME). Lastly, we discuss remaining grand challenges, prospect, and future work with key recommendations that must be done prior to the full commercialization of water electrolysis systems.

ACS Style

Shams Anwar; Faisal Khan; Yahui Zhang; Abdoulaye Djire. Recent development in electrocatalysts for hydrogen production through water electrolysis. International Journal of Hydrogen Energy 2021, 46, 32284 -32317.

AMA Style

Shams Anwar, Faisal Khan, Yahui Zhang, Abdoulaye Djire. Recent development in electrocatalysts for hydrogen production through water electrolysis. International Journal of Hydrogen Energy. 2021; 46 (63):32284-32317.

Chicago/Turabian Style

Shams Anwar; Faisal Khan; Yahui Zhang; Abdoulaye Djire. 2021. "Recent development in electrocatalysts for hydrogen production through water electrolysis." International Journal of Hydrogen Energy 46, no. 63: 32284-32317.

Research article
Published: 17 July 2021 in Geofluids
Reads 0
Downloads 0

A number of different factors can affect flow performance in perforated completions, such as perforation density, perforation damage, and tunnel geometry. In perforation damage, any compaction at the perforation tunnels will lead to reduced permeability, more significant pressure drop, and lower productivity of the reservoir. The reduced permeability of the crushed zone around the perforation can be formulated as a crushed-zone skin factor. For reservoir flow, earlier research studies show how crushed (compacted) zones cause heightened resistance in radially converging vertical and horizontal flow entering perforations. However, the effects related to crushed zones on the total skin factor are still a moot point, especially for horizontal flows in perforations. Therefore, the present study will look into the varied effects occurring in the crushed zone in relation to the vertical and horizontal flows. The experimental test was carried out using a geotechnical radial flow set-up to measure the differential pressure in the perforation tunnel with a crushed zone. Computational fluid dynamics (CFD) software was used for simulating pressure gradient in a cylindrical perforation tunnel. The single-phase water was radially injected into the core sample with the same flow boundary conditions in the experimental and numerical procedures. In this work, two crushed zone configuration scenarios were applied in conjunction with different perforation parameters, perforation length, crushed zone radius, and crushed zone permeability. In the initial scenario, the crushed zone is assumed to be located at the perforation tunnel’s side only, while in the second scenario, the crushed zone is assumed to be located at a side and the tip of perforation (a tip-crushed zone). The simulated results indicate a good comparison with regard to the two scenarios’ pressure gradients. Furthermore, the simulations’ comparison reveals another pressure drop caused by the tip crushed zone related to the horizontal or plane flow in the perforations. The differences between the two simulations’ results show that currently available models for estimating the skin factor for vertical perforated completions need to be improved based on which of the two cases is closer to reality. This study has presented a better understanding of crushed zone characteristics by employing a different approach to the composition and shape of the crushed zone and permeability reduction levels for the crushed zone in the axial direction of the perforation.

ACS Style

Ekhwaiter Abobaker; Abadelhalim Elsanoose; Faisal Khan; Mohammad Azizur Rahman; Amer Aborig; Stephen Butt. Comparison of Crushed-Zone Skin Factor for Cased and Perforated Wells Calculated with and without including a Tip-Crushed Zone Effect. Geofluids 2021, 2021, 1 -13.

AMA Style

Ekhwaiter Abobaker, Abadelhalim Elsanoose, Faisal Khan, Mohammad Azizur Rahman, Amer Aborig, Stephen Butt. Comparison of Crushed-Zone Skin Factor for Cased and Perforated Wells Calculated with and without including a Tip-Crushed Zone Effect. Geofluids. 2021; 2021 ():1-13.

Chicago/Turabian Style

Ekhwaiter Abobaker; Abadelhalim Elsanoose; Faisal Khan; Mohammad Azizur Rahman; Amer Aborig; Stephen Butt. 2021. "Comparison of Crushed-Zone Skin Factor for Cased and Perforated Wells Calculated with and without including a Tip-Crushed Zone Effect." Geofluids 2021, no. : 1-13.

