欧洲和德国的钢铁工业的环保现状和发展(一)

欧洲钢铁工业的环境保护已经达到了很高的水平.20世纪60年代初,保护环境一般认为只是局限于限制工业对人们的影响.现在,尤其是在罗马协议(Club of Rome)的努力之下,环保的范围从节约资源延伸到整个自然环境的保护,而后产生了目前应用中的工业过程和产品生产周期.整体来说,保护环境的所有途径都被视为是未来平衡发展的先决条件. 欧盟环境保护技术的情况可以德国为例,更准确地说是杜伊斯堡的蒂森克虏伯钢厂(ThyssenKrupp Stahl AG),其粗钢产量约占德国总产量的35%. 钢铁工业环境保护的重点仍然是维护空气质量,在此目的下,进行除尘处理.由烟囱处取样获得目前的除尘水平来看,今后的除尘主要是控制粉尘扩散和减少细小微粒的排放. 钢铁工业环境保护的关键策略就是循环利用,它能起到与环境协调一致的作用,能够解决相关工艺中需要的大量的原料问题.德国的蒂森克虏伯钢厂(ThyssenKrupp Stahl AG)现在炉渣的综合回收率水平已接近95%,其中冶金渣的回收利用率尤为突出.在德国,高炉渣利用率达到了100%,同时炼钢渣的利用率也超过了90%,每吨粗钢的耗水量也控制在100 m3的范围内.大量的努力不仅保证了有效的回收和再生利用工序,而且冷却水的利用也保证了新水的补充率降低到每吨粗钢15m3左右. 污染控制除与主要环境即空气、水和土壤有关外,已扩展到回收过程再利用的水平,更多地重点渐渐地放到相关产品的环境保护.产品和原料却日益受到生产商和消费者的重点检验,有点与环境协调性标准相反.组织机构的环境保护手段包括环境管理系统的认可、生态平衡、政府部门的自愿协议以及提高公众的信息、教育水平. 欧洲的钢铁工业,当然也包括德国,都已经意识到环境保护的重要性同工业研究与开发的重要性处于同等地位.一个典型的例子就是超薄板汽车用钢项目,这个与产品相关的研究引入了35家合作厂商共同研究一个比目前传统的汽车车身更结实、更轻的项目.欧洲钢铁厂家的联合合作已经成功研究出应用于烧结厂的减少二恶英(dioxin)排放物的吸收工艺.用褐煤焦尘作为吸附剂,已能够平均减少排放物大约70%.很可能2003年在杜伊斯堡的ThyssenKrupp Stahl AG将被授权建全世界最先进的焦化厂.在这个项目过程中研究出的主要部分成果,例如低尘低烟煤湿熄法和减少PAH排放物的单室压力控制系统,会恰当地应用于新的工厂.另一个主要进步是ThyssenKrupp Stahl AG研制的Hamborn OxiCup竖炉,它有湿法冶炼和排除含铁残渣的功能.此技术预计成为回收利用铁水和钢产品中产生的粉尘和泥浆这一难题的解决方案. 德国的局势尤其显示出环境法规的落实正开始对经济发展和劳动力市场起到不利影响.盛行的经济技术条件需要合法的体制治理环境保护的合理化.将来任何进一步的措施都要考虑这一事实,既要获得可持续发展,又要考虑到经济发展和社会需求.     

Laboratory-scale liquefiers for natural gas: A design and assessment study

Exact knowledge of natural gas composition is essential in custody transfer to determine the energy content of the delivery. However, for liquefied natural gas (LNG), a reliable composition determination is difficult. Here, we describe the design of a laboratory-scale reference liquefier that enables the validation and calibration of optical spectroscopy sensors by providing them with a sample of metrologically traceable composition. Hence, it is crucial to avoid fractionation of the sample during liquefaction. This is realized by supercritical liquefaction of a reference gas mixture in conjunction with a special vapor–liquid-equilibrium (VLE) cell. As this is a demanding high-pressure application, low-pressure condensation as liquefaction method was also assessed. Through experimental investigations and VLE calculations, preservation of the composition of the produced liquid sample during condensation was studied. We conclude that under optimized conditions, validation, and calibration measurements of optical sensors can be performed on condensed liquids, which, however, needs further confirmation.     

A non-invasive form finding method with application to metal forming

Inverse form finding aims in determining the optimal material configuration of a workpiece for a specific forming process. A gradient- and parameter-free (nodal-based) form finding approach has recently been developed, which can be coupled non-invasively as a black box to arbitrary finite element software. Additionally the algorithm is independent from the constitutive behavior. Consequently, the user has not to struggle with the underlying optimization theory behind. Benchmark tests showed already that the approach works robustly and efficiently. This contribution demonstrates that the optimization algorithm is also applicable to more sophisticated forming processes including orthotropic large strain plasticity, combined hardening and frictional contact. A cup deep drawing process with solid-shell elements and a combined deep drawing and upsetting process to form a functional component with external teeth are investigated.     

Training echo state networks for rotation-invariant bone marrow cell classification

The main principle of diagnostic pathology is the reliable interpretation of individual cells in context of the tissue architecture. Especially a confident examination of bone marrow specimen is dependent on a valid classification of myeloid cells. In this work, we propose a novel rotation-invariant learning scheme for multi-class echo state networks (ESNs), which achieves very high performance in automated bone marrow cell classification. Based on representing static images as temporal sequence of rotations, we show how ESNs robustly recognize cells of arbitrary rotations by taking advantage of their short-term memory capacity. The performance of our approach is compared to a classification random forest that learns rotation-invariance in a conventional way by exhaustively training on multiple rotations of individual samples. The methods were evaluated on a human bone marrow image database consisting of granulopoietic and erythropoietic cells in different maturation stages. Our ESN approach to cell classification does not rely on segmentation of cells or manual feature extraction and can therefore directly be applied to image data.

     

Das IT-Sicherheitsgesetz

Datenschutz und Datensicherheit - DuD - Die öffentliche Sicherheit zählt zu den wichtigsten Aufgaben der Innenpolitik. Dazu gehören der Schutz der Bürgerinnen und Bürger...     

Multi-agent programming contest 2017: lampe team description

In this paper we describe our participation in the 2017 edition of the Multi-Agent Programming Contest as team ‘lampe’. Our strategy was implemented in C++; it uses a centralised organisation of agents and evaluates different strategies based on an internal simulation of the future game state. Strategies are generated using handwritten heuristics in a randomised fashion, also relying on the internal simulation.