The objective of this task is to validate the methodology to minimize energy use in multiple, concurrent manufacturing operations using the CCAT testbeds. Initially incorporating two different manufacturing machines in a single manufacturing facility (the testbed), the methodology will be expandable to additional manufacturing operations and multiple facilities. The initial, nonoptimized baseline energy footprint of the testbed configuration will be measured and documented. The sensor network will be installed, and the fully instrumented testbed will be operated for optimal energy efficiency. Analyses of energy footprint, before and after implementation of the sensor network, will be performed and a technical report of the results will be delivered.

• All aspects of the testbed operation for this project will be defined: Specific manufacturing operations (machine, part, and material), ancillary components, data management and status monitoring. The test plan will define the timing and type of operations, data to be measured, and data analysis methods. The test plan, including data management, will be reviewed/revised with the project team. (subtask 6.1)
• Per the energy optimization requirements and preliminary results of the models, the selection of sensors will be finalized, installed and calibrated. The process monitoring interface, such as MTConnect, will be established for data logging and remote monitoring. (subtask 6.2)
• Testing will commence in standard, non-optimized mode to establish and document baseline operation. Using results of the models, testbed operating parameters will be modified in real time to optimize energy use. Data will be logged per the data management and communication protocols established in Task 5. (subtask 6.3)
• Testbed data will be sorted, presented in visualized format, and analyzed. Overall energy usage of the testbed will be analyzed for significant variables and trends. All data will be archived, and transferred in reportable formats to the SM Platform. (subtask 6.4)

 

Clean Energy Smart Manufacturing Innovation Institute

Investigators

 

George Bollas

george.bollas@uconn.edu

Dr. George Bollas is the Director of the Institute for Advanced Systems Engineering (IASE) at the University of Connecticut. He is an Associate Professor with the Department of Chemical and Biomolecular Engineering at the University of Connecticut, a process design expert and winner of the prestigious NSF CAREER Award and the ACS PRF DNI Award. He received B.E. and Ph.D. degrees from the Aristotle University of Thessaloniki in Greece and then worked as a postdoctoral research associate at the Chemical Engineering Department of MIT. At UConn, he is leading efforts to develop and explore novel system representations (steady state and dynamic models) of thermal fluid systems (TFS) in equation-oriented environments that allow system dynamic optimization, sensitivity and uncertainty analysis, fault detection and optimal control. Dr. Bollas is also the director of the Process Design Simulation and Optimization Laboratory (PDSOL). The lab pursues a balanced approach to experimentation guided by robust modeling and simulation of chemical processes, including experimental design, process scaling and control.

 

Kenneth Creasy

Senior Director of Supply Chain Technology & Strategy at Johnson & Johnson (J&J). He has had the additional
roles as Director of Research and Development for Codman Neuro, the neurosciences company within
Johnson & Johnson (J&J), Director of Quality for Codman and leading the Six Sigma and Design Excellence
efforts for the DePuySynthes franchise within Johnson & Johnson. Prior to his career at J&J, he formed and led
the Process Analytical Chemistry Center of Excellence at Honeywell, developing sensors and analytical
equipment for processes as diverse as commodity materials, high purity chemicals and pharmaceutical actives
and intermediates. He is a certified Six Sigma and Design for Six Sigma Master Black Belt, leading teams
through significant remediation efforts and organizational changes.

 

Shalabh Gupta

shalabh.gupta@engr.uconn.edu

Dr. Gupta’s research is focused on the science of autonomy with emphasis on two key areas: Data Analytics and Networked-Intelligent systems. Application examples include complex human-engineered systems such as a network of unmanned vehicles, medical robotics, distributed sensor networks, power grids, aircraft control systems, hybrid vehicles, etc. Some key research areas include machine perception, information fusion, distributed learning, adaptive decision & control in presence of uncertainties, cooperative tasking and adaptive navigation of unmanned vehicles, intelligent sensor networks for Intelligence, Surveillance & Reconnaissance (ISR) operations, and fault diagnosis & prognosis in networked-control systems. In essence, his research is centered around the essential characteristic of cyber-physical systems that links the domain of underlying system dynamics with the domain of information & control.

 

Krishna Pattipati

krishna.pattipati@uconn.edu

Dr. Krishna Pattipati received the B. Tech. degree in electrical engineering with highest honors from the Indian Institute of Technology, Kharagpur, in 1975, and the M.S. and Ph.D. degrees in systems engineering from UConn, Storrs, in 1977 and 1980, respectively. He was with ALPHATECH, Inc., Burlington, MA from 1980 to 1986. He has been with the department of Electrical and Computer Engineering at UConn, where he is currently the Board of Trustees Distinguished Professor and the Chair Professor in Systems Engineering. Dr. Pattipati’s research activities are in the areas of proactive decision support, uncertainty quantification, smart manufacturing, autonomy, knowledge representation, and optimization-based learning and inference. A common theme among these applications is that they are characterized by a great deal of uncertainty, complexity, and computational intractability. He is a cofounder of Qualtech Systems, Inc., a firm specializing in advanced integrated diagnostics software tools (TEAMS, TEAMS-RT, TEAMS-RDS, TEAMATE), and serves on the board of Aptima, Inc.

Dr. Pattipati was selected by the IEEE Systems, Man, and Cybernetics (SMC) Society as the Outstanding Young Engineer of 1984, and received the Centennial Key to the Future award. He has served as the Editor-in-Chief of the IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS–PART B from 1998 to 2001. He was co-recipient of the Andrew P. Sage Award for the Best SMC Transactions Paper for 1999, the Barry Carlton Award for the Best AES Transactions Paper for 2000, the 2002 and 2008 NASA Space Act Awards for “A Comprehensive Toolset for Model-based Health Monitoring and Diagnosis,” and “Real-time Update of Fault-Test Dependencies of Dynamic Systems: A Comprehensive Toolset for Model-Based Health Monitoring and Diagnostics”, and the 2003 AAUP Research Excellence Award at UCONN. He is an elected Fellow of IEEE and of the Connecticut Academy of Science and Engineering.