  This study presents a broad perspective of hybrid process modeling and
optimization combining the scientific knowledge and data analytics in
bioprocessing and chemical engineering with a science-guided machine learning
(SGML) approach. We divide the approach into two major categories. The first
refers to the case where a data-based ML model compliments and makes the
first-principle science-based model more accurate in prediction, and the second
corresponds to the case where scientific knowledge helps make the ML model more
scientifically consistent. We present a detailed review of scientific and
engineering literature relating to the hybrid SGML approach, and propose a
systematic classification of hybrid SGML models. For applying ML to improve
science-based models, we present expositions of the sub-categories of direct
serial and parallel hybrid modeling and their combinations, inverse modeling,
reduced-order modeling, quantifying uncertainty in the process and even
discovering governing equations of the process model. For applying scientific
principles to improve ML models, we discuss the sub-categories of
science-guided design, learning and refinement. For each sub-category, we
identify its requirements, advantages and limitations, together with their
published and potential areas of applications in bioprocessing and chemical
engineering.We also present several examples to illustrate different hybrid
SGML methodologies for modeling polymer processes.
