Chemical incidents can be prevented or mitigated by improving safety performance and implementing the lessons learned from past incidents. Despite some limitations in the range of information they provide, chemical incident databases can be utilized as sources of lessons learned from incidents by evaluating patterns and relationships that exist between the data variables. Much of the previous research focused on studying the causal factors of incidents; hence, this research analyzes the chemical incidents from both the causal and consequence elements of the incidents. A subset of incidents data reported to the Hazardous Substance Emergency Events Surveillance (HSEES) chemical incident database from 2002-2006 was analyzed using data mining and text mining methodologies. Both methodologies were performed with the aid of STATISTICA software. The analysis studied 12,737 chemical process related incidents and extracted descriptions of incidents in free-text data format from 3,316 incident reports. The structured data was analyzed using data mining tools such as classification and regression trees, association rules, and cluster analysis. The unstructured data (textual data) was transformed into structured data using text mining, and subsequently analyzed further using data mining tools such as, feature selections and cluster analysis. The data mining analysis demonstrated that this technique can be used in estimating the incident severity based on input variables of release quantity and distance between victims and source of release. Using the subset data of ammonia release, the classification and regression tree produced 23 final nodes. Each of the final nodes corresponded to a range of release quantity and, of distance between victims and source of release. For each node, the severity of injury was estimated from the observed severity scores' average. The association rule identified the conditional probability for incidents involving piping, chlorine, ammonia, and benzene in the value of 0.19, 0.04, 0.12, and 0.04 respectively. The text mining was utilized successfully to generate elements of incidents that can be used in developing incident scenarios. Also, the research has identified information gaps in the HSEES database that can be improved to enhance future data analysis. The findings from data mining and text mining should then be used to modify or revise design, operation, emergency response planning or other management strategies.

The sustainability and continued development of the chemical industry is to a large extent dependent on learning from past incidents. The failure to learn from past mistakes is rather not deliberate but due to unawareness of the situation. Incident databases are excellent resources to learn from past mistakes; however, in order to be effective, incident databases need to be functional in disseminating the lessons learned to users. Therefore, this research is dedicated to improving user accessibility of incident databases. The objective of this research is twofold. The first objective is improving accessibility of the database system by allowing the option of word search as well as folder search for the users. This will satisfy research needs of users who are aware of the hazards at hand and need to access the relevant information. The second objective is to activate the database via integration of the database with an operational software. This will benefit research needs of users who are unaware of the existing hazards. Literature review and text mining of Major Accident Reporting System (MARS) database short reports are employed to develop an initial taxonomy, which is then refined and modified by expert review. The incident reports in MARS database is classified to the right folders in the taxonomy and implemented in a database system based on Microsoft Excel, where the users can retrieve information using folder search as well as word search option via a user friendly interface. A program coded in JAVA is prepared for integrating the incident database with a Management of Change (MOC) software prototype. A collection of keywords on hazardous substances and equipment is prepared. If the keywords exist in the MOC interface, they will be highlighted, and with the click of a button, will return up to ten relevant incident reports. Using an active and knowledge-based system, people can learn from incidents and near-misses and will be more active to reduce the frequency of recurring incidents.