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ORIGINAL RESEARCH article

Front. Environ. Sci.
Sec. Big Data, AI, and the Environment
Volume 12 - 2024 | doi: 10.3389/fenvs.2024.1389639

Enhancing Environmental Sustainability through Code-Driven Process Integration in the Petrochemical Industry

Provisionally accepted
  • 1 School of Mathematical Sciences, Jiangsu University, Jiangsu 212013,China, Jiangsu, China
  • 2 Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
  • 3 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, Egypt

The final, formatted version of the article will be published soon.

    Balancing various objectives and navigating uncertainties, reducing CO2 emissions and enhancing energy efficiency in industry presents a complex challenge. While previous studies primarily focused on conventional optimization methods, this research introduces an innovative approach: a multi-criteria optimization framework tailored to address uncertainties. The primary objective is to optimize energy consumption, minimize emissions, and improve cost efficiency simultaneously within the petrochemical industry. To effectively manage uncertain variables, this study integrates decision-making simulations and expert insights through a hybrid methodology to yield optimal outcomes. Employing three distinct preference categories, the model formulates comprehensive decision-making strategies. Empirical findings underscore the model's efficacy in reducing CO2 emissions, bridging crucial gaps in existing research, and advocating sustainable 1 Enhancing Environmental Sustainability through Code-Driven Process Integration in the Petrochemical Industry practices in the sector. Departing from conventional methodologies, this research leverages advanced decision-making techniques adept at handling uncertainty. The framework identifies pivotal emission sources and advocates economically viable reduction strategies. Its adaptability enriches our comprehension of emission challenges by considering diverse factors and expert perspectives. Professional assessments affirm the model's success and propose a Coding-Based Prototype as a strategic tool for addressing uncertainties. These results underscore the imperative for policy reforms, such as embracing carbon capture technologies, to bolster global sustainability and foster enduring growth in the industrial domain.

    Keywords: Multi-criteria optimization, decision-making, petrochemical, coding, emission

    Received: 21 Feb 2024; Accepted: 25 Nov 2024.

    Copyright: © 2024 Ahsan, Tian, Du, Alhussan and El-kenawy. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: El-Sayed M. El-kenawy, Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, Egypt

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.