Artificial Intelligence (AI) (1) is becoming the latest go to technology/methodology for almost every application. Numerous articles have been published in the areas of chemical properties, discoveries, process design and development and manufacturing areas (2,3,4,5,6,7). If the machine language can be used to simplify conventional chemical process development and design, it is my speculation that commercialization of products can be sped up multifold. Chemical manufacturing landscape could change dramatically. This will lead to improved profits through faster availability and reduced shortages of products in every segment of the chemical industry that includes fine/specialty chemicals, additives, cosmetics, flavors and fragrances and pharmaceuticals. In addition, every chemical related business can make significant progress towards “net zero” (8,9) emissions.
I am presenting my perspective on how AI could be used to develop, simplify, speed up development, commercialization and manufacturing of the products. Perspective presented here is my own. There is no financial relationship with any “for profit” and nonprofit organization.
AI and Process Development:
For most of the product categories mentioned above development and commercialization take their own course. This is due to availability of data such as properties and mutual behavior of the chemicals needed and used in product evaluation, formulation and process design. Development moves from the laboratory to commercial scale following their established paths that have been taught and practiced over the last 70+ years.
For product development physical and chemical properties of chemicals are needed. Much of the data about chemicals (physical and chemical properties, safety and health value of the chemicals) used and produced might be available from Chemical Abstract Services (10) NIST Chemistry Web Book (11), product suppliers and can be generated internally at the companies. AI should accelerate and facilitate availability of solubility and safety data. Safety data and solubilities of solvents identified by AI would have to be verified in the laboratory. It could also be used to define process conditions for chemical processing. Process development and commercialization will be accelerated.
Many of the chemicals are raw materials are solid at ambient temperatures. To facilitate processing and ease of handling, they are solubilized or slurried in inert solvent/s. These are recovered and reused. This has been the tradition for the last century. Even though the solvents are recovered and reused, they still leave a large environmental footprint than a process which is all liquid process i.e. solids do not have to be slurried to facilitate processing.
Sociochemicology (12, 13) i.e. mutual behavior of chemicals plays a significant part in development and commercialization of chemical synthesis processes. Some of the methods that can be used to facilitate incorporation of physical properties of the chemicals are reviewed (14,15). However, the methods should not be limited to what has been discussed in literature.
Some of the methods that value use of liquids in chemical processing are reviewed (16). Between AI and “collaborative creativity” (17) of the village (16) excellent processes can be developed. I believe that with the help of artificial intelligence it can and will become possible, if it is not already being tried. Having knowledge that all liquid processes could minimize solvent use would be giant leap to “Net Zero” processes. Developers can take advantage of point of addition and heat of reaction to control the reaction kinetics to speed up the processes.
Having an all liquid process could lead to use of smaller size reactors or alternate reaction systems (16) that could eliminate or significantly reduce the amount of solvent use. It would be a significant step to achieving “Net Zero”.
I believe that AI could scour the processing equipment world and propose equipment that is commercially used in chemical and other industries and would be suitable for the chemical synthesis and blended product applications. It will be interesting to see how AI will change chemical engineering practices. My conjecture is that it could propose processing methods and sequences that would simplify chemical process manufacturing and further augment “collaborative creativity” (16) of the “village” (15). Business models should improve.
For the AI proposed designs my expectation is that the unit processes and unit operations might not change much from traditional ways but not having seen any such design it would enlightening to see the results of what will change. Proposed process designs would be readily accepted in the companies after they have been tested internally and product quality meets or exceeds expectations. Adoption in most companies will come quickly. However, companies that are regulated e.g. pharmaceuticals will be skeptical and apprehensive. This will be due major regulatory acceptance challenges as most of the regulatory folks have essentially no or very little chemical process development, design, commercialization and manufacturing experience. Lack of the knowledge and experience at regulators could be a major expenditures and time delays as the regulators will have to be taught value of better technologies. There will be cost implications to the companies and they might not or be slow to venture out in using AI based designs for their product development and manufacturing.
Brand pharma companies could adopt the AI based technologies quickly as they are single product focused. However, generics since they are fragmented and do not have dedicated equipment could be reluctant to accept better technologies that could be AI generated. If AI based manufacturing is accepted overall equipment running time in pharmaceuticals could be lower than the current times. This might deter adoption of AI based technologies. Regulations would have to re-written.
With the progress that is being made in AI it is expected that process development and manufacturing technologies will be of great benefit to humanity (18, 19). With the changing landscape regulators will have change their posturing especially when it comes to pharmaceutical product approvals. If they don’t and will not keep up with time, stone age will be a burden on space age needs.
