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

Dynamic control of sensor and actuator failures in multivariable distillation column

This paper examines the impact of sensor and actuator failures in the operation of a multivariable distillation column. Several failure scenarios are evaluated including failures of sensors and actuators in various scales of magnitudes and durations. The results obtained illustrate the ability of process controllers in suppressing the impact of these unwanted events. Closed-loop dynamic responses of the process revealed capabilities of these controllers in dealing with upsets that are small in magnitude and duration. In the case of larger and longer process upsets, process controllers are not adequate in providing the necessary corrective measures. This leaves the necessary interventions to be taken by the plant operators, following alarms that would have been triggered in typical plant operation scenario

Modeling the impact of natural and security hazards in an LNG processing facility

Development of accident models based on cause and effect relationships facilitates the formulation of accident prevention and mitigation plans in the Chemical Process Industries (CPIs). In this paper, failures of accident prevention barriers triggered by manmade and natural hazards are causally modeled using Fault Trees (FTs) models. Additionally, updated technique of FTs basic and top events failure probabilities was applied using Hierarchy Bayesian Approach (HBA) based on basic events precursor data. This updated methodology overcomes the uncertainty limitation in the determination of FTs reliability data, as well as converge them into their accurate values. Moreover, it provides valuable information supporting risk based decision. The methodology was applied to LNG pipeline and liquefaction plant Dispersion Prevention Barrier (DPB). The result shows the capability of the methodology to model natural and security hazards (NE&ISHs) in both qualitative and quantitative manners, as well as, to update FT events failure probabilities through the use of the precursor data to the HBA. Outcomes demonstrate that the average posterior failure probability of DPB of that particular case study increased from 0.0613 to 0.204232 which represents a 3.33 times increment compared with the prior

Unknown input observer design for fault detection and diagnosis in a continuous stirred-tank reactor

Early and accurate fault detection and diagnosis (FDD) minimises downtime, increases the safety and reliability of plant operation, and reduces manufacturing costs. This paper presents a robust FDD strategy for a nonlinear system using a bank of unknown input observers (UIO). The approach is based on structure residual generation that provides not only decoupling of faults from model uncertainties and unknown input disturbance but also decoupling the effect of a fault from the effects of other faults. The generated residual was evaluated through the statistical threshold to avoid fault missing or false alarm. The performance of the robust FDD scheme was assessed by some sensor fault scenarios created in a continuous stirred-tank reactor (CSTR). The simulation result showed the effectiveness of the proposed approach

Modelling ultrasound waves bubble formation in ethanol/ethyl acetate azeotrope mixture

The separation of an azeotropic mixture such as ethanol/ethyl acetate in distillation process can be enhanced by ultrasound wave. The application of ultrasound wave creates bubble cavitation in the mixture and shifts the vapour-liquid equilibrium favouring the separation of the azeotropic mixture. This study investigates the formation of bubbles in the mixture through modelling and simulation. The results obtained show that bubble formation at low ultrasound frequency is favoured by the increase in intensity, which has a direct relation to sonic pressure. The optimal sonic pressure for bubble formation at equilibrium is 5 atm and conforms to the model for small bubble formation with radius of 0.14 /<m. Furthermore, the maximum possible number of bubbles at equilibrium in the ethanol/ethyl acetate azeotropic mixture of 1 L is 91 × 1015. The developed model can be used to determine the optimal sonic pressure, sound intensity, size of bubble, and possible number of bubbles formed at equilibrium

Growth of Scenedesmus dimorphus in different algal media and pH profile due to secreted metabolites

In this study investigation was made to evaluate the effects of different algal media components to get optimized cell count of Scenedesmus dimorphus. Five different fresh water algal media such as Bold’s Basal Medium (BBM), M4N medium, BG-11 medium, N-8 medium and M-8 medium were used for culturing S. dimorphus in flask culture. A set of environmental factors including light, temperature, air flow rate and nutritional components was standardized to obtain the highest productivity of 0.1406 g/L with specific growth rate of 0.10483/day. This study designates the bold basal medium as advantageous one for S. dimorphus and also reveals that production of metabolites by the same algal strain depends mostly on the nature of constituents of media and might have different influence on the pH.Keywords: Scenedesmus dimorphus, bold basal medium, algal growthAfrican Journal of Biotechnology, Vol 13(16), 1714-172

MODELING THE IMPACT OF NATURAL AND SECURITY HAZARDS IN AN LNG PROCESSING FACILITITY

Development of accident models based on cause and effect relationships facilitates the formulation of accident prevention and mitigation plans in the Chemical Process Industries (CPIs). In this paper, failures of accident prevention barriers triggered by man-made and natural hazards are causally modeled using Fault Trees (FTs) models. Additionally, updated technique of FTs basic and top events failure probabilities was applied using Hierarchy Bayesian Approach (HBA) based on basic events precursor data. This updated methodology overcomes the uncertainty limitation in the determination of FTs reliability data, as well as converge them into their accurate values. Moreover, it provides valuable information supporting risk based decision. The methodology was applied to LNG pipeline and liquefaction plant Dispersion Prevention Barrier (DPB). The result shows the capability of the methodology to model natural and security hazards (NE&amp;ISHs) in both qualitative and quantitative manners, as well as, to update FT events failure probabilities through the use of the precursor data to the HBA. Outcomes demonstrate that the average posterior failure probability of DPB of that particular case study increased from 0.0613 to 0.204232 which represents a 3.33 times increment compared with the prior. </jats:p

Dynamic Control of Sensor and Actuator failures in Multivariable Distillation Column

This paper examines the impact of sensor and actuator failures in the operation of a multivariable distillation column. Several failure scenarios are evaluated including failures of sensors and actuators in various scales of magnitudes and durations. The results obtained illustrate the ability of process controllers in suppressing the impact of these unwanted events. Closed-loop dynamic responses of the process revealed capabilities of these controllers in dealing with upsets that are small in magnitude and duration. In the case of larger and longer process upsets, process controllers are not adequate in providing the necessary corrective measures. This leaves the necessary interventions to be taken by the plant operators, following alarms that would have been triggered in typical plant operation scenario.</jats:p

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