Towards artificial intelligence at scale in the chemical industry

被引：25

作者：

Chiang, Leo H. ^{[1
]}

Braun, Birgit ^{[1
]}

Wang, Zhenyu ^{[2
]}

Castillo, Ivan ^{[2
]}

机构：

[1] Dow Chem Co USA, Core R&D, Lake Jackson, TX 77566 USA

[2] Dow Chem Co USA, AI & Stat, Chemometr, Lake Jackson, TX 77566 USA

来源：

AICHE JOURNAL | 2022年 / 68卷 / 06期

关键词：

artificial intelligence; fault diagnosis; industrial applications; machine learning; optimization; PARTIAL LEAST-SQUARES; ROBUST OPTIMIZATION; SENSOR FUSION; SYSTEM; ANALYTICS; MODELS; IDENTIFICATION; ARCHITECTURE;

D O I：

10.1002/aic.17644

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

In the Industry 4.0 era, the chemical industry is embracing broad adoption of artificial intelligence (AI) and machine learning (ML) methods. This article provides a holistic view of how the industry is transforming digitally towards AI at scale. First, a historical perspective on how the industry used AI to aid humans in better decision-making is shown. Then state-of-the-art AI research addressing industrial needs on reliability and safety, process optimization, supply chain, material discovery, and reaction engineering is highlighted. Finally, a vision of the plant of the future is illustrated with critical components of AI-ready culture, model life cycle management, and renewed role of humans in chemical manufacturing.

引用

页数：20

共 148 条

[101] Wastewater quality monitoring system using sensor fusion and machine learning techniques [J].

Qin, Xusong ;

Gao, Furong ;

Chen, Guohua .

WATER RESEARCH, 2012, 46 (04) :1133-1144

[102] Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing [J].

Raji, Inioluwa Deborah ;

Smart, Andrew ;

White, Rebecca N. ;

Mitchell, Margaret ;

Gebru, Timnit ;

Hutchinson, Ben ;

Smith-Loud, Jamila ;

Theron, Daniel ;

Barnes, Parker .

FAT* '20: PROCEEDINGS OF THE 2020 CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, 2020, :33-44

[103] Digital Twin: Values, Challenges and Enablers From a Modeling Perspective [J].

Rasheed, Adil ;

San, Omer ;

Kvamsdal, Trond .

IEEE ACCESS, 2020, 8 :21980-22012

[104] A Systematic Methodology for Comparing Batch Process Monitoring Methods: Part I-Assessing Detection Strength [J].

Rato, Tiago J. ;

Rendall, Ricardo ;

Gomes, Veronique ;

Chin, Swee-Teng ;

Chiang, Leo H. ;

Saraiva, Pedro M. ;

Reis, Marco S. .

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2016, 55 (18) :5342-5358

[105] Incorporation of process-specific structure in statistical process monitoring: A review [J].

Reis, Marco S. ;

Gins, Geert ;

Rato, Tiago J. .

JOURNAL OF QUALITY TECHNOLOGY, 2019, 51 (04) :407-421

[106] Data-driven methods for batch data analysis - A critical overview and mapping on the complexity scale [J].

Rendall, Ricardo ;

Chiang, Leo H. ;

Reis, Marco S. .

COMPUTERS & CHEMICAL ENGINEERING, 2019, 124 :1-13

[107] Image-based manufacturing analytics: Improving the accuracy of an industrial pellet classification system using deep neural networks [J].

Rendall, Ricardo ;

Castillo, Ivan ;

Lu, Bo ;

Colegrove, Brenda ;

Broadway, Michael ;

Chiang, Leo H. ;

Reis, Marco S. .

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2018, 180 :26-35

[108] A Unifying and Integrated Framework for Feature Oriented Analysis of Batch Processes [J].

Rendall, Ricardo ;

Lu, Bo ;

Castillo, Ivan ;

Chin, Swee-Teng ;

Chiang, Leo H. ;

Reis, Marco S. .

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2017, 56 (30) :8590-8605

[109]

Ridsdale Chantel., 2015, STRATEGIES BEST PRAC

[110] Inverse molecular design using machine learning: Generative models for matter engineering [J].

Sanchez-Lengeling, Benjamin ;

Aspuru-Guzik, Alan .

SCIENCE, 2018, 361 (6400) :360-365

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