Vikram Garaniya

Dynamic risk-based maintenance for offshore processing facility

Processing facilities in a marine environment may not remain safe and available if they are not well maintained. Dynamic risk‐based maintenance (RBM) methodology is a tool for maintenance planning and decision making, used to enhance the safety and availability of the equipment. It also assists in identifying and prioritizing the maintenance of equipment based on the level of risk. This article discusses an advanced methodology for the design of an optimum maintenance program integrating a dynamic risk‐based approach with a maintenance optimization technique. In this study, Bayesian Network (BN) is employed to develop a new dynamic RBM methodology that is capable of using accident precursor information in order to revise the risk profile. The use of this methodology is based on its failure prediction capability which optimizes the cost of maintenance. The developed methodology is applied to a case study involving a failure of a separator system in the offshore oil and gas production platform considering marine environments. The result shows it is essential that the valve system in the separator needs to be planned for maintenance once every 25 days; however, the cooler system can be planned for repairs once only biennially. A sensitivity analysis is also conducted to study the criticality of the operating system.

Determination of Human Error Probabilities for the Maintenance Operations of Marine Engines

Human error is a crucial factor in the shipping industry and not to mention numerous human errors occur during the maintenance procedures of marine engines. Determination of human error probabilities (HEPs) is important to reduce the human errors and prevent the accidents. Nevertheless, determination of HEPs in the maintenance procedures of marine engines has not been given desired attention. The aim of this study is to determine the HEPs for the maintenance procedures of the marine engines to minimize the human errors and preclude accidents from the shipping industry. The Success Likelihood Index Method is used to determine the HEPs due to the unavailability of human error data for maintenance procedures of marine engines. The results showed that among the 43 considered activities in this study, inspection and overhauls piston/piston rings have the lowest HEP meaning it has a lower consequence for accidents. On the other hand, fuel and lubricating oil filters pressure difference checking and renews filter elements activity have the highest HEP indicating it has high chances for accidents.

A network based approach to envisage potential accidents in offshore process facilities

Envisaging potential accidents in large scale offshore process facilities such as Floating Liquefied Natural Gas (FLNG) is complex and could be best characterized through evolving scenarios. In the present work, a new methodology is developed to incorporate evolving scenarios in a single model and predicts the likelihood of accident. The methodology comprises; (a) evolving scenario identification, (b) accident consequence framework development, (c) accident scenario likelihood estimation, and (d) ranking of the scenarios. Resulting events in the present work are modeled using a Bayesian network approach, which represents accident scenarios as cause‐consequences networks. The methodology developed in this article is compared with case studies of ammonia and Liquefied Natural Gas from chemical and offshore process facility, respectively. The proposed method is able to differentiate the consequence of specific events and predict probabilities for such events along with continual updating of consequence probabilities of fire and explosion scenarios taking into account. The developed methodology can be used to envisage evolving scenarios that occur in the offshore oil and gas process industry; however, with further modification it can be applied to different sections of marine industry to predict the likelihood of such accidents.

Pitting Degradation Modeling of Ocean Steel Structures Using Bayesian Network

Modelling depth of long-term pitting corrosion is of interest for engineers in predicting the structural longevity of ocean infrastructures. Conventional models demonstrate poor quality in predicting the long-term pitting corrosion depth. Recently developed phenomenological models provide a strong understanding of the pitting process however they have limited engineering applications. In this study, a novel probabilistic model is developed for predicting the long-term pitting corrosion depth of steel structures in marine environment using Bayesian Network. The proposed Bayesian Network model combines an understanding of corrosion phenomenological model and empirical model calibrated using real-world data. A case study, which exemplifies the application of methodology to predict the pit depth of structural steel in long-term marine environment, is presented. The result shows that the proposed methodology succeeds in predicting the time dependent, long-term anaerobic pitting corrosion depth of structural steel in different environmental and operational conditions.

Performance assessment of aeration and radial oxygen loss assisted cathode based integrated constructed wetland-microbial fuel cell systems

The present study explores low-cost cathode development possibility using radial oxygen loss (ROL) of Canna indica plants and intermittent aeration (IA) for wastewater treatment and electricity generation in constructed wetland-microbial fuel cell (CW-MFC) system. Two CW-MFC microcosms were developed. Amongst them, one microcosm was planted with Canna indica plants for evaluating the ROL dependent cathode reaction (CW-MFC dependent on ROL) and another microcosm was equipped with intermittent aeration for evaluating the intermittent aeration dependent cathode reaction (CW-MFC with additional IA). The CW-MFC with additional IA has achieved 78.71% and 53.23%, and CW-MFC dependent on ROL has achieved 72.17% and 46.77% COD removal from synthetic wastewater containing glucose loads of 0.7gL-1and 2.0gL-1, respectively. The maximum power density of 31.04mWm-3 and 19.60mWm-3 was achieved in CW-MFC with additional IA and CW-MFC dependent on ROL, respectively.

