Statistical Process Optimization

Statistical Process Optimization is a systematic methodology that employs statistical models and experimental design principles to identify optimal operating conditions and enhance performance in bioprocesses by quantitatively analyzing the relationships between input variables and critical quality or productivity outcomes.

1K

Publications

43.2K

Citations

3.4K

Authors

1.1K

Institutions

Publications per year

2017–2026

384

Authors

3.4K

  1. NE
    Noura El‐Ahmady El‐Naggar

    City of Scientific Research and Technological Applications, Egypt

    9 publications · 504 citations

    9

    h-index

  2. SM
    Seied Mahdi Pourmortazavi

    Malek Ashtar University of Technology, Iran

    8 publications · 270 citations

    8

    h-index

  3. IE
    Ignacio E. Grossmann

    Carnegie Mellon University, United States

    7 publications · 557 citations

    7

    h-index

  4. JA
    Jiju Antony

    Heriot-Watt University, United Kingdom

    7 publications · 222 citations

    7

    h-index

  5. AH
    Ali H. Jawad

    Universiti Teknologi MARA, Malaysia

    7 publications · 355 citations

    6

    h-index

1–5 of 3.4K

Venues

  1. Bioresource Technology

    23 publications · 1.6K citations

    22

    h-index

  2. Computers & Chemical Engineering

    30 publications · 2.4K citations

    22

    h-index

  3. Journal of Quality Technology

    16 publications · 1K citations

    14

    h-index

  4. Technometrics

    19 publications · 1.2K citations

    14

    h-index

  5. Process Safety and Environmental Protection

    13 publications · 657 citations

    13

    h-index

1–5