Artificial intelligence in the chemical industry – risks and opportunities

• Autorzy: Laska Mateusz , Karwala Izabela

Identyfikatory

DOI

10.29119/1641-3466.2023.172.25

• Języki publikacji: EN

Abstrakty

EN

Purpose: The aim of the article is to review the literature on the risks and opportunities of implementing Industry 4.0 - Artificial Intelligence solutions in the chemical industry. Design/methodology/approach: The review was carried out using available scientific articles, popular science publications, and media reports from the world's largest companies in the chemical industry. Findings: The analysis indicates that there are more benefits than risks arising from the implementation of Artificial Intelligence solutions in the chemical industry. Research limitations/implications: The frequent lack of specific economic indicators makes it difficult to clearly indicate the implementation potential of a specific solution in other companies in the chemical industry. Social implications: The implementation of AI in chemical industry companies can reduce environmental pollution, raw material consumption, and optimize production processes. Originality/value: The article, based on real data, is aimed at middle and senior management of companies in the chemical industry, presenting the advantages and disadvantages of implementing AI solutions in the chemical industry.

Słowa kluczowe

EN

AI; chemical industry; machine learning; risks; opportunities

PL

sztuczna inteligencja; przemysł chemiczny; nauczanie maszynowe; ryzyko; możliwości

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
• Rocznik: 2023
• Tom: z. 172
• Strony: 403--416
• Opis fizyczny: Bibliogr. 41 poz.

Twórcy

autor

Laska Mateusz

• Grupa Azoty S.A.