Journal article
Published: 06 July 2021 in Ocean Engineering
Reads 0
Downloads 0

Metamodel combined with simulation type reliability method is an effective way to determine the probability of failure (Pf) of complex structural systems and reduce the burden of computational models. However, some existing challenges in structural reliability analysis are minimizing the number of calls to the numerical model and reducing the computational time. Most research work considers adaptive methods based on ordinary Kriging with a single point enrichment of the experimental design (ED). This paper presents an active learning reliability method using a hybrid metamodel with multiple point enrichment of ED for structural reliability analysis. The hybrid method (termed as APCKKm-MCS) takes advantage of the global prediction and local interpolation capability of Polynomial Chaos Expansion (PCE) and Kriging, respectively. The U learning function drives active learning in this approach, while K-means clustering is proposed for multiple point enrichment purposes. Two benchmark functions and two practical marine structural cases validate the performance and efficiency of the method. The results confirm that the APCKKm-MCS approach is efficient and reduces the computational time for reliability analysis of complex structures with nonlinearity, high dimension input random variables, or implicit limit state function.

ACS Style

Aghatise Okoro; Faisal Khan; Salim Ahmed. An Active Learning Polynomial Chaos Kriging metamodel for reliability assessment of marine structures. Ocean Engineering 2021, 235, 109399 .

AMA Style

Aghatise Okoro, Faisal Khan, Salim Ahmed. An Active Learning Polynomial Chaos Kriging metamodel for reliability assessment of marine structures. Ocean Engineering. 2021; 235 ():109399.

Chicago/Turabian Style

Aghatise Okoro; Faisal Khan; Salim Ahmed. 2021. "An Active Learning Polynomial Chaos Kriging metamodel for reliability assessment of marine structures." Ocean Engineering 235, no. : 109399.

Journal article
Published: 17 June 2021 in Reliability Engineering & System Safety
Reads 0
Downloads 0

The stochastic nature of microbial corrosion creates spatial interdependencies among random corrosion parameters and their failure modes. These interdependencies need to be captured for robust offshore system reliability prediction considering complex multispecies biofilms. This research paper presents a hybrid methodology for the prediction of system reliability, considering multiple failure modes’ interdependencies. The methodology integrates the Bayesian Network with Copula-based Monte Carlo (BN-CMC) simulation. The BN captures the dynamic interactions among physio-chemical parameters and microbes to predict the corrosion rate of an offshore system. The random corrosion parameters dependencies and the failure modes that define the performance functions under microbial corrosion are modeled using CMC. The methodology is assessed with an example, and the impact of dynamic interactions of the parameters and their failure modes on the system reliability is investigated. The results reveal that the system's probability of failure differs diversely as the degree of dependencies among the random corrosion parameters and their failure modes increases. The proposed methodology can predict the failure indexes that could aid system integrity management for a sustainable offshore operation experiencing microbial corrosion.

ACS Style

Sidum Adumene; Faisal Khan; Sunday Adedigba; Sohrab Zendehboudi. Offshore system safety and reliability considering microbial influenced multiple failure modes and their interdependencies. Reliability Engineering & System Safety 2021, 215, 107862 .

AMA Style

Sidum Adumene, Faisal Khan, Sunday Adedigba, Sohrab Zendehboudi. Offshore system safety and reliability considering microbial influenced multiple failure modes and their interdependencies. Reliability Engineering & System Safety. 2021; 215 ():107862.

Chicago/Turabian Style

Sidum Adumene; Faisal Khan; Sunday Adedigba; Sohrab Zendehboudi. 2021. "Offshore system safety and reliability considering microbial influenced multiple failure modes and their interdependencies." Reliability Engineering & System Safety 215, no. : 107862.

Book chapter
Published: 12 June 2021 in Methods in Chemical Process Safety
Reads 0
Downloads 0

Despite having an extremely lower frequency, domino accidents pose enormous losses in process industries when they occur. Over the years, rigorous efforts have been made to demystify the mechanism of domino accidents, assess the possibility of domino effect occurrence, set the thresholds for escalation vectors, and develop the polices to prevent such catastrophes in chemical plants. This chapter presents an overview of the advanced methods used for domino effect assessment and prevention. Several definitions of domino accidents are also included to identify the typical characteristics of a domino effect. Additionally, a brief history of research growth, driving forces, and significant contributions from global regulatory bodies is presented to perceive how this field is evolving to react to major domino induced accidents.

