Digital Twin and AI Powered Optimization Framework for Smart Petrochemical Operations under Saudi Vision 2030

• Author(s): Syed Arif Ali Bukharee
• Paper ID: 1719700
• Page: 1042-1053
• Published Date: 10-07-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 10 Issue 1 July-2026

Abstract

Digital transformation is reshaping petrochemical value creation as producers seek higher reliability, lower energy intensity, safer operations, and stronger environmental performance under increasingly volatile market conditions. Within this shift, digital twins and artificial intelligence have moved from isolated pilots to an integrated capability that can synchronize plant data, process models, asset histories, and optimization logic across the full operating lifecycle. This review examines how digital twin architectures, AI techniques, and industrial connectivity can be combined into an optimization framework for smart petrochemical operations aligned with Saudi Vision 2030. The study has three objectives: to synthesize the technological foundations of digital twins for petrochemical plants; to assess how AI supports prediction, diagnosis, control, maintenance, and decision optimization; and to develop an implementation framework tailored to Saudi industrial priorities, including productivity, localization, sustainability, and workforce capability. A structured review methodology inspired by recent review designs in chemical engineering and future industrial systems was used to organize evidence published between 2020 and 2025. The analysis indicates that the strongest operational value emerges when physics-based process knowledge is fused with machine learning, historian data, IIoT streams, and enterprise systems rather than when AI or digital twins are deployed as stand-alone tools. Across the literature, high-value applications include soft sensing, abnormal situation detection, energy optimization, predictive maintenance, production scheduling, emissions monitoring, and operator decision support. Yet the review also finds persistent barriers in model governance, cyber risk, data quality, semantic interoperability, and organizational readiness. To address these gaps, the paper proposes a layered framework linking sensing, contextualization, hybrid modelling, AI analytics, prescriptive optimization, and human-in-the-loop governance. The framework is discussed in relation to petrochemical complexes in Saudi Arabia, where integration with process simulation, SAP-like enterprise workflows, reliability systems, and sustainability reporting can accelerate both operational excellence and Vision 2030 outcomes.

Keywords

Digital Twin, Artificial Intelligence, Petrochemical Operations, Hybrid Modelling, Predictive Maintenance, Energy Optimization, Saudi Vision 2030

Citations

IRE Journals:
Syed Arif Ali Bukharee "Digital Twin and AI Powered Optimization Framework for Smart Petrochemical Operations under Saudi Vision 2030" Iconic Research And Engineering Journals Volume 10 Issue 1 2026 Page 1042-1053

IEEE:
Syed Arif Ali Bukharee "Digital Twin and AI Powered Optimization Framework for Smart Petrochemical Operations under Saudi Vision 2030" Iconic Research And Engineering Journals, vol. 10, no. 1, Jul. 2026

APA:
Syed Arif Ali Bukharee (2026). Digital Twin and AI Powered Optimization Framework for Smart Petrochemical Operations under Saudi Vision 2030. Iconic Research And Engineering Journals, 10(1).

MLA:
Syed Arif Ali Bukharee "Digital Twin and AI Powered Optimization Framework for Smart Petrochemical Operations under Saudi Vision 2030" Iconic Research And Engineering Journals, vol. 10, no. 1, Jul. 2026.

BibTeX

@article{1719700,
author = {Syed Arif Ali Bukharee},
title = {Digital Twin and AI Powered Optimization Framework for Smart Petrochemical Operations under Saudi Vision 2030},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {1},
pages = {1042-1053},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1719700.pdf},
abstract = {Digital transformation is reshaping petrochemical value creation as producers seek higher reliability, lower energy intensity, safer operations, and stronger environmental performance under increasingly volatile market conditions. Within this shift, digital twins and artificial intelligence have moved from isolated pilots to an integrated capability that can synchronize plant data, process models, asset histories, and optimization logic across the full operating lifecycle. This review examines how digital twin architectures, AI techniques, and industrial connectivity can be combined into an optimization framework for smart petrochemical operations aligned with Saudi Vision 2030. The study has three objectives: to synthesize the technological foundations of digital twins for petrochemical plants; to assess how AI supports prediction, diagnosis, control, maintenance, and decision optimization; and to develop an implementation framework tailored to Saudi industrial priorities, including productivity, localization, sustainability, and workforce capability. A structured review methodology inspired by recent review designs in chemical engineering and future industrial systems was used to organize evidence published between 2020 and 2025. The analysis indicates that the strongest operational value emerges when physics-based process knowledge is fused with machine learning, historian data, IIoT streams, and enterprise systems rather than when AI or digital twins are deployed as stand-alone tools. Across the literature, high-value applications include soft sensing, abnormal situation detection, energy optimization, predictive maintenance, production scheduling, emissions monitoring, and operator decision support. Yet the review also finds persistent barriers in model governance, cyber risk, data quality, semantic interoperability, and organizational readiness. To address these gaps, the paper proposes a layered framework linking sensing, contextualization, hybrid modelling, AI analytics, prescriptive optimization, and human-in-the-loop governance. The framework is discussed in relation to petrochemical complexes in Saudi Arabia, where integration with process simulation, SAP-like enterprise workflows, reliability systems, and sustainability reporting can accelerate both operational excellence and Vision 2030 outcomes.},
keywords = {Digital Twin, Artificial Intelligence, Petrochemical Operations, Hybrid Modelling, Predictive Maintenance, Energy Optimization, Saudi Vision 2030},
month = {July}
}