The role of industry 4.0 enabling technologies for predicting, and managing of algal blooms: Bridging gaps and unlocking potential.

Journal: Marine pollution bulletin
PMID: 39740519

Abstract

Recent advancements in data analytics, predictive modeling, and optimization have highlighted the potential of integrating algal blooms (ABs) with Industry 4.0 technologies. Among these innovations, digital twins (DT) have gained prominence, driven by the rapid development of artificial intelligence (AI) and machine learning (ML) technologies, particularly those associated with the Internet of Things (IoT). AI is pivotal in enabling IoT and DT by enhancing decision-making, automating processes, and delivering actionable insights. The intersection of DT and AI in the context of ABs presents a promising new area for research exploration. Digital twins, which serve as virtual replicas of physical entities, systems, or processes, offer significant potential when combined with AI technologies, paving the way for novel research avenues in algal management (AM). This literature review examines digital twins' challenges and applications within AM. It also comprehensively analyzes the current state of IoT-based applications developed using AI and DT. The review further explores the tools for implementing DT systems and surveys existing AI techniques incorporating DTs. Additionally, it discusses the opportunities and challenges associated with creating various IoT-based applications by integrating AI and DT. The review concludes by identifying unexplored research avenues in this emerging field, underscoring the potential for future advancements in Artificial Intelligence of Things (AIoT) within AM.

Authors

  • Abdul Gaffar Sheik
    Institute for Water and Wastewater Technology, Durban University of Technology, Durban-4001, South Africa; School of Engineering, The University of British Columbia Okanagan, 3333 University Way, Kelowna, BC V1V 1V7, Canada.
  • Mantena Sireesha
    Center for Geospatial and Saline Studies, Sasi Institute of Technology & Engineering, Tadepalligudem, Andhra Pradesh-534101, India; Department of Computer Science and Engineering, Sasi Institute of Technology & Engineering, Tadepalligudem, Andhra Pradesh-534101, India.
  • Arvind Kumar
    International Rice Research Institute, Los Baños, Philippines.
  • Purushottama Rao Dasari
    Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada.
  • Reeza Patnaik
    Institute for Water and Wastewater Technology, Durban University of Technology, Durban-4001, South Africa.
  • Sourav Kumar Bagchi
    Institute for Water and Wastewater Technology, Durban University of Technology, Durban-4001, South Africa.
  • Faiz Ahmad Ansari
    Institute for Water and Wastewater Technology, Durban University of Technology, Durban-4001, South Africa.
  • Faizal Bux
    Institute for Water and Wastewater Technology, Durban University of Technology, Durban, 4000, South Africa. faizalb@dut.ac.za.

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