Patent Publication Number: US-2017357928-A1

Title: System and method for industrial process control and automation system operator evaluation and training

Description:
CROSS-REFERENCE TO RELATED APPLICATION AND PRIORITY CLAIM 
     This application claims priority under 35 U.S.C. §119(e) to U.S. Provisional Patent Application No. 62/347,352 filed on Jun. 8, 2016 and entitled “SYSTEM AND METHOD FOR INDUSTRIAL PROCESS CONTROL AND AUTOMATION SYSTEM OPERATOR EVALUATION AND TRAINING,” the content of which is hereby incorporated by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     This disclosure relates generally to industrial process control and automation. More specifically, this disclosure relates to a system and method for industrial process control and automation system operator evaluation and training. 
     BACKGROUND 
     Industrial process control and automation systems are often used to automate and operate large and complex industrial processes. These types of systems routinely include sensors, actuators, and controllers. The controllers are often arranged hierarchically in a control and automation system. For example, lower-level controllers are often used to receive measurements from the sensors and perform process control operations to generate control signals for the actuators. Higher-level controllers are often used to perform higher-level functions, such as planning, scheduling, and optimization operations. Human operators routinely interact with controllers and other devices in a control and automation system, such as to review warnings, alarms, or other notifications associated with various events and make adjustments to control or other operations. 
     SUMMARY 
     This disclosure provides a system and method for industrial process control and automation system operator evaluation and training. 
     In a first embodiment, a method includes obtaining at least one model associating areas of competency with job roles and job responsibilities of personnel, the at least one model also associating the areas of competency with curricula of training exercises and content. The method also includes obtaining a library of intervention assets associated with the areas of competency, the intervention assets comprising content for training personnel in at least one of the areas of competency as part of the curricula of training exercises and content. The method further includes evaluating a trainee, by a competency management system, to determine a competency gap analysis of the trainee, the competency gap analysis comprising at least one competency gap associated with job responsibilities of the trainee, the at least one competency gap identifying at least one of the areas of competency in which the trainee requires training. In addition, the method includes providing, by a training system, web-based training to the trainee based on the at least one competency gap, the training comprising at least one of the intervention assets and at least one intervention activity. 
     In a second embodiment, an apparatus includes at least one interface and at least one processing device. The at least one interface is configured to exchange information over a cloud-based network. The at least one processing device is configured to obtain at least one model associating areas of competency with job roles and job responsibilities of personnel, the at least one model also associating the areas of competency with curricula of training exercises and content. The at least one processing device is also configured to obtain a library of intervention assets associated with the areas of competency, the intervention assets comprising content for training personnel in at least one of the areas of competency as part of the curricula of training exercises and content. The at least one processing device is further configured to control a competency management system to evaluate a trainee in order to determine a competency gap analysis of the trainee, the competency gap analysis comprising at least one competency gap associated with job responsibilities of the trainee, the at least one competency gap identifying at least one of the areas of competency in which the trainee requires training. In addition, the at least one processing device is configured to control a training system to provide web-based training to the trainee based on the at least one competency gap, the training comprising at least one of the intervention assets and at least one intervention activity. 
     In a third embodiment, a non-transitory computer readable medium contains instructions that, when executed by at least one processing device, cause the at least one processing device to obtain at least one model associating areas of competency with job roles and job responsibilities of personnel, the at least one model also associating the areas of competency with curricula of training exercises and content. The medium also contains instructions that, when executed by the at least one processing device, cause the at least one processing device to obtain a library of intervention assets associated with the areas of competency, the intervention assets comprising content for training personnel in at least one of the areas of competency as part of the curricula of training exercises and content. The medium further contains instructions that, when executed by the at least one processing device, cause the at least one processing device to control a competency management system to evaluate a trainee in order to determine a competency gap analysis of the trainee, the competency gap analysis comprising at least one competency gap associated with job responsibilities of the trainee, the at least one competency gap identifying at least one of the areas of competency in which the trainee requires training. In addition, the medium contains instructions that, when executed by the at least one processing device, cause the at least one processing device to control a training system to provide web-based training to the trainee based on the at least one competency gap, the training comprising at least one of the intervention assets and at least one intervention activity. 
     Other technical features may be readily apparent to one skilled in the art from the following figures, descriptions, and claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a more complete understanding of this disclosure, reference is now made to the following description, taken in conjunction with the accompanying drawings, in which: 
         FIG. 1  illustrates an example industrial process control and automation system according to this disclosure; 
         FIGS. 2 through 5  illustrate an example system for industrial process control and automation system operator evaluation and training according to this disclosure; 
         FIG. 6  illustrates an example method for industrial process control and automation system operator evaluation and training according to this disclosure; and 
         FIG. 7  illustrates an example device supporting industrial process control and automation system operator evaluation and training according to this disclosure. 