• https://orcid.org/0000-0003-0124-3496

autor

Karwala Izabela

• Grupa Azoty S.A.
• Akademia WSB

• https://orcid.org/0000-0003-0991-7301

Bibliografia

• 1. 5 Pharma companies using artificial intelligence. Retrieved from: https://www.kantify.com/insights/5-pharma-companies-using-artificial-intelligence, 21.01.2023.
• 2. A definition of Artificial Intelligence: main capabilities and scientific disciplines. Retrieved from: https://digital-strategy.ec.europa.eu/en/library/definition-artificial-intelligence-main-capabilities-and-scientific-disciplines, 4.02.2023.
• 3. Accenture global digital chemicals survey. Retrieved from: https://www.accenture.com/us-en/~/media/accenture/conversion-assets/dotcom/documents/global/pdf/dualpub_6/ accenture-insight-digital-chemical-survey-infographic.pdf, 30.01.2023.
• 4. Andulkar, M., Dow uses vision AI at the edge to boost employee safety and security with Azure. Retrieved from: https://customers.microsoft.com/en-us/story/1349423518578860 629 -dow-chemicals-azure-video-analyzer, 16.01.2023.
• 5. Azadeh, A., Bonab, N.A., Salehi, V., Zarrin, M. (2015). A unique algorithm for the assessment and improvement of job satisfaction by resilience engineering: Hazardous labs. International Journal of Industrial Ergonomics, 49, 68-77. https://doi.org/10.1016/j.ergon. 2015.06.002.
• 6. Beamon, B.M. (1998). Supply chain design and analysis: Models and methods. International Journal of Production Economics, 55(3), 281-294. https://doi.org/10.1016/ S0925-5273(98)00079-6.
• 7. Behdani, B., Lukszo, Z., Srinivasan, R. (2019). Agent-oriented simulation framework for handling disruptions in chemical supply chains. Computers and Chemical Engineering, 122, 306-325. https://doi.org/10.1016/j.compchemeng.2018.0.
• 8. Behdani, B., Lukszo, Z., Adhitya, A., Srinivasan, R. (2009). Agent based model for performance analysis of a global chemical supply chain during normal and abnormal situations. Computer Aided Chemical Engineering, 26, 979-984. https://doi.org/10.1016/ S1570-7946(09)70163-4.
• 9. Berning, G., Brandenburg, M., Gürsoy, K., Kussi, J.S., Mehta, V., Tölle, F.J. (2004). Integrating collaborative planning and supply chain optimization for the chemical process industry (I) - Methodology. Computers and Chemical Engineering, 28(6-7), 913-927. https://doi.org/10.1016/j.compchemeng.2003.
• 10. Blue Yonder Powers Supply Chain Planning for The Source. Retrieved from: https://media.blueyonder.com/blue-yonder-powers-supply-chain-planning-for-the-source/, 19.01.2023.
• 11. Castillo-Villar, K.K. (2014). Metaheuristic algorithms applied to bioenergy supply chain problems: Theory, review, challenges, and future. Energies, 7(11), 7640-7672. https://doi.org/10.3390/en7117640.
• 12. Confronting the risks of artificial intelligence. Retrieved from: https://www.cognitivescale.com/wp-content/uploads/2019/06/Confronting_AI_risks_-_McKinsey.pdf, 16.01.2023.
• 13. Digital transformation initiative chemistry and advanced materials industry. Retrieved from: http://reports.weforum.org/digital-transformation/wp-content/blogs.dir/94/mp/files/ pages/files/dti-chemistry-and-advanced-materials-industry-white-paper.pdf, 30.01.2023.
• 14. Doan, H.A., Agarwal, G., Qian, H., Counihan, M.J., Rodríguez-López, J., Moore, J.S., Assary, R.S. (2020). Quantum chemistry-informed active learning to accelerate the design and discovery of sustainable energy storage materials. Chemistry of Materials, 32(15), 6338-6346. https://doi.org/10.1021/acs.chemmater.0c007.
• 15. Ehlen, M.A., Sun, A.C., Pepple, M.A., Eidson, E.D., Jones, B.S. (2014). Chemical supply chain modeling for analysis of homeland security events. Computers and Chemical Engineering, 60, 102-111. https://doi.org/10.1016/j.compchemeng.2013.07.
• 16. Ghaderi, H., Pishvaee, M.S., Moini, A. (2016). Biomass supply chain network design: An optimization-oriented review and analysis. Industrial Crops and Products, 94, 9721000. https://doi.org/10.1016/j.indcrop.2016.09.027.
• 17. Gu, G.H., Noh, J., Kim, I., Jung, Y. (2019). Machine learning for renewable energy materials. Journal of Materials Chemistry A, 7(29), 17096-17117. https://doi.org/10.1039/ c9ta02356a.
• 18. Guillén, G., Mele, F.D., Espuna, A., Puigjaner, L. (2006). Addressing the design of chemical supply chains under demand uncertainty. Industrial & Engineering Chemistry Research, 45, 7566-7581. https://doi.org/10.1021/ie051424a.
• 19. Hájek, P., Stejskal, J. (2018). R&D cooperation and knowledge spillover effects for sustainable business innovation in the chemical industry. Sustainability, 10(4), 1064. https://doi.org/10.3390/su10041064.
• 20. Hajjar, Z., Tayyebi, S., Hosein, M., Ahmadi, E. (2016). Application of AI in chemical engineering. In: M.A. Aceves-Fernandez (Ed.), Artificial intelligence - Emerging trends and applications (pp. 399-416). https://doi.org/10.5772/intechopen.76027.
