Accelerating discovery through high-throughput experimentation and machine learning - Year two

Canonical methods of molecular discovery and reaction optimization rely on “trial-and-error” approaches and slow experimentation with low discovery rates. By harnessing high-throughput experimentation (HTE) with machine learning (ML) methods, artificial intelligence (AI) and robotics, we have the potential to dramatically accelerate the discovery and preparation of next generation molecules and materials. We will extract, unify, and transform data from literature into actionable intelligence, and generate a robust workflow for the automated synthesis of catalysts and resins at NOVA Chemicals.

Accelerating discovery through high-throughput experimentation and machine learning

Canonical methods of molecular discovery and reaction optimization rely on “trial-and-error” approaches and slow experimentation with low discovery rates. By harnessing high-throughput experimentation (HTE) with machine learning (ML) methods, artificial intelligence (AI) and robotics, we have the potential to dramatically accelerate the discovery and preparation of next generation molecules and materials. We will extract, unify, and transform data from literature into actionable intelligence, and generate a robust workflow for the automated synthesis of catalysts and resins at NOVA Chemicals.

Mathematical Modelling of Coke Formation in Thermal Cracking Units

Thermal cracking of ethane (from natural gas) is used to make ethylene, a chemical used for producing plastics. Thermal cracking occurs in metal tubes that pass through a furnace, where heat generated in the combustion chamber outside of the tubes makes the cracking reactions inside the tubes occur at high rates. Coke is an undesirable side product that deposits on the inner surface of the tubes during ethane cracking, influencing rates of chemical reactions and the distribution of chemical products that emerge from the reactor.

Application of Spatial Statistics to Quantify Mixing and the Potential for Reaction

Many chemical reactions can produce unwanted byproducts which require additional purification steps and lead to unwanted waste. Additional purification steps consume large amounts of energy, and waste products can have a significant environmental impact. The chemistry can be modified so that the desired products are favored over the unwanted byproducts, and the mixing can be intensified so that molecules are more rapidly and intimately mixed. Both of these approaches will reduce byproducts.