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Understanding the Kinetics & Thermodynamics of Cannabis Extraction with the help of machine learning

Markus RoggenFollow

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- 1. Slow is Smooth & Smooth is Fast! Understanding the Kinetics & Thermodynamics of Cannabis Extraction Dr. Markus Roggen President & CSO
- 2. Introduction DELIC Labs is a research venture that seeks to add fundamental scientific insight to the field of cannabis and mushroom production. We seek to support the cannabis and mushroom industries by establishing a centralized hub in Vancouver, BC, for collaborative research focused on: • Process Design • Process Optimization • Process Analytics • Formulation Research
- 3. Collaborative Research DELIC Labs collaborates with academic, industry and private groups around the globe. Some highlights of those collaborations are: • University of British Columbia, Vancouver • Loyalist College, Belleville • Via Innovations by Dr. Monica Vialpando • Veridient Science by Dr. Linda Klumpers Fundamental Collaboration
- 4. Research Topics • Chemometrics and data analytics for process control and optimization • Kinetic studies to understand mechanisms • In-process analytics for process control • Computational studies to understand mechanisms • Process development, like crystallization Fundamental Cannabis and Mushroom Chemistry
- 5. Abstract • CO2 Extraction Optimization and its Limitations • Optimization beyond Single Case • Flow Rate and Yield • Gaussian Processes and Bayesian Optimization • Machine Learning and Artificial Intelligence
- 6. Don’t Panic
- 7. CO2 Extraction Optimization and its Limitations • Extraction is an Art!
- 8. CO2 Extraction Optimization and its Limitations • Linear optimization, even multifactorial, is cost prohibitive • Looking at one parameter at time is to slow • Optimizing more than 3 factors takes to long Temperature (˚C) Pressure (psi) Yield THC (g) 34 60 1100 1900 47 1500 0
- 9. CO2 Extraction Optimization and its Limitations • Just looking at pressure and temperature is not enough • We look at >20 controls, every minute, all the time
- 10. CO2 Extraction Optimization and its Limitations • Optimization is conventionally done empirically • explore how altering conditions changes the outcome • This is resource intensive (time, materials, money) • Discounts or does not use all the data generated • Results are often biased by the optimization and not entirely generalizable to different inputs or extractors • Garbage in, garbage out • Not for material • But for data
- 11. Optimization beyond Single Case • Producers: XXX • Instrument Types: 6 • Individual Runs: XXXX • Datapoints: ~100,000
- 12. Flow Rate and Yield • Counterintuitive observation from dataset: • Slower flow rate decreases CO2 needed • Slower flow rate increases extract purity
- 13. 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 Gaussian Processes and Bayesian Optimization Linear Models • Classical dimensionality reduction techniques and linear regression models probe the relationship between various input variables and yield • Challenges in proper cross validation • Test errors were noisy • Linear techniques do not provide good accuracy • Do not provide a built-in uncertainty estimate
- 14. Gaussian Processes and Bayesian Optimization Gaussian Processes (GPs) • Fit an entire family of curves to the observations • Infinite number of functions fit our finite set of observations • GPs assign a probability to each of these functions • Mean of probability distribution represents most likely prediction • Use the variance of the distribution as an uncertainty estimate.
- 15. Gaussian Processes and Bayesian Optimization Bayesian Optimization (BO) • GP model provides mean and uncertainty • BO identifies highest uncertainty of GP • BO choose the next best point to sample based on both its uncertainty and the function value at that point
- 16. Gaussian Processes and Bayesian Optimization • Visuals for GP and BO
- 17. Gaussian Processes and Bayesian Optimization • 42
- 18. Gaussian Processes and Bayesian Optimization • Bayesian optimizer outputs a set of experimental conditions to further to refine model
- 19. Gaussian Processes and Bayesian Optimization • Bayesian optimizer outputs a set of experimental conditions to further to refine model
- 20. Machine Learning and Artificial Intelligence • Working model for subset of data • Can see effect of input material on optimal conditions • Probe effects of more factors • Expand to whole dataset, fill gaps in dataset • Partner with more producers to get everyone better results • Biggest problem is bad data
- 21. Expertise CSO: Dr. Markus Roggen Dr. Roggen has been actively involved in the cannabis industry for over 5 years in executive positions overseeing production, R&D and process optimization for multiple producers. Dr. Roggen is also a trusted advisor and mentor for multiple startups, startup accelerators and organizations. DELIC Labs Team Our team covers a wide range of expertise, including analytical chemistry, process chemistry, engineering physics, data science and statistics. Scientific Advisor: Prof. Glenn Sammis Prof. Sammis is an Associate Professor in the Chemistry Department at the University of British Columbia. He has built an internationally recognized research group working on the development of novel synthetic methods for the preparation of natural products and pharmaceuticals.
- 22. Dr. Markus Roggen markus@cbdvl.com