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New technologies for operator training

Advanced technologies including cloud hosting and virtual reality can enhance the effectiveness of operator training simulators

JOHN J ROFFEL, Honeywell Limited
MARTIN J ROSS, Honeywell Control Systems
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Article Summary
Many studies have concluded that operator competency directly impacts safe, reliable and profitable process operations. The benefits of well-trained operators are fully understood in terms of better health, safety and environmental performance, lower cycle times, faster recovery from incidents, fewer off-spec products, and improved morale. Developing competent operators in today’s environment presents significant challenges. In summary, people need to be trained fast and right. The established techniques for developing operator competencies generally follow a tiered approach with classroom or online training delivered through learning management systems (LMS) followed by on the job training (OJT) and in some cases simulator based training of various types. It is generally accepted that people learn best when they are engaged hands on with the systems they are working with; however, in the case of process plant there are obvious dangers associated with exposing trainees to the real work operating environment too early in their development. The industry has responded to this challenge with the development of the operator training simulator (OTS).

An OTS typically comprises three key functional components:
• The simulation model – the process simulation
• The information model – the operator’s control environment
• The training model – the tools and content used by instructors to deliver training.

Of the three training simulator components, the training model is the least mature, and is therefore an effective opportunity to improve process simulator training effectiveness. The deployment and use of specific tools, prior to executing simulator training exercises, can evaluate requisite skills, knowledge and capabilities against the tasks required to achieve specific goals. A new technology is described that focuses on the pre-training needs for basic process and control knowledge.

Historically, when compared to other domains such as the mining industry, the process industry field operator has not been well served by training simulators. Maturing technologies now make it possible to integrate the simulation model with various forms of augmented reality, virtual reality and photographic techniques to provide a compelling environment for the field operator. Examples are presented of how this technology can be deployed today to enhance the effectiveness of individual and team based training.

Computing technology is entering the age of distributed resources and connected devices which can provide new opportunities for a progressive organisation to improve access to learning. A vision is presented with early adopter examples where cloud computing can provide a geographically diverse audience of users affordable access to advanced training technologies.

The operator training challenges of the future require innovative solutions against the needs for accelerated learning, easier access and improved overall outcome. Technologies available today and emerging in the future provide opportunities for continuous improvement against the goal of safe, reliable and profitable process operations.

Employers of process operators must ensure people are selected and trained to execute their duties safely and effectively. Training, in particular, seeks to transfer appropriate knowledge and skills and is motivated by awareness that people will make mistakes which if left undetected can lead to a range of non-optimal outcomes. In addition, the introduction of complex control systems over the last 40 years has removed the ‘hands on’ nature of the operator’s role which in the past provided the basis for much of training’s needs.1 The Abnormal Situation Management (ASM) Consortium suggests people are directly responsible for 40% of abnormal situation losses,2 (see Figure 1) with a number of reasons for this, including:
• Insufficient employee knowledge
• Operator error
• Maintenance worker errors.

The business driver for operator training programmes is clear. Most training programmes either explicitly or implicitly follow a performance management approach. Figure 2 shows a typical training lifecycle for a process operator. At the start, the operator will attend some classroom based orientation training. Transfer of knowledge is assessed by tests and interviews. At this point, a population of trainees would be expected to demonstrate a wide range of abilities. Some who pass the test continue, others would need to retake training to reach the desired performance standard. Orientation and classroom based training is followed by OJT. OJT is typically provided by a senior operator or mentor. Again the performance of such newly trained operators can be expected to be widely distributed. When an operator performance deviation is detected either through observation, plant KPI deviation, or other anomaly, an intervention is initiated. Typical interventions may include closer supervision, completing specific training simulator exercises, or attending refresher training on a particular topic. Over time, fewer performance deviations and anomalies occur and the operator is eventually considered ‘certified’. Once certified, the operator’s performance is further assessed for deviations from expected performance standards and if necessary further training interventions will be triggered.

Although organisations employ various processes to deploy a performance based training programme, at the heart of most programmes is a competency management system (CMS). These generally map job roles to competencies and appropriate training activities or interventions (see Figure 3). An employee in a new role is first assessed against the role’s competency requirements defined by the CMS and any required training is scheduled via an LMS. The LMS acts as the store of information about what activities a trainee has completed, activities in progress and those activities scheduled to complete in future. If any performance deviations are observed, the competency gap is assessed and the required refresher training is identified by reference to the CMS and scheduled via the LMS.

For guidance regarding the effectiveness of the training intervention, it is common to refer to the learning pyramid shown in Figure 4 (attributed originally to the National Training Laboratories, Bethel, Maine, US).

The pyramid tells us that dynamic practice is a great way to teach people; it is very effective in terms of knowledge and skills retention (75%) when compared to static methods such as reading (10%). The challenge for the process industry is practising on the real plant creates inherent and obvious risks. To solve this problem, organisations will often turn to OTS.

An OTS contains the following key components:
• A mathematical simulation model to accurately reproduce the real industrial process
• An information model that reproduces the operator’s information, process control and logic environment
• A training model that comprises comprehensive and automated tools to provide an instructor all the capabilities to deliver effective competency development and assurance.

Today’s state of the art for an OTS is that the simulation and information models are considered mature. These aspects will not be considered further in this article. The training model or more generally the framing of the OTS within a competency management programme and recent advances in application of new technology will be discussed further.
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