AI-ENABLED BLOCKCHAIN ARCHITECTURE FOR TRUSTED IoT DATA MANAGEMENT ACROSS THE CHEMICAL LIFECYCLE: A REFERENCE FRAMEWORK FOR SAUDI ARABIA'S CHEMICAL INDUSTRY

Abstract

Chemical manufacturers and distributors are increasingly dependent upon a steady stream of data from sensors, lab systems, logistics platforms and connected assets. However, when records are fragmented, unverifiable, or difficult to exchange across organisational boundaries, lifecycle decisions remain exposed to avoidable uncertainty. This review examines the integration of artificial intelligence, blockchain, and the Industrial Internet of Things within a trusted data architecture for the chemical lifecycle in Saudi Arabia. A structured integrative review synthesised peer-reviewed studies mainly published between 2020 and 2025 on industrial IoT security, edge intelligence, blockchain provenance, federated learning, digital twins, smart-contract governance, and supply chain traceability. The evidence indicates that no single technology resolves the trust problem in isolation. Artificial intelligence can spot anomalous or implausible process data, but its outputs inherit the weaknesses of the data pipeline. Blockchain can preserve event provenance and enforce multi-party rules, but high-frequency telemetry should not be directly stored on-chain. The IoT offers operational visibility but the attack surface is expanded by heterogeneous devices, legacy protocols, and weak identities. The review therefore develops a layered architecture where raw telemetry resides in governed off-chain stores, critical lifecycle events are hashed and signed to a permissioned consortium ledger, and edge or federated intelligence provides validation, anomaly detection, predictive functions, and decision support. The proposed design offers auditable event commitments that tie feedstock receipt, processing, quality release, storage, transport, downstream handoff and recovery. For organisations operating in Saudi Arabia's chemical industry, identity governance, semantic interoperability, latency-aware partitioning, privacy-preserving analytics, accountable smart contracts, and phased integration with existing operational technology are central implementation priorities. The review concludes that trusted lifecycle management is less about maximising blockchain usage and more about allocating a narrow, verifiable role for each technology within an end-to-end governance model.