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The TOBUS software has been developed to facilitate the implementation of the TOBUS building diagnosis and decision-making method for retrofit studies. This user-friendly software runs under the Windows® operating system and includes several modules, each of which addresses a particular aspect of the diagnosis including: building description, dimensions, cost coefficients, building diagnosis of current physical state and functional obsolescence, indoor environmental quality (IEQ), energy use, elaboration of retrofit scenarios, cost analysis, reporting results. The