     
    
    
     DETAILED DESCRIPTION 
       FIGS. 1 through 7 , discussed below, and the various embodiments used to describe the principles of the present invention in this patent document are by way of illustration only and should not be construed in any way to limit the scope of the invention. Those skilled in the art will understand that the principles of the invention may be implemented in any type of suitably arranged device or system. 
     As described above, human operators routinely interact with controllers and other devices in an industrial process control and automation system, such as to review warnings, alarms, or other notifications associated with various events and make adjustments to control or other operations. Errors by human operators can have negative economic, safety, or other consequences to the owners of an industrial facility and potentially to the general public. Owners of industrial facilities therefore typically require or desire competent staff to operate the industrial facilities and their associated equipment. Owners are financially incentivized (and in some cases mandated) to employ operators with the appropriate skill levels and knowledge for safe and efficient execution of process operations. 
     Operator evaluations and training simulators are often deployed as effective training tools by companies wishing to develop operator competency. However, research has identified that many companies lack an effective process for deploying training interventions, including simulator-based training. Typical issues observed are that interventions are not competency-based and that feedback is weak and not timely. 
     To address these and other issues, embodiments of this disclosure provide web-based or cloud-hosted course curricula that are competency-based and provide clear and timely feedback to trainees. The combination of interventions and course structure is designed to address problems such as a lack of design and feedback, thereby providing a superior training outcome. 
     In some embodiments, a trainee can be trained in a particular set of competencies using the web-based or cloud-hosted system. Rather than have to go to multiple locations to receive training (possibly from multiple vendors), the trainee can access the training from his or her workstation. Simulations can be performed or executed at the workstation, rather than in another location. In some embodiments, the competencies are developed based on (or in accordance with) the Abnormal Situation Management (ASM) Consortium. The ASM Consortium has developed a competency framework for defining competencies for roles that are relevant in industrial process and control systems. Additionally or alternatively, the competencies can be developed based on (or in accordance with) other competency models. In general, the disclosed embodiments are flexible and can accommodate one or multiple different competency models. 
       FIG. 1  illustrates an example industrial process control and automation system  100  according to this disclosure. As shown in  FIG. 1 , the system  100  includes various components that facilitate production or processing of at least one product or other material. For instance, the system  100  is used here to facilitate control over components in one or multiple plants  101   a - 101   n . Each plant  101   a - 101   n  represents one or more processing facilities (or one or more portions thereof), such as one or more manufacturing facilities for producing at least one product or other material. In general, each plant  101   a - 101   n  may implement one or more processes and can individually or collectively be referred to as a process system. A process system generally represents any system or portion thereof configured to process one or more products or other materials in some manner. 
     In  FIG. 1 , the system  100  is implemented using the Purdue model of process control. In the Purdue model, “Level 0” may include one or more sensors  102   a  and one or more actuators  102   b . The sensors  102   a  and actuators  102   b  represent components in a process system that may perform any of a wide variety of functions. For example, the sensors  102   a  could measure a wide variety of characteristics in the process system, such as temperature, pressure, or flow rate. Also, the actuators  102   b  could alter a wide variety of characteristics in the process system. The sensors  102   a  and actuators  102   b  could represent any other or additional components in any suitable process system. Each of the sensors  102   a  includes any suitable structure for measuring one or more characteristics in a process system. Each of the actuators  102   b  includes any suitable structure for operating on or affecting one or more conditions in a process system. 
     One or more networks  104  are coupled to the sensors  102   a  and actuators  102   b . The network  104  facilitates interaction with the sensors  102   a  and actuators  102   b . For example, the network  104  could transport measurement data from the sensors  102   a  and provide control signals to the actuators  102   b . The network  104  could represent any suitable network or combination of networks. As particular examples, the network  104  could represent an Ethernet network, an electrical signal network (such as a HART or FOUNDATION FIELDBUS network), a pneumatic control signal network, or any other or additional type(s) of network(s). 
     In the Purdue model, “Level 1” includes one or more controllers  106 , which are coupled to the network  104 . Among other things, each controller  106  may use the measurements from one or more sensors  102   a  to control the operation of one or more actuators  102   b . Each controller  106  includes any suitable structure for controlling one or more aspects of a process system. As a particular example, each controller  106  could represent a computing device running a real-time operating system. 