ACS Style

Faisal Khan; Tanjin Amin; Valerio Cozzani; Genserik Reniers. Domino effect: Its prediction and prevention—An overview. Methods in Chemical Process Safety 2021, 5, 1 -35.

AMA Style

Faisal Khan, Tanjin Amin, Valerio Cozzani, Genserik Reniers. Domino effect: Its prediction and prevention—An overview. Methods in Chemical Process Safety. 2021; 5 ():1-35.

Chicago/Turabian Style

Faisal Khan; Tanjin Amin; Valerio Cozzani; Genserik Reniers. 2021. "Domino effect: Its prediction and prevention—An overview." Methods in Chemical Process Safety 5, no. : 1-35.

Book chapter
Published: 12 June 2021 in Methods in Chemical Process Safety
Reads 0
Downloads 0

This chapter presents a methodology to assess the probability of a domino effect due to pool fire and boiling liquid expanding vapor explosion (BLEVE). A computational fluid dynamics (CFD)-based approach has been adopted to develop a wide range of scenarios. Unlike using the existing probit models, an empirical nonlinear equation has been developed using the results from generated scenarios. This equation can predict dynamic domino effect probabilities based on the estimated heat radiation which is highly pertinent in the Industry 4.0 framework. Besides, this method can aid in determining the safety distance between two process equipment. A total of two successful applications suggests the efficacy of the displayed method for domino effect assessment.

ACS Style

Faisal Khan; Tanjin Amin; Valerio Cozzani; Genserik Reniers. Domino effect assessment in the framework of industry 4.0. Methods in Chemical Process Safety 2021, 5, 495 -517.

AMA Style

Faisal Khan, Tanjin Amin, Valerio Cozzani, Genserik Reniers. Domino effect assessment in the framework of industry 4.0. Methods in Chemical Process Safety. 2021; 5 ():495-517.

Chicago/Turabian Style

Faisal Khan; Tanjin Amin; Valerio Cozzani; Genserik Reniers. 2021. "Domino effect assessment in the framework of industry 4.0." Methods in Chemical Process Safety 5, no. : 495-517.

Research article
Published: 08 June 2021 in Safety in Extreme Environments
Reads 0
Downloads 0

Multivariate models to estimate environmental load on an offshore structure are an essential consideration. A reliable approach is necessary to capture the dependency among parameters and to be flexible in handling the statistical characteristics of ocean parameters. Previous studies on marine engineering usually assumed symmetric dependence when considering multivariate models. In this paper, both symmetric and asymmetric copula functions are constructed for modelling ocean parameters and the estimation of total environmental load. Results are compared with the traditional joint probability approach to demonstrate the advantage of using copula functions. The results reveal that environmental loads strongly depend on the significance of dependence, which is defined as the copula function’s correlation. The probability of failure, estimated using copula functions, is higher than the traditional joint probability estimate. In addition to this, Root-mean-square errors (RMSE) and the mean absolute errors using copula functions are lower than those of the traditional joint probability modeling. Thus, the use of copula functions provides a more conservative approach to safety. Therefore, the use of copula functions to capture both dependence types, while estimating ocean environmental loads provide a better understanding of the environmental load and its contribution to the failure probability.

ACS Style

Adhitya Ramadhani; Faisal Khan; Bruce Colbourne; Salim Ahmed; Mohammed Taleb-Berrouane. Environmental load estimation for offshore structures considering parametric dependencies. Safety in Extreme Environments 2021, 1 -27.

AMA Style

Adhitya Ramadhani, Faisal Khan, Bruce Colbourne, Salim Ahmed, Mohammed Taleb-Berrouane. Environmental load estimation for offshore structures considering parametric dependencies. Safety in Extreme Environments. 2021; ():1-27.

Chicago/Turabian Style

Adhitya Ramadhani; Faisal Khan; Bruce Colbourne; Salim Ahmed; Mohammed Taleb-Berrouane. 2021. "Environmental load estimation for offshore structures considering parametric dependencies." Safety in Extreme Environments , no. : 1-27.

