Browsing by Author "Polat, Serhan"

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    Predictive Analysis and Recommendation for Managing Risk and Avoiding Hazard in Chemical and Oil & Gas Industrial Infrastructures
    (2018-12-07) Polat, Serhan; Rokne, Jon G.; Alhajj, Reda S.; Moshirpour, Mohammad
    Chemical processing industrial infrastructures such as oil & gas plants are operated with the risk of hazardous events which may lead to casualties, economic and/or environmental consequences. Fortunately, a variety of devices and mechanisms are already available or rapidly emerging to capture data which may be used to develop techniques that may assist in issuing timely hazard alerts. This would help to avoid or prevent the hazard and hence save lives, the environment and the economy. Thus, the aim of this thesis is to develop an approach capable of analyzing the reports data captured after operations of infrastructure which can be used to guide domain experts in handling various causes and consequences of hazards. Available data may be publicly available or may exist in private repositories of processing companies. The latter data may not be accessible outside the company premises. However, the data available for this thesis has been crawled from publicly available data which exists as reports in various formats varying from plain text, semi-structured to structured. The crawled reports have been preprocessed using natural language processing techniques. Domain ontology has been used to guide the whole processes of clustering, and classification and a multiagent system have been integrated into the developed approach. Utilizing a multiagent system in the process allows for multiple perspectives to be incorporated into the process. These aspects are represented by independent agents who collaborate and negotiate to reach a consensus. The developed approach has been successfully applied to some publicly available gas and oil infrastructure hazard related data. The reported results may be used to issue recommendations to use certain safeguards to reduce the risk level in the processes.