     Redundant networks  108  are coupled to the controllers  106 . The networks  108  facilitate interaction with the controllers  106 , such as by transporting data to and from the controllers  106 . The networks  108  could represent any suitable redundant networks. As particular examples, the networks  108  could represent a pair of Ethernet networks or a redundant pair of Ethernet networks, such as a FAULT TOLERANT ETHERNET (FTE) network from HONEYWELL INTERNATIONAL INC. 
     At least one switch/firewall  110  couples the networks  108  to two networks  112 . The switch/firewall  110  may transport traffic from one network to another. The switch/firewall  110  may also block traffic on one network from reaching another network. The switch/firewall  110  includes any suitable structure for providing communication between networks, such as a HONEYWELL CONTROL FIREWALL (CF9) device. The networks  112  could represent any suitable networks, such as a pair of Ethernet networks or an FTE network. 
     In the Purdue model, “Level 2” may include one or more machine-level controllers  114  coupled to the networks  112 . The machine-level controllers  114  perform various functions to support the operation and control of the controllers  106 , sensors  102   a , and actuators  102   b , which could be associated with a particular piece of industrial equipment (such as a boiler or other machine). For example, the machine-level controllers  114  could log information collected or generated by the controllers  106 , such as measurement data from the sensors  102   a  or control signals for the actuators  102   b . The machine-level controllers  114  could also execute applications that control the operation of the controllers  106 , thereby controlling the operation of the actuators  102   b . In addition, the machine-level controllers  114  could provide secure access to the controllers  106 . Each of the machine-level controllers  114  includes any suitable structure for providing access to, control of, or operations related to a machine or other individual piece of equipment. Each of the machine-level controllers  114  could, for example, represent a server computing device running a MICROSOFT WINDOWS operating system. Although not shown, different machine-level controllers  114  could be used to control different pieces of equipment in a process system (where each piece of equipment is associated with one or more controllers  106 , sensors  102   a , and actuators  102   b ). 
     One or more operator stations  116  are coupled to the networks  112 . The operator stations  116  represent computing or communication devices providing user access to the machine-level controllers  114 , which could then provide user access to the controllers  106  (and possibly the sensors  102   a  and actuators  102   b ). As particular examples, the operator stations  116  could allow users to review the operational history of the sensors  102   a  and actuators  102   b  using information collected by the controllers  106  and/or the machine-level controllers  114 . The operator stations  116  could also allow the users to adjust the operation of the sensors  102   a , actuators  102   b , controllers  106 , or machine-level controllers  114 . In addition, the operator stations  116  could receive and display warnings, alerts, or other messages or displays generated by the controllers  106  or the machine-level controllers  114 . Each of the operator stations  116  includes any suitable structure for supporting user access and control of one or more components in the system  100 . Each of the operator stations  116  could, for example, represent a computing device running a MICROSOFT WINDOWS operating system. 
     At least one router/firewall  118  couples the networks  112  to two networks  120 . The router/firewall  118  includes any suitable structure for providing communication between networks, such as a secure router or combination router/firewall. The networks  120  could represent any suitable networks, such as a pair of Ethernet networks or an H E network. 
     In the Purdue model, “Level 3” may include one or more unit-level controllers  122  coupled to the networks  120 . Each unit-level controller  122  is typically associated with a unit in a process system, which represents a collection of different machines operating together to implement at least part of a process. The unit-level controllers  122  perform various functions to support the operation and control of components in the lower levels. For example, the unit-level controllers  122  could log information collected or generated by the components in the lower levels, execute applications that control the components in the lower levels, and provide secure access to the components in the lower levels. Each of the unit-level controllers  122  includes any suitable structure for providing access to, control of, or operations related to one or more machines or other pieces of equipment in a process unit. Each of the unit-level controllers  122  could, for example, represent a server computing device running a MICROSOFT WINDOWS operating system. Although not shown, different unit-level controllers  122  could be used to control different units in a process system (where each unit is associated with one or more machine-level controllers  114 , controllers  106 , sensors  102   a , and actuators  102   b ). 
     Access to the unit-level controllers  122  may be provided by one or more operator stations  124 . Each of the operator stations  124  includes any suitable structure for supporting user access and control of one or more components in the system  100 . Each of the operator stations  124  could, for example, represent a computing device running a MICROSOFT WINDOWS operating system. 
     At least one router/firewall  126  couples the networks  120  to two networks  128 . The router/firewall  126  includes any suitable structure for providing communication between networks, such as a secure router or combination router/firewall. The networks  128  could represent any suitable networks, such as a pair of Ethernet networks or an FTE network. 
