Aims and scope

The Journal focuses on publishing original, high-quality research at the intersection of chemical engineering, industrial chemistry, and artificial intelligence. Special emphasis is placed on data-driven, intelligent, and computational approaches that enhance the design, optimization, safety, sustainability, and digital transformation of chemical processes and materials. The main thematic areas of interest include, but are not limited to, the following:

1. Artificial Intelligence and Machine Learning in Industrial Chemistry

Application of machine learning techniques for prediction of material properties

Deep learning–based modeling of chemical and industrial processes

Neural network applications in chemical reaction simulation

Intelligent algorithms for pattern recognition in chemical data

Expert systems and decision-support tools in chemical industries

2. Intelligent Optimization and Control of Industrial Processes

Optimization of chemical processes using evolutionary and metaheuristic algorithms

Advanced process control using intelligent, fuzzy, and neural control methods

Real-time monitoring and quality control in chemical industries using AI

Fault prediction and predictive maintenance in industrial units

Digital Twin technologies in chemical and process industries

3. Computational Chemistry and Intelligent Modeling

AI-assisted molecular and multiscale simulations

Prediction of reaction pathways and reaction kinetics using artificial intelligence

Data-driven models in physical chemistry and industrial chemistry

Applications of artificial intelligence in quantum chemical calculations

4. Materials and Catalyst Design and Development

Intelligent design of advanced materials and nanomaterials

Machine learning–based discovery and optimization of catalysts

Prediction of mechanical, thermal, and chemical properties of materials

Development of sustainable and green materials using intelligent algorithms

5. Data Mining and Big Data in Chemical Industries

Analysis of large-scale industrial and experimental datasets

Data mining in chemical and materials databases

Unsupervised learning methods for clustering and classification of chemical data

Big data management and processing in chemical plants and industrial facilities

6. Artificial Intelligence in Green and Sustainable Chemistry

Intelligent optimization for reduction of energy and raw material consumption

AI-based approaches for pollution reduction and chemical waste management

Design of low-carbon and sustainable chemical processes

Life Cycle Assessment (LCA) using intelligent and data-driven methods

7. Industrial Applications and Case Studies

Case studies on implementation of artificial intelligence in chemical industries

Applications of AI in petrochemical, refinery, polymer, pharmaceutical, and coating industries

Intelligent automation of production lines

Knowledge and technology transfer from academia to industry in AI-driven chemical engineering

8. Safety, Risk Assessment, and Intelligent Decision-Making

AI-based risk assessment of chemical processes

Prediction and prevention of industrial accidents

Intelligent decision-support systems in chemical industries

Analysis of human errors using intelligent and data-driven models

9. Education, Policy, and Foresight

Artificial intelligence education and training for chemical engineers

Challenges and opportunities of AI adoption in chemical industries at national and global levels

Technology foresight and future trends in industrial chemistry

Ethical, legal, and regulatory aspects of artificial intelligence in industry

10. Emerging and Interdisciplinary Topics

Integration of artificial intelligence with the Internet of Things (IoT) in chemical industries

Applications of blockchain and AI in chemical supply chains

Intelligent robots in laboratories and industrial units

Generative artificial intelligence for materials discovery and reaction design