Multi-state analysis of process status using multilevel flow modelling and Bayesian network

Multilevel Flow Modeling (MFM) model maps functionality of components in a system through logical interconnections and is effective in predicting success rates of tasks undertaken. However, the output of this model is binary, which is taken at its extrema, i.e., success and failure, while in reality, the operational status of plant components often spans between these end. In this paper, a multi-state model is proposed by adding probabilistic information to the modelling framework. Using a heat exchanger pilot plant as a case study, the MFM model is transformed into its fault tree [1] equivalent to incorporate failure probability information. To facilitate computations, the FT model is transformed into Bayesian Network model, and applied for fault detection and diagnosis problems. The results obtained illustrate the effectiveness and feasibility of the proposed method

Multi-state analysis functional models using Bayesian networks

Multilevel Flow Modeling (MFM) model maps functionality of components in a system through logical interconnections and is effective in predicting success rates of tasks undertaken. However, the output of this model is binary, which is taken at its extrema, i.e., success and failure, while in reality, the operational status of plant components often spans between these end. In this paper, a multi-state model is proposed by adding probabilistic information to the modelling framework. Using a heat exchanger pilot plant as a case study, the MFM model is transformed into its fault tree [1] equivalent to incorporate failure probability information. To facilitate computations, the FT model is transformed into Bayesian Network model, and applied for fault detection and diagnosis problems. The results obtained illustrate the effectiveness and feasibility of the proposed method

Risk-based interventions for safer operation of a hydrogen station

In ensuring safety of plant operation, both the reliability of plant components and human resources are important. Moving towards optimising resources, there is a need to prioritise intervention measures such as maintenance program and review of standard operating procedures. In this paper, a technique known as basic event ranking approach (BERA) is applied to a hydrogen station. BERA examines the relative importance of plant components based on their probability of failure within the realm of fault tree analysis model, and yields values of importance index for each basic event investigated. To incorporate changes in reliability data throughout the plant lifetime, a dynamic extension to BERA is introduced. The vulnerability of plant components and human actions are ranked with respect to the selected top events of fault trees generated from plant functions. The results revealed the potential of BERA to facilitate risk-based intervention initiatives to support process safety

Failure analysis using functional model and Bayesian network

A class of functional model known as multilevel flow model (MFM) is used to represent a pilot scale heat exchanger system. MFM is effective in representing chemical process qualitatively through graphical representation, but lacks the ability to quantify the impact of successes or failures of process events, and is not able to quantitatively distinguish between steps in a goal and their contributions towards achieving the main goal. To address this issue, the MFM is converted into its equivalent fault tree (FT) model to accommodate logical sequence of events along with the needed quantifications. The FT model is then converted into Bayesian network (BN) model to facilitate updates of probabilities. Using Hugin 8.1 software, the BN model is simulated to investigate the response of the process when subjected to various faults. The results highlight the capability of the model in detecting process faults and in identifying the associated root causes, thus pointing to the potentials of the proposed strategy in modeling complex chemical processes for higher level functions in plant operations such as facilitating alarm system and fault diagnosis

Control and optimization of aromatic compounds in multivariable distillation column

Product separations in petroleum refineries depend significantly on distillation process, which is known to be challenging to be optimally managed, especially when multiple products with variety of purity requirements are involved due to nonlinear dynamics and high degree of process interactions. In this paper, control and optimization aspects of a multivariable distillation process are discussed. A mathematical model of the system is simulated in MATLAB programming environment, and analyses of process behavior and control performances are carried out. The controllers are tuned using conventional Ziegler-Nichols method and L-V control configuration was adopted. The results on disturbance rejection and set point tracking capabilities, in order to maintain the purity of benzene in the distillate above 98.5 % are discussed. Based on these insights, the optimum operating conditions were determined, which serves as a good starting point for further works in addressing variety of problems related to process operations