Relevant Publications

Perspectives on the Integration between First-Principles and Data-Driven Modeling

Computers & Chemical Engineering 2022

Efficiently embedding and/or integrating mechanistic information with data-driven models is essential if it is desired to simultaneously take advantage of both engineering principles and data-science. The opportunity for hybridization occurs in many scenarios, such as the development of a faster…

Two-Stage Approach to Parameter Estimation of Differential Equations Using Neural ODEs

Industrial & Engineering Chemistry Research 2021

Modeling physiochemical relationships using dynamic data is a common task in fields throughout science and engineering. A common step in developing generalizable, mechanistic models is to fit unmeasured parameters to measured data. However, fitting differential equation-based models can be computation-intensive…

AC-Optimal Power Flow Solutions with Security Constraints from Deep Neural Network Models

Computer Aided Chemical Engineering 2021

In power grid operation, optimal power flow (OPF) problems are solved several times per day to find economically optimal generator setpoints that balance given load demands. Ideally, we seek an optimal solution that is also “N-1 secure”, meaning the system can…

Surrogate-Based Optimization for Mixed-Integer Nonlinear Problems

Computers & Chemical Engineering 2020

Sun Hye Kim Fani Boukouvala

Simulation-based optimization using surrogate models enables decision-making through the exchange of data from high-fidelity models and development of approximations. Many chemical engineering optimization problems, such as process design and synthesis, rely on simulations and contain both discrete and continuous decision…

Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques

Optimization Letters 2019

Optimization of simulation-based or data-driven systems is a challenging task, which has attracted significant attention in the recent literature. A very efficient approach for optimizing systems without analytical expressions is through fitting surrogate models. Due to their increased flexibility, nonlinear…

Nonlinear Variable Selection Algorithms for Surrogate Modeling

AIChE Journal 2019

Jianyuan Zhai Fani Boukouvala

Having the ability to analyze, simulate and optimize complex systems is becoming more important in all engineering disciplines. Decision-making using complex systems usually leads to nonlinear optimization problems, which rely on computationally expensive simulations. Therefore, it is often challenging to…

Teaching data-analytics through process design

Computer Aided Chemical Engineering 2018

F. Boukouvala J. Zhai SH Kim

Data-analytics is becoming a very influential tool for decision-making today both in industry and academia. The incorporation of data-driven concepts in the core chemical engineering curriculum would be very beneficial to our graduates, making them competitive in today’s market. The…

Surrogate-based optimization of expensive flowsheet models for continuous pharmaceutical manufacturing

Journal of Pharmaceutical Information 2013

F. Boukouvala F.J. Muzzio M.G. Ierapetritou

Simulation-based optimization is a research area that is currently attracting a lot of attention in many industrial applications, where expensive simulators are used to approximate, design, and optimize real systems. Pharmaceuticals are typical examples of high-cost products which involve expensive…

Reduced order Discrete Element Method (DEM) modeling

Chemical Engineering Science 2013

F. Boukouvala F.J. Muzzio M.G. Ierapetritou Y. Gao

Reduced-order modeling (ROM) techniques are playing a very significant role in the recent literature bridging the gap between computationally expensive simulators and their application in optimization and control of distributed parameter systems. In modeling of solid-based processes, Discrete Element Method…

Improving continuous powder blending performance using Projection to Latent Structures regression

Journal of Pharmaceutical Innovation 2013

Y. Gao F. Boukouvala W. Englisch W. Meng F.J. Muzzio M.G. Ierapetritou

Purpose There has been increasing interest in the last few years especially within the pharmaceutical industry towards continuous powder blending. In this paper, the effects of different design and operating parameters are investigated, which include blade speed, shaft angle, weir…

Relevant Presentations

Data-Centric Optimization: Methods and Applications

2015 AIChE Annual Meeting in Exhibit Hall 1 (Salt Palace Convention Center)

November 08, 2015

F. Boukouvala

As an engineering community we have been working towards developing detailed mathematical simulation models which accurately capture the behavior of complex systems. This trend is reinforced by our drive to couple multiscale information which ranges from the atomistic and molecular…

Constrained Grey-Box Global Optimization of High Dimensional Problems

2015 AIChE Annual Conference in Salon F (Salt Lake Marriott Downtown at City Creek)

November 11, 2015 at 12.55

C.A. Floudas F. Boukouvala

The use of deterministic global optimization methods based on analytical C2 functions is prohibitive in a large portion of engineering applications which have one or multiple of the following characteristics: (a) high computational cost which implies that the calculation of derivatives…

Mathematical Modeling and Global Optimization for Entire Petrochemical Planning Operations

2014 AIChE Annual Meeting in Atlanta

November 20, 2014

J. Lie F. Boukouvala X. Xiao C.A. Floudas

In the last twenty years, the petrochemical industry has succeeded by creating markets and supplying them with suitable products used to create goods such as plastics, cosmetics, lubricants, and paints. Petrochemical production begins in a refinery that separates crude oils…

From Experimental Data to Building Integrated Dynamic Flowsheet Models for Pharmaceutical Processes

2012 AIChE Annual Conference in Pittsburgh

October 29, 2012

F. Boukouvala F.J. Muzzio M.G. Ierapetritou

FDA harmonization guidelines [1] aim to promote building quality into design of pharmaceutical processes through mechanistic understanding of powder process behavior. Based on this paradigm, the industry is moving with a fast pace towards a model-based process and product design,…

Multivariate Analysis and Reduced Order Modeling Based On Discrete Element Method (DEM) Simulations for a Powder Blender

AIChE Annual Meeting in Minneapolis

October 20, 2011

Y. Gao F. Boukouvala F.J. Muzzio M.G. Ierapetritou

In this work, a set of Discrete Element Method (DEM) simulations are performed based on a computer experimental design for the qualitative and quantitative characterization of a periodic section of a powder blender. Mixing of powders is a crucial operation…

Application of Kriging for dynamic data- driven modeling of pharmaceutical processes

AIChE Annual Meeting in Salt Lake City

November 11, 2010

F. Boukouvala F.J. Muzzio M.G. Ierapetritou

In emergent technologies it is very common that a functional form for the input-output relationships is unavailable and the process behavior is symbolically described using black-box models. This is the case for many pharmaceutical process operations, for which first-principles models…

Design Space of pharmaceutical processes using data-driven based methods

AIChE Annual Meeting in Salt Lake City

November 08, 2010

F. Boukouvala F.J. Muzzio M.G. Ierapetritou

The identification and graphical representation of process design space is very critical in locating not only feasible but also optimum operating variable ranges and design configurations. Design space of a process is the area of the parametric space within which…

Predictive modeling of pharmaceutical processes with missing and noisy data

2009 AIChE Annual Meeting in Nashville

November 12, 2009

F. Boukouvala F.J. Muzzio M.G. Ierapetritou

Current trends in pharmaceutical product development focus on the fundamental understanding and characterization of all process unit operations, which will lead to the development of predictive models in order to minimize the variability in product performance. The pharmaceutical industry is…