Polyolefins are one of the most widely used commodity polymers with applications in films, packaging and automotive industry. The modeling of polymerization processes producing polyolefins, including high-density polyethylene (HDPE), polypropylene (PP), and linear low-density polyethylene (LLDPE) using Ziegler-Natta catalysts with multiple active sites, is a complex and challenging task. In our study, we integrate process modeling and data analytics for improving and optimizing polyolefin manufacturing processes. Most of the current literature on polyolefin modeling does not consider all of the commercially important production targets when quantifying the relevant polymerization reactions and their kinetic parameters based on measurable plant data. We develop an effective methodology to estimate kinetic parameters that have the most significant impacts on specific production targets, and to develop the kinetics using all commercially important production targets validated over industrial polyolefin processes. We showcase the utility of dynamic models for efficient grade transition in polyolefin processes. We also use the dynamic models for inferential control of polymer processes. Thus, we showcase the methodology for making first-principle polyolefin process models which are scientifically consistent, but tend to be less accurate due to many modeling assumptions in a complex system. Data analytics and machine learning (ML) have been applied in the chemical process industry for accurate predictions for data-based soft sensors and process monitoring/control. Specifically, for polymer processes, they are very useful since the polymer quality measurements like polymer melt index, molecular weight etc. are usually less frequent compared to the continuous process variable measurements. We showcase the use of predictive machine learning models like neural networks for predicting polymer quality indicators and demonstrate the utility of causal models like partial least squares to study the causal effect of the process parameters on the polymer quality variables. ML models produce accurate results can over-fit the data and also produce scientifically inconsistent results beyond the operating data range. Thus, it is growingly important to develop hybrid models combining data-based ML models and first-principle models. We present a broad perspective of hybrid process modeling and optimization combining the scientific knowledge and data analytics in bioprocessing and chemical engineering with a science-guided machine learning (SGML) approach and not just the direct combinations of first-principle and ML models. We present a detailed review of scientific literature relating to the hybrid SGML approach, and propose a systematic classification of hybrid SGML models according to their methodology and objective. We identify the themes and methodologies which have not been explored much in chemical engineering applications, like the use of scientific knowledge to help improve the ML model architecture and learning process for more scientifically consistent solutions. We apply these hybrid SGML techniques to industrial polyolefin processes such as inverse modeling, science guided loss and many others which have not been applied previously to such polymer applications. / Doctor of Philosophy / Almost everything we see around us from furniture, electronics to bottles, cars, etc. are made fully or partially from plastic polymers. The two most popular polymers which comprise almost two-thirds of polymer production globally are polyethylene (PE) and polypropylene (PP), collectively known as polyolefins. Hence, the optimization of polyolefin manufacturing processes with the aid of simulation models is critical and profitable for chemical industry. Modeling of a chemical/polymer process is helpful for process-scale up, product quality estimation/monitoring and new process development. For making a good simulation model, we need to validate the predictions with actual industrial data. Polyolefin process has complex reaction kinetics with multiple parameters that need to be estimated to accurately match the industrial process. We have developed a novel strategy for estimating the kinetics for the model, including the reaction chemistry and the polymer quality information validating with industrial process. Thus, we have developed a science-based model which includes the knowledge of reaction kinetics, thermodynamics, heat and mass balance for the polyolefin process. The science-based model is scientifically consistent, but may not be very accurate due to many model assumptions. Therefore, for applications requiring very high accuracy predicting any polymer quality targets such as melt index (MI), density, data-based techniques might be more appropriate. Recently, we may have heard a lot about artificial intelligence (AI) and machine learning (ML) the basic principle behind these methods is to making the model learn from data for prediction. The process data that are measured in a chemical/polymer plant can be utilized for data analysis. We can build ML models to predict polymer targets like MI as a function of the input process variables. The ML model predictions are very accurate in the process operating range of the dataset on which the model is learned, but outside the prediction range, they may tend to give scientifically inconsistent results. Thus, there is a need to combine the data-based models and scientific models. In our research, we showcase novel approaches to integrate the science-based models and the data-based ML methodology which we term as the hybrid science-guided machine learning methods (SGML). The hybrid SGML methods applied to polyolefin processes yield not only accurate, but scientifically consistent predictions which can be used for polyolefin process optimization for applications like process development and quality monitoring.

While machine learning has shown success in many fields, it can be challenging when there are limitations with insufficient training data. By incorporating knowledge into the machine learning pipeline, one can overcome such limitations. Therefore, eliciting expert knowledge can play an important role in the machine learning project pipeline. Expert knowledge can come in many forms, and it is seldom easy to elicit and formalize it in a way that is easily implementable into a machine learning project. While it has been done, not much focus has been on how. Furthermore, the motivations for why knowledge was elicited in a particular way as well as the challenges that may exist with the elicitation, are not always focused on either. Making educated decisions for knowledge elicitation can therefore be challenging for researchers. Hence, this work aims to explore and categorize how expert knowledge elicitation has been done by researchers previously. This was done by developing a taxonomy that was then used for analyzing articles. A total of 43 articles were found, containing 97 elicitation paths that were categorized in order to identify trends and common approaches. The findings from our study were used to provide guidance for an industrial case in its initial stage to show how the taxonomy presented in this work can be applied in a real-world scenario.