(333b) Iot-Enabled Cybermanufacturing: Challenges and Possibilities
AIChE Annual Meeting
2016
2016 AIChE Annual Meeting
Computing and Systems Technology Division
Big Data Analytics in Chemical Engineering
Tuesday, November 15, 2016 - 12:45pm to 1:00pm
One potential enabler for cybermanufacturing is the Internet of Things (IoT), and there is a general consensus that factories and plants connected to the Internet are more efficient, productive and smarter than their non-connected counterparts[2], [3].
Industrial IoT devices are sensors/actuators, computers with wireless networks, and, most importantly, systems that are small and easy to embed. IoT devices offer the opportunity to instrument systems with massive numbers of sensors. With the huge amount of data and the programmability of IoT devices, comes the opportunity to shape the data received, to address local redundancy of information, and to improve both the accuracy and precision of measurements locally and across a distributed parameter system such as a reactor. However, many challenges remain in the areas of hardware such as sensor/actuator/network reliability, sensor accuracy; software such as protocols, data management, cyber security; and data analytics such as how to address the 4Vâ??s of Big Data[4].
In this work, we test IoT temperature sensors on a batch system with the goal of better understanding and control of the system. Complete description of the batch system setup, including IoT hardware and software, will be given. Results obtained based on the batch system, challenges encountered and ways to address them, and potential application of IoT devices in chemical processes will be discussed.
References:
[1] S. C. of the A. M. P. 2. 0. (AMP2.0), â??Report to the president: accelerating U.S. advanced manufacturing.â? 2014.
[2] P. C. Evans and M. Annunziata, â??Industrial internet: Pushing the boundaries of minds and machines,â? Gen. Electr., 2012.
[3] F. Shrouf, J. Ordieres, and G. Miragliotta, â??Smart factories in Industry 4.0: A review of the concept and of energy management approached in production based on the Internet of Things paradigm,â? in Industrial Engineering and Engineering Management (IEEM), 2014 IEEE International Conference on, 2014, pp. 697â??701.
[4] S. J. Qin, â??Process data analytics in the era of big data,â? AIChE J., vol. 60, pp. 3092â??3100, 2014.