Abnormal operation of chemical processec sa used by equipment and sensor faults, such as plugging of pipes, control system failure or improper operation by personnel can result in poor product quality, equipment damage, or a catastrophe process failure leading to loss of equipment and worker injury, as well as significant economic losses. It is estimated that the cost attributable to preventable
losses in the petrochemical industry only is around several billion pounds per year. Independent studies of case
histories by the Health and Safety Commission in the UK and by a Honeywell led industrial consortium in the US and world wide show that human errors represent the major cause
of failures. In contrast to this discovery, the majority of pervious studies on computer aided systems for fault detection and diagnosis has focussed on the process side only. It is now widely acknowledged that there is only limited information on how human factors can be assessed
and even less that is specific to chemical industry,
therefore research is much needed in this area.
This study presents a methodology to involve human
factors into the development of systems for automatic identification and diagnosis of abnormal operations and
develops methods and techniques that can be used to simultaneously capture, characterise and assess the performance of operators as well as of the process. A joint process operator simulation platform was developed which was used as a test-bed for carrying out the studies. The process part is a simulator, which emulates in high fidelity the dynamic behaviour of the process, which is subject to influence of various disturbances and operators intervention. The operator module was developed as a real-time expert system, which emulates operator's behaviour in interpretation of received signals, planning and executions of the decisions. The interaction between the two modules
is managed through an interaction module, which handles the real-time exchange of data using DDE (Dynamic Data Exchange). The interaction module also contains the toolkits for analysing the dynamic behaviour of the joint process-operator system.
The operator simulation module was developed based on a theoretical model of human behaviour, which breaks operator's activities into perception of signals an interpretation of the received information, planning for actions and execution of the decisions. The system was implemented as a real-time expert system using visual Prolog. Numerical models were also integrated into the expert system, e. g. stress models of operators. This flexible system allows studies on individual operators actions, stress, intervene time, the frequency of intervene and near-miss or near-hit in operation.
As part of the effort to use the platform to develop methods and tools for characterising and assessing the dynamic behaviour of the joint process-operator system,
a digraph method for qualitative/quantitative modelling of the dynamic behaviour of the combined system was proposed.
The method involves categorical characterisation of
dynamic trends using principal component analysis and fuzzy c-means and sectioning of the clusters. An iterative method for determining the number of the clusters and sections
based on the global performance was derived. Compared with pervious studies on qualitative process modelling, the proposed approach is more accurate and has higher
resolution, and more importantly is able to deal with joint process-operator systems.
The methods and systems developed were illustrated and fully tested using simulated and industrial case studies.