A multivariable model for estimation of vapor cloud explosion occurrence possibility based on a Fuzzy logic approach for flammable materials

In this paper, a new method based on Fuzzy theory is presented to estimate the occurrence possibility of vapor cloud explosion (VCE) of flammable materials. This new method helps the analyst to overcome some uncertainties associated with estimating VCE possibility with the Event Tree (ET) technique. In this multi-variable model, the physical properties of the released material and the characteristics of the surrounding environment are used as the parameters specifying the occurrence possibility of intermediate events leading to a VCE. Factors such as area classification, degree of congestion of a plant and release rate are notably affecting the output results. Moreover, the proposed method benefits from experts' opinions in the estimation of the VCE possibility. A refrigeration cycle is used as the case study and the probability of VCE occurrence is determined for different scenarios. In this study, sensitivity analysis is performed on the model parameters to assess their effect on the final values of the VCE possibility. Furthermore, the results are compared with the results obtained using other existing models.     

Safety-based process plant layout using genetic algorithm

This paper presents a method based on a genetic algorithm for optimizing process plant layout. The relative location of main process units is determined to minimize an annual cost function including the cost of material transfer between process units (piping and pumping costs), land cost, and the expected annual loss resulting from damage to each secondary unit caused by primary accidents occurring in nearby process units. This method is an improvement over previous attempts using genetic algorithms or mathematical programming techniques to optimize plant layout, which neglected pumping costs and included safety issues by evaluating the infringement of predefined safety distances only. In this approach the operating cost of material transfer is included and the likelihood of accidents is taken into account thus providing good practical solutions to the plant layout problem incorporating more realistic cost functions and constraints. In the paper, after discussing the structure of the annual cost function and describing the working logic of the layout generating algorithm, a case study is described to demonstrate the effectiveness of the proposed methodology.     

Modeling and optimization of activated sludge bulking for a real wastewater treatment plant using hybrid artificial neural networks-genetic algorithm approach

Prediction of sludge bulking is a matter of growing importance around the world. Sludge volume index (SVI) should be monitored to predict sludge bulking for a wastewater treatment plant. This study was an effort to develop hybrid artificial neural network-genetic algorithm models (MLPANN-GA and RBFANN-GA) to accurately predict SVI. Operating parameters, including MLVSS, pH, DO, temperature, TSS, COD and total nitrogen were the inputs of neural networks. Genetic algorithm was utilized in order to optimize weights and thresholds of the MLPANN and RFBANN models. Training procedures for SVI estimation were successful for both the MLPANN-GA and RBFANN-GA models. The training and validation models showed an almost perfect match between experimental and predicted values of SVI. The results indicated that with low experimental values of input data to train ANNs, the MLPANN-GA compared with the RBFANN-GA is more accurate due to higher coefficient of determination (R²) and lower root mean squared error (RMSE) values. The values of RMSE and R² for the optimal models approached 0 and 1, respectively. The mean average error for the ANN models did not exceed 3% of the input values of the measured SVI. The GA increased the accuracy of all the MLPANN and RBFANN models.     

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.     

Pre-evacuation path planning in chemicals-concentrated areas with risk assessment and individual character analysis

With the ever-increasing development of those chemical parks (concentrated areas), the inherent hazards may remain the major leading cause of serious casualties, causing dramatic increases in deaths and injuries. Despite this, proper path beforehand can effectively minimise the number of deaths or injured. In this study, in order to better address the aforesaid issue, the pre-evacuation path planning was adopted to do so. This method can serve to prepare emergency response in case of extreme events, such as fires, explosions, or dangerous leakages, because these accidents could happen in chemical parks (concentrated areas). To that end, a framework was therefore proposed. First, the general risk representation was conducted. After the main hazards as well as the vulnerability within the facilities was identified, the interaction between those two factors could be expressed with matrices. This was followed by the analysis of the domino effect, which tends to occur under such circumstances. Second, individuals' visibility and inclination at each location to choose the nearest exit gate or shelter zone were analyzed by space syntax analysis. Third, a weighted risk map mainly composed of risk, individual's visibility, and inclination of exits was therefore generated. And the lowest cumulative risk path was simulated and analyzed accordingly. Finally, the map modified with received risks suggests that each individual's safest route from their current locations can be possibly simulated with Dijkstra's algorithm, which corresponds to the lowest cumulative risk. For the purposes of illustration and validation, a real case was adopted. The results demonstrated that this framework could provide both technical and theoretical support for the pre-evacuation path planning in chemicals-concentrated areas like chemicals-concentrated areas.     

