Advanced Applications in Process Control and Training Needs of Field and Control Room Operators

OCCUPATIONAL APPLICATIONS Operators play a vital role in production and safety in industrial processes. Since the introduction of advanced control techniques, such as model predictive control and real-time optimization, operators’ acquisition of adequate mental models to develop complex cause-and-effect relationship explaining plant behavior has been increasingly challenged. Additionally, distinct challenges have arisen with respect to crew coordination between control room and field operators to orchestrate a coordinated flow of actions to assess situations or choose a course of action. Based on an analysis of training needs, it is argued that traditional training practice, such as the use of operator training simulators, could be advanced by using current training environments, such as virtual reality training simulators. This would allow using modern training technology and its advancements in parallel to the advancements of control techniques to support production and safety at its best. TECHNICAL ABSTRACT Background: Extensive integration of various modern methods in the process industry has changed the tasks of industrial operators. The integration of advanced technology and control algorithms lead to new challenges faced by control room and field operators, from both technical and crew-coordination complexity perspectives. From a technical perspective, couplings, dynamic effects, non-transparency, conflicting goals, comprehension of model predictive control, and real-time optimization challenge the development of an accurate mental model. From a crew-coordination complexity perspective, control room operators and field operators face the challenge to orchestrate their individual actions into a coordination flow of actions to assess a situation and solve problems. Purpose: The purpose of this article is to highlight the cognitive and teamwork requirements of operators and to note the limitations of current training practices compared to the training objectives that need to be achieved individually and as a team. Methods: Evidence is presented from instance-based learning theory and theories addressing the acquisition of mental models, instances, and skills for crew-coordination complexity; this is used to suggest that current training practices match only a subset of the challenging training objectives that are essential to use technology efficiently and safely. Results: Findings from the cognitive training need analysis are linked to training objectives and training methods based on the learning theories presented. Additionally, arguments for using different training environments (operator training simulators, virtual reality training simulators) to achieve the training objectives in an optimal way are presented. Conclusions: It is concluded that advancements in the applications of process control techniques call for a new mindset in the training of operators. Advanced training methods and environments can be one way of helping the operator to improve performance reduce errors and enhance safety.