On improving the efficiency of modifier adaptation via directional information

• Tipo: Artigo em Revista Científica Internacional
• Data: 2022

Autores

Rodrigues, D (Autor) Outra; Marchetti, AG (Autor) Outra; Bonvin, D (Autor) Outra

Revista

Título: Computers and Chemical Engineering

Vol. 164

ISSN: 0098-1354

Editora: Elsevier

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00W-STN

DOI: 10.1016/j.compchemeng.2022.107867

Abstract (EN): In real-time optimization, the solution quality depends on the model ability to predict the plant KarushKuhn-Tucker (KKT) conditions. In the case of non-parametric plant-model mismatch, one can add input-affine modifiers to the model cost and constraints as is done in modifier adaptation (MA). These modifiers require estimating the plant cost and constraint gradients. This paper discusses two ways of reducing the number of input directions, thereby improving the efficiency of MA in practice. The first approach capitalizes on the knowledge of the active set to reduce the number of KKT conditions. The second approach determines the dominant gradients using sensitivity analysis. This way, MA reaches near plant optimality efficiently by adapting the first-order modifiers only along the dominant input directions. These approaches allow generating several alternative MA schemes, which are analyzed in terms of the number of degrees of freedom and compared in a simulated study of the Williams-Otto plant.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 16