Abstract:

Teaching long-term biological experiments in an undergraduate biochemical engineering laboratory setting is difficult due to the time constraints of a traditional lab class format. Long-term bioprocess operations experiments, such as continuous culture and fed-batch experiments are especially difficult to implement and teach when students’ lab time is restricted to one or two lab sessions a week. These constraints lead to students missing out on retrieving and reacting to important data to make changes to the experiment in real time. In this work, we introduce real-time data analytics software that enables instantaneous data retrieval, analysis, and acquisition simultaneously from multiple experiments. Data compiled from different process and analytical platforms such as bioreactors, biochemical analyzers, and mass spectrometers facilitate daily access to long-term experiments without being physically present in the lab. Students have access not only to their own current and previous data but to their fellow classmates’ data allowing for more informed decision-making and collaborative efforts when reacting to their experiment. The real-time data historian system facilitates student learning by allowing them to be actively engaged in the entirety of the experimental process from the design of the experiment through the implementation and subsequent data retrieval and response. Specifically, this system gives the students the tools to react to the data received in real time and make immediate changes to parameters along with the capability to recognize and troubleshoot experimental problems that arise. This troubleshooting experience further prepares students for industrial settings which often utilize data historians to monitor processes daily and troubleshoot issues as they arise. The results from this work indicate enhanced student engagement in the entire experimental process, more comprehensive experimental troubleshooting, and increased understanding of industrial workflows.

Keywords:

Real-time, data analysis, learning.