(8a) Multi-Scale Process Systems Engineering

Authors: 
Calfa, B. A., University of Wisconsin-Madison
Multi-Scale Process Systems Engineering

Bruno A. Calfa

bruno.calfa@wisc.edu

In this poster session, I will discuss my Ph.D. work in the area of Process Systems Engineering (PSE) at Carnegie Mellon University, my Postdoc work at UW-Madison, 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 Interests:

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-drivenapproaches for modeling uncertainty in Enterprise-wide Optimization (EWO) problems [4-11]. 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; conceptual/operational analysis of sustainable technologies, e.g., solar fuels [1].
  • Small Scale: property prediction [3] and computer-aided materials design (e.g., crystals) via optimal inverse problems; microkinetics and optimal catalyst design.

I am the lead solver of a winning solution-a systematic crystal design framework-to the Materials Science and Engineering Data Challenge put forward by the Air Force Research Lab. 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 Interests:

I had extensive teaching experience as a TA at CMU. I prepared several teaching materials, and gave guest lectures and tutorials. 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.

At UW-Madison, I have developed case studies that are incorporated into a Chemical Business Simulator (http://uwchembussim.che.wisc.edu/), which is an education tool that integrates engineering and business best practices [2]. I have also volunteered as a tutor at the Undergraduate Learning Center for freshman and sophomore Chemical Engineering courses.

References

[1] B. A. Calfa and C. T. Maravelias. Conceptual Analysis of Process Alternatives for Solar Thermochemical Methanol Production: The Role of Chemical Storage. In Preparation.

[2] B. A. Calfa and W. F. Banholzer. Web-Based Simulation Games for the Integration of Engineering and Business Best Practices. In Preparation.

[3] B. A. Calfa and J. R. Kitchin. Property Prediction of Crystalline Solids from Composition and Crystal Structure. In: AIChE Journal. (2016). DOI: http://dx.doi.org/10.1002/aic.15251.

[4] I. E. Grossmann, R. M. Apap, B. A. Calfa, P. Garcia-Herreros, and Q. Zhang. Recent Advances in Mathematical Programming Techniques for the Optimization of Process Systems under Uncertainty. In: Computers & Chemical Engineering. (2016). DOI: http://dx.doi.org/10.1016/j.compchemeng.2016.03.002.

[5] 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. 54.29 (2015), pp. 7261-7272.

[6] B. A. Calfa and I. E. Grossmann. Optimal Procurement Contract Selection with Price Optimization under Uncertainty for Process Networks. In: Computers & Chemical Engineering. 82.1 (2015), pp. 330-343.

[7] 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.

[8] 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/.

[9] 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.

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

[11] 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.

[12] B. A. Calfa and M. L. Torem. Bioreagents: their use in the removal of heavy metals from liquid streams by biosorption/bioflotation. (in Portuguese). In: Revista Escola de Minas. 60.3 (2007), pp. 537-542.

[13] B. A. Calfa and M. L. Torem. On the Fundamentals of Cr(III) Removal from Liquid Streams by a Bacterial Strain. In: Minerals Engineering. 21.1 (2007), pp. 48-54.

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