强化管理机制推进机械制造企业快速发展

随着国有企业改革的不断深化,市场竞争愈加激烈,针对机械行业的现状,从3个方面探讨了强化管理机制,推进机械制造企业可持续快速发展的途径.     

浅谈机械化施工的组织管理与设备调配

结合公路机械化施工的意义和作用，介绍了施工机械调配的基本要求，阐述了机械设备的运行规律及其调配原则，提出了主导机械的调配方法及相关注意事项。     

对ERP的认识——观念的转变

试图澄清国内对ERP、管理信息系统、数据库概念间的误解。强调了ERP中的管理思想和组织的特点，给出了作者的ERP观点，指出了实现了的ERP、实现中的ERP与ERP软件的不同，强调ERP软件实际上就是一个没有企业实体、但是有自己的组织结构、规模的运作流程，可以科学管理的虚拟企业。而ERP的实施过程可以看作是将实体企业装入虚拟企业的过程。     

调整煤源结构优化炼焦煤质量管理

介绍了湘钢在调整煤源结构、优化炼焦煤质量管理方面所做的一些工作。近年来 ,湘钢依靠科技进步 ,坚持精料方针 ,不断强化企业管理 ,使焦炭质量有了较大的提高 ,为降低高炉焦比 ,提高高炉的利用系数做出了应有的贡献     

城市二次加压供水设施污染的防治与对策

从设计、施工、管理等方面，分析了城市二次加压设施污染的原因，提出设计及施工中的防治对策及二次加压设施管理上的对策，以确保城市安全供水。     

浅谈建设项目的管理控制

就如何加强建设项目的科学管理，谈一些经验和体会。     

强化乙烯生产管理提升企业竞争能力

回顾了中国石油天然气股份公司所属乙烯装置2002年的生产情况，总结了乙烯生产管理中取得的经验和存在的问题，并提出了今后乙烯生产管理的目标。     

Automated model generation for performance engineering of building automation networks

Large technical systems need to be designed both reliable and efficient. Specialized design tools offer therefore a simplified, abstract design and extend details autonomously in the background. Analytic and simulation based models could improve the quality by testing and dimensioning the design before implementation, but setting up the necessary models is time-consuming and expensive. Therefore many developers ask for analysis tools which are able to create their models from the available information in the design tools. This paper presents such an automated modeling approach basing on an existing design database at the example of a network analysis for building automation fieldbuses. The process of automated modeling is unfolded, and the potentials and limitations are discussed.     

Predictive modeling of mercury speciation in combustion flue gases using GMDH-based abductive networks

Modeling mercury speciation is an important requirement for estimating harmful emissions from coal-fired power plants and developing strategies to reduce them. First-principle models based on chemical, kinetic, and thermodynamic aspects exist, but these are complex and difficult to develop. The use of modern data-based machine learning techniques has been recently introduced, including neural networks. Here we propose an alternative approach using abductive networks based on the group method of data handling (GMDH) algorithm, with the advantages of simplified and more automated model synthesis, automatic selection of significant inputs, and more transparent input–output model relationships. Models were developed for predicting three types of mercury speciation (elemental, oxidized, and particulate) using a small dataset containing six inputs parameters on the composition of the coal used and boiler operating conditions. Prediction performance compares favourably with neural network models developed using the same dataset, with correlation coefficients as high as 0.97 for training data. Network committees (ensembles) are proposed as a means of improving prediction accuracy, and suggestions are made for future work to further improve performance.