城市湖库蓝藻水华形成机理综合建模研究

蓝藻水华形成是诸多营养及环境因素相互作用的结果,因此,本文对城市湖库蓝藻水华形成(包括复苏、萌芽、生长、暴发阶段)这一复杂生态过程进行了综合建模研究.通过在阳光房中模拟湖库蓝藻水华形成过程,采用正交实验分析获得蓝藻生长的关键影响因素,并为蓝藻水华形成机理建模提供相应参量.在此基础上,构建了用于模拟湖库蓝藻水华形成过程的蓝藻生长机理模型,采用遗传算法对机理模型中涉及的参数进行优化率定;同时,考虑蓝藻水华暴发阶段具有突变特性,建立了描述蓝藻水华暴发状态的尖点突变模型,进而构建了城市湖库蓝藻水华形成各阶段的综合机理模型.实验仿真结果表明,该综合机理模型能较好地模拟城市湖库蓝藻从复苏到暴发整个过程的变化规律,且该模型结合了数学机理建模和智能方法的优势,克服了单一蓝藻水华机理模型的缺陷,为湖库蓝藻水华形成机理的深入研究提供了新思路.     

潮流场作用下的航标漂移计算方法研究

针对目前航标遥控遥测系统中误报警过多的问题,对当前航标漂移计算方法做出优化。首先采集了航标遥测数据并通过拉依达数学准则对采集数据进行预处理;结合Kmeans算法和ISODATA算法,对预处理数据进行聚类,对比分析计算出的聚类中心,选取精确度更高的聚类中心作为计算航标漂移量的基准点并计算航标漂移距离;采用Person相关性分析方法和回归分析方法,构建潮流场作用下的航标漂移模型。结合航标和潮流场实际数据进行回归分析,确定模型参数,计算均方根误差,对模型进行验证。结果表明,该模型能较好地反映潮流场作用下的航标漂移运动,能有效减少航标漂移误报警,提高航标的管理效率和智能化水平。     

A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

● A novel VMD-IGOA-LSTM model has proposed for the prediction of water quality. ● Improved model quickly converges to the global optimal fitness and remains stable. ● The prediction accuracy of water quality parameters is significantly improved. Water quality prediction is vital for solving water pollution and protecting the water environment. In terms of the characteristics of nonlinearity, instability, and randomness of water quality parameters, a short-term water quality prediction model was proposed based on variational mode decomposition (VMD) and improved grasshopper optimization algorithm (IGOA), so as to optimize long short-term memory neural network (LSTM). First, VMD was adopted to decompose the water quality data into a series of relatively stable components, with the aim to reduce the instability of the original data and increase the predictability, then each component was input into the IGOA-LSTM model for prediction. Finally, each component was added to obtain the predicted values. In this study, the monitoring data from Dayangzhou Station and Shengmi Station of the Ganjiang River was used for training and prediction. The experimental results showed that the prediction accuracy of the VMD-IGOA-LSTM model proposed was higher than that of the integrated model of Ensemble Empirical Mode Decomposition (EEMD), the integrated model of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Nonlinear Autoregressive Network with Exogenous Inputs (NARX), Recurrent Neural Network (RNN), as well as other models, showing better performance in short-term prediction. The current study will provide a reliable solution for water quality prediction studies in other areas.     

矿井主风机不停风倒机系统最优控制研究

为解决矿井停机切换主要通风机过程中引起的井下风量波动很大,易造成瓦斯积聚等引发的安全问题,提出1种基于智能控制的主通风机稳风切换系统.建立动态风机倒机数学模型,提出基于改进粒子群算法优化的模糊自适应PID的控制方法,并应用MATLAB进行仿真实验,结果表明:控制方法控制效果明显,在系统运行的120 s内,井下风量波动始...     

基于机器学习的浮标实时自动化赤潮预警研究

实现赤潮预警对于减轻海洋环境灾害、避免海洋产业特别是海洋渔业重大经济损失具有重要意义。针对当前水文监测数据海量却难以实现实时自动化监测与预警,特别是难以利用传统监测手段实现对危害更大的赤潮的精准实时预测这一显著问题,提出利用浮标数据作为依据,借助机器学习在大数据分析和智能决策方面的优势,建立一种新颖的双重递进式赤潮预警机制的方法。首先,通过相关算法分析历史数据,以确认赤潮初步预警阈值;其次,对叶绿素a、pH、溶解氧等重要监测指标的当前和阶段性变化进行初步分析,判断是否达到预警触发条件;然后,进一步联合分类、回归、聚类、神经网络等机器学习相关方法,对数据进行深度挖掘;最后,通过这种递进式的机制对短期内是否会发生赤潮作出判断,以实现赤潮自动化预警预报。在此基础上,利用宁波梅山湾实际监测数据,证实了该方法在赤潮实时自动化预警中的有效性。