Dissertação
Investigação de abordagens evolutivas e otimização Bayesiana restrita no problema de otimização em tempo-real
Fecha
2021-12-16Autor
Victor São Paulo Ruela
Institución
Resumen
Real-time optimization (RTO) is a technique capable of iteratively leading an industrial
process (plant) to its optimal economic operation, using for this an approximate mathematical model combined with the solution of a non-linear optimization problem. In order
to deal with model-plant mismatch, different approaches are available in the literature,
among which the Modifier Adaptation (MA) stands out. It applies first-order corrections
to the cost and constraints functions in order to achieve plant optimality upon convergence.
However, the calculation of these corrections depends on plant gradient information, which
is difficult to obtain. Promising approaches to overcome this limitation are to perform
Gaussian Processes (GP) regression to model the mismatch and use Bayesian optimization
techniques. Aiming to initiate a further discussion of the numerical problems expected with
the application of these new approaches, the objective of this work is to study the effect of
the optimizer on the performance of randomly initialized RTO systems in the presence of
measurement noise. For this, we consider the MA with GPs adaptation (MA-GP) and a new
approach using constrained Bayesian optimization through the Expected Improvement with
Constraints (EIC) acquisition function. Based on the convergence to the plant optimum,
iteration feasibility and computational cost, the performance of deterministic nonlinear
optimization algorithms (Sequential Quadratic Programming (SQP) and Nelder-Mead
Simplex (NM)) and an evolutionary heuristic (Differential Evolution (DE)) are compared.
For two benchmark models available in the literature, it is illustrated that the SQP and
NM algorithms may fail to find the optimum during the RTO system iterations. As a
result, the system’s performance is degraded, presenting higher variability and sensitivity
to the initialization step. For a confidence interval of 95%, DE outperformed the other
algorithms, although it requires a higher computational effort. Furthermore, it is possible
to prove the proposed technique’s potential via constrained Bayesian optimization. By
allowing the use of the unrestricted NM algorithm, it becomes efficient when compared
to MA-GP with SQP, requiring at the same time a low computational cost. However, its
convergence is still uncertain and its performance is inferior to MA-GP, especially for
problems where the plant’s optimal operating point is at the intersection of its constraints.
Ítems relacionados
Mostrando ítems relacionados por Título, autor o materia.
-
Separação sólido-líquido em efluentes da suinocultura com uso de extratos tanantes modificados e aplicação de modelos de otimização multivariada
Radis Steinmetz, Ricardo Luís; Kunz, Airton Kunz; Ramme, Marco; Luiz Dressler, Valderi; Marlon de Moraes Flores, Érico -
REATIVAÇÃO E OTIMIZAÇÃO DO SISTEMA DE AUTOMAÇÃO DA LINHA DE CORTE E REINSPEÇÃO DA CSN
Lucas Vieira Prazeres