     In the Purdue model, “Level 4” may include one or more plant-level controllers  130  coupled to the networks  128 . Each plant-level controller  130  is typically associated with one of the plants  101   a - 101   n , which may include one or more process units that implement the same, similar, or different processes. The plant-level controllers  130  perform various functions to support the operation and control of components in the lower levels. As particular examples, the plant-level controller  130  could execute one or more manufacturing execution system (MES) applications, scheduling applications, or other or additional plant or process control applications. Each of the plant-level controllers  130  includes any suitable structure for providing access to, control of, or operations related to one or more process units in a process plant. Each of the plant-level controllers  130  could, for example, represent a server computing device running a MICROSOFT WINDOWS operating system. 
     Access to the plant-level controllers  130  may be provided by one or more operator stations  132 . Each of the operator stations  132  includes any suitable structure for supporting user access and control of one or more components in the system  100 . Each of the operator stations  132  could, for example, represent a computing device running a MICROSOFT WINDOWS operating system. 
     At least one router/firewall  134  couples the networks  128  to one or more networks  136 . The router/firewall  134  includes any suitable structure for providing communication between networks, such as a secure router or combination router/firewall. The network  136  could represent any suitable network, such as an enterprise-wide Ethernet or other network or all or a portion of a larger network (such as the Internet). 
     In the Purdue model, “Level 5” may include one or more enterprise-level controllers  138  coupled to the network  136 . Each enterprise-level controller  138  is typically able to perform planning operations for multiple plants  101   a - 101   n  and to control various aspects of the plants  101   a - 101   n . The enterprise-level controllers  138  can also perform various functions to support the operation and control of components in the plants  101   a - 101   n . As particular examples, the enterprise-level controller  138  could execute one or more order processing applications, enterprise resource planning (ERP) applications, advanced planning and scheduling (APS) applications, or any other or additional enterprise control applications. Each of the enterprise-level controllers  138  includes any suitable structure for providing access to, control of, or operations related to the control of one or more plants. Each of the enterprise-level controllers  138  could, for example, represent a server computing device running a MICROSOFT WINDOWS operating system. In this document, the term “enterprise” refers to an organization having one or more plants or other processing facilities to be managed. Note that if a single plant  101   a  is to be managed, the functionality of the enterprise-level controller  138  could be incorporated into the plant-level controller  130 . 
     Access to the enterprise-level controllers  138  may be provided by one or more operator stations  140 . Each of the operator stations  140  includes any suitable structure for supporting user access and control of one or more components in the system  100 . Each of the operator stations  140  could, for example, represent a computing device running a MICROSOFT WINDOWS operating system. 
     A historian  142  is also coupled to the network  136  in this example. The historian  142  could represent a component that stores various information about the system  100 . The historian  142  could, for example, store information used during production scheduling and optimization. The historian  142  represents any suitable structure for storing and facilitating retrieval of information. Although shown as a single centralized component coupled to the network  136 , the historian  142  could be located elsewhere in the system  100 , or multiple historians could be distributed in different locations in the system  100 . 
     Operators may use various devices (such as various operator stations described above) to oversee, control, and adjust operations of other devices (such as various controllers described above) in the system  100 . Operators may also use various devices to review warnings, alarms, or other notifications associated with the system  100  and take corrective action. As a result, evaluating the competencies of the operators and providing training to improve the competencies of the operators can be extremely important or even required. The description below describes how industrial process control and automation system operator evaluation and training can be supported. 
     Although  FIG. 1  illustrates one example of an industrial process control and automation system  100 , various changes may be made to  FIG. 1 . For example, industrial control and automation systems come in a wide variety of configurations. The system  100  shown in  FIG. 1  is meant to illustrate one example operational environment in which industrial process control and automation system operator evaluation and training may be needed or desired. However,  FIG. 1  does not limit this disclosure to any particular configuration or operational environment. 
       FIGS. 2 through 5  illustrate an example system for industrial process control and automation system operator evaluation and training according to this disclosure. As shown in  FIG. 2 , a system  200  includes a number of computing nodes  202   a - 202   n . The computing nodes  202   a - 202   n  denote any suitable computing or communication devices that can be used by operators or other personnel. The computing nodes  202   a - 202   n  could, for instance, denote desktop computers, laptop computers, tablet computers, or mobile smartphones. The computing nodes  202   a - 202   n  could be used by various personnel, such as operators or other personnel requiring or desiring training related to an industrial process control and automation system, such as the system  100  of  FIG. 1 . 
     The computing nodes  202   a - 202   n  are coupled to at least one network  204 . The network  204  facilitates interaction and exchange of data involving the computing nodes  202   a - 202   n . For example, data transmitted by or received at any of the computing nodes  202   a - 202   n  may pass through the network  204 . The network  204  denotes any suitable network or combination of networks, such as one or more local area networks, metropolitan area networks, wide area networks, or global networks such as the Internet. 