Tomorrow is here:
Some might say AI will arrive tomorrow. However, we all know that TOMORROW never comes. AI’s TOMORROW is HERE. We have to learn and capitalize on what we do especially when it comes to chemical products and processes. Listening to the linked (20, 21, 22, 23) my conjecture is that it will have a powerful impact on chemical process synthesis and could deliver us pathways and processes that we think are or thought to be impossible. Acceptance in chemical process development, design and commercialization could be fast in non-FDA approved products. Incorporation of AI in drug development could be fast. However, as suggested earlier, incorporation of AI in manufacturing could lag “quite” some time due to regulator’s lack of hands on knowledge and experience in product, process development, design and manufacturing practices. My conjecture is that combination of “Creative Destruction” (24, 25) and “Nondestructive Creation” (16, 26) will come in play. Business Models, Regulatory policies and procedures will require a monumental change.
Girish Malhotra, PE
1. Artificial Intelligence, Wikipedia https://en.wikipedia.org/wiki/Artificial_intelligence Accessed March 21, 2023
2. Chemical Engineering And Artificial Intelligence https://aichatgpt.co.za/chemical-engineering-and-artificial-intelligence/ Accessed March 21, 2023
3. Artificial Intelligence In Chemical https://aichatgpt.co.za/artificial-intelligence-in-chemical-engineering/#AI_For_Developing_Novel_Chemicals_and_Products Accessed March 22, 2023
4. Stephanopoulos, G. Artificial Intelligence in Process Engineering, Chemical Engineering Education https://journals.flvc.org/cee/article/view/124525/123536 pg. 182-185, 192 Accessed March 21, 2023
5. Lou, H. H., Gai, H. Lamar University, How AI can better serve the chemical process industry, July 2020 Accessed March 21, 2023
6. Trinh, C.; Meimaroglou, D.; Hoppe, S. Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers. Processes 2021, 9, 1456. https://doi.org/10.3390/pr9081456 Accessed March 21, 2023
7. Gasteiger J.: Chemistry in Times of Artificial Intelligence ChemPhysChem 2020, 21, 2233– 2242 Accessed March 21, 2023
8. Burke, J. What does net zero mean? https://www.greenbiz.com/article/what-does-net-zero-mean, May 2, 2019 Accessed April 27, 2021
9. Sheldon R.A. The E factor 25 years on: the rise of green chemistry and sustainability, Green Chemistry https://pubs.rsc.org/en/content/articlelanding/2017/gc/c6gc02157c/unauth#!divAbstract , 2017, 19, 18-43 Accessed February 17, 2021
10. Chemical Abstract Service https://www.cas.org/cas-data Accessed March 21, 2023
11. NIST Chemistry Web Book SRD 69 https://webbook.nist.gov/chemistry/ Accessed March 21, 2023
12. Malhotra, Girish: Process Simplification and The Art of Exploiting Physical Properties, Profitability through Simplicity March 10, 2017 Accessed March 21, 2023
13. Malhotra, Girish: Sociochemicology, 2014 Accessed March 21, 2023
14. AI for chemistry https://chemintelligence.com/ai-for-chemistry Accessed March 21, 2023
15. Butler, Keith T. et.al Machine learning for molecular and materials science, Nature 559, 547–555 (2018) Accessed March 22, 2023
16. Malhotra, Girish: Active Pharmaceutical Ingredient Manufacturing: Nondestructive Creation April 19, 2022 https://www.degruyter.com/document/doi/10.1515/9783110702842/html
18. Artificial Intelligence In Chemical Engineering https://aichatgpt.co.za/artificial-intelligence-in-chemical engineering/#AI_For_Developing_Novel_Chemicals_and_Products Accessed March 22, 2023
19. Chemical Engineering And Artificial Intelligence https://aichatgpt.co.za/chemical-engineering-and-artificial-intelligence/ March 21, 2023
20. A360 Day 1 Emad Mostaque https://www.youtube.com/watch?v=aEWHrqxniLM Accessed March 24, 2023
21. Bubeck S. et.al. Sparks of Artificial General Intelligence: Early experiments with GPT-4, https://arxiv.org/pdf/2303.12712.pdf Accessed March 24, 2023
22. Gates Bill: AI and the rapidly evolving future of computing https://www.youtube.com/watch?v=bHb_eG46v2cAccessed March 24, 2023
23. Gates Bill, The Age of AI has begun https://www.gatesnotes.com/The-Age-of-AI-Has-Begun March 21, 2023 Accessed March 25, 2023
24. Malhotra, Girish: Is "Creative Destruction" the way to go for the Pharmaceuticals? Profitability through Simplicity, December 12, 2008 Accessed March 22, 2023
25. Creative Destruction Schumpeter: Definition https://youmatter.world/en/definition/creativedestruction-schumpeter-definition/ April 20, 2020 Accessed August 15, 2022
26. Kim, Chan W., Mauborgne: Nondisruptive Creation: Rethinking Innovation and Growth, MIT Sloan Review, February 21, 2019