Data-driven optimization for process systems engineering applications

TL;DR: A review of existing heat, mass, and momentum transfer models for pyrolysis can be found in this paper , where the effects of transport properties on pyroolysis are dealt with.

TL;DR: In this article , an adversarially robust Bayesian optimization (ARBO) method is proposed for auto-tuning problems with significant time-invariant uncertainties in an expensive ''high fidelity'' system model used for closed-loop simulations.

TL;DR: Multi-fidelity data-driven design and analysis of reactor and tube simulations optimizes complex reactor geometries using Gaussian processes and a weighted acquisition function.

TL;DR: In this paper , Bayesian symbolic learning is applied to derive simple closed-form expressions from rigorous process simulations, streamlining the process modeling task and making process models more accessible to experimental groups.

TL;DR: In this article, the adaptive estimates of lower-order moments are used for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimate of lowerorder moments.

TL;DR: A method is described for the minimization of a function of n variables, which depends on the comparison of function values at the (n 41) vertices of a general simplex, followed by the replacement of the vertex with the highest value by another point.

TL;DR: A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving and a number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class.

TL;DR: This work describes new algorithms that take into account the variable cost of learning algorithm experiments and that can leverage the presence of multiple cores for parallel experimentation and shows that these proposed algorithms improve on previous automatic procedures and can reach or surpass human expert-level optimization for many algorithms.