The physical properties required in polypropylene composites (PPCs) vary depending on the purpose of use. In the manufacturing of PPCs, it is crucial to determine the types and quantities of numerous reinforcements to meet the required physical properties. Owing to industrial complexity, most PPC manufacturers produce the composites repeatedly until the desired physical properties are obtained. Hence, to reduce trial and error, we developed prediction models for the physical properties of PPCs based on commercial recipe data. The recipe data included information about five physical properties of composites manufactured using 90 materials. In complex industrial environments, because one recipe is usually composed of 2–12 materials, numerous combinations of data sets are created. It causes the lack of the same material combination data sets and thus makes it difficult to develop a good performance model. Therefore, a novel categorization process is suggested as data preprocessing to overcome the data imbalance problem. The models for predicting the five physical properties (flexural strength, melting index, tensile strength, specific gravity, and flexural modulus) were developed using random forest, and the performance of the prediction models was improved via hyperparameter optimization. Furthermore, the effects of the materials on the performance of the models were numerically described through variable importance analysis. Finally, a software was developed to implement the prediction models in the industry. The software was applied to a commercial composite and achieved high accuracy, demonstrating the effectiveness of this study. Thus, the software suggests decision-making solutions to save cost and time by reducing the trial and error in the industrial environment with high complexity.

Manufacturing polypropylene (PP) composites to meet customers’ needs is difficult, time-consuming, and costly, owing to the ever-increasing diversity and complexity of the corresponding specifications and the trial-and-error method currently used to satisfy the required physical properties. To address this issue, we developed three models for predicting the physical properties of PP composites using three machine learning (ML) methods: multiple linear regression (MLR), deep neural network (DNN), and random forest (RF). Further, the industrial data of 811 recipes were acquired to verify the developed models. Data categorization was performed to account for the differences between data and the fact that different recipes require different materials. The three models were then deployed to predict the flexural strength (FS), melting index (MI), and tensile strength (TS) of the PP composites in nine case studies. The predictive performance results differed according to the physical properties of the composites. The FS and MI prediction models with MLR exhibited the highest R² values of 0.9291 and 0.9406. The TS model with DNN exhibited the highest R² value of 0.9587. The proposed models and study findings are useful for predicting the physical properties of PP composites for recipes and the development of new recipes with specific physical properties.

The distillation process is one of the most common and energy‐intensive processes in the chemical industry. Most chemical processes are nonlinear and complex, because of which, it is difficult to find optimal operating conditions. To solve this problem, we developed a framework for energy optimization of the distillation process based on a machine learning (ML) model. The framework enables the efficient operation of the process by using the optimal operating conditions recommended by the ML‐based predictive model. The predictive model, which is a key component, is developed in three steps: learning, validation, and improvement. In the learning step, we select an algorithm suitable for the purpose of the process and learn process data. In the validation step, the model is validated using hold‐out cross‐validation. Finally, in the improvement step, the model performance is improved through hyper‐parameter optimization. We applied the framework to a commercial mixed butane distillation columns of 45,000 metric tons per annum capacity. The predictive model was based on commercial process data, and it can be used to predict the temperature at the product stage. The model recommended the steam flow rate required to maintain the target temperature of the product stage as per the operating conditions. The recommended steam flow rate will be guideline for the on‐site operator. The software is developed that the predictive model can be easily applied to the commercial processes, and it identifies the state of the process and recommends optimal operating conditions.