Batch Uncertainty Analysis

Batch Uncertainty Analysis is a systematic methodological framework within batch process systems that quantifies and propagates uncertainties arising from input variability, model parameters, and measurement errors across discrete batch operations to assess the reliability and robustness of process outcomes.

10

Publications

622

Citations

28

Authors

13

Institutions

Publications per year

2017–2026

1

Authors

28

  1. HG
    Harvey G. Stenger

    Lehigh University, United States

    2 publications · 44 citations

    2

    h-index

  2. CG
    Christos Georgakis

    Lehigh University, United States

    2 publications · 44 citations

    2

    h-index

  3. JF
    Jake Fotopoulos

    Lehigh University, United States

    2 publications · 44 citations

    2

    h-index

  4. SU
    Saadet Ulas

    University of Illinois Chicago, United States

    2 publications · 58 citations

    2

    h-index

  5. UM
    Urmila M. Diwekar

    Vishwamitra Research Institute, United States

    2 publications · 58 citations

    2

    h-index

1–5 of 28

Venues

  1. Computers & Chemical Engineering

    4 publications · 107 citations

    4

    h-index

  2. Information Sciences

    1 publications · 43 citations

    1

    h-index

  3. Applied Soft Computing

    1 publications · 67 citations

    1

    h-index

  4. 1

    h-index

  5. Chemical Engineering Science

    1 publications · 29 citations

    1

    h-index

1–5