Economic analysis of the circular economy based on waste plastic pyrolysis oil: a case of the university campus.

Recently, the concept of a circular economy for carbon neutrality is emerging. In particular, waste plastics are one of the key wastes, and efforts are being made to recycle them as energy rather than dispose of them. Accordingly, the technology of producing and utilizing pyrolysis oil from waste plastics attracts attention. As it is an early stage of technology development, however, there are not many demonstrations and papers that analyze the technology broadly. The goal of this study is to propose building a circular economy on a university campus through waste plastic pyrolysis oil technology. To show its feasibility, waste plastic pyrolysis oil technology is analyzed comprehensively from economic, environmental, and policy perspectives using the scenario analysis technique on the university campus level. A methodology of the scenario analysis technique enables predicting the uncertainties. Since plastic pyrolysis oil technologies and carbon neutrality are accompanied by many uncertainties, this technique is expected to be an appropriate methodology for this study. First, the amount of pyrolysis oil production from waste plastics from the campus is estimated. Then, the cost and carbon emissions from waste plastics are estimated if the pyrolysis oil technology is used instead of the traditional waste disposal process. As a result, the total economic profits of up to 425,484,022 won/year (354,570.01 $/year) are expected when a circular economy is built using waste plastic pyrolysis oil. In addition, it is also confirmed that greenhouse gas (GHG) emissions can be reduced by up to 840,891 kgCO2eq/year. The waste plastic pyrolysis oil satisfies Korea's gas pollutant standards and is consistent with the GHG reduction policy. It can be concluded that building a circular economy at the university campus level using waste plastic pyrolysis oil technology is suitable from economic, environmental, and policy perspectives.

A method for detecting the quality of cotton seeds based on an improved ResNet50 model.

The accurate and rapid detection of cotton seed quality is crucial for safeguarding cotton cultivation. To increase the accuracy and efficiency of cotton seed detection, a deep learning model, which was called the improved ResNet50 (Impro-ResNet50), was used to detect cotton seed quality. First, the convolutional block attention module (CBAM) was embedded into the ResNet50 model to allow the model to learn both the vital channel information and spatial location information of the image, thereby enhancing the model's feature extraction capability and robustness. The model's fully connected layer was then modified to accommodate the cotton seed quality detection task. An improved LRelu-Softplus activation function was implemented to facilitate the rapid and straightforward quantification of the model training procedure. Transfer learning and the Adam optimization algorithm were used to train the model to reduce the number of parameters and accelerate the model's convergence. Finally, 4419 images of cotton seeds were collected for training models under controlled conditions. Experimental results demonstrated that the Impro-ResNet50 model could achieve an average detection accuracy of 97.23% and process a single image in 0.11s. Compared with Squeeze-and-Excitation Networks (SE) and Coordination Attention (CA), the model's feature extraction capability was superior. At the same time, compared with classical models such as AlexNet, VGG16, GoogLeNet, EfficientNet, and ResNet18, this model had superior detection accuracy and complexity balances. The results indicate that the Impro-ResNet50 model has a high detection accuracy and a short recognition time, which meet the requirements for accurate and rapid detection of cotton seed quality.

Classification of plug seedling quality by improved convolutional neural network with an attention mechanism.

The classification of plug seedling quality plays an active role in enhancing the quality of seedlings. The EfficientNet-B7-CBAM model, an improved convolutional neural network (CNN) model, was proposed to improve classification efficiency and reduce high cost. To ensure that the EfficientNet-B7 model simultaneously learns crucial channel and spatial location information, the convolutional block attention module (CBAM) has been incorporated. To improve the model's ability to generalize, a transfer learning strategy and Adam optimization algorithm were introduced. A system for image acquisition collected 8,109 images of pepper plug seedlings, and data augmentation techniques improved the resulting data set. The proposed EfficientNet-B7-CBAM model achieved an average accuracy of 97.99% on the test set, 7.32% higher than before the improvement. Under the same experimental conditions, the classification accuracy increased by 8.88-20.05% to classical network models such as AlexNet, VGG16, InceptionV3, ResNet50, and DenseNet121. The proposed method had high accuracy in the plug seedling quality classification task. It was well-adapted to numerous types of plug seedlings, providing a reference for developing a fast and accurate algorithm for plug seedling quality classification.