Failure analysis of the tripping operation and its impact on well control

As the cost of drilling and completion of offshore well is soaring, efforts are required for better well planning. Safety is to be given the highest priority over all other aspects of well planning. Among different element of drilling, well control is one of the most critical components for the safety of the operation, employees and the environment. Primary well control is ensured by keeping the hydrostatic pressure of the mud above the pore pressure across an open hole section. A loss of well control implies an influx of formation fluid into the wellbore which can culminate to a blowout if uncontrollable. Among the factors that contribute to a blowout are: stuck pipe, casing failure, swabbing, cementing, equipment failure and drilling into other well. Swabbing often occurs during tripping out of an open hole. In this study, investigations of the effects of tripping operation on primary well control are conducted. Failure scenarios of tripping operations in conventional overbalanced drilling and managed pressure drilling are studied using fault tree analysis. These scenarios are subsequently mapped into Bayesian Networks to overcome fault tree modelling limitations such s dependability assessment and common cause failure. The analysis of the BN models identified RCD failure, BHP reduction due to insufficient mud density and lost circulation, DAPC integrated control system, DAPC choke manifold, DAPC back pressure pump, and human error as critical elements in the loss of well control through tripping out operation.

Improving droplet sizing methodology for spray dynamics investigation

Spray modelling is one of the most useful techniques to characterize engine performance, efficiency and emissions. The size of droplets is one of the key variables that govern the efficiency of combustion of the liquid fuel. This study aims to develop an interactive tool using MATLAB codes that identifies the droplets and their sizes from the image taken with the long distance microscope in the spray chamber setup. In this developed method, firstly the background of the image was removed and then image processing techniques, dilation and erosion, were applied to the image file to refine the image files. Subsequently, circle detection method based on the Hough Transform algorithm with the function of imfindcircles was implemented. This function of the program allows the user to identify size droplets from the image files. A statistical study was conducted with the results automated from the MATLAB program using a different set of threshold values of black and white contrast. The results showed an optimal range for the threshold (black and white) values between 40 and 70. This optimal threshold range was established based on consideration of the correct and incorrect identification of the droplets. The results indicated that the program has the ability to identify the droplet providing size and numbers. The MATLAB program was developed using MATLAB compiler and can be used at different workstations.

A step-by-step approach for evaluating the reliability of the main engine lube oil system for a ship's propulsion system

Effective and efficient maintenance is essential to ensure reliability of a ships main propulsion system, which in turn is interdependent on the reliability of a number of associated sub-systems. A primary step in evaluating the reliability of the ships propulsion system will be to evaluate the reliability of each of the sub- system. This paper discusses the methodology adopted to quantify reliability of one of the vital sub-system viz. the lubricating oil system, and development of a model, based on Markov analysis thereof. Having developed the model, means to improve reliability of the system should be considered. The cost of the incremental reliability should be measured to evaluate cost benefits. A maintenance plan can then be devised to achieve the higher level of reliability. Similar approach could be considered to evaluate the reliability of all other sub-systems. This will finally lead to development of a model to evaluate and improve the reliability of the main propulsion system.

Constructed Wetland Coupled Microbial Fuel Cell Technology

Traditional wastewater treatments require high energy, operation, and maintenance costs and produce a large amount of sludge during treatment. This situation is becoming more complex with increasing population growth and urban areas. Thus, a new paradigm of water-energy nexus is required to meet the new water and energy demands at an affordable cost. The constructed wetlands (CWs) or treatment wetlands are low cost engineered systems that are designed to utilize the natural processes for wastewater treatment. In general, CWs run without any chemical dosing or external energy requirements and are easy to operate and maintain. Thus, CWs need very less cost for operation. The CWs have been established in a large number throughout the world as an alternative to the conventional wastewater treatment systems [1,2]. The foundation of CWs for the wastewater treatment technology was laid by early experiments of Dr. Kӓthe Seidel in the 1960s [3] and by Reinhold Kickuth in the 1970s [4,5]. At the beginning of CW establishment, the CWs were mainly used for the treatment of traditional tertiary and secondary domestic/municipal wastewaters [6]. The early types of CW were often dominated by free water surface CWs in North America and horizontal subsurface flow (HSSF) CWs in Europe and Australia [7,8]. In later years, the application of CWs has also been significantly stretched to purify agricultural effluents [9,10], industrial effluents [11,12]; landfill leachates [13]; agricultural drainage waters [14,15]; acid mine drainage [16]; aquaculture waters [17]; and urban and highway runoff [18,19].

An ecological risk assessment model for Arctic oil spills from a subsea pipeline

There is significant risk associated with increased oil and gas exploration activities in the Arctic Ocean. This paper presents a probabilistic methodology for Ecological Risk Assessment (ERA) of accidental oil spills in this region. A fugacity approach is adopted to model the fate and transport of released oil, taking into account the uncertainty of input variables. This assists in predicting the 95th percentile Predicted Exposure Concentration (PEC95%) of pollutants in different media. The 5th percentile Predicted No Effect Concentration (PNEC5%) is obtained from toxicity data for 19 species. A model based on Dynamic Bayesian Network (DBN) is developed to assess the ecological risk posed to the aquatic community. The model enables accounting for the occurrence likelihood of input parameters, as well as analyzing the time-variable risk profile caused by seasonal changes. It is observed through the results that previous probabilistic methods developed for ERA can be overestimating the risk level.