Advances and applications of chance-constrained approaches to systems optimisation under uncertainty
A Geletu, M Klöppel, H Zhang, P Li - International Journal of …, 2013 - Taylor & Francis
A chance-constrained optimisation (CCOPT) model has a dual goal: guaranteeing
performance as well as system reliability under uncertainty. The beginning of CCOPT …
performance as well as system reliability under uncertainty. The beginning of CCOPT …
Risk-sensitive reinforcement learning applied to control under constraints
P Geibel, F Wysotzki - Journal of Artificial Intelligence Research, 2005 - jair.org
In this paper, we consider Markov Decision Processes (MDPs) with error states. Error states
are those states entering which is undesirable or dangerous. We define the risk with respect …
are those states entering which is undesirable or dangerous. We define the risk with respect …
Chance constrained programming approach to process optimization under uncertainty
Deterministic optimization approaches have been well developed and widely used in the
process industry to accomplish off-line and on-line process optimization. The challenging …
process industry to accomplish off-line and on-line process optimization. The challenging …
Nonlinear chance-constrained process optimization under uncertainty
M Wendt, P Li, G Wozny - Industrial & engineering chemistry …, 2002 - ACS Publications
Optimization under uncertainty is considered necessary for robust process design and
operation. In this work, a new approach is proposed to solve a kind of nonlinear optimization …
operation. In this work, a new approach is proposed to solve a kind of nonlinear optimization …
An inner-outer approximation approach to chance constrained optimization
A Geletu, A Hoffmann, M Kloppel, P Li - SIAM Journal on Optimization, 2017 - SIAM
Nonlinear chance constrained optimization (CCOPT) problems are known to be difficult to
solve. This work proposes a smooth approximation approach consisting of an inner and an …
solve. This work proposes a smooth approximation approach consisting of an inner and an …
Chance constrained optimization of process systems under uncertainty: I. Strict monotonicity
H Arellano-Garcia, G Wozny - Computers & Chemical Engineering, 2009 - Elsevier
An approach for chance constrained programming of large-scale nonlinear dynamic
systems is presented. The stochastic property of the uncertainties is explicitly considered in …
systems is presented. The stochastic property of the uncertainties is explicitly considered in …
A tractable approximation of non-convex chance constrained optimization with non-Gaussian uncertainties
A Geletu, M Klöppel, A Hoffmann, P Li - Engineering Optimization, 2015 - Taylor & Francis
Chance constrained optimization problems in engineering applications possess highly
nonlinear process models and non-convex structures. As a result, solving a nonlinear non …
nonlinear process models and non-convex structures. As a result, solving a nonlinear non …
Optimization of refinery hydrogen network based on chance constrained programming
Deterministic optimization approaches have been developed and used in the optimization of
hydrogen network in refinery. However, uncertainties may have a large impact on the …
hydrogen network in refinery. However, uncertainties may have a large impact on the …
A quasi‐sequential approach to large‐scale dynamic optimization problems
A novel sequential approach for solving dynamic optimization problems containing path
constraints on state variables is presented and its performance analyzed. As in the …
constraints on state variables is presented and its performance analyzed. As in the …
Uncertainty in chemical process systems engineering: a critical review
Uncertainty or error occurs as a result of a lack or misuse of knowledge about specific topics
or situations. In this review, we recall the differences between error and uncertainty briefly …
or situations. In this review, we recall the differences between error and uncertainty briefly …