Transforming chemical process engineering: The role of <scp>AI</scp> and machine learning in revolutionizing process systems
Bibliographic record
Abstract
Abstract This review examines the transformative impact of artificial intelligence (AI) and machine learning (ML) in advancing process systems engineering (PSE) within the chemical process industries. AI/ML techniques, including neural networks, reinforcement learning, and hybrid modelling, address challenges of process nonlinearity, uncertainty, and real‐time optimization demands. Successful applications in energy optimization, predictive maintenance, and fault detection demonstrate enhanced process efficiency, predictive accuracy, and operational adaptability. Innovations such as digital twins and cyber‐physical systems enable real‐time monitoring and autonomous control. However, adoption barriers, including data quality, computational complexity, legacy system integration, and the need for interpretable models in regulated environments, persist. Addressing these challenges requires scalable, adaptive AI/ML systems, interdisciplinary collaboration, and workforce training. Future advancements in transfer learning, explainable AI, and Internet of Things (IoT) integration under Industry 4.0 frameworks are critical. This review provides a comprehensive guide for researchers and practitioners, outlining strategies to harness AI/ML for sustainable and resilient operations in the chemical process industries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".