COMPUTERS & CHEMICAL ENGINEERING

Scope & Guideline

Driving Breakthroughs in Chemical Engineering Through Advanced Computing

Introduction

Explore the comprehensive scope of COMPUTERS & CHEMICAL ENGINEERING through our detailed guidelines, including its aims and scope. Stay updated with trending and emerging topics, and delve into declining areas to understand shifts in academic interest. Our guidelines also showcase highly cited topics, featuring influential research making a significant impact. Additionally, discover the latest published papers and those with high citation counts, offering a snapshot of current scholarly conversations. Use these guidelines to explore COMPUTERS & CHEMICAL ENGINEERING in depth and align your research initiatives with current academic trends.
LanguageMulti-Language
ISSN0098-1354
PublisherPERGAMON-ELSEVIER SCIENCE LTD
Support Open AccessNo
CountryUnited Kingdom
TypeJournal
Convergefrom 1977 to 2025
AbbreviationCOMPUT CHEM ENG / Comput. Chem. Eng.
Frequency12 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND

Aims and Scopes

The journal 'COMPUTERS & CHEMICAL ENGINEERING' aims to advance the field of chemical engineering through innovative computational methodologies and applications. It focuses on the integration of computer science and chemical engineering principles to enhance the modeling, simulation, and optimization of chemical processes.
  1. Computational Modeling and Simulation:
    The journal emphasizes the development and application of computational models for simulating chemical processes, including dynamic simulations, process optimization, and system identification.
  2. Data-Driven Approaches:
    A significant focus is on employing data-driven methodologies such as machine learning, artificial intelligence, and statistical methods for process optimization, fault detection, and predictive modeling.
  3. Process Optimization:
    The journal covers various optimization techniques, including mixed-integer linear programming (MILP), dynamic optimization, and robust optimization, aimed at improving the efficiency and sustainability of chemical processes.
  4. Integration of Environmental and Economic Factors:
    Research published in the journal often explores the techno-economic and environmental assessments of chemical processes, ensuring that sustainability is a core consideration in process design and operation.
  5. Innovations in Process Control:
    The journal highlights advancements in control strategies, including model predictive control (MPC), reinforcement learning-based control, and adaptive control strategies tailored for complex chemical processes.
  6. Chemical Engineering Education:
    The journal also addresses the educational aspects of chemical engineering, focusing on innovative teaching methodologies and the integration of computational tools into the curriculum.
The journal reflects a dynamic research landscape with emerging themes that showcase the integration of advanced computational techniques and innovative methodologies in chemical engineering.
  1. Machine Learning and AI Applications:
    Recent publications have increasingly focused on the application of machine learning and artificial intelligence in chemical engineering, particularly for process optimization, fault detection, and predictive maintenance.
  2. Sustainability and Green Engineering:
    There is a growing emphasis on sustainability, with research exploring eco-friendly processes, circular economy principles, and the integration of renewable energy in chemical manufacturing.
  3. Advanced Process Control Techniques:
    Emerging control strategies such as reinforcement learning-based control and adaptive model predictive control are gaining traction, reflecting the industry's shift towards more intelligent and responsive systems.
  4. Data-Driven Decision Making:
    The trend towards data-driven decision-making processes is evident, with research focusing on the use of real-time data analytics and big data approaches for enhancing operational efficiency in chemical processes.
  5. Integration of Quantum Computing:
    There is an emerging interest in the application of quantum computing techniques for solving complex optimization problems in chemical engineering, indicating a forward-looking trend in computational capabilities.

Declining or Waning

While 'COMPUTERS & CHEMICAL ENGINEERING' continues to evolve, certain research themes appear to be declining in prominence. This trend may reflect shifts in industry needs and academic focus.
  1. Traditional Chemical Process Design:
    There is a noticeable decrease in publications focusing solely on conventional chemical process design without the integration of computational tools or data-driven approaches.
  2. Static Optimization Models:
    Research centered on static optimization models that do not incorporate real-time data or dynamic elements is becoming less common, as the field moves towards more adaptive and responsive methodologies.
  3. Basic Process Control Strategies:
    Basic control strategies that do not leverage advanced computational techniques or adaptive learning methods are seeing reduced interest, as more sophisticated control approaches take precedence.
  4. Single-Disciplinary Approaches:
    There is a decline in research that does not integrate interdisciplinary methods, particularly those that do not combine chemical engineering with computer science, data analytics, or environmental science.

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