B-COPNA resin formation from ethylene tar light fractions:Process development and mechanical exploration by molecular simulation

An efficient utilization strategy of ethylene tar(ET),the main by-product of the ethylene cracking unit,is urgently required to meet demands for modern petrochemical industry.On the other hand,condensed polynuclear aromatic resin of moderate condensation degree(B-COPNA)is a widely used carbon material due to its superb processability,the production of which is,however,seriously limited by the high cost of raw materials.Under such context,an interesting strategy was proposed in this study for producing B-COPNA resin using crosslinked light fractions of ethylene tar(ETLF,boiling point<260℃)facilitated by molecular simulation.1,4-Benzenedimethanol(PXG)was first selected as the crosslinking agent according to the findings of molecular simulation.The effects of operating conditions,including reactions temperature,crosslinking agent,and catalyst content on the softening point and yield of B-COPNA resin products were then investigated to optimize the process.The reaction mechanism of resin production was studied by analyzing the molecular structure and transition state of ETLF and crosslinking agents.It was shown that PXG exhibited a superior capacity of withdrawing electrons and a higher electrophilic reactivity than other crosslinking agents.In addition to the highest yield and greatest heat properties,PXG-prepared resin contained the most condensed aromatics.The corresponding optimized conditions of resin preparation were 180℃,1:1.9(PXG:ETLF),and 3%(mass)of catalyst content with a resin yield of 78.57%.It was the electrophilic substitution reaction that occurred between the ETLF and crosslinking agent molecules that were responsible for the resin formation,according to the experimental characterization and molecular simulation.Hence,it was confirmed that the proposed strategy and demonstrated process can achieve a clean and high value-added utilization of ETLF via B-COPNA resin preparation,bringing huge economic value to the current petrochemical industry.

An algorithm for automatic identification of multiple developmental stages of rice spikes based on improved Faster R-CNN

Spike development directly affects the yield and quality of rice. We describe an algorithm for automatically identifying multiple developmental stages of rice spikes(AI-MDSRS) that transforms the automatic identification of multiple developmental stages of rice spikes into the detection of rice spikes of diverse maturity levels. The scales vary greatly in different growth and development stages because rice spikes are dense and small, posing challenges for their effective and accurate detection. We describe a rice spike detection model based on an improved faster regions with convolutional neural network(Faster R-CNN).The model incorporates the following optimization strategies: first, Inception_Res Net-v2 replaces VGG16 as a feature extraction network;second, a feature pyramid network(FPN) replaces single-scale feature maps to fuse with region proposal network(RPN);third, region of interest(Ro I) alignment replaces Ro I pooling, and distance-intersection over union(DIo U) is used as a standard for non-maximum suppression(NMS). The performance of the proposed model was compared with that of the original Faster R-CNN and YOLOv4 models. The mean average precision(m AP) of the rice spike detection model was92.47%, a substantial improvement on the original Faster R-CNN model(with 40.96% m AP) and 3.4%higher than that of the YOLOv4 model, experimentally indicating that the model is more accurate and reliable. The identification results of the model for the heading–flowering, milky maturity, and full maturity stages were within two days of the results of manual observation, fully meeting the needs of agricultural activities.