GRNN模型在煤与瓦斯突出及瓦斯含量预测中的应用

煤与瓦斯突出的作用机理非常复杂,是诸多因素如地应力、煤层瓦斯、煤体物理力学性质等共同作用的结果。在分析广义回归神经网络(GRNN)的基本原理和算法的基础上,建立煤与瓦斯突出等级以及基于构造复杂程度定量评价的瓦斯含量GRNN模型。然后用收集到的工程实例样本训练和检验该模型。结果表明,GRNN模型具有很好的预测能力和泛化能力,能较好揭示瓦斯含量和诸影响因素间的关系,可用于煤与瓦斯突出判别以及瓦斯含量预测。同时可以看出,光滑因子的合理选取对于提高GRNN模型的预测精度非常重要,因此,在以后的实际应用中需要不断尝试,找出最合理的光滑因子。     

基于神经网络模型的燃料空气混合物爆炸威力预测研究

燃料空气混合物爆炸威力准确预测研究是学术界的一个难题。针对燃料空气混合物爆炸威力有效预测问题,采用神经网络方法,设计多层神经网络模型,进行实际预测应用。应用结果表明,采用的预测方法简便、可行,可以为燃料空气混合物爆炸威力预测提供一种新途径。相比3层BP模型,设计的预测模型可以减少训练次数,缩短训练时间,提高预测正确率,应用优势较明显。     

基于人工神经网络的莺落峡月径流模拟预测

莺落峡是黑河干流出山口径流量的重要控制站,莺落峡径流量的多少直接影响着该流域经济、社会的发展和生态环境保护,水资源分配和调度的管理和决策。论文基于人工神经网络,对莺落峡径流进行了模拟预测。将月径流分为汛期和非汛期,分别建立BP人工神经网络,通过对径流分类前后的模型进行比较,发现分类后的月径流BP模型的性能显然优于未分类的模型,故此设计了4种不同气候情景,采用分类后的模型对莺落峡2030年的径流量进行了预测。即,在降水量不变、气温增加0.5℃,2030年莺落峡年径流量将增加8.92%;气温增加1℃、降水量不变,年径流量将减少5.414%;气温不变、降水量增加10%,年径流量将增加9.905%;气温增加0.5℃、降水量增加10%,年径流量将增加8.98%。     

江苏数字地震台网地震监测能力估算

采用理论计算和实测相结合的方法估计了江苏数字地震台网的地震监测能力。虽然用两种方法得到的监测区略有差异 ,但几何形状相近 ,其公共区域部分更趋合理地反映了目前江苏数字地震台网的监测范围。比较了速度型和位移型地震记录的差异 ,并讨论了其监测范围差异的原因     

江苏省防震减灾信息网络主页的实现

介绍了江苏省防震减灾信息网络主页的设计方案 ,探讨了Web服务器的选择、浏览器兼容性问题、网页内容与结构布局、地震数据的动态交互 ,HTML/DHTML功能扩展及网络安全 5个方面的问题。重点讨论了防震减灾信息与数据的Web实现和网络安全。     

多水源用水网络优化模型研究

为了解决含有多类供水水源的用水网络优化设计问题,研究了过程工业用水特点,将各用水单元看成水阱,各单元的排水看成潜在水源,从而将供水问题转化为水阱和水源的匹配问题,建立用于多水源用水网络优化的配水模型,并详细阐述该模型应用步骤及其特点。     

区域空气监测网络建设策略与实践

根据区域空气质量管理的发展形势,提出建设区域空气自动监测网络的思路和建设内容,探索建立区域空气监测QA/QC技术体系的策略与解决方案。     

2015—2019年汾渭平原臭氧污染状况分析

基于全国空气质量监测网数据,分析了2015—2019年汾渭平原11个城市臭氧(O₃)污染状况。结果表明:2015—2019年,汾渭平原11个城市O₃平均浓度总体呈升高趋势,年平均升高12.2μg/m³,其中,2017—2019年均超过二级标准限值(160μg/m³)。O₃单项污染物的空气质量分指数占空气质量指数的比例逐年升高,O₃超标使汾渭平原2015—2019年各年度空气质量优良天数比例分别减少了1.4、5.4、13.0、11.1、14.4个百分点。O₃浓度呈春夏季(5—9月)高、秋冬季(11—12月)低的特点,其中,5—9月O₃超标天数占全部O₃超标天数的97%以上。各年度O₃日最大8小时平均质量浓度(O₃-8 h)的最大值分别为152、176、224、195、202μg/m³,均出现在5—7月。O₃...     

Internal corrosion hazard assessment of oil & gas pipelines using Bayesian belief network model

A substantial amount of oil & gas products are transported and distributed via pipelines, which can stretch for thousands of kilometers. In British Columbia (BC), Canada, alone there are over 40,000 km of pipelines currently being operated. Because of the adverse environmental impact, public outrage and significant financial losses, the integrity of the pipelines is essential. More than 37 pipe failures per year occur in BC causing liquid spills and gas releases, damaging both property and environment. BC oil & gas commission (BCOGS) has indicated metal loss due to internal corrosion as one of the primary causes of these failures. Therefore, it is of a paramount importance to timely identify pipelines subjected to severe internal corrosion in order to improve corrosion mitigation and pipeline maintenance strategies, thus minimizing the likelihood of failure. To accomplish this task, this paper presents a Bayesian belief network (BBN)-based probabilistic internal corrosion hazard assessment approach for oil & gas pipelines. A cause-effect BBN model has been developed by considering various information, such as analytical corrosion models, expert knowledge and published literature. Multiple corrosion models and failure pressure models have been incorporated into a single flexible network to estimate corrosion defects and associated probability of failure (PoF). This paper also explores the influence of fluid composition and operating conditions on the corrosion rate and PoF. To demonstrate the application of the BBN model, a case study of the Northeastern BC oil & gas pipeline infrastructure is presented. Based on the pipeline's mechanical characteristics and operating conditions, spatial and probabilistic distributions of corrosion defect and PoF have been obtained and visualized with the aid of the Geographic Information System (GIS). The developed BBN model can identify vulnerable pipeline sections and rank them accordingly to enhance the informed decision-making process.     

A neuro-fuzzy algorithm for assessment of health,safety, environment and ergonomics in a large petrochemical plant

Process plants such as petrochemical units have been continuously trying to improve Health, Safety, Environment and Ergonomics (HSEE) programs. This study proposes an adaptive network-based fuzzy inference system (ANFIS) for assessment of HSEE programs in a petrochemical plant. The proposed neuro-fuzzy approach is applied to a set of operators in the petrochemical unit to show its applicability and superiority. To achieve the objectives of this study, standard questionnaires with respect to HSEE are completed by operators. The average results for each category of HSEE are used as inputs and accomplishment of HSEE programs is used as output for the algorithm. Moreover, this algorithm is used to rank operators performance with respect to HSEE. Finally, the algorithm identifies efficient operators with respect to HSEE. This is the first study that introduces an intelligence algorithm for assessment and improvement of HSEE program in a petrochemical plant.