Journal article
Published: 05 May 2021 in Reliability Engineering & System Safety
Reads 0
Downloads 0

Corrosion is a major threat to safety and asset integrity in oil and gas production and processing facilities. This paper proposes a simple, yet practical model for quantitative corrosion risk assessment from safety and economic perspectives. This includes an Adaptive Bow-Tie (ABT) model for the corrosion risk with a specific focus on microbiologically influenced corrosion, along with corrosion economic risk profile. The ABT model is logically tested using corrosion science and statistically verified using a pipeline corrosion database, including cases of corrosion-induced failures. The ABT model also provides opportunities to run root-cause contribution (RCC) analysis and to estimate the probability of corrosion, corrosion-induced failures, and highly probable sequences leading to failure. The model application is demonstrated using a crude oil transportation pipeline system. The study identifies and quantifies parameters that helps to prioritize the actions needed to prevent and control corrosion and avoid failures. The proposed model can serve as an important mechanism to identify, assess, and manage corrosion threat to an asset.

ACS Style

Mohammed Taleb-Berrouane; Faisal Khan; Kelly Hawboldt. Corrosion risk assessment using adaptive bow-tie (ABT) analysis. Reliability Engineering & System Safety 2021, 214, 107731 .

AMA Style

Mohammed Taleb-Berrouane, Faisal Khan, Kelly Hawboldt. Corrosion risk assessment using adaptive bow-tie (ABT) analysis. Reliability Engineering & System Safety. 2021; 214 ():107731.

Chicago/Turabian Style

Mohammed Taleb-Berrouane; Faisal Khan; Kelly Hawboldt. 2021. "Corrosion risk assessment using adaptive bow-tie (ABT) analysis." Reliability Engineering & System Safety 214, no. : 107731.

Journal article
Published: 02 May 2021 in Process Safety and Environmental Protection
Reads 0
Downloads 0

This study presents a dynamic risk modeling strategy for a hydrocarbon sub-surface production system under a gas lift mechanism. A data-driven probabilistic methodology is employed to conduct a risk analysis. The integrated approach comprises a multilayer perceptron (MLP) – artificial neural network (ANN) model and a Bayesian network (BN) technique. The MLP-ANN model performs the production forecast, and the BN model analyzes dynamic risks (the production response) and evaluates the impact of the sand face pressure on risks. The introduced model offers an effective strategy to avoid production failure and to monitor dynamic risks. The dynamic risk analysis yields predictive outcomes at any production time in the well’s production life. It offers field operators an early warning system based on the Bayesian model with prognostic capabilities. The proposed strategy effectively manages production risks and assists in production decision-making, especially in complex production systems.

ACS Style

Abbas Mamudu; Faisal Khan; Sohrab Zendehboudi; Sunday Adedigba. Dynamic risk modeling of complex hydrocarbon production systems. Process Safety and Environmental Protection 2021, 151, 71 -84.

AMA Style

Abbas Mamudu, Faisal Khan, Sohrab Zendehboudi, Sunday Adedigba. Dynamic risk modeling of complex hydrocarbon production systems. Process Safety and Environmental Protection. 2021; 151 ():71-84.

Chicago/Turabian Style

Abbas Mamudu; Faisal Khan; Sohrab Zendehboudi; Sunday Adedigba. 2021. "Dynamic risk modeling of complex hydrocarbon production systems." Process Safety and Environmental Protection 151, no. : 71-84.

Journal article
Published: 23 April 2021 in Ocean Engineering
Reads 0
Downloads 0

For an improved estimation of marine structural reliability, a consideration of random variable dependency is essential. With a limited study on dependence modelling of marine structures, this study proposes a framework for reliability assessment of ocean structural systems with multidimensional variables. This framework captures possible nonlinearity and tail dependence in the variables using vine copula. The proposed method develops a graphical structure of random variables consisting of nodes, edges, and trees using the Drawable Vine (D-vine) approach. This study demonstrates the developed framework on a jacket support structure subjected to the extreme environmental load conditions at Jeanne D′ Arc basin on Canada's east coast. The structure's reliability is evaluated with optimally selected copulas in the D-vine trees and associated marginal distributions. A comparison between the reliability result using the D-vine copula method, Gaussian coupling assumption, and statistical independence between variables proved its superiority in modelling variable dependence of complex marine systems. The probability of failure (Pf) using D-vine copula was closer to the reference Importance Sampling (IS) results than other methods.

ACS Style

Okoro Aghatise; Faisal Khan; Salim Ahmed. Reliability assessment of marine structures considering multidimensional dependency of the variables. Ocean Engineering 2021, 230, 109021 .

