喀斯特石漠化综合防治空间决策支持系统研究

为了更好地配合喀斯特石漠化的综合治理,结合地理信息系统(GIS)和决策支持系统(DSS)技术,采用Map Object开发方法,开发了喀斯特石漠化综合防治空间决策支持系统。充分利用喀斯特石漠化地区各种空间信息,建立石漠化强度现状分析、石漠化发生率计算、石漠化年变化率分析等决策分析模型以及石漠化综合防治效果监测模块。通过实际应用可知,基于空间决策支持系统(SDSS)建立空间信息系统可以很好的为喀斯特石漠化的防治提供决策分析,可以更好地监测石漠化强度的变化,有助于石漠化的治理工作。     

Development of HGAPSO-SVR corrosion prediction approach for offshore oil and gas pipelines

Managing the oil and gas pipelines against corrosion is one of the major challenges of the oil and gas sector because of the complexities associated with the initiation, stabilization, and growth of the corrosion defects. The present research attempts to develop a model for predicting the maximum depth of pitting corrosion in oil and gas pipelines using SVM algorithm. In order to improve the SVM performance, Hybrid PSO and GA was utilized. Monte Carlo simulation was used to determine the time lapse for the pit depth growth. In order to implement the above modeling approaches and to prove their efficiency and accuracy against a large database, a total of 340 data samples for corrosion depth and rate are retrieved from the Iranian Oilfields. The performance of the new algorithm shows that it has higher stability and accuracy. In addition, the forecasting results of the new algorithm are compared with the 11 intelligent optimization algorithms, it shows that the novel hybrid algorithm has higher accuracy, better generalization ability, and stronger robustness. The coefficient of determination (R²) value in the testing phase for SVM-HGAPSO was estimated by 0.99. Proposed hybrid model and Monte-Carlo simulations pitting corrosion based on Poisson square wave process have been used to predict the time evolution of the mean value of the pit depth distribution for different categories of maximum pitting rates (low, moderate, high and sever). The models was validated with 4 field data for each of the pitting corrosion categories and the results agreed well. The pipelines under severe pitting corrosion rate were, more conservatively predicted by HGAPSO-SVR than those under low, moderate and high pitting corrosion rates. The results obtained demonstrate the potentials of this technique for the integrity management of corroded aged pipelines.     

Prediction of gas explosion pressures: A machine learning algorithm based on KPCA and an optimized LSSVM

A gas explosion, as a common accident in public life and industry, poses a great threat to the safety of life and property. The determination and prediction of gas explosion pressures are greatly important for safety issues and emergency rescue after an accident occurs. Compared with traditional empirical and numerical models, machine learning models are definitely a superior approach. However, the application of machine learning in gas explosion pressure prediction has not reached its full potential. In this study, a hybrid gas explosion pressure prediction model based on kernel principal component analysis (KPCA), a least square support vector machine (LSSVM), and a gray wolf optimization (GWO) algorithm is proposed. A dataset consisting of 12 influencing factors of gas explosion pressures and 317 groups of data is constructed for developing and evaluating the KPCA-GWO-LSSVM model. The results show that the correlations among the 12 influencing factors are eliminated and dimensioned down by the KPCA method, and 5 composite indicators are obtained. The proposed KPCA-GWO-LSSVM hybrid model performs well in predicting gas explosion pressures, with coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) values of 0.928, 26.234, and 12.494, respectively, for the training set; and 0.826, 25.951, and 13.964, respectively, for the test set. The proposed model outperforms the LSSVM, GWO-LSSVM, KPCA-LSSVM, beetle antennae search improved BP neural network (BAS-BPNN) models and reported empirical models. In addition, the sensitivity of influencing factors to the model is evaluated based on the constructed database, and the geometric parameters X1 and X2 of the confined structure are the most critical variables for gas explosion pressure prediction. The findings of this study can help expand the application of machine learning in gas explosion prediction and can truly benefit the treatment of gas explosion accidents.     

Risk information in decision-making: definitions,requirements and various functions

Inappropriate decisions are often regarded as causes of major accidents in the process industries. To improve the quality of decisions, it is important to make the right information available at the right time. The objective of this work is to investigate what types of risk information is needed for risk-related decisions in various decision-making processes. A framework is proposed to facilitate future research for easing information deficiency. In this paper, risk information is examined through common decision-making processes, and is identified serving to 1) detect and characterize risk-related decision problems, 2) indicate the severity and urgency of decisions, 3) state requirements and constraints of workable solutions, 4) represent attributes for comparing and evaluating solutions, and 5) act as rules to maintain safety or control risk. These usages of risk information in different decision problems imply the large diversity in information needs for decision-making. An adaptive information support is thus suggested to provide targeted risk information to specific decision-makers for effective and efficient decision-making in accident prevention in the process industries.     

