地应力条件下的凿岩爆破数值模拟

为了研究地应力对凿岩爆破的影响,采用DDA方法模拟爆炸应力波作用下考虑地应力条件时的单孔和多孔凿岩爆破破岩过程。模拟发现,随着初始地应力水平的增加,裂纹扩展半径和破岩区域面积减小,裂纹发育主方向趋于地应力的最大主应力方向,初始地应力对裂纹的抑制和引导作用明显;初始地应力水平的增加,对拉伸裂纹的抑制作用更为显著,从而降低了拉伸破坏对爆破破岩的贡献。模拟也表明,在初始地应力存在的条件下,通过对爆破载荷和炮孔布置进行针对性的优化,可以克服地应力带来的影响,并取得预期的爆破效果。本研究对地应力条件下的凿岩爆破工程具有理论和参考意义。     

Notwendigkeit und Vermarktung von Flexibilitätsoptionen im Energiesystem der Zukunft

Demands on production, distribution and consumption of electrical energy change fundamentally with the energy revolution. Energy purchasing costs for inflexible consumers are rising and proceeds of inflexible producers are sinking. Companies are able to reduce costs by marketing operational flexibility options. This article sets out the need of flexibility in the energy system and the new marketing options. Flexibility potentials within infrastructure plants of a chemical park are analyzed, evaluated regarding marketing and activated in the presented research project FlexChemistry.     

Benchmark value determination of energy efficiency indexes for coal-fired power units based on data mining methods

The operational optimisation of coal-fired power units is important for saving energy and reducing losses in the electric power industry. One of the key issues is how to determine the benchmark values of the energy efficiency indexes of the units. Therefore, a new framework for determining these benchmark values is proposed, based on data mining methods. First, the energy efficiency key performance indicators (KPIs) associated with the net coal consumption rate (NCCR) were selected based on the domain knowledge. Second, the decision-making samples with minimal NCCR were acquired with the fuzzy C-means (FCM) clustering algorithm, and the corresponding clustering centres were employed as the benchmark values. Finally, based on the support vector regression (SVR) algorithm, the target values of the NCCR were obtained with the KPIs as input, and the energy saving potential was evaluated by comparing the target values with the historical values of the NCCR. An actual on-duty 1000 MW unit was taken as study unit, and the results show that the energy saving potential is remarkable when the operators adjust the KPIs based on the calculated benchmark values.     

Smart control of the assembly process with a fuzzy control system in the context of Industry 4.0

Assembly line balancing is important for the efficiency of the assembly process, however, a wide range of disruptions can break the current workload balance. Some researchers explored the task assignment plan for the assembly line balancing problem with the assumption that the assembly process is smooth with no disruption. Other researchers considered the impacts of disruptions, but they only explored the task re-assignment solutions for the assembly line re-balancing problem with the assumption that the re-balancing decision has been made already. There is limited literature exploring on-line adjustment solutions (layout adjustment and production rate adjustment) for an assembly line in a dynamic environment. This is because real-time monitoring of an assembly process was impossible in the past, and it is difficult to incorporate uncertainty factors into the balancing process because of the randomness and non-linearity of these factors. However, Industry 4.0 breaks the information barriers between different parts of an assembly line, since smart, connected products, which are enabled by advanced information and communication technology, can intelligently interact and communicate with each other and collect, process and produce information. Smart control of an assembly line becomes possible with the large amounts of real-time production data in the era of Industry 4.0, but there is little literature considering this new context. In this study, a fuzzy control system is developed to analyze the real-time information of an assembly line, with two types of fuzzy controllers in the fuzzy system. Type 1 fuzzy controller is used to determine whether the assembly line should be re-balanced to satisfy the demand, and type 2 fuzzy controller is used to adjust the production rate of each workstation in time to eliminate blockage and starvation, and increase the utilization of machines. Compared with three assembly lines without the proposed fuzzy control system, the assembly line with the fuzzy control system performs better, in terms of blockage ratio, starvation ratio and buffer level. Additionally, with the improvement of information transparency, the performance of an assembly line will be better. The research findings shed light on the smart control of the assembly process, and provide insights into the impacts of Industry 4.0 on assembly line balancing.     

