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Integrating HSE Governance with Intelligent Leakage Detection Systems for Sustainable Industrial Development in Saudi Arabia
Subject area: Science,Engineering and Technology · Area of research: HSE Governance and Smart Leak Detection
Abstract
Saudi Arabia’s industrial expansion under Vision 2030 increases the strategic importance of preventing loss of containment across oil and gas, petrochemical, water, utilities, and infrastructure assets. This review examines how health, safety, and environment (HSE) governance can be integrated with intelligent leakage detection so that sensing technologies become part of accountable risk management rather than isolated technical tools. An integrative review of peer-reviewed literature published during 2020–2025 synthesizes evidence on acoustic sensing, pressure and flow analytics, Industrial Internet of Things (IIoT), machine and deep learning, digital twins, risk-based inspection, dynamic risk analysis, human factors, and safety governance. The synthesis shows that intelligent systems can improve detection speed, localization, and predictive integrity assessment, but technical accuracy alone does not ensure safer outcomes. Governance quality determines whether alerts are validated, escalated, translated into barrier actions, documented, and learned from. The paper proposes an HSE-integrated architecture linking sensor assurance, contextual analytics, dynamic risk ranking, human authorization, emergency response, maintenance, and sustainability metrics. It argues that Saudi industrial operators should treat leakage intelligence as a safety-critical decision system with defined ownership, cybersecurity controls, model assurance, and lifecycle verification. Research priorities include Saudi field datasets, explainable and uncertainty-aware models, cross-asset interoperability, human–AI decision protocols, and evidence connecting intelligent detection to measurable reductions in environmental releases and major-accident risk.
Keywords
HSE governance; intelligent leakage detection; process safety; machine learning; IIoT; digital twins; risk-based inspection; Saudi Arabia; sustainable industrial development
How to cite this paper
@article{1724089,
author = {Muhammad Ashfaq},
title = {Integrating HSE Governance with Intelligent Leakage Detection Systems for Sustainable Industrial Development in Saudi Arabia},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {4},
pages = {2577-2590},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724089.pdf},
abstract = {Saudi Arabia’s industrial expansion under Vision 2030 increases the strategic importance of preventing loss of containment across oil and gas, petrochemical, water, utilities, and infrastructure assets. This review examines how health, safety, and environment (HSE) governance can be integrated with intelligent leakage detection so that sensing technologies become part of accountable risk management rather than isolated technical tools. An integrative review of peer-reviewed literature published during 2020–2025 synthesizes evidence on acoustic sensing, pressure and flow analytics, Industrial Internet of Things (IIoT), machine and deep learning, digital twins, risk-based inspection, dynamic risk analysis, human factors, and safety governance. The synthesis shows that intelligent systems can improve detection speed, localization, and predictive integrity assessment, but technical accuracy alone does not ensure safer outcomes. Governance quality determines whether alerts are validated, escalated, translated into barrier actions, documented, and learned from. The paper proposes an HSE-integrated architecture linking sensor assurance, contextual analytics, dynamic risk ranking, human authorization, emergency response, maintenance, and sustainability metrics. It argues that Saudi industrial operators should treat leakage intelligence as a safety-critical decision system with defined ownership, cybersecurity controls, model assurance, and lifecycle verification. Research priorities include Saudi field datasets, explainable and uncertainty-aware models, cross-asset interoperability, human–AI decision protocols, and evidence connecting intelligent detection to measurable reductions in environmental releases and major-accident risk.},
keywords = {HSE governance; intelligent leakage detection; process safety; machine learning; IIoT; digital twins; risk-based inspection; Saudi Arabia; sustainable industrial development},
month = {October},
}