Water quality prediction of copper-molybdenum mining-beneficiation wastewater based on the PSO-SVR model

● Data acquisition and pre-processing for wastewater treatment were summarized. ● A PSO-SVR model for predicting COD_eff in wastewater was proposed. ● The COD_eff prediction performances of the three models in the paper were compared. ● The COD_eff prediction effects of different models in other studies were discussed. The mining-beneficiation wastewater treatment is highly complex and nonlinear. Various factors like influent quality, flow rate, pH and chemical dose, tend to restrict the effluent effectiveness of mining-beneficiation wastewater treatment. Chemical oxygen demand (COD) is a crucial indicator to measure the quality of mining-beneficiation wastewater. Predicting COD concentration accurately of mining-beneficiation wastewater after treatment is essential for achieving stable and compliant discharge. This reduces environmental risk and significantly improves the discharge quality of wastewater. This paper presents a novel AI algorithm PSO-SVR, to predict water quality. Hyperparameter optimization of our proposed model PSO-SVR, uses particle swarm optimization to improve support vector regression for COD prediction. The generalization capacity tested on out-of-distribution (OOD) data for our PSO-SVR model is strong, with the following performance metrics of root means square error (RMSE) is 1.51, mean absolute error (MAE) is 1.26, and the coefficient of determination (R²) is 0.85. We compare the performance of PSO-SVR model with back propagation neural network (BPNN) and radial basis function neural network (RBFNN) and shows it edges over in terms of the performance metrics of RMSE, MAE and R², and is the best model for COD prediction of mining-beneficiation wastewater. This is because of the less overfitting tendency of PSO-SVR compared with neural network architectures. Our proposed PSO-SVR model is optimum for the prediction of COD in copper-molybdenum mining-beneficiation wastewater treatment. In addition, PSO-SVR can be used to predict COD on a wide variety of wastewater through the process of transfer learning.     

精神支持与物质支持对建筑工人不安全行为影响对比

为向建筑工人提供更具针对性的支持,对比分析精神支持和物质支持的影响效果,分别构建精神支持和物质支持对建筑工人不安全行为的影响机理,基于351名建筑工人调查数据,采用结构方程模型展开实证研究.研究结果表明:精神支持和物质支持对建筑工人安全意识的提高和工作压力的降低都具有显著影响;相较于物质支持,精神支持影响效果更好,更能...     

支持向量机法在煤与瓦斯突出分析中的应用研究

通过分析采煤工作面煤与瓦斯涌出量与地质构造指标的对应关系,应用支持向量机(SVM)方法对煤与瓦斯涌出类型及涌出量进行分析。建立两类突出识别的SVM模型、多类型突出识别的H-SVMs模型以及预测瓦斯涌出量的支持向量回归模型。研究结果表明:SVM方法能够很好地对煤与瓦斯突出模式进行识别,所建立的采煤工作面瓦斯涌出量预测模型的精度高于应用BP神经网络预测精度;SVM理论基础严谨,决策函数结构简单,泛化能力强,并且决策函数中的法向量W可以反映突出模式识别的地质结构指标的权重。     

基于支持向量回归机的煤层瓦斯含量预测研究

为了对煤层瓦斯含量进行准确预测,应用支持向量回归机(SVR)理论建立煤层瓦斯含量预测模型,结合现场实测数据利用支持向量机(SVM)工具箱进行模型的求解及预测,并从均方根误差、希尔不等系数和平均绝对百分误差3个不同误差指标与人工神经网络预测模型进行比较分析。研究结果表明:SVR模型其预测精度及可行性高于神经网络模型,而且运算快,实时性较好,用于煤层瓦斯含量的预测较理想,具有良好的应用前景,可以为煤矿瓦斯防治提供理论依据。     

城市环境实用决策支持系统(UEDSS)的研制

我国研制并推广通用性和实用性城市环境决策支持系统(UFDSS)时机已经成熟。该文探讨了该系统研制中的几个关键问题,包括系统功能需求分析;城市环境优先决策问题;半结构化问题的结构化处理;通用性城市环境决策模型(IPSUE);GIS和DSS的关系;GIS对UEDSS的贡献等。最后给出了UEDSS的总体框架结构．      

某型地面武器系统严寒地区保障问题与对策研究

目的 破解特殊自然环境影响产生的某型地面武器系统保障难题。方法 根据历史故障情况,采用FMEA(Failure Mode Effect Analysis,故障模式影响分析)进行低温影响分析,采用Pearson相关系数法分析低温与武器系统故障关系,应用回归分析进行严寒条件下装备故障情况预测。结合实际应用情况,全面系统总结该型地面武器系统在严寒条件下,使用和保障过程中可能出现的技术准备、性能指标、信息体系构建、伪装防护、维修保障相关问题。结果 得出某型地面武器系统低温情况下故障数量与温度负相关的结论,且预测在严寒条件下该型武器系统故障数量将大幅上升。通过总结保障经验,有针对性地提出了配置地域、物资筹措、安全运输、维护保障、维修训练等方面的对策措施。结论 根据作战任务的等级转换、机动输送、战前集结、战斗抗击和战后总结等环节,提出装备保障流程建议,以提高严寒地区某型地面武器系统保障效能。