Prediction of high-density polyethylene pyrolysis using kinetic parameters based on thermogravimetric and artificial neural networks

● Reducting the sampling frequency can enhance the modelling process. ● The pyrolysis of HDPE was investigated at three different heating rates. ● The average E_a and k₀ were calculated by Friedman, KAS, FWO, and CR methods. ● ANN was employed to predict the HDPE weight loss with the optimal MSE and R². Pyrolysis is considered an attractive option and a promising way to dispose waste plastics. The thermogravimetric experiments of high-density polyethylene (HDPE) were conducted from 105 °C to 900 °C at different heating rates (10 °C/min, 20 °C/min, and 30 °C/min) to investigate their thermal pyrolysis behavior. We investigated four methods including three model-free methods and one model-fitting method to estimate dynamic parameters. Additionally, an artificial neural network model was developed by providing the heating rates and temperatures to predict the weight loss (wt.%) of HDPE, and optimized via assessing mean squared error and determination coefficient on the test set. The optimal MSE (2.6297 × 10⁻²) and R² value (R² > 0.999) were obtained. Activation energy and pre-exponential factor obtained from four different models achieves the acceptable value between experimental and predicted results. The relative error of the model increased from 2.4 % to 6.8 % when the sampling frequency changed from 50 s to 60 s, but showed no significant difference when the sampling frequency was below 50 s. This result provides a promising approach to simplify the further modelling work and to reduce the required data storage space. This study revealed the possibility of simulating the HDPE pyrolysis process via machine learning with no significant accuracy loss of the kinetic parameters. It is hoped that this work could potentially benefit to the development of pyrolysis process modelling of HDPE and the other plastics.     

《昆明-蒙特利尔全球生物多样性框架》下的生物多样性监测

生物多样性监测是执行和评估《昆明-蒙特利尔全球生物多样性保护框架》（简称《昆-蒙框架》）长期目标和行动指标所需要的关键数据的主要来源。简要介绍了《昆-蒙框架》下的全球生物多样性观测网络（The global biodiversity observation network,GEO BON）概况、中国已有的生物多样性监测/观测网络,构建全球生物多样性观测系统（A global biodiversity observation system,GBiOS）的目的和核心监测指标,分析了生物多样性监测网络建设的一般步骤和GEO BON建议的4种生物多样性监测新方法。提出,地理空间单元与生物类群的全覆盖监测是建设生物多样性监测网络的主要目标,传统监测方法和新监测技术的互补是当前生物多样性监测的重点工作内容。     

海南省省控土壤环境监测基础点位布设思路与方法

为满足新时期土壤污染防治管理要求,掌握海南省土壤环境质量状况及变化趋势,海南省亟需加强省控土壤环境监测基础点位布设,完善土壤环境监测网。通过梳理海南省土壤环境监测工作基础和监测网络构建情况,形成了省控基础点位布设思路、原则和方法,并应用GIS技术完成点位布设。海南省省控土壤环境监测基础点位基本覆盖了海南省农用地主要土壤类型和成土母岩,监测结果可以反映海南省农用地土壤环境质量整体状况。     

深度学习在裸地扬尘源监测中的应用研究

裸地是扬尘的重要来源,施工建设过程中形成的裸地极易在大风天气作用下造成扬尘污染。因此,快速、有效地定位裸地位置,并确认其管控措施落实情况,对于开展裸地扬尘源监管具有重要意义。基于高分辨率遥感监测数据,结合人工解译裸地扬尘源数据集,以北京市大兴区为例,利用深度学习方法对裸地和防尘网覆盖裸地进行分类识别。同时,利用颜色匹配法对大兴区防尘网覆盖裸地进行识别,横向评估深度学习方法的识别精度。结果显示：深度学习方法对防尘网覆盖裸地的识别精度达97%,对裸地的识别精度达61%;颜色匹配法对防尘网覆盖裸地的识别精度达85%。防尘网覆盖裸地的颜色特征鲜明,深度学习方法和颜色匹配法对防尘网覆盖裸地的识别精度都在85%以上。深度学习方法对于面积大于2 000 m²的图斑有着较好的识别精度。深度学习方法可以提高裸地遥感解译的效率,实现规范化图像识别,可以作为人工判读的辅助手段。在实际应用中,可通过进一步积累样本来增强模型性能。深度学习方法适用于裸地扬尘源线索快速发现、工地防尘网措施落实情况快速检测等场景。     