AMA Style

Okoro Aghatise, Faisal Khan, Salim Ahmed. Reliability assessment of marine structures considering multidimensional dependency of the variables. Ocean Engineering. 2021; 230 ():109021.

Chicago/Turabian Style

Okoro Aghatise; Faisal Khan; Salim Ahmed. 2021. "Reliability assessment of marine structures considering multidimensional dependency of the variables." Ocean Engineering 230, no. : 109021.

Journal article
Published: 23 April 2021 in Ocean Engineering
Reads 0
Downloads 0

One major accident scenario aboard fishing vessels is “man overboard” (MOB). Prevention of this accident scenario would reduce the high fatality rate in the fishing industry. Critical understanding of the risk factors is vital for a robust risk assessment of this accident scenario and to develop interventions. This paper presents the Objected-Oriented Bayesian Network (OOBN) application for risk assessment of the MOB scenario. The OOBN model is developed to probabilistically capture the key accident influencing factors in fragmented structures. The proposed methodology is demonstrated in an accident scenario, and the model captures the dynamic dependencies and interdependencies among basic variables and establishes their degree of influence on the accident occurrence probability. The vulnerability path was identified, and a pre-and post-accident intervention plan was proposed to minimize the accident occurrence and its associated risk. Applying the methodology provides vital safety-based information that could be adopted for small vessel operation and maritime administration regulation.

ACS Style

Vindex Domeh; Francis Obeng; Faisal Khan; Neil Bose; Elizabeth Sanli. Risk analysis of man overboard scenario in a small fishing vessel. Ocean Engineering 2021, 229, 108979 .

AMA Style

Vindex Domeh, Francis Obeng, Faisal Khan, Neil Bose, Elizabeth Sanli. Risk analysis of man overboard scenario in a small fishing vessel. Ocean Engineering. 2021; 229 ():108979.

Chicago/Turabian Style

Vindex Domeh; Francis Obeng; Faisal Khan; Neil Bose; Elizabeth Sanli. 2021. "Risk analysis of man overboard scenario in a small fishing vessel." Ocean Engineering 229, no. : 108979.

Journal article
Published: 21 April 2021 in Journal of Petroleum Science and Engineering
Reads 0
Downloads 0

Pore pressure prediction represents an important safety aspect of drilling engineering. Accurate pore pressure prediction is required for appropriate mud weight usage. Kick can occur when mud weight is lower than pore pressure gradient and this can result in disastrous events such as blowout when the kick is not properly controlled. Likewise, too high mud density can fracture the reservoir which can lead to several problems. Thus, the need to research on means of improving accurate pore pressure prediction during drilling is in order. In this article, two methodologies are presented. One of the methodologies is developed to utilize resistivity data for pore pressure prediction, and the other methodology is developed if resistivity and porosity data are available for pore pressure prediction. Several methodologies already exist for pore pressure prediction with resistivity data. Therefore, the methodology presented in this article is compared to other resistivity-based methodologies in order to observe their pore pressure prediction capabilities. Field data is used for testing prediction performance in terms of mean absolute percentage error, root mean square error and Pearson product moment correlation coefficient. Results of the test show that the methodology developed in this article performed best. Different logging/measurement parameters are used for pore pressure prediction e.g., resistivity log, sonic velocity, corrected d-exponent, etc. One way to improve accuracy of pore pressure prediction is utilizing pore pressure prediction from different logging/measurement parameters. For the other methodology which utilizes resistivity and porosity for pore pressure prediction, the methodology is proposed to utilize the change in Archie's cementation exponent. This is because the effect of cementation on pore pressure prediction could become significant at greater depths. Testing with field data showed that this methodology also performs better than simply averaging pore pressure prediction from resistivity and porosity logs using other conventional equations. In addition to the methodology developed for combining porosity and resistivity log for pore pressure prediction, machine learning can also be utilized. Results obtained using artificial neural network indicate better performance in comparison to simply averaging predictions from conventional means of using resistivity and porosity.

ACS Style

Augustine Uhunoma Osarogiagbon; Olalere Oloruntobi; Faisal Khan; Ramachandran Venkatesan; Paul Gillard. Combining porosity and resistivity logs for pore pressure prediction. Journal of Petroleum Science and Engineering 2021, 205, 108819 .

