Characterization of wood biochar and evaluation of its adsorption potential for removal of sulphate from produced water in offshore oil and gas industry

This thesis analyses the potential of wood biochar as an adsorbent in removal of sulphate from produced water. In worldwide offshore oil and gas industry, a large volume of waste water is generated as produced water. Sulphur compounds present in these produced water streams can cause environmental problems, regulatory problems and operational issues. Among the various sulphur removal technologies, the adsorption technique is considered as a suitable method since the design is simple, compact, economical and robust. Biochar has been studied as an adsorbent for removal of contaminants from water in a number of studies due to its low cost, potential availability, and adsorptive characteristics. In this study, biochar produced through fast pyrolysis of bark, hardwood sawdust, and softwood sawdust were characterized through a series of tests and were analysed for adsorbent properties. Treating produced water using biochar sourced from wood waste is a two-fold solution to environmental problems as it reduces the volume of these wastes. Batch adsorption tests were carried out to obtain adsorption capacities of each biochar sample using sodium sulphate solutions. The highest sulphur adsorption capacities obtained for hardwood char, softwood char and bark char were 11.81 mg/g, 9.44 mg/g, and 7.94 mg/g respectively at 10 °C and pH=4. The adsorption process followed the second order kinetic model and the Freundlich isotherm model. Adsorption reaction was thermodynamically favourable and exothermic. The overall analysis concludes that the wood biochar is a feasible, economical, and environmental adsorbent for removal of sulphate from produced water.

Industrial fault detection and diagnosis using Bayesian belief network

Rapid development in industry have contributed to more complex systems that are prone to failure. In applications where the presence of faults may lead to premature failure, fault detection and diagnostics tools are often implemented. The goal of this research is to improve the diagnostic ability of existing FDD methods. Kernel Principal Component Analysis has good fault detection capability, however it can only detect the fault and identify few variables that have contribution on occurrence of fault and thus not precise in diagnosing. Hence, KPCA was used to detect abnormal events and the most contributed variables were taken out for more analysis in diagnosis phase. The diagnosis phase was done in both qualitative and quantitative manner. In qualitative mode, a networked-base causality analysis method was developed to show the causal effect between the most contributing variables in occurrence of the fault. In order to have more quantitative diagnosis, a Bayesian network was constructed to analyze the problem in probabilistic perspective.