Anshu Gupta Won the Best Poster Award at 3rd European Symposium on Computer Aided Process Engineering
Graduate Student Anshu Gupta was the winner of the Best Poster Award given at the 23rd European Symposium on Computer Aided Process Engineering held in Lappeenranta Finland, June 9-12, 2013. Anshu presented a poster entitled "Intelligent Alarm System applied to Continuous Pharmaceutical Manufacturing". His poster is based on the PhD research that he is carrying out under NSF ERC SOPS sponsorship and was selected from the 110 posters presented at the meeting.
Anshu Gupta joined the School of Chemical Engineering in Fall 2010 and is advised by Professor Reklaitis. His BS degree is from the Indian Institute of Technology, Madras. Mr. Gupta's research is multidisciplinary in nature, combining process systems engineering methodologies and pharmaceutical engineering-based models and sensing methods, and is being carried out in collaboration with researchers at three other universities. The objective is to develop a comprehensive framework that can detect and diagnose abnormal events and provide mitigation advice to the operator. The research addresses one of the important challenges in effective real time process management; i.e., the implementation of intelligent systems that can assist human operators in making supervisory control decisions, instead of simply sounding an alarm when process variables go out of range. Operator failures to exercise the appropriate mitigation actions often have an adverse effect on product quality, process safety, occupational health and environmental impact. The economic effect of such exceptional events is immense; an estimated $20billion/year losses in petrochemical industry have been reported. The challenges and opportunities for improvements are even larger in the pharmaceutical manufacturing domain because it involves particulate and granular systems whose processing tends to be more problematic than that of fluids.
IAS deals with fault detection, diagnosis (FDD) and mitigation of conditions that result from process anomalies. Early detection and diagnosis of process faults while the plant is still operating in a controllable region can help avoid abnormal event progression, production disruptions and productivity losses. An IAS framework has been developed using a combination of Wavelet Analysis, Principal Component Analysis (PCA), Signed Digraphs (SDG) and Qualitative Trend Analysis (QTA) and applied on pilot scale continuous tableting line. The framework was able to detect and diagnose several types of faults within a few seconds of their inception and provide mitigation advisories to the operator.