• 21. Kądzielawski, G. (2022) The state of development of artificial intelligence in polish industry: opinions of employees. International Journal of Contemporary Management, https://doi.org/10.2478/ijcm-2022-0015.
• 22. Komunikat Ministerstwa Rozwoju Pracy i Technologii: Polityka Przemysłowa Polski. Retrieved from: https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd= &ved=2ahUKEwjO7ezMi7X9AhXnhP0HHUPMAYwQFnoECBEQAQ&url=https%3A %2F%2Fwww.gov.pl%2Fattachment%2F555c07a8-4d95-49af-9ec1-282fafdbcac5&usg=AOvVaw1ln80rSkzPhEPBuyvJ0LF7, 16.01.2023.
• 23. Lan, K., Park, S., Yao, Y. (2019). Key issue, challenges, and status quo of models for biofuel supply chain design. In: J. Ren, A. Scipioni, A. Manzardo, H. Liang (Eds.), Biofuels for a more sustainable future: Life cycle sustainability assessment and multi-criteria decision making (pp. 273-315). Elsevier. https://doi.org/10.1016/B978-0-12-815581-3.00010-5.
• 24. Łapińska, J., Kądzielawski, G., Sudolska, A., Górka, J. (2021). Zaufanie pracowników do sztucznej inteligencji w przedsiębiorstwach przemysłu chemicznego. Przemysł Chemiczny 100(2), 127-131.
• 25. Li, Z., Ma, X., Xin, H. (2017). Feature engineering of machine-learning chemisorption models for catalyst design. Catalysis Today, 280, 232-238. https://doi.org/10.1016/j.cattod.2016.04.013.
• 26. Lin, S., Krishnan, V., Womack, D., Optimizing the chemicals value chain with AI. Retrieved from: https://www.ibm.com/thought-leadership/institute-business-value/report/chemicals-value-chain-ai, 16.02.2023.
• 27. Marcou, G., Aires De Sousa, J., Latino, D.A.R.S., de Luca, A., Horvath, D., Rietsch, V., Varnek, A. (2015). Expert system for predicting reaction conditions: The Michael reaction case. Journal of Chemical Information and Modeling, 55(2), 239-250. https://doi.org/10.1021/ci500698a.
• 28. Matt, Ch., Hess, T., Benlian, A. (2015) Digital Transformation Strategies. Business & Information Systems Engineering, 57(5), pp. 339-343.
• 29. Mitsui Chemicals Teams up With NEC and dotData to Trial AI-Based Price Change Forecasting for Market-Sensitive Products. Retrieved from: https://eu.mitsuichemicals.com/release/2021/2021_0924.htm, 19.01.2023.
• 30. Mohammadi, Y., Penlidis, A. (2018). Optimulation in chemical reaction engineering: Oxidative coupling of methane as a case study. Industrial and Engineering Chemistry Research, 57(26), 8664-8678. https://doi.org/10.1021/acs.iecr.8b01424.
• 31. OECD (2019). Scoping the OECD AI principles: Deliberations of the Expert Group on Artificial Intelligence at the OECD (AIGO). OECD Digital Economy Papers, No. 291. Paris: OECD Publishing, https://doi.org/10.1787/d62f618a-en.
• 32. Pace, R., Elevating pharma inspection with deep learning. Retrieved from: https://www.pharmamanufacturing.com/industry-perspectives-sponsored/article/ 11293580/elevating-pharma-inspection-with-deep-learning, 4.02.2023.
• 33. Pozo, C., Ruíz-Femenia, R., Caballero, J., Guillén-Gosálbez, G., Jiménez, L. (2012). On the use of Principal Component Analysis for reducing the number of environmental objectives in multi-objective optimization: Application to the design of chemical supply chains. Chemical Engineering Science, 69(1), 146-158. https://doi.org/10.1016/j.ces.2011.10.018.
• 34. Raport Polskiej Izby Przemysłu Chemicznego - pozycja, wyzwania, perspektywy (2022).
• 35. Segler, M.H.S., Preuss, M., Waller, M.P. (2018). Planning chemical syntheses with deep neural networks and symbolic AI. Nature, 555(7698), 604-610. https://doi.org/10.1038/ nature25978.
• 36. Smartening up with Artificial Intelligence (AI) - What’s in it for Germany and its Industrial Sector? Retrieved from: https://www.mckinsey.com/~/media/mckinsey/industries/semiconductors/our%20insights/smartening%20up%20with%20artificial%20intelligence/ smartening-up-with-artificial-intelligence.ashx, 30.01.2023.
• 37. Specialty Chemicals Market Size, Share & Trends Analysis Report By Product (Institutional & Industrial Cleaners, Flavor & Fragrances, Food & Feed Additives), By Region, And Segment Forecasts, 2023-2030. Retrieved from: https://www.grandviewresearch.com/ industry-analysis/specialty-chemicals-market, 2.02.2023.
• 38. Vinuesa, R., Azizpour, H., Leite, I. et al. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nat Commun, 11, 233. https://doi.org/10.1038/s41467-019-14108-y.
• 39. Zahrt, A.F., Henle, J.J., Rose, B.T., Wang, Y., Darrow, W.T., Denmark, S.E. (2019). Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning. Science, 363(6424), 1-11. https://doi.org/10.1126/science.aau5631.
• 40. Zhao, K., Yu, X. (2011). A case based reasoning approach on supplier selection in petroleum enterprises. Expert Systems with Applications, 38(6), 6839-6847. https://doi.org/10.1016/j.eswa.2010.12.055.
• 41. Zhou, Z., Li, X., Zare, R.N. (2017). Optimizing chemical reactions with deep reinforcement learning. ACS Central Science, 3(12), 1337-1344. https://doi.org/10.1021/acscentsci.7b00492.