The global energy demand is expected to increase by 30% within the next two decades. Plastic thermochemical recycling is a potential alternative to meet this tremendous demand because of its availability and high heating value. Polypropylene (PP) and polyethylene (PE) are considered in this study because of their substantial worldwide availability in the category of plastic wastes. Two cases were modeled to produce hydrogen from the waste plastics using Aspen Plus^®. Case 1 is the base design containing three main processes (plastic gasification, syngas conversion, and acid gas removal), where the results were validated with the literature. On the other hand, case 2 integrates the plastic gasification with steam methane reforming (SMR) to enhance the overall hydrogen production. The two cases were then analyzed in terms of syngas heating values, hydrogen production rates, energy efficiency, greenhouse gas emissions, and process economics. The results reveal that case 2 produces 5.6% more hydrogen than case 1. The overall process efficiency was enhanced by 4.13%. Case 2 reduces the CO₂ specific emissions by 4.0% and lowers the hydrogen production cost by 29%. This substantial reduction in the H₂ production cost confirms the dominance of the integrated model over the standalone plastic gasification model.

Methanol is a clean fuel and an important feedstock for the petrochemical industry. Conventionally, the coal-to-methanol process generates a substantial amount of CO₂ emissions with a low yield of methanol. In this study, we propose the conceptual design development of coal-to-methanol process using captured CO₂ from the gasification plant by implying process intensification. The base case and three alternative designs have been developed using the Aspen Plus to analyze the process performance. The four designs have been compared in terms of their energy consumption, economics, methanol production rate, and carbon emissions while maintaining the gasifier operation conditions, sulfur content in the syngas, and stoichiometric number at the methanol synthesis reactor. Among the developed designs, the most feasible design requires an energy requirement and product cost of 21.9 GJ and $142.5 per ton of methanol product. The study also showed that the coal-to-methanol process assisted with external hydrogen from electrolysis plant for achieving higher production rates and low CO₂ emissions is currently not economical due to the high H₂ cost.

Piping and instrument diagrams (P&IDs) are a key component of the process industry; they contain information about the plant, including the instruments, lines, valves, and control logic. However, the complexity of these diagrams makes it difficult to extract the information automatically. In this study, we implement an object-detection method to recognize graphical symbols in P&IDs. The framework consists of three parts—region proposal, data annotation, and classification. Sequential image processing is applied as the region proposal step for P&IDs. After getting the proposed regions, the unsupervised learning methods, k-means, and deep adaptive clustering are implemented to decompose the detected dummy symbols and assign negative classes for them. By training a convolutional network, it becomes possible to classify the proposed regions and extract the symbolic information. The results indicate that the proposed framework delivers a superior symbol-recognition performance through dummy detection.