Machine learning for physicochemical property prediction of complex hydrocarbon mixtures

Machine learning has proven effective for predicting properties of pure compounds from molecular structures, but properties of mixtures, in particular oil fractions, are rarely dealt with. At best, the bulk properties are estimated based on pure compound properties, linear mixing rules, and a reconstructed composition of the feedstock. As the detailed composition of such mixtures is rarely well determined and often approximated by lumps, the accuracy of the estimated bulk properties can be improved. In this work, we demonstrate for a naphtha case study our bulk property estimation method. First, a detailed PIONA composition is delumped into a molecule-level composition, and a machine learning-based approach is used to predict properties of those molecules, which are further combined in another deep neural network for the prediction of bulk properties. The latter machine learning models are trained on mixture properties using vectors that represent the mixture. The first vector is a linear combination of the molecular representation vectors and the representation of the molecular geometries that make up the mixture. The second vector applies linear mixing rules on boiling temperatures, critical temperatures, liquid densities, and vapor pressures that are predicted with machine learning. The last vector consists of a learned distillation curve. We show that an integrated machine learning approach that starts from the molecular structures in the mixture offers significant improvements in predicting mixture properties over existing approaches applied in industry and academia

Active learning-based exploration of the catalytic pyrolysis of plastic waste

Research in chemical engineering requires experiments, which are often expensive, time-consuming, and labo-rious. Design of experiments (DoE) aims to extract maximal information from a minimum number of experi-ments. The combination of DoE with machine learning leads to the field of active learning, which results in a more flexible, multi-dimensional selection of experiments. Active learning has not yet been applied in reaction modeling, as most active learning techniques still require an excessive amount of data. In this work, a novel active learning framework called GandALF that combines Gaussian processes and clustering is proposed and validated for yield prediction. The performance of GandALF is compared to other active learning strategies in a virtual case study for hydrocracking. Compared to these active learning methods, the novel framework out-performs the state-of-the-art and achieves a 33%-reduction in experiments. The proposed active learning approach is the first to also perform well for data-scarce applications, which is demonstrated by selecting ex-periments to investigate the ex-situ catalytic pyrolysis of plastic waste. Both a common DoE-technique, and our methodology selected 18 experiments to study the effect of temperature, space time, and catalyst on the olefin yield for the catalytic pyrolysis of LDPE. The experiments selected with active learning were significantly more informative than the regular DoE-technique, proving the applicability of GandALF for reaction modeling and experimental campaigns

Active Machine Learning for Chemical Engineers: a Bright Future Lies Ahead!

By combining machine learning with design of experiments, so-called active machine learning, more efficient and cheaper research can be conducted. Machine learning algorithms are more flexible, and are better at investigating the processes spanning all length scales of chemical engineering. While the active machine learning algorithms are maturing, its applications are lacking behind. Three types of challenges faced by active machine learning are identified and ways to overcome them are discussed: the convincing of the experimental researcher, the flexibility of data creation, and the robustness of the active machine learning algorithms. A bright future lies ahead for active machine learning in chemical engineering thanks to increasing automation and more efficient algorithms to drive novel discoveries