Fault detection and root cause diagnosis using dynamic Bayesian network

This thesis presents two real time process fault detection and diagnosis (FDD) techniques incorporating process data and prior knowledge. Unlike supervised monitoring techniques, both these methods can perform without having any prior information of a fault. In the first part of this research, a hybrid methodology is developed combining principal component analysis (PCA), Bayesian network (BN) and multiple uncertain (likelihood) evidence to improve the diagnostic capacity of PCA and existing PCA-BN schemes with hard evidence based updating. A dynamic BN (DBN) based FDD methodology is proposed in the later part of this work which provides detection and accurate diagnosis by a single tool. Furthermore, fault propagation pathway is analyzed using the predictive feature of a BN and cause-effect relationships among the process variables. Proposed frameworks are successfully validated by applying to several process models

Multivariate data-based safety analysis in digitalized process systems

Chemical process industries are vulnerable to accidents due to their inherent hazardous nature, complex operations, and growing size. Although the control system works as the first safety layer and is designed to maintain the setpoint within the safety limit, it cannot suppress all the deviations. Therefore, a warning system is used above the control layer to provide alarm(s) to the operators about unpermitted process deviations that cannot be negated by the controllers. Data-based process fault detection and diagnosis (FDD) and dynamic risk assessment (DRA) tools play a pivotal role to ensure that any significant process deviation is efficiently captured and necessary maintenance has been done to restore the process to normal operating mode. Besides, these tools can provide a detailed analysis of failure paths which are significant to prevent a fault from propagating into an accident. Conventional univariate monitoring is easier to implement and comes as standard with distributed control systems (DCS). This conventional approach is unsuitable in digitalized process systems due to increased close loop control, large process dimension, and complex interaction among variables. Modern process industries require techniques that can handle the complexity and scale of process plants. Timely detection of faults, diagnosis of root cause(s) of faults that affect multiple variables, and predicting a quantitative measure of consequence is vital to ensure process safety and reliability. The thesis deals with multivariate data-driven FDD and DRA for digitalized process systems. This research aims to reduce the technological gaps between the current methods and prerequisites of automated FDD and DRA tools for multivariate safety analysis. This thesis looks at improving all aspects of FDD and DRA methods, starting from data pre-processing to consequence analysis due to fault(s). First, the effect of data pre-processing is investigated in the context of multivariate FDD. Multivariate exponentially weighted moving average (MEWMA) is found to be an effective way of filtering process data without adversely affecting their correlation structure. The MEWMA is combined with PCA-BN, and a new method called MEWMA-PCA-BN is proposed. The developed framework can detect and diagnose the fault earlier than many contemporary multivariate process monitoring models. In this work, a novel methodology has been proposed to construct the BNs from historical fault symptoms. Second, the selection of the principal components (PCs) for the PCA-BN method is made automated; the correlation dimension (CD) is used in this regard. Also, a new methodology is proposed for developing BNs from continuous process data. Third, the prediction of the consequences of a fault has been adapted for multivariate process systems. A novel data-driven framework has been proposed for concurrent FDD and DRA using the naïve Bayes classifier (NBC), BN, and event tree analysis (ETA). This work utilizes a multivariate fault probability from NBC for dynamic failure prediction. It overcomes the limitation of using univariate probability in DRA. Finally, this thesis looks into improving the FDD performance by capturing the correlation structure of process variables and considering the consequence analysis. The R-vine copula is used to demystify the correlation structure accurately while the ETA predicts the consequences. Unacceptable deviation of risk is used as an indicator of a fault, and subsequently, root cause(s) diagnosis is performed using density quantile analysis (DQA). Industrial, experimental, and simulated datasets are used to test and validate the performance of the developed models. This thesis is an important step for multivariate data-driven FDD and DRA research

Dynamic Failure Risk Assessment of Wastewater Treatment and Reclamation Plant: An Industrial Case Study

Due to the growing scarcity of water resources, wastewater reuse has become one of the most effective solutions for industrial consumption. However, various factors can detrimentally affect the performance of a wastewater treatment plant (WWTP), which is considered a risk of not fulfilling the effluent requirements. Thus, to ensure the quality of treated wastewater, it is essential to analyze system failure causes and their potential outcomes and mitigation measures through a systematic dynamic risk assessment approach. This work shows how a dynamic Bayesian network (DBN) can be effectively used in this context. Like the conventional Bayesian network (BN), the DBN can capture complex interactions between failure contributory factors. Additionally, it can forecast the upcoming failure likelihood using a prediction inference. This proposed methodology was applied to a WWTP of the Moorchekhort Industrial Complex (MIC), located in the center of Iran. A total of 15 years’ time frame (2016–2030) has been considered in this work. The first six years’ data have been used to develop the DBN model and to identify the crucial risk factors that are further used to reduce the risk in the remaining nine years. The risk increased from 21% to 42% in 2016–2021. Applying the proposed risk mitigation measures can decrease the failure risk from 33% to 9% in 2022–2030. The proposed model showed the capability of the DBN in risk management of a WWTP system which can help WWTPs’ managers and operators achieve better performance for higher reclaimed water quality

