인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
ABSTRACT Recent hardware developments within industrial digitalization make more evident the need of high‐fidelity models to exploit process system engineering potential in biomanufacturing. Hybrid models, which combine first‐principles (white‐box) and data‐driven (black‐box) approaches, aim to leverage the strengths of both methodologies to mitigate their individual limitations to provide high predictive power. This article explores the integration of hybrid modeling techniques in bioprocess engineering, emphasizing their potential to facilitate product development, accelerate experimental design, enhance process monitoring, drive optimization and catalyze the implementation of digitalization strategies to achieve a more robust and sustainable production in the framework of the goals set by the UN 2030 agenda. In this review, we analyze 270 publications from the early 1990s to 2024, extracting application domains, hybrid‐model architectures, implementation software, and nonparametric output structures. Our findings highlight marked improvements in predictive accuracy, and extrapolation capabilities while revealing persistent challenges in integrating mechanistic and empirical components. This review on hybrid modeling implementations shows their potential to enhance accuracy, calibration, and extrapolation capabilities, highly required for bioprocesses. This work underscores the importance of integrating mechanistic and data‐driven approaches to improve the flexibility and interpretability of models in complex engineering systems, offering significant implications for more sustainable and innovative bioprocesses.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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