提高人工神经网络洪水峰值预报精度的研究

以动量项与学习率自适应调整相结合的改进BP算法作为人工神经网络计算方法，阐述了洪水预报网络模型的构造和数据处理，以及转移函数对洪水预报精度的影响，提出了合理的洪水预报网络模型的设计方案。     

污水处理过程自动控制研究与应用

就国内外近几年的污水处理过程自动控制的研究进展进行了综述,介绍了各种先进控制理论和先进控制技术在污水处理主要工段和典型参数控制中的应用,并提出污水处理过程中先进控制的前景。     

建筑内人员疏散的一种网络模型算法的讨论

针对疏散模型问题进行了一些讨论,在分析疏散模型的基础上,认为网络模型较为适合工程应用,确定发展网络疏散模型。在讨论网络疏散模型路径时,首先将整个建筑物模型划分为四种不同的结构,并假设了模型成立的条件,再按照典型的流量限制原理和该单元内疏散的特点,分别讨论了这四种不同类型的结构的算法。     

Dynamic risk analysis of offloading process in floating liquefied natural gas (FLNG) platform using Bayesian Network

The growing demand for natural gas has pushed oil and gas exploration to more isolated and previously untapped regions around the world where construction of LNG processing plants is not always a viable option. The development of FLNG will allow floating plants to be positioned in remote offshore areas and subsequently produce, liquefy, store and offload LNG in the one position. The offloading process from an FLNG platform to a gas tanker can be a high risk operation. It consists of LNG being transferred, in hostile environments, through loading arms or flexible cryogenic hoses into a carrier which then transports the LNG to onshore facilities. During the carrier's offloading process at onshore terminals, it again involves risk that may result in an accident such as collision, leakage and/or grounding. It is therefore critical to assess and monitor all risks associated with the offloading operation. This study is aimed at developing a novel methodology using Bayesian Network (BN) to conduct the dynamic safety analysis for the offloading process of an LNG carrier. It investigates different risk factors associated with LNG offloading procedures in order to predict the probability of undesirable accidents. Dynamic failure assessment using Bayesian theory can estimate the likelihood of the occurrence of an event. It can also estimate the failure probability of the safety system and thereby develop a dynamic failure assessment tool for the offloading process at a particular FLNG plant. The main objectives of this paper are: to understand the LNG offloading process, to identify hazardous events during offloading operation, and to perform failure analysis (modelling) of critical accidents and/or events. Most importantly, it is to evaluate and compare risks. A sensitivity analysis has been performed to validate the risk models and to study the behaviour of the most influential factors. The results have indicated that collision is the most probable accident to occur during the offloading process of an LNG carrier at berth, which may have catastrophic consequences.     

神经网络预测催化超临界水氧化废水效果的研究

在间歇式反应器中进行催化超临界水氧化DDNP废水试验,考察催化剂浓度、反应温度、压力、停留时间对氧化效果的影响。在实验基础上采用BP神经网络算法,建立以催化剂浓度、反应温度、压力、停留时间作为网络模型的输入层,COD去除率作为输出层的双隐层BP神经网络预测模型,预测催化超临界水氧化废水的效果,仿真结果表明模型预测效果较好。     

区域高速公路网紧急事件救援协作体系构建

从加强高速公路紧急事件应急处置、协同管理的角度出发,给出区域高速公路网和紧急事件的定义,界定了紧急事件的分类和特征,阐明建立救援协作体系的必要性。从工作流程和环节控制入手,构建包括决策组织、信息共享、交通管控、现场救援、应急储备等内容在内的区域高速公路网紧急事件救援协作体系,提出各模块构成要件及典型运作模式,有助于统筹区域高速公路网决策指挥,优化应急资源配置和调度,密切紧急事件协同处置,提高救援效率和水平。