Conductor selection and economic analysis of D.R. congo–guinea ±800 kV UHVDC transmission project

Africa is embracing new opportunities featured with industrialization, urbanization and regionalization. Based on co-development of ‘Electricity, Mining, Metallurgy, Industry and Trade’ and grids interconnection proposed by Global Energy Interconnection Development and Cooperation Organization (GEIDCO), the high-quality hydropower resource of the Congo River can be exploited in large scale under the wide-range interconnected framework of African Energy Interconnection (AEI), forging a new engine for Africa economy. The transmission distance of the Congo River hydropower reaches 6,000 km at its farthest end in North Africa, which brings forth challenges to economics of proposed projects. Under this novel continental energy interconnection scheme in Africa, economics of those projects have not yet been in detail studied. This paper has implemented China’s mature engineering experiences and analytical tools of UHVDC project planning into the AEI structure, through exploring the economic behavior of ultra-long distance UHVDC projects in the scope of conductor selection in the Congo River hydropower transmission for the first time, and has provided concerned parties with a technical and analytical results of their economics comparison. This paper has chosen the D.R. Congo - Guinea ±800 kV UHVDC project as a typical example. Its preliminary system planning is introduced and three types of conductor are selected for scheme comparison. Later in this paper, the transmission loss, total investment and equivalent annual cost of the project have been calculated and analyzed. In the final part, sensitivity analysis results of the annual cost to utilization hours, transmission loss, loss tariff and construction cost has been provided     

新型有机玻璃在站台门的应用及有限元分析

介绍了新型有机玻璃的性能,通过对有机玻璃的相关性能进行分析,确认有机玻璃在站台门系统应用的可行性,为站台门节能减重的设计提供相关依据。     

Implementing lean practices in manufacturing SMEs: testing ‘critical success factors’ using Necessary Condition Analysis

Lean practices are known to increase operational performance. Previous research has identified critical success factors for implementing lean practices. This research aims to examine the extent to which success factors are critical for various degrees of lean practice implementation. Using multiple-respondent self-assessments from 33 Dutch manufacturing small and medium-sized enterprises (SMEs), we conducted a Necessary Condition Analysis. Our findings indicated that the criticality of success factors is progression dependent. In the initial stages of the lean journey, SMEs could improve their lean practices in a bottom-up manner through local factors such as a learning focus, improvement training and support congruence. When lean practices are more advanced, some company-wide factors must be present: top management support, a shared improvement vision and a supplier link. Our findings question the universality of success factors such as strategic involvement and indicate the need for a more dynamic model of lean implementation.     

The sociotechnical constitution of cognitive work analysis: roles,affordances and malfunctions

Analysis of everyday work practices in sociotechnical systems for eliciting design/intervention requirements involves appropriate work analysis frameworks. The current article provides an extension to one such framework — Cognitive Work Analysis (CWA) — by scrutinising its sociotechnical basis. CWA's forte depends on its ‘design for adaptation’, system related operations, and operators. In contrast, sociotechnical work systems require not only operators and adaptation, but also a significant emphasis on ‘users’ and ‘appropriation’. The current article extends CWA (based on Rasmussen's original concepts) for users; subsequently allowing for system flexibility and possibilities of ‘appropriation’ within acceptable boundaries of the system's correct functioning. To this end, the first phase of Work Domain Analysis is extended by adding a new layer to the abstraction hierarchy (AH), based on Rasmussen's original insights.     

磷矿山开采系统对黄柏河东支的影响研究

从黄柏河东支流域磷矿山绿色开发要求出发，基于地表水环境系统和矿业开采的耦合影响作用，开展了磷矿山开采对黄柏河东支的水环境的影响研究。通过定量和定性分析，在磷矿山开采过程中对黄柏河东支的各种影响因素中，矿井涌水(占比为43.19%)、尾渣淋溶水(占比为15.51%)、矿废石淋溶水(占比为14.64%)和生产废水(占比为10.84%)为磷矿山开采对黄柏河东支水环境的主要影响因素，尤其是矿井涌水携带的总污染物量大，影响占比接近50%。该研究影响分析为黄柏河东支水环境的治理提供有力指引支撑。     

Adaptive neuro-fuzzy algorithm applied to predict and control multi-phase flow rates through wellhead chokes

A Takagi-Sugeno adaptive neuro-fuzzy inference system (TSFIS) model is developed and applied to a dataset of wellhead flow-test data for the Resalat oil field located offshore southern Iran, the objective is to assist in the prediction and control of multi-phase flow rates of oil and gas through the wellhead chokes. For this purpose, 182 test data points (Appendix 1) related to the Resalat field are evaluated. In order to predict production flow rate (Q_L) expressed as stock-tank barrels per day (STB/D), this dataset includes four selected input variables: upstream pressure (Pwh); wellhead choke sizes (D64); gas to liquid ratio (GLR); and, base solids and water including some water-soluble oil emulsion (BS&W). The test data points evaluated include a wide range of oil flow rate conditions and values for the four input variables recorded. The TSFIS algorithm applied involves five data processing steps: a) pre-processing, b) fuzzification, c) rules base and adaptive neuro-fuzzy inference engine, d) defuzzification, and e) post-processing of the fuzzy model. The developed TSFIS model for the Resalat oil field database predicted oil flow rate to a high degree of accuracy (root mean square error = 247 STB/D, correlation coefficient = 0.9987), which improves substantially on the commonly used empirical algorithms used for such predictions. TSFIS can potentially be applied in wellhead choke fuzzy controllers to stabilize flow in specific wells based on real-time input data records.