Dr. James Henry awarded $141K for industry research
November 20, 2020 | by Shelly Vitanza
The Center for Operator Performance has awarded Dr. James Henry, assistant professor of chemical engineering, a total of $141,027 for research related to the workload of machine operators.
COP, a consortium of U.S. industry and academia representatives that focuses on human factors engineering awarded Henry $57,404 for his proposal titled, “Effect of Action Demand Load on Operator
Response Time and Accuracy.” The research will identify the optimal amount of work load for machine operators, so that they neither become bored and less responsive or overwhelmed.
“There is a lot of focus on the negative effects at the high-end of the work load,” Henry said. “There is less evidence about the effects of having too little work to keep our minds occupied and on task.”
Henry received an additional $83,623 of funding for his proposal, “The Effect of Advanced Process Control on the Degradation of Process Knowledge.” The research will seek to determine how much operator knowledge and skills are lost when companies implement the Advanced Process Control, an automated process machine. Henry hopes to discern how much skill atrophy occurs when automated systems are used and to establish best practices to help operators keep their skillset intact.
For both studies, Henry intends to use student volunteers who will use simulation equipment to approximate the work environment of process operators.
“Using the simulations, we can teach the students many of the skills that operators use and then see how quick and accurate their response time is in different scenarios,” said Henry. “We should also be able to see how well students recall those skills when they don’t use them for a period of time.”
Henry has an industry background in alarm management and hazard analysis.
“This is a great example of how industry can drive research by letting us know where they need answers and knowledge,” said Henry. “And the location of LU makes us very attractive to industry for this type of collaboration.”
Advanced Process Control has become one of the most powerful industry tools for improving process profitability and safety. Knowing that APC will go offline periodically it is important that operators have the ability to react to process changes by utilizing process knowledge and understanding in a timely manner. In this phase 1 project, we will utilize student test groups on simulations and industrial alarm and operator action data (if possible and available) in an attempt to understand to what extent and how quickly a degradation of process knowledge occurs when not utilized regularly. This study hopes to show a time dependence of loss of process knowledge and to determine if historical alarm and action data can be used to validate this study.
In addition, operator performance is one of the most important factors associated with effective operation of a facility. However, it is also the most difficult factor to quantify. In this project, we will utilize a unique display design and random action generation to effectively isolate the effects of action load from other variables (particularly pattern recognition and experiential learning) to develop and algorithm for scoring operator performance. This scoring system will allow for the effective evaluation of operator loading concerns and be utilized in future research to effectively quantify and decouple the effects of multiple DCS and process variables.
COP, a consortium of U.S. industry and academia representatives that focuses on human factors engineering awarded Henry $57,404 for his proposal titled, “Effect of Action Demand Load on Operator
Response Time and Accuracy.” The research will identify the optimal amount of work load for machine operators, so that they neither become bored and less responsive or overwhelmed. “There is a lot of focus on the negative effects at the high-end of the work load,” Henry said. “There is less evidence about the effects of having too little work to keep our minds occupied and on task.”
Henry received an additional $83,623 of funding for his proposal, “The Effect of Advanced Process Control on the Degradation of Process Knowledge.” The research will seek to determine how much operator knowledge and skills are lost when companies implement the Advanced Process Control, an automated process machine. Henry hopes to discern how much skill atrophy occurs when automated systems are used and to establish best practices to help operators keep their skillset intact.
For both studies, Henry intends to use student volunteers who will use simulation equipment to approximate the work environment of process operators.
“Using the simulations, we can teach the students many of the skills that operators use and then see how quick and accurate their response time is in different scenarios,” said Henry. “We should also be able to see how well students recall those skills when they don’t use them for a period of time.”
Henry has an industry background in alarm management and hazard analysis.
“This is a great example of how industry can drive research by letting us know where they need answers and knowledge,” said Henry. “And the location of LU makes us very attractive to industry for this type of collaboration.”
Advanced Process Control has become one of the most powerful industry tools for improving process profitability and safety. Knowing that APC will go offline periodically it is important that operators have the ability to react to process changes by utilizing process knowledge and understanding in a timely manner. In this phase 1 project, we will utilize student test groups on simulations and industrial alarm and operator action data (if possible and available) in an attempt to understand to what extent and how quickly a degradation of process knowledge occurs when not utilized regularly. This study hopes to show a time dependence of loss of process knowledge and to determine if historical alarm and action data can be used to validate this study.
In addition, operator performance is one of the most important factors associated with effective operation of a facility. However, it is also the most difficult factor to quantify. In this project, we will utilize a unique display design and random action generation to effectively isolate the effects of action load from other variables (particularly pattern recognition and experiential learning) to develop and algorithm for scoring operator performance. This scoring system will allow for the effective evaluation of operator loading concerns and be utilized in future research to effectively quantify and decouple the effects of multiple DCS and process variables.