     Operator evaluation and training could be provided in various ways in  FIG. 2 . For example, in some embodiments, the operator evaluation and training to could be supported by one or more servers  206  or other standalone computing devices. Each server  206  could include one or more processing devices, one or more memories, and one or more interfaces. Each processing device includes any suitable processing or computing device, such as a microprocessor, microcontroller, digital signal processor, field programmable gate array, application specific integrated circuit, or discrete logic devices. Each memory includes any suitable storage and retrieval device, such as random access memory (RAM), Flash memory, or read only memory (ROM). Each interface includes any suitable structure facilitating communication over a connection or network, such as a wired interface (like an Ethernet interface) or a wireless interface (like a radio frequency transceiver). At least one database  208  could be coupled to the network  204  or otherwise be accessible to the server(s)  206 . Each database  208  could store any suitable information associated with operator evaluation and training in any suitable format. 
     In other embodiments, operator evaluation and training could be supported within a network-based environment  210 , such as a computing cloud. The network-based environment  210  could include various components that support cloud-based operator evaluation and training. For example, the network-based environment  210  could include one or more servers or other computing devices  212  that execute logic supporting operator evaluation and training, as well as one or more databases  214  that store data used for operator evaluation and training. As is typical with computing clouds, the specific device or devices executing the logic and storing the data can change over time, such as when different servers are selected at different times for executing the logic based on load balancing or other factors. 
     The use of a cloud environment can provide a number of technical advantages over non-cloud based environments. For example, a cloud environment allows the system  200  to be easily scaled up or down to provide resources of an appropriate scale to accommodate changing training needs. For example, if an enterprise acquires or builds new industrial plants that are staffed by additional operators, the system  200  could be expanded easily to accommodate additional training needs by adding one or more computing devices  212 , databases  208 ,  214 , or servers  206 . Similarly, if training needs are reduced, various components of the computing cloud could be eliminated or downsized. 
     However implemented, the operator evaluation and training functionality of the system  200  allows administrators or other users to prepare a library of simulations, supporting materials, and applications (referred to as “content”) and bundle this content into an offering (referred to as a “curriculum”). In some embodiments, the content could be accessible to trainees via standard web browsers. A curriculum is used as a tool for management and review of competency development for each trainee. In some embodiments, the trainees may be operators or other personnel associated with an industrial process control and automation system, such as the system  100  of  FIG. 1 . 
     The use of training and evaluation content that is accessible from a web browser provides a number of technical advantages over manual, paper, and even single computer-based systems. For example, since the content is accessible via a standard web browser, a trainee could access training and evaluation content from anywhere in the world, as long as the trainee has an Internet connection. This allows great flexibility in providing training quickly and efficiently to geographically dispersed user groups. This also allows the components of the computing cloud to be substantially centralized, which can provide efficiencies in managing the system  200 . Further, the computing device executing a standard web browser typically does not require any training or evaluation licenses, software, or hardware to be installed locally in order to provide a training environment to the trainee. In addition, the computing device is not required to be connected to a corporate computing network in order to provide training. 
       FIG. 3  illustrates an example functional overview of the system  200  shown in  FIG. 2 . The functional components in  FIG. 3  could be executed or otherwise supported using the server  206  and database  208 , the network-based environment  210 , or in any other suitable manner. As shown in  FIG. 3 , the system  200  receives or accesses information  302  defining various job roles and responsibilities of personnel associated with at least one industrial process control and automation system. The information  302  could be provided by owners or operators of a control and automation system, standards bodies, industry or government regulatory agencies, or other source(s). 
     The information  302  is used to create at least one competency model  304 , which defines at least one curriculum of training exercises and content for trainees. Each competency model  304  could include any suitable information for defining the areas of competency needed in various job roles and the curricula of training exercises and content for those areas of competency. Details of example competency models and how competency models may be used can be found in U.S. Patent Application Publication No. 2011/0307301 and U.S. Patent Application Publication No. 2014/0349255, the contents of which are incorporated by reference herein. 
     The system  200  also receives or has access to at least one library  306  of intervention assets  307 . The intervention assets  307  denote simulations, supporting materials, applications, or other content that can be used to measure trainees&#39; competence at delivering various outcomes or that can be used to provide training related to the outcomes. As a particular example, an intervention asset  307  could include a process model that simulates a process or operation within an industrial process and control system (such as a distillation process or a slide valve operation in a refinery). As another particular example, an intervention asset  207  could include a business ethics course or an environmental compliance course. Each library  306  could include information from any suitable source(s), such as personnel associated with an industrial process control and automation system or a third-party who provides training materials or other materials. 