AMA Style

Augustine Uhunoma Osarogiagbon, Olalere Oloruntobi, Faisal Khan, Ramachandran Venkatesan, Paul Gillard. Combining porosity and resistivity logs for pore pressure prediction. Journal of Petroleum Science and Engineering. 2021; 205 ():108819.

Chicago/Turabian Style

Augustine Uhunoma Osarogiagbon; Olalere Oloruntobi; Faisal Khan; Ramachandran Venkatesan; Paul Gillard. 2021. "Combining porosity and resistivity logs for pore pressure prediction." Journal of Petroleum Science and Engineering 205, no. : 108819.

Journal article
Published: 16 April 2021 in Process Safety and Environmental Protection
Reads 0
Downloads 0

This paper presents a risk-based fault detection and diagnosis methodology for nonlinear and non-Gaussian process systems using the R-vine copula and the event tree. The R-vine model provides a multivariate probability that is used in the event tree to generate a dynamic risk profile. An abnormal situation is detected from the monitored risk profile; subsequently, root cause(s) diagnosis is carried out. A fault diagnosis module is also proposed using the density quantiles, developed from marginal probabilities. The performance of this methodology is benchmarked using the Tennessee Eastman chemical process. The proposed risk-based framework has also been applied to an experimental setup and a real industrial isomer separator unit. The diagnosis module is found sensitive to both single and simultaneous faults. The results confirm that the proposed methodology provides better performance than the conventional principal component analysis and transfer entropy-based fault diagnosis techniques using the advantage of marginal density quantile analysis.

ACS Style

Tanjin Amin; Faisal Khan; Salim Ahmed; Syed Imtiaz. Risk-based fault detection and diagnosis for nonlinear and non-Gaussian process systems using R-vine copula. Process Safety and Environmental Protection 2021, 150, 123 -136.

AMA Style

Tanjin Amin, Faisal Khan, Salim Ahmed, Syed Imtiaz. Risk-based fault detection and diagnosis for nonlinear and non-Gaussian process systems using R-vine copula. Process Safety and Environmental Protection. 2021; 150 ():123-136.

Chicago/Turabian Style

Tanjin Amin; Faisal Khan; Salim Ahmed; Syed Imtiaz. 2021. "Risk-based fault detection and diagnosis for nonlinear and non-Gaussian process systems using R-vine copula." Process Safety and Environmental Protection 150, no. : 123-136.

Journal article
Published: 15 April 2021 in Process Safety and Environmental Protection
Reads 0
Downloads 0

The containment of infectious diseases is challenging due to complex transmutation in the biological system, intricate global interactions, intense mobility, and multiple transmission modes. An emergent disease has the potential to turn into a pandemic impacting millions of people with loss of life, mental health, and severe economic impairment. Multifarious approaches to risk management have been explored for combating an epidemic spread. This work presents the implementation of engineering safety principles to pandemic risk management. We have assessed the pandemic risk using Paté-Cornell's six levels of uncertainty. The susceptible, exposed, infected, quarantined, recovered, deceased (SEIQRD), an advanced mechanistic model, along with the Monte Carlo simulation, has been used to estimate the fatality risk. The risk minimization strategies have been categorized into hierarchical safety measures. We have developed an event tree model of pandemic risk management for distinct risk-reducing strategies realized due to natural evolution, government interventions, societal responses, and individual practices. The roles of distinct interventions have also been investigated for an infected individual's survivability with the existing healthcare facilities. We have studied the Corona Virus Disease of 2019 (COVID-19) for pandemic risk management using the proposed framework. The results highlight effectiveness of the proposed strategies in containing a pandemic.

ACS Style

Alauddin; Faisal Khan; Syed Imtiaz; Salim Ahmed; Paul Amyotte. Pandemic risk management using engineering safety principles. Process Safety and Environmental Protection 2021, 150, 416 -432.

AMA Style

Alauddin, Faisal Khan, Syed Imtiaz, Salim Ahmed, Paul Amyotte. Pandemic risk management using engineering safety principles. Process Safety and Environmental Protection. 2021; 150 ():416-432.

