438641 Multi-Scale Process Systems Engineering

Sunday, November 8, 2015
Exhibit Hall 1 (Salt Palace Convention Center)
Bruno A. Calfa, Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI

Multi-Scale Process Systems Engineering

In this poster session, I will discuss my Ph.D. work in the area of Process Systems Engineering (PSE) at Carnegie Mellon University (advisor: Dr. Ignacio E. Grossmann), the research topics and projects I am proposing to pursue, and my teaching interests and experience. For more information, please visit my website: http://bacalfa.com/.

Research

Figure 1. Multiple scales in Process Systems Engineering (PSE) research.

My Ph.D. research focused on the "large" scale top blocks in Figure 1. I integrated and efficiently solved planning and scheduling models for a network of batch plants, and developed data-driven approaches for modeling uncertainty in Enterprise-wide Optimization (EWO) problems [1-8]. I am interested in developing PSE methods (modeling, simulation, optimization, and control) to solve multi-scale problems of practical importance.

       Large Scale: novel data-driven models for uncertainty in sales and operations planning; multilevel optimization with contracts and pricing.

       Intermediate Scale: reduced-order modeling and optimization; analysis of sustainable technologies (e.g., solar fuels); material and energy integration through water and wastewater network optimization (e.g, water desalination, renewables).

       Small Scale: property prediction and computer-aided material design (e.g., crystals) via optimal inverse problems; microkinetics and optimal catalyst design.

I received the 2015 Ken Meyer Award for Excellence in Graduate Research, which is given by the Department of Chemical Engineering at CMU in which the faculty base their selection of the student on research quality, productivity, recognition, and impact.

Teaching

I had extensive teaching experience as a TA at CMU. I prepared several teaching materials, and gave guest lectures and tutorials. I am particularly interested in enhancing the students' exposure to computing, as well as helping them develop teamwork and communication skills. In addition to process design, I am also very interested in teaching introduction to chemical engineering, numerical methods, thermodynamics, and unit operations.

I received the 2012 Mark Dennis Karl Outstanding Graduate Teaching Award, which is given by the Department of Chemical Engineering at CMU to a student judged by the faculty to have done an outstanding job as a teaching assistant.

References

[1] B. A. Calfa, A. Agarwal, S. J. Bury, J. M. Wassick, and I. E. Grossmann. "Data-Driven Simulation and Optimization Approaches to Incorporate Production Variability in Sales and Operations Planning". In: Industrial & Engineering Chemistry Research. (2015). Just Accepted. DOI: 10.1021/acs.iecr.5b01273.

[2] B. A. Calfa and I. E. Grossmann. "Optimal Procurement Contract Selection with Price Optimization under Uncertainty for Process Networks". In: Computers & Chemical Engineering. (2015). Submitted.

[3] B. A. Calfa, I. E. Grossmann, A. Agarwal, S. J. Bury, and J. M. Wassick. "Data-Driven Individual and Joint Chance-Constrained Optimization via Kernel Smoothing". In: Computers & Chemical Engineering. 78.1 (2015), pp. 51–69.

[4] I. E. Grossmann, R. M. Apap, B. A. Calfa, P. García-Herreros, and Q. Zhang. "Recent Advances in Mathematical Programming Techniques for the Optimization of Process Systems under Uncertainty". In: 12th International Symposium on Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering. Ed. by Jakob K. Huusom Krist V. Gernaey and Rafiqul Gani. To appear in Proceedings. . 2015.

[5] B. A. Calfa. A Memory-Efficient Implementation of Multi-Period Two- and Multi-Stage Stochastic Programming Models. Carnegie Mellon University. Technical Report, 2014. URL: http://repository.cmu.edu/cheme/246/.

[6] B. A. Calfa, A. Agarwal, I. E. Grossmann, and J. M. Wassick. "Data-Driven Multi-Stage Scenario Tree Generation via Statistical Property and Distribution Matching". In: Computers & Chemical Engineering. 68.1 (2014), pp. 7–23.

[7] I. E. Grossmann, B. A. Calfa, and P. García-Herreros. "Evolution of Concepts and Models for Quantifying Resiliency and Flexibility of Chemical Processes". In: Computers & Chemical Engineering. 70 (2014), pp. 22–34.

[8] B. A. Calfa, A. Agarwal, I. E. Grossmann, and J. M. Wassick. "Hybrid Bilevel-Lagrangean Decomposition Scheme for the Integration of Planning and Scheduling of a Network of Batch Plants". In: Industrial & Engineering Chemistry Research. 52.5 (2013), pp. 2152–2167.


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