A strategy for reducing CO2 emissions from buildings with the Kaya identity – A Swiss energy system analysis and a case study

Within the general context of Greenhouse Gas (GHG) emissions reduction, decomposition analysis allows the quantification of the contribution of different factors to changes in emissions as well as the assessment of the effectiveness of policy and technology measures. The Kaya identity has been widely used for that purpose in order to disaggregate carbon emissions into various driving forces. In this paper, it is applied for the analysis of emissions resulting from energy use at three different scales. First, a decomposition analysis of the carbon emissions for the complete Swiss energy system is presented using the future projections from the Swiss Energy Strategy 2050. The Kaya identity is then applied to the Swiss building sector after it is adapted with factors that are more relatable to building parameters, such as floor area instead of Gross Domestic Product (GDP). Finally, the last level of analysis is a small scale community energy system for a unique Swiss village that aims to significantly reduce its emissions. An energy strategy is developed and its effectiveness is assessed with the adapted Kaya identity and benchmarked against the Swiss average values. The presented method demonstrates how the performance of buildings under various retrofitting scenarios can be benchmarked against future emission targets.     

A systematic estimation model for fraction nonconforming of a wafer in semiconductor manufacturing research

The clustering phenomenon of defects usually occurs in semiconductor manufacturing. However, previous studies did not pay much attention to the influence of clustering phenomenon for estimating fraction nonconforming of a wafer. Thus, this paper presents a systematic estimation model with considering relevant variables about clustering defects for fraction nonconforming of a wafer. The method combines back-propagation neural network (BPNN) with genetic algorithm (GA) to obtain an estimation model. In this study, GA aims to optimize the parameters of BPNN. Five relevant variables: number of defects (ND), squared coefficient of angle variation (SCV_A) for defects, squared coefficient of distance variation (SCV_D) for defects, defect cluster index (CI_M), and the number of cluster groups (NCG) for defects by self-organized map (SOM) are utilized as inputs for GA–BPNN. Finally, a simulation case and a real-world case are used to confirm the effectiveness of proposed method.     

Transportation interface facilities design using interactive high-level computer graphics

A prototype pedestrian facilities design system is described. The design system exploits the capabilities of an intelligent high-resolution vector graphics workstation which features a highly interactive computer graphics environment. A summary of historical developments in pedestrian facility design and graphics workstation architecture is presented, followed by a discussion of the stochastic model and interactive computer graphics features utilized by a prototype facilities design system. Potential applications of such a design system are discussed, and promising areas of future development are outlined.     

Sustainability decision support system based on collaborative control theory

Industries nowadays have more insight into corporate environmental, social and economic sustainability than ever before. Sustainability issues in various industries are all about choices – finding ways to be more strategic and reducing waste and energy, while benefiting the bottom line. Because of the complexity of sustainability decisions and strategies, these insights call for applying advanced control techniques. In this article we review the concepts of sustainability and its practical issues, specifically focusing on the issues of supply networks. Sustainability issues are usually complex because beyond their inherent challenges, there are conflicts among stakeholders within organizations and between organizations; hence, more useful methods are required for effective solutions. We consider Decision Support Systems (DSSs) to help optimize solutions related with sustainability issues, and review their concepts and usefulness based on previous work. We also suggest strategies applying the Collaborative Control Theory (CCT) principles to augment DSS by a new Sustainability – Decision Support Protocol (S-DSP) in order to overcome certain weaknesses. To model and illustrate the benefit of S-DSP as a control protocol, two practical supply delivery/production problems are analyzed. The results highlight how better collaborative solutions can be achieved to maximize the sustainability of supply networks. It is envisioned that sustainability decision support by such cyber-supported collaboration protocols will contribute to overcome the emerging challenges of sustainability planning and control.     

历史建筑节能技术在上海大厦保护修缮中的探究

在保护历史建筑的同时降低历史建筑的能耗,提高其居住舒适度,是对历史建筑高层次的保护,有利于历史建筑的可持续发展。以上海大厦为例,分析其在修缮工程中应用的节能技术,对改造前后上海大厦的能耗状况进行评估比较和总结,为其他历史建筑应用节能技术提供参考。     

Dynamic planning and control methodology for strategic and operational construction project management

This paper introduces the system's perspective of the dynamic planning and control methodology (DPM), aimed to support both the strategic and the operational aspects of project management. For this objective, a new modeling framework that integrates system dynamics and network-based tools is presented in this paper. The proposed framework adopts system dynamics as a core simulation engine for strategic project management and network-based tools as a wrap layer for operational project management. To implement the DPM framework, a web-based system has been developed within a collaborative environment. The developed system provides great support to both the strategic and operational aspects of project management by integrating familiar network concepts with system dynamics to analyze the overall strategic and operational project performance.     

Performance and long-term durability of direct-methane flat-tube solid oxide fuel cells with symmetric double-sided cathodes

In this work, we investigated the performance and stability of a large flat-tube SOFC with symmetric double-sided cathodes (DSC), which was directly fueled with methane. The effect of steam/carbon (S/C) ratio, temperature, and current density on the performance, and long-term stability of the DSC as well as the catalytic behavior of the anode was investigated in details. The thick anode support and inner channels of the DSC formed an efficient microreactor for steam-reforming of methane, resulting in high conversion rate of methane and CO selectivity. In particular, when the S/C was 2, the conversion of CH₄ at 750 °C achieved 100% in the DSC and no carbon deposition was observed. Moreover, the voltage of DSC with was stable throughout 190 h under a discharge current density of 0.257 A cm⁻².     

Convolutional neural network: Deep learning-based classification of building quality problems

The rapid development of the construction industry in China has introduced unprecedented quality-related problems in the country’s building industry. In response to this issue, the government has established various complaint channels to report quality problems. Therefore, building quality complaints (BQCs) need to be classified and solved by respective agencies or departments rapidly for avoiding adverse impact on the safety, health, and well-being of people. However, the current process of classifying BQCs is labor intensive, time consuming, and error prone. An automatic complaint classification is required to improve the effectiveness and efficiency of complaint handling, but studies on this issue are limited. Prevailing text classification research in construction has focused on utilizing conventional shallow machine learning. By contrast, this study explores a novel convolutional neural network (CNN)-based approach that incorporates a deep-learning method to automatically classify the short texts contained within BQCs. The presented approach enables capturing the semantic features in BQC texts and automatic classification of the BQCs into predefined categories. After the model optimization, tests are conducted to examine the practical application of the text classification approach compared with Bayes-based and support vector machine classifiers. Results indicate that the developed CNN-based approach performs well in the Chinese BQC classification with limited manual intervention and few complicated feature engineering.     

不同构造参数双层窗隔声性能试验研究

通过试验研究玻璃构造形式与规格、开启方式与密封形式、双层窗间距及窗框型材对双层窗隔声性能的影响,分析得出:玻璃构造形式与规格对双层窗的计权隔声量影响较小,其空气声隔声量频率特性曲线相近,低频共振频率主要出现在160~250 Hz范围;外平开/内推拉形式双层窗的隔声性能与双平开形式双层窗相当,均显著优于双推拉形式双层窗,且推拉窗采用包覆式密封条替换普通毛条,对提升外平开/内推拉形式双层窗的隔声量有一定效果;在相同条件下,当两层窗之间的间距大于90 mm时,双层窗构件的计权隔声量相近,隔声性能优于间距为80 mm的双层窗。