Data-driven Process Systems Engineering Lab
Relevant Publications
Data-driven Spatial Branch-and-bound Algorithm for Box-constrained Simulation-based Optimization
Journal of Global Optimization 2021
The ability to use complex computer simulations in quantitative analysis and decision-making is highly desired in science and engineering, at the same rate as computation capabilities and first-principle knowledge advance. Due to the complexity of simulation models, direct embedding of…
Managing Uncertainty in Data-Driven Simulation-Based Optimization
Computers & Chemical Engineering 2019
Optimization using data from complex simulations has become an attractive decision-making option, due to ability to embed high-fidelity, non-linear understanding of processes within the search for optimal values. Due to lack of tractable algebraic equations, the link between simulations and…
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…
Optimization of black-box problems using Smolyak grids and polynomial approximations
Journal of Global Optimization 2018
A surrogate-based optimization method is presented, which aims to locate the global optimum of box-constrained problems using input–output data. The method starts with a global search of the n-dimensional space, using a Smolyak (Sparse) grid which is constructed using Chebyshev extrema…
Global optimization of grey-box computational systems using surrogate functions and application to highly constrained oil-field operations
Computers & Chemical Engineering 2018
This work presents recent advances within the AlgoRithms for Global Optimization of coNstrAined grey-box compUTational problems (ARGONAUT) framework, developed for optimization of systems which lack analytical forms and derivatives. A new parallel version of ARGONAUT (p-ARGONAUT) is introduced to solve…
Optimal design of energy systems using constrained grey-box multi-objective optimization
Computers & Chemical Engineering 2018
The (global) optimization of energy systems, commonly characterized by high-fidelity and large-scale complex models, poses a formidable challenge partially due to the high noise and/or computational expense associated with the calculation of derivatives. This complexity is further amplified in the…
Teaching data-analytics through process design
Computer Aided Chemical Engineering 2018
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…
Dimensionality reduction for production optimization using polynomial approximations
Computational Geosciences 2017
The objective of this paper is to introduce a novel paradigm to reduce the computational effort in waterflooding global optimization problems while realizing smooth well control trajectories amenable for practical deployments in the field. In order to overcome the problems…
ARGONAUT: AlgoRithms for Global Optimization of coNstrAined grey-box compUTational problems
Optimization Letters 2016
The algorithmic framework ARGONAUT is presented for the global optimization of general constrained grey-box problems. ARGONAUT incorporates variable selection, bounds tightening and constrained sampling techniques, in order to develop accurate surrogate representations of unknown equations, which are globally optimized. ARGONAUT…
Global Optimization Advances in Mixed-Integer Nonlinear Programming, MINLP, and Constrained Derivative-Free Optimization, CDFO
European Journal of Operational Research 2015
This manuscript reviews recent advances in deterministic global optimization for Mixed-Integer Nonlinear Programming (MINLP), as well as Constrained Derivative-Free Optimization (CDFO). This work provides a comprehensive and detailed literature review in terms of significant theoretical contributions, algorithmic developments, software implementations…
Relevant Presentations
Uncertainty Considerations for Surrogate Functions for Constrained Grey-Box Optimization
AIChE Annual Meeting 2016 in San Francisco
November 10, 2016
Data-Centric Optimization: Methods and Applications
2015 AIChE Annual Meeting in Exhibit Hall 1 (Salt Palace Convention Center)
November 08, 2015
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
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…
Global Optimization of Constrained Grey-Box Computational Problems
World Congress on Global Optimization 2015 in Gainesville, Florida
February 24, 2015
Grey-box global optimization refers to problems for which derivatives of the objective function and/or constraints of the original model are not directly employed for obtaining the global optimum. In typical applications of grey-box optimization, derivative information is either: (1) available…
Argonaut: Algorithms for Global Optimization of Constrained Grey-Box Computational Problems
2014 AIChE Annual Meeting in Atlanta
November 19, 2014
Constrained grey-box optimization methods do not employ derivative information of the objective function and/or constraints of the original model for obtaining the global optimum. In typical applications of the above methods, derivative information may be available but deceptive or prohibitively…
Optimization of CO2 Capture, Utilization and Sequestration (CCUS) Supply Chain Networks
2013 AIChE Annual Meeting in San Fransisco
November 08, 2013
CO2 capture, utilization and storage (CCUS) is an enabling technology toward reducing CO2 emissions from stationary sources which include power plants, cement production plants, iron and steel plants, refineries, petrochemicals and gas processing plants. More than 60% of the total…
Global Optimization of Grey-Box Constrained Models
2013 AIChE Annual Meeting in San Fransisco
November 05, 2013
Grey-box optimization methods have a wide range of applicability in various fields which rely on expensive simulations or solely input-output data [1]. Consequently, there has been great interest in the development of methods for the optimization of models for which…
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