     At least one intervention assignment function  308  uses the competency model or models  304  and the library or libraries  306  to map different intervention assets  307  to different job roles and responsibilities. The intervention assignment function  308  operates to identify which simulations, supporting materials, applications, or other content should be provided to different job roles and responsibilities in order to help increase operator competency. The intervention assignment function  308  could also analyze information associated with specific trainees to identify areas where the trainees require training. 
     In some cases, a newly-implemented training exercise could be required across multiple trainee groups. For example, certain training (such as business ethics) might be newly required for all or a large group of employees of an enterprise. Once the training exercise (such as a business ethics course) is added as an intervention asset  207 , the intervention assignment function  308  could automatically map the new intervention asset  207  to all job roles and responsibilities. In some cases, training might be geographically based. For example, a business ethics course might be required only for employees in certain countries or regions in order to comply with local laws. In such cases, the intervention assignment function  308  can easily limit the mapping to job roles and responsibilities in only those countries or regions. 
     In this example, different ones of the intervention assets  307  can be provided to different trainees via a web-based deployment platform  310 , which makes the intervention assets  307  available to at least one training system  312 . The web-based deployment platform  310  includes any suitable logic for making content available via a web browser or other suitable interface. The training system  312  includes any suitable structure for providing content to trainees and obtaining information from the trainees. For example, the training system  312  can include a computer, laptop, or tablet having network access to the web-based deployment platform  310 . 
       FIG. 4  illustrates example processes  402  and  404  that could occur in the system  200 . The process  402  generally involves the use of a competency management system  406  to evaluate a trainee  408  and provide a competency gap analysis  410 . The competency management system  406  could form part of the intervention assignment functionality of  FIG. 3 . The competency management system  406  operates to identify, for a trainee with a given job role and given job responsibilities, what competencies the trainee should possess. The competency management system  406  could also provide simulations (such as computer-based simulations of an industrial process), ask questions, receive responses or answers from the trainee in response to the simulations or questions, or otherwise attempt to identify what competencies the trainee actually possesses. Any differences in competencies can be used to generate the competency gap analysis  410 , which identifies any gaps between the competencies that the trainee should possess and the competencies that the trainee actually possesses. 
     The process  404  generally involves the use of the training system  312  to provide training to a trainee  408 , where the training involves one or more intervention activities  412 . The intervention activities  412  could, for example, involve the use of various ones of the intervention assets  307  in the library  306 . For example, the intervention activities  412  could involve providing the trainee  408  with audio or video content, running simulations to see how the trainee  408  reacts in certain situations, or replaying historical time series and event data for past abnormal situations together with lessons learned from those scenarios. In some cases, multiple intervention activities could be combined into one training exercise. For example, a simulation of a distillation process in a refinery could be combined with documentation to be read by the trainee, a video showing a distillation process expert to be watched by the trainee, and a quiz to be completed by the trainee. 
     An activity scheduler  414  could be used to schedule training as part of the process  404  based on the results of the process  402 . For example, the activity scheduler  414  could analyze a competency gap analysis  410  for a trainee  408 , identify the intervention activities  412  needed by the trainee  408 , and schedule those intervention activities  412 . 
     Multiple types of feedback can be provided as shown in  FIG. 4 . For example, feedback  416  generated through the use of the intervention activities  412  can be provided directly to the trainee  408 . This could include, for example, the trainee  408  being informed of how the trainee  408  performed during a simulation. Because the training is computer-based and web-based, results of the trainee&#39;s performance can be obtained and processed by the training system  312  and provided directly and immediately to the trainee (such as when displayed on the web browser) in real time. This real-time feedback  416  to the trainee promotes understanding by the trainee and helps to ensure that the training is useful, beneficial, and relevant. 
     Feedback  418  generated through the use of the intervention activities  412  can also be provided to the process  402  so that the competency gap analysis  410  of the trainee  408  can be updated. This could include, for example, the process  402  receiving information indicating that the trainee  408  is no longer deficient in certain competencies. 
     The feedback  418  could also include information used for aggregate data collection or heuristics. For example, feedback  418  from multiple trainees could be collected and aggregated by the system  200  in order to determine benchmarks, training successes, and training progressions of populations of trainees. Such populations could be divided and compared based on years of experience, geographical location, job roles, demographics, or any other suitable characteristic(s). As a particular example, by aggregating feedback  418  from a large number of trainees over a period of time, it may be possible to determine that some types of training (such as simulations) are more effective or successful in one geographic region, while another type of training (such as quizzes) are more effective or successful in another region. A centralized, cloud-based system facilitates the rapid procurement and use of such feedback  418 . 