Chicago/Turabian Style

Alauddin; Faisal Khan; Syed Imtiaz; Salim Ahmed; Paul Amyotte. 2021. "Pandemic risk management using engineering safety principles." Process Safety and Environmental Protection 150, no. : 416-432.

Journal article
Published: 14 April 2021 in Process Safety and Environmental Protection
Reads 0
Downloads 0

This paper presents a data-driven methodology for fault detection and diagnosis (FDD) by integrating the principal component analysis (PCA) with the Bayesian network (BN). Though the integration of PCA-BN for FDD purposes has been studied in the past, the present work makes two contributions for process systems. First, the application of correlation dimension (CD) to select principal components (PCs) automatically. Second, the use of Kullback-Leibler divergence (KLD) and copula theory to develop a data-based BN learning technique. The proposed method uses a combination of vine copula and Bayes’ theorem (BT) to capture nonlinear dependence of high-dimensional process data which eliminates the need for discretization of continuous data. The data-driven integrated PCA-BN framework has been applied to two processing systems. Performance of the proposed methodology is compared with the independent component analysis (ICA), kernel principal component analysis (KPCA), kernel independent component analysis (KICA), and their integrated frameworks with the BN. The comparative study suggests that the proposed framework provides superior performance.

ACS Style

Tanjin Amin; Faisal Khan; Salim Ahmed; Syed Imtiaz. A data-driven Bayesian network learning method for process fault diagnosis. Process Safety and Environmental Protection 2021, 150, 110 -122.

AMA Style

Tanjin Amin, Faisal Khan, Salim Ahmed, Syed Imtiaz. A data-driven Bayesian network learning method for process fault diagnosis. Process Safety and Environmental Protection. 2021; 150 ():110-122.

Chicago/Turabian Style

Tanjin Amin; Faisal Khan; Salim Ahmed; Syed Imtiaz. 2021. "A data-driven Bayesian network learning method for process fault diagnosis." Process Safety and Environmental Protection 150, no. : 110-122.

Journal article
Published: 19 March 2021 in Ocean Engineering
Reads 0
Downloads 0

Microbiologically influenced corrosion (MIC) is a complex phenomenon that occurs when a microbial community is involved in the degradation of an asset (e.g. pipelines). It is widely recognized as a significant cause of hazardous hydrocarbon release and subsequently, fires, explosions, and economic and environmental impacts. This paper presents a new MIC management methodology. The proposed methodology assists in accurately monitoring MIC activity and accordingly develop strategies to manage it. The MIC monitoring and management activities are achieved using Continuous Bayesian Network (CBN) technique with Hierarchical Bayesian Analysis (HBA). The integration of HBA and CBN helps overcome the Bayesian network's discrete value limitations (BN) and source-to-source uncertainty for each node in the network. The methodology can provide the precise value of parameters, such as failure probability and MIC occurrence rate which are verified using observed data. The application of the methodology is demonstrated on a subsea pipeline. The study provides a better understanding of the influencing factors of MIC rate and failure probability. This assists in developing effective MIC management strategies.

ACS Style

Mohammad Yazdi; Faisal Khan; Rouzbeh Abbassi. Microbiologically influenced corrosion (MIC) management using Bayesian inference. Ocean Engineering 2021, 226, 108852 .

AMA Style

Mohammad Yazdi, Faisal Khan, Rouzbeh Abbassi. Microbiologically influenced corrosion (MIC) management using Bayesian inference. Ocean Engineering. 2021; 226 ():108852.

Chicago/Turabian Style

Mohammad Yazdi; Faisal Khan; Rouzbeh Abbassi. 2021. "Microbiologically influenced corrosion (MIC) management using Bayesian inference." Ocean Engineering 226, no. : 108852.

Review article
Published: 17 March 2021 in Reliability Engineering & System Safety
Reads 0
Downloads 0

Chemical process industries (CPIs) work with a variety of hazardous materials in quantities which have the potential to have large health, environmental and financial impacts and as such are exposed to the risk of major accidents. The experience with accidents in this domain shows many cases which involve complex human-machine interactions. Human Reliability Analysis (HRA) has been utilized as a proactive approach to identify, model, and quantify human error highlighted as the leading cause of accidents. Consequently, researchers have actively worked on enhancing process safety and risk engineering since the '70s. However, despite its importance and practical implications for improving human reliability, there has not been a review of human reliability related to processing systems. The present study is aimed at presenting a systematic attempt to identify the needs, gaps, and challenges of HRA in CPI. An in-depth analysis of the literature in Web of Science core collection and Scopus databases from 1975 to August 2020 is conducted. This analysis focuses on human factors in three critical elements of CPIs: maintenance operations, emergency operations, and control room operations. The analysis synthesizes the theoretical and empirical findings, shedding light on the strengths and shortcomings of current literature and identifying research opportunities. A comparison of HRA in CPIs is undertaken with nuclear power plants (NPPs) to better understand the current stage of research and research challenges and opportunities.