Dynamic Failure Risk Assessment of Wastewater Treatment and Reclamation Plant: An Industrial Case Study

Due to the growing scarcity of water resources, wastewater reuse has become one of the most effective solutions for industrial consumption. However, various factors can detrimentally affect the performance of a wastewater treatment plant (WWTP), which is considered a risk of not fulfilling the effluent requirements. Thus, to ensure the quality of treated wastewater, it is essential to analyze system failure causes and their potential outcomes and mitigation measures through a systematic dynamic risk assessment approach. This work shows how a dynamic Bayesian network (DBN) can be effectively used in this context. Like the conventional Bayesian network (BN), the DBN can capture complex interactions between failure contributory factors. Additionally, it can forecast the upcoming failure likelihood using a prediction inference. This proposed methodology was applied to a WWTP of the Moorchekhort Industrial Complex (MIC), located in the center of Iran. A total of 15 years’ time frame (2016–2030) has been considered in this work. The first six years’ data have been used to develop the DBN model and to identify the crucial risk factors that are further used to reduce the risk in the remaining nine years. The risk increased from 21% to 42% in 2016–2021. Applying the proposed risk mitigation measures can decrease the failure risk from 33% to 9% in 2022–2030. The proposed model showed the capability of the DBN in risk management of a WWTP system which can help WWTPs’ managers and operators achieve better performance for higher reclaimed water quality

A neural network approach to predict the time-to-failure of atmospheric tanks exposed to external fire

Domino scenarios triggered by fire pose severe risks to workers, assets, and the environment. Accurate quantitative models are needed to support mitigation actions addressing the prevention of fire escalation, especially considering sensitive targets such as atmospheric tanks containing large quantities of dangerous substances. A novel approach based on neural networks was developed, allowing the accurate quantification of the time-to-failure (TTF) of atmospheric tanks exposed to external fires accounting for mitigation actions. Data from a lumped parameter model were used to train and assess neural networks' performance. The toolbox of models obtained provides the TTF of atmospheric tanks both in the case of unmitigated fire scenarios and considering safety barriers and protection measures, such as water deluges and fire monitors. Model predictions are fast, accurate, and supplemented with confidence intervals. The comparative analysis demonstrated the better performance of the model developed compared to simplified correlations widely used in the literature to predict TTF. The approach developed, based on the integration of neural networks in consequence analysis tools, shows significant potential for the advancement of a quantitative assessment of domino scenarios, providing accurate and user-friendly tools for a quick evaluation of domino fire scenarios under both mitigated and unmitigated conditions

An immunoinformatics and extended molecular dynamics approach for designing a polyvalent vaccine against multiple strains of Human T-lymphotropic virus (HTLV).

Human T-lymphotropic virus (HTLV), a group of retroviruses belonging to the oncovirus family, has long been associated with various inflammatory and immunosuppressive disorders. At present, there is no approved vaccine capable of effectively combating all the highly pathogenic strains of HTLV that makes this group of viruses a potential threat to human health. To combat the devastating impact of any potential future outbreak caused by this virus group, our study employed a reverse vaccinology approach to design a novel polyvalent vaccine targeting the highly virulent subtypes of HTLV. Moreover, we comprehensively analyzed the molecular interactions between the designed vaccine and corresponding Toll-like receptors (TLRs), providing valuable insights for future research on preventing and managing HTLV-related diseases and any possible outbreaks. The vaccine was designed by focusing on the envelope glycoprotein gp62, a crucial protein involved in the infectious process and immune mechanisms of HTLV inside the human body. Epitope mapping identified T cell and B cell epitopes with low binding energies, ensuring their immunogenicity and safety. Linkers and adjuvants were incorporated to enhance the vaccine's stability, antigenicity, and immunogenicity. Initially, two vaccine constructs were formulated, and among them, vaccine construct-2 exhibited superior solubility and structural stability. Molecular docking analyses also revealed strong binding affinity between the vaccine construct-2 and both targeted TLR2 and TLR4. Molecular dynamics simulations demonstrated enhanced stability, compactness, and consistent hydrogen bonding within TLR-vaccine complexes, suggesting a strong binding affinity. The stability of the complexes was further corroborated by contact, free energy, structure, and MM-PBSA analyses. Consequently, our research proposes a vaccine targeting multiple HTLV subtypes, offering valuable insights into the molecular interactions between the vaccine and TLRs. These findings should contribute to developing effective preventive and treatment approaches against HTLV-related diseases and preventing possible outbreaks. However, future research should focus on in-depth validation through experimental studies to confirm the interactions identified in silico and to evaluate the vaccine's efficacy in relevant animal models and, eventually, in clinical trials