Digital Chemical Engineering

Scope & Guideline

Redefining Chemical Engineering: The Digital Transformation Journey

Introduction

Welcome to your portal for understanding Digital Chemical Engineering, featuring guidelines for its aims and scope. Our guidelines cover trending and emerging topics, identifying the forefront of research. Additionally, we track declining topics, offering insights into areas experiencing reduced scholarly attention. Key highlights include highly cited topics and recently published papers, curated within these guidelines to assist you in navigating influential academic dialogues.
LanguageEnglish
ISSN2772-5081
PublisherELSEVIER SCI LTD
Support Open AccessYes
CountryUnited Kingdom
TypeJournal
Convergefrom 2021 to 2024
AbbreviationDIGIT CHEM ENG / Digit. Chem. Eng.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address125 London Wall, London EC2Y 5AS, ENGLAND

Aims and Scopes

The journal 'Digital Chemical Engineering' focuses on the intersection of chemical engineering and digital technologies. It aims to advance knowledge and practices in the field through the integration of computational methods, data analytics, and automation in chemical processes.
  1. Integration of Artificial Intelligence and Machine Learning:
    The journal emphasizes the application of AI and machine learning techniques to optimize chemical processes, enhance predictive control, and facilitate decision-making in complex systems.
  2. Digital Twins and Virtual Simulations:
    Research frequently explores the development of digital twins and virtual simulation models to improve training, monitoring, and optimization of chemical engineering processes.
  3. Data-Driven Approaches:
    A significant focus is on data-driven methodologies, including machine learning and statistical techniques, for modeling, monitoring, and control of chemical processes.
  4. Process Automation and Control:
    The journal covers advancements in automation technologies and control strategies, particularly model predictive control and smart manufacturing solutions.
  5. Sustainability and Environmental Impact:
    There is a consistent emphasis on sustainable practices within chemical engineering, including studies on carbon capture, renewable materials, and energy-efficient processes.
  6. Educational Innovations in Chemical Engineering:
    The journal also addresses educational methodologies and tools, particularly the use of digital tools and AI in teaching chemical engineering concepts.
The journal has witnessed a surge in interest in specific themes that reflect current advancements and priorities in digital chemical engineering. This section outlines the trending and emerging scopes.
  1. Machine Learning and Predictive Analytics:
    There is a growing emphasis on machine learning applications for predictive modeling and optimization of chemical processes, showcasing the industry's shift towards data-centric methodologies.
  2. Digital Twins and Smart Manufacturing:
    The development of digital twins and their applications in smart manufacturing and process monitoring are emerging as crucial themes, reflecting the industry's move towards enhanced operational efficiency.
  3. Sustainable Engineering Practices:
    Research on sustainability, including carbon capture technologies and green chemistry approaches, is trending as industries seek to reduce their environmental footprint.
  4. Interdisciplinary Applications of AI:
    The integration of artificial intelligence across various domains, including energy systems and bioengineering, is increasingly popular, highlighting the versatility of AI in chemical engineering.
  5. Educational Tools for Chemical Engineering:
    The emergence of digital tools and AI in educational frameworks indicates a trend towards enhancing learning experiences and methodologies in chemical engineering education.

Declining or Waning

While 'Digital Chemical Engineering' continues to grow in various innovative areas, certain themes have shown signs of decline in recent publications. This section highlights those waning scopes.
  1. Traditional Experimental Methods:
    There is a noticeable decline in publications focusing solely on traditional experimental methods without the integration of digital technologies. This shift reflects the increasing importance of computational and data-driven approaches.
  2. Basic Process Design Without Digital Integration:
    Research centered on basic chemical process design that does not incorporate digital tools or data analytics is becoming less frequent, indicating a move towards more sophisticated and integrated approaches.
  3. Conventional Control Strategies:
    The focus on conventional control strategies, such as PID control without enhancements from digital or AI technologies, has diminished as more advanced methodologies gain prominence.

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