ACS Style

Esmaeil Zarei; Faisal Khan; Rouzbeh Abbassi. Importance of human reliability in process operation: A critical analysis. Reliability Engineering & System Safety 2021, 211, 107607 .

AMA Style

Esmaeil Zarei, Faisal Khan, Rouzbeh Abbassi. Importance of human reliability in process operation: A critical analysis. Reliability Engineering & System Safety. 2021; 211 ():107607.

Chicago/Turabian Style

Esmaeil Zarei; Faisal Khan; Rouzbeh Abbassi. 2021. "Importance of human reliability in process operation: A critical analysis." Reliability Engineering & System Safety 211, no. : 107607.

Review article
Published: 08 March 2021 in Journal of Pipeline Science and Engineering
Reads 0
Downloads 0

Pipelines are the most vital energy-transportation mediums of today's energy-intensive economies. To a level, pipeline integrity is tied to the continuous development and robustness of modern societies, where major failures may result in dire environmental, societal, and economic consequences. Therefore, pipeline safety and integrity are crucial for a sustainable future and responsible development. Pipeline integrity management has been a topic of interest for regulators, practitioners, and academicians alike. Over the past four decades, integrity management has evolved from prescriptive visual inspection and assessment to risk-based integrity management using real-time data. This paper aims to capture the evolution of risk-based methods in integrity management, focusing on the last two decades. The paper answers four primary questions:

ACS Style

Faisal Khan; Rioshar Yarveisy; Rouzbeh Abbassi. Risk-based pipeline integrity management: A road map for the resilient pipelines. Journal of Pipeline Science and Engineering 2021, 1, 74 -87.

AMA Style

Faisal Khan, Rioshar Yarveisy, Rouzbeh Abbassi. Risk-based pipeline integrity management: A road map for the resilient pipelines. Journal of Pipeline Science and Engineering. 2021; 1 (1):74-87.

Chicago/Turabian Style

Faisal Khan; Rioshar Yarveisy; Rouzbeh Abbassi. 2021. "Risk-based pipeline integrity management: A road map for the resilient pipelines." Journal of Pipeline Science and Engineering 1, no. 1: 74-87.

Journal article
Published: 26 February 2021 in Marine Pollution Bulletin
Reads 0
Downloads 0

This paper investigates the linkage between the acute impacts on apex marine mammals with polar cod responses to an oil spill. It proposes a Bayesian network-based model to link these direct and indirect effects on the apex marine mammals. The model predicts a recruitment collapse (for the scenarios considered), causing a higher risk of mortality of polar bears, beluga whales, and Narwhals in the Arctic region. Whales (adult and calves) were predicted to be at higher risk when the spill was under thick ice, while adult polar bears were at higher risk when the spill occurred on thin ice. A spill over the thick ice caused the least risk to whale and adult polar bears. The spill's timing and location have a significant impact on the animals in the Arctic region due to its unique sea ice dynamics, simple food web, and short periods of food abundance.

ACS Style

Faisal Fahd; Ming Yang; Faisal Khan; Brian Veitch. A food chain-based ecological risk assessment model for oil spills in the Arctic environment. Marine Pollution Bulletin 2021, 166, 112164 .

AMA Style

Faisal Fahd, Ming Yang, Faisal Khan, Brian Veitch. A food chain-based ecological risk assessment model for oil spills in the Arctic environment. Marine Pollution Bulletin. 2021; 166 ():112164.

Chicago/Turabian Style

Faisal Fahd; Ming Yang; Faisal Khan; Brian Veitch. 2021. "A food chain-based ecological risk assessment model for oil spills in the Arctic environment." Marine Pollution Bulletin 166, no. : 112164.