     The feedback  418  could further include information offered by or learned from trainees used to improve the training experience for future users. For example, during an intervention activity  412  that includes a simulation exercise, a trainee may come up with a novel solution to a simulation problem. The solution may be recorded by the training system  312  and then provided as feedback  418  to the system  200  as an additional or improved solution that can be incorporated into future training or incorporated into a process used in a real-time system, such as the system  100 . 
     One goal of the functionality shown in  FIGS. 2 through 4  is to tie training to an operator&#39;s competencies rather than to specific tasks. Various training programs can be used to train an operator to be proficient at a specific task. For example, training programs can be used to train an operator how to start up or shut down an industrial process or how to respond to an alarm. It is then assumed that an operator has desired competencies when the operator can complete specific tasks. Feedback is typically limited to whether or not each specific task can be completed. 
     The system  200  shown in  FIG. 2  and its functionalities shown in  FIGS. 3 and 4  operate to provide competency-based training and feedback instead of task-based training and feedback. The curricula defined by the competency model  304  and the intervention assets  307  identified by the library  306  are used to measure and help increase a trainee&#39;s competency to deliver various outcomes related to his or her job role and job responsibilities. As a result, the intervention assets  307  can be aligned and assigned based on trainee competencies and not merely whether the trainee can perform certain tasks. 
       FIG. 5  illustrates the cyclical nature of how operator evaluation and training may occur. As shown in  FIG. 5 , the trainee  408  can undergo one or more training interventions  502 , where each training intervention  502  involves one or more intervention activities  412  designed to increase the trainee&#39;s competency in some area. Each competency can be assigned one or more training interventions  502 , and the same training intervention  502  may be assigned to more than one competency. Once completed, a measure of the trainee&#39;s proficiency  504  for one or more competencies can be generated and provided to the trainee  408 . For example, the trainee&#39;s proficiency  504  at each competency could be expressed in terms of a “score” that identifies the proficiency level of the trainee that is attained at the end of each intervention. If necessary, additional training interventions  502  could then occur. 
     Although  FIGS. 2 through 5  illustrate one example of a system for industrial process control and automation system operator evaluation and training, various changes may be made to  FIGS. 2 through 5 . For example, while described as being server-based or cloud-based, the system  200  could be implemented in other ways. 
       FIG. 6  illustrates an example method  600  for industrial process control and automation system operator evaluation and training according to this disclosure. For ease of explanation, the method  600  is described as being performed using the system  200  of  FIG. 2 . However, the method  600  could be used with any suitable device or system. 
     At step  601 , at least one model associating areas of competency with job roles and job responsibilities of personnel is obtained. This could include, for example, the system  200  obtaining at least one curriculum competency model  304 . The model could also associate the areas of competency with curricula of training exercises and content. 
     At step  603 , a library of intervention assets associated with the areas of competency is obtained. This could include, for example, the system  200  obtaining the library  306  of intervention assets  307 . The intervention assets include content for training personnel in at least one of the areas of competency as part of the curricula of training exercises and content. 
     At step  605 , a trainee is evaluated by a competency management system. This could include, for example, the competency management system  406  evaluating a trainee  408  to determine a competency gap analysis  410  of the trainee  408 . The competency gap analysis includes at least one competency gap associated with job responsibilities of the trainee. Each competency gap identifies at least one the areas of competency in which the trainee requires training. 
     At step  607 , training for the trainee is scheduled. This could include, for example, the activity scheduler  414  scheduling the training by analyzing the competency gap analysis and identifying at least one intervention activity  412  for the training. 
     At step  609 , the training is provided to the trainee. This could include, for example, the training system  312  providing web-based training to the trainee  408  based on the at least one competency gap. The training includes at least one of the intervention assets and at least one intervention activity. 
     At step  611 , first and second feedback is provided in response to completion of the training. This could include, for example, the training system  312  providing feedback  416  to the trainee  408  and providing feedback  418  to the competency management system  406 . The second feedback can be used by the competency management system to update the competency gap analysis of the trainee. 
     Although  FIG. 6  illustrates one example of a method  600  for industrial process control and automation system operator evaluation and training, various changes may be made to  FIG. 6 . For example, while shown as a series of steps, various steps shown in  FIG. 6  could overlap, occur in parallel, occur in a different order, or occur multiple times. Moreover, some steps could be combined or removed and additional steps could be added according to particular needs. In addition, while the method  600  is described with respect to the system  200  (which itself was described with respect to one or more industrial process control and automation systems), the method  600  may be used in conjunction with other types of devices and systems. 
       FIG. 7  illustrates an example device  700  supporting industrial process control and automation system operator evaluation and training according to this disclosure. The device  700  could, for example, denote various computing devices in the system  100  of  FIG. 1  or the nodes, servers, or computing devices in the system  200  of  FIG. 2 . The device  700  could be used to perform one or more operations of the method  600 . 
