We are pleased to announce a Special Issue on Chemical Engineering Digitalization for the Chemical Engineering Transactions Journal, focused on the transformative integration of Digital Twins, Artificial Intelligence (AI), and Machine Learning (ML) in chemical engineering processes.
This special issue aims to highlight the latest advancements and applications of these cutting-edge technologies in optimizing and transforming chemical engineering operations.

Scope and Topics:
This special issue welcomes original research articles, reviews, and case studies that explore the role of digitalization technologies in chemical engineering. Topics of interest include, but are not limited to:

" Digital Twin Technologies:
o Development and implementation of digital twin models for chemical processes.
o Real-time process monitoring and control using digital twins.
o Digital twin applications in plant design, optimization, and operation.
" Artificial Intelligence and Machine Learning in Chemical Engineering:
o Machine learning models for process prediction and optimization.
o AI-driven decision-making and fault detection in chemical engineering.
o Integration of AI and ML into process simulations, modeling, and system identification.
" Data-Driven Approaches:
o Big data analytics in chemical engineering applications.
o AI and ML for real-time data processing and optimization.
o Predictive maintenance using AI models and data analytics.
" Industrial Case Studies and Applications:
o Successful case studies of AI, ML, and digital twin adoption in chemical plants.
o Practical applications and challenges of implementing digitalization in the chemical industry.
o Future trends and vision of digital twins, AI, and ML in chemical engineering.

Submission and publication:
Manuscripts should be submitted via the journal's submission system.
Manuscripts should respect the Formatting guidelines
Publication of final accepted Manuscripts is subjected to a publication fee