循环经济与江苏产业结构调整

为寻求一种能实现环境保护与经济发展相互促进的新途径,在分析循环经济的内涵及其与产业结构变动关系的基础上,对江苏产业结构进行了较深层次的分析,结果表明：江苏产业结构中资源消耗型和环境污染型行业仍占有较大比重。按照循环经济的原理,江苏产业结构调整的目标是：在“稳定一产,优化二产,壮大三产”的同时,各产业内部要优先发展资源节约型、环境友好型和废物利用型行业。工业主要是实现生态化转型,对不同行业分别给予鼓励、控制或限制;农业主要是投入品减量化、废弃物资源化和产业纵横链接化;服务业主要是围绕经济、社会和环境和谐发展的目标推进产业的环保化。     

体系安全性分析的双层次框架

为识别体系(So S)中的薄弱环节、评价威胁风险并提高体系安全性,根据安全系统工程和体系工程理论,提出体系安全性基本概念。基于复杂网络科学和传统安全分析技术,提出一种双层次分析框架,从脆弱性角度和威胁风险角度研究体系安全性。脆弱性分析可从拓扑结构和非拓扑因素2方面识别对体系安全较关键的脆弱点;威胁风险分析能得到脆弱点面临的任意威胁组合,并识别后果较严重的威胁风险。结果表明:体系安全性包括宏观的脆弱性应对能力,和微观的威胁风险控制能力。体系安全性分析需宏观和微观双层次相结合,全面考虑体系安全性诸因素。基于识别结果的威胁评价将提高安全分析的针对性和效率。     

基于贝叶斯网络的非正常情况下高铁行车调度人因可靠性分析

行车调度子系统在整个高铁运输系统中处于关键位置,虽然行车调度大多已经采用了信息化、自动化的作业程序,但发生设备故障、交通事故、自然灾害等非正常情况时,很多具体的作业组织和管理仍需行车调度作业人员的参与.提出了一种基于贝叶斯网络考虑情境环境因子交互关系的人因可靠性分析方法,以某铁路局的人因失误数据统计表为学习样本,推理出行车调度作业人员工作中存在的主要问题.结果表明,“完成任务的可用时间”、“培训”及“人员配置”的节点状态变化会对作业人员的可靠性产生很大影响.最后,以高铁行车调度人员在某种特定情境状态下的实际作业为例进行了实证研究.研究表明,所建立的方法正确可靠,能够准确、高效地辨识人因可靠性.     

烟气脱硝催化剂的回收利用工艺

随着国家对氮氧化物排放的控制越来越严格,选择性催化还原（SCR）烟气脱硝技术在燃煤电厂脱硝工程中被广泛应用。文章介绍了我国烟气脱硝催化剂的产生及回收利用情况,并对现有的回收利用工艺进行了分析,重点介绍了一种可促进烟气脱硝催化剂资源利用最大化、避免造成二次环境污染的脱硝催化剂回收利用工艺。     

磷酸铵镁热解产物循环沉氨技术进展研究

化学药剂费用过高以及磷酸盐元素的消耗和化学污泥的利用问题是阻碍磷酸铵镁结晶技术应用推广的难题之一,磷酸铵镁热解产物循环技术可解决上述难题.本文叙述了磷酸铵镁热解产物沉氨机理,磷酸铵镁热解影响因素以及热解产物沉氨过程中的影响因素及机理;并阐述了磷酸铵镁热解产物循环过程中带来的新的问题以及可能的解决方法.     

海岛地区非常规水资源开发利用现状及保障对策研究

中国海岛众多,且多数海岛属于丘陵区,普遍存在水资源紧缺的问题.非常规水资源可以增加水资源供给,将是海岛地区解决水资源危机的重要途径.本文通过分析海岛地区海水淡化,主要介绍了海水淡化应用现状、存在的问题和不足,探讨了雨水利用的方法,包括雨水收集、存在的问题和不足,对污水回用进行了探讨,对非常水资源的开发利用现状及存在的问题,提出了海岛地区开发利用非常规水资源的保障对策,为海岛地区水资源可持续利用提供了参考.     

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.     

Concept for cost effective improvement of river morphology in the context of the European Water Framework Directive

The EU Water Framework Directive calls for cost effective measures to achieve a “good status” in all European ground, surface and coastal waters. Besides eutrophication, the degradation of hydromorphology is the main reason for failing this objective. In this paper, we conceptualise the interactive decision support process BASINFORM-M for finding proper locations for river restoration. The concept combines the recently proposed “stepping stone approach” from aquatic ecology with elements from cost effectiveness analysis, multi-criteria analysis and participatory approaches. BASINFORM-M exemplifies a shift away from the isolated restoration of single river reaches towards a consideration of functional relationships within river networks.     

A hybrid machine learning model for predicting crater width formed by explosions of natural gas pipelines

Despite the existence of industry models for estimating the crater width formed by the explosion of natural gas pipelines, their applicability is still limited since the complex formation mechanisms. In this work, a novel hybrid model was developed to predict crater width formed by explosions of natural gas pipelines, using artificial neural networks (ANN) as the fundamental predictor. Based on the historical accident records, the proposed hybrid model was trained by the pipeline parameter, the operating condition, the installation parameter, and the crater width. A novel nature-inspired optimization algorithm, i.e., the Lévy-Weighted Quantum particle swarm optimization (LWQPSO) algorithm, was proposed to optimize the ANN model's parameters. Three machine learning models were developed for comparative reasons to predict the crater width. The use of precision and error analysis indicators assesses prediction performance. The results show that the proposed hybrid model (LWQPSO-ANN) has high prediction accuracy and stability, which outperforms QPSO-ANN-based benchmark hybrid models and the model without an optimizer (Support Vector Machine, SVM). The parameter sensitivities of the proposed algorithm, including the maximum number of iterations, population size and contraction-expansion coefficient, were determined. The proposed hybrid model is expected to support the quantitative risk assessment (QRA), Right-of-Way (ROW) definition and the inherently safer design of the underground parallel pipelines.     

Association analysis of accident factors in petrochemical storage tank farms

In order to identify and clarify the association between the factors leading to accidents in a petrochemical tank area, this study analyzes investigation reports of 212 petrochemical tank farm accidents and combines this with the “association rule” mining and science related to complex networks. The main risk factors are determined and a risk factor data set is constructed; 75 association rules are extracted from the factor data set based on the Apriori algorithm. Then the obtained association rules are used to construct an accident factors network of the petrochemical storage tank area, and the topology characteristics of the network are further analyzed to reveal the importance of factors. Factors with large node degree, betweenness, and clustering coefficients are obtained, such as “violation of operating regulations”, “high concentration of flammable gas in the air”, “lack of experience and professional skills”, etc. These factors play an important role in the formation and development of accidents. The results also show that the accident cause network of the petrochemical storage tank area has a small average shortest path length and a large cluster coefficient, indicating a relatively close connection between the accident factors. The contributions of this study is not only extracting the hidden relationships among contributory factors to tank farm accidents using association analysis, but also revealing which factors are more important for the tank farm safety through the complex network.