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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.

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A novel design of a double-tube steam methane reforming (SMR) reactor was evaluated in terms of conversion and reactor temperature, compared with the conventional, single-tube, fixed bed reactor. The heat from the reformate could be recovered through the double-tube reactor, which increased the conversion from 71.7 to 89.3% and lowered the reactor outlet temperature from 732.7 to 674.5 °C. An actual plant was then designed, wherein the entire operating process was tested using the doubletube reactor, which produced 100 N m 3 /h of pure hydrogen. Last, to maximize the thermal efficiency and to achieve a hydrogenproduction rate of >100 N m 3 /h, the operating conditions were optimized with the decision variables and constraints based on actual operating experiences. Consequently, our developed optimal SMR system gave a thermal efficiency of 81.3%, higher than that of the current commercial products (approximately 70%), and achieved a hydrogen-production rate of 124.8 N m 3 /h.

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