     As shown in  FIG. 7 , the device  700  includes at least one processor  702 , at least one storage device  704 , at least one communications unit  706 , and at least one input/output (I/O) unit  708 . Each processor  702  can execute instructions, such as those that may be loaded into a memory  710 . Each processor  702  denotes any suitable processing device, such as one or more microprocessors, microcontrollers, digital signal processors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or discrete circuitry. 
     The memory  710  and a persistent storage  712  are examples of storage devices  704 , which represent any structure(s) capable of storing and facilitating retrieval of information (such as data, program code, and/or other suitable information on a temporary or permanent basis). The memory  710  may represent a random access memory or any other suitable volatile or non-volatile storage device(s). The persistent storage  712  may contain one or more components or devices supporting longer-term storage of data, such as a read only memory, hard drive, Flash memory, or optical disc. 
     The communications unit  706  supports communications with other systems or devices. For example, the communications unit  706  could include a network interface card or a wireless transceiver facilitating communications over a wired or wireless network (such as the network  204 ). The communications unit  706  may support communications through any suitable physical or wireless communication link(s). 
     The I/O unit  708  allows for input and output of data. For example, the I/O unit  708  may provide a connection for user input through a keyboard, mouse, keypad, touchscreen, or other suitable input device. The I/O unit  708  may also send output to a display, printer, or other suitable output device. 
     Although  FIG. 7  illustrates one example of a device  700  supporting industrial process control and automation system operator evaluation and training, various changes may be made to  FIG. 7 . For example, various components in  FIG. 7  could be combined, further subdivided, or omitted and additional components could be added according to particular needs. Also, computing devices can come in a wide variety of configurations, and  FIG. 7  does not limit this disclosure to any particular configuration of computing device. 
     In some embodiments, various functions described in this patent document are implemented or supported by a computer program that is formed from computer readable program code and that is embodied in a computer readable medium. The phrase “computer readable program code” includes any type of computer code, including source code, object code, and executable code. The phrase “computer readable medium” includes any type of medium capable of being accessed by a computer, such as read only memory (ROM), random access memory (RAM), a hard disk drive, a compact disc (CD), a digital video disc (DVD), or any other type of memory. A “non-transitory” computer readable medium excludes wired, wireless, optical, or other communication links that transport transitory electrical or other signals. A non-transitory computer readable medium includes media where data can be permanently stored and media where data can be stored and later overwritten, such as a rewritable optical disc or an erasable storage device. 
     It may be advantageous to set forth definitions of certain words and phrases used throughout this patent document. The terms “application” and “program” refer to one or more computer programs, software components, sets of instructions, procedures, functions, objects, classes, instances, related data, or a portion thereof adapted for implementation in a suitable computer code (including source code, object code, or executable code). The term “communicate,” as well as derivatives thereof, encompasses both direct and indirect communication. The terms “include” and “comprise,” as well as derivatives thereof, mean inclusion without limitation. The term “or” is inclusive, meaning and/or. The phrase “associated with,” as well as derivatives thereof, may mean to include, be included within, interconnect with, contain, be contained within, connect to or with, couple to or with, be communicable with, cooperate with, interleave, juxtapose, be proximate to, be bound to or with, have, have a property of, have a relationship to or with, or the like. The phrase “at least one of,” when used with a list of items, means that different combinations of one or more of the listed items may be used, and only one item in the list may be needed. For example, “at least one of: A, B, and C” includes any of the following combinations: A, B, C, A and B, A and C, B and C, and A and B and C. 
     The description in the present application should not be read as implying that any particular element, step, or function is an essential or critical element that must be included in the claim scope. The scope of patented subject matter is defined only by the allowed claims. Moreover, none of the claims invokes 35 U.S.C. §112(f) with respect to any of the appended claims or claim elements unless the exact words “means for” or “step for” are explicitly used in the particular claim, followed by a participle phrase identifying a function. Use of terms such as (but not limited to) “mechanism,” “module,” “device,” “unit,” “component,” “element,” “member,” “apparatus,” “machine,” “system,” “processor,” or “controller” within a claim is understood and intended to refer to structures known to those skilled in the relevant art, as further modified or enhanced by the features of the claims themselves, and is not intended to invoke 35 U.S.C. §112(f). 
     While this disclosure has described certain embodiments and generally associated methods, alterations and permutations of these embodiments and methods will be apparent to those skilled in the art. Accordingly, the above description of example embodiments does not define or constrain this disclosure. Other changes, substitutions, and alterations are also possible without departing from the spirit and scope of this disclosure, as defined by the following claims.