Recent trends on hybrid modeling for Industry 4.0
The chemical processing industry has relied on modeling techniques for process monitoring,
control, diagnosis, optimization, and design, especially since the third industrial revolution …
control, diagnosis, optimization, and design, especially since the third industrial revolution …
A critical review on intelligent optimization algorithms and surrogate models for conventional and unconventional reservoir production optimization
Aiming to find the most suitable development schemes of conventional and unconventional
reservoirs for maximum energy supply or economic benefits, reservoir production …
reservoirs for maximum energy supply or economic benefits, reservoir production …
ReLU networks as surrogate models in mixed-integer linear programs
B Grimstad, H Andersson - Computers & Chemical Engineering, 2019 - Elsevier
We consider the embedding of piecewise-linear deep neural networks (ReLU networks) as
surrogate models in mixed-integer linear programming (MILP) problems. A MILP formulation …
surrogate models in mixed-integer linear programming (MILP) problems. A MILP formulation …
Sustainable ammonia production through process synthesis and global optimization
CD Demirhan, WW Tso, JB Powell… - AIChE …, 2019 - Wiley Online Library
Current ammonia production technologies have a significant carbon footprint. In this study,
we present a process synthesis and global optimization framework to discover the efficient …
we present a process synthesis and global optimization framework to discover the efficient …
Thinking inside the box: A tutorial on grey-box Bayesian optimization
R Astudillo, PI Frazier - 2021 Winter Simulation Conference …, 2021 - ieeexplore.ieee.org
Bayesian optimization (BO) is a framework for global optimization of expensive-to-evaluate
objective functions. Classical BO methods assume that the objective function is a black box …
objective functions. Classical BO methods assume that the objective function is a black box …
Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques
SH Kim, F Boukouvala - Optimization Letters, 2020 - Springer
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 …
attracted significant attention in the recent literature. A very efficient approach for optimizing …
Data-driven optimization for process systems engineering applications
D Van De Berg, T Savage, P Petsagkourakis… - Chemical Engineering …, 2022 - Elsevier
Most optimization problems in engineering can be formulated as 'expensive'black box
problems whose solutions are limited by the number of function evaluations. Frequently …
problems whose solutions are limited by the number of function evaluations. Frequently …
A nonlinear support vector machine‐based feature selection approach for fault detection and diagnosis: Application to the Tennessee Eastman process
In this article, we present (1) a feature selection algorithm based on nonlinear support vector
machine (SVM) for fault detection and diagnosis in continuous processes and (2) results for …
machine (SVM) for fault detection and diagnosis in continuous processes and (2) results for …
Bayesian optimization with reference models: A case study in MPC for HVAC central plants
We present a framework for exploiting reference models in Bayesian optimization (BO). Our
approach is motivated by a model predictive control (MPC) tuning application for central …
approach is motivated by a model predictive control (MPC) tuning application for central …
Surrogate-assisted evolutionary algorithm with dimensionality reduction method for water flooding production optimization
The objective of oil reservoir production optimization is finding optimal scheme of each well
to maximize the net present value (NPV) or the hydrocarbon production. Various …
to maximize the net present value (NPV) or the hydrocarbon production. Various …