Hybrid Real-time Optimization

Hybrid Real-Time Optimization is a real-time optimization paradigm that integrates model-based and data-driven approaches within a unified framework to dynamically adjust system operations by leveraging the strengths of both mechanistic models and empirical data for improved accuracy and adaptability under time-varying conditions.

30

Publications

1.2K

Citations

118

Authors

62

Institutions

Publications per year

2017–2026

18

Authors

118

  1. AF
    Antonio Ferramosca

    Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina

    2 publications · 92 citations

    2

    h-index

  2. AH
    Alejandro H. González

    Instituto de Desarrollo Tecnológico para la Industria Química, Argentina

    2 publications · 92 citations

    2

    h-index

  3. RA
    Raymond A. DeCarlo

    Purdue University West Lafayette, United States

    1 publications · 34 citations

    1

    h-index

  4. FT
    Fangcheng Tang

    Beijing University of Chemical Technology, China

    1 publications · 51 citations

    1

    h-index

  5. ZM
    Zhizhong Mao

    Northeastern University, China

    1 publications · 43 citations

    1

    h-index

1–5 of 118

Venues

  1. Industrial & Engineering Chemistry Research

    2 publications · 46 citations

    2

    h-index

  2. Journal of Process Control

    2 publications · 208 citations

    2

    h-index

  3. AIChE Journal

    1 publications · 36 citations

    1

    h-index

  4. Applied Energy

    1 publications · 22 citations

    1

    h-index

  5. Knowledge-Based Systems

    1 publications · 23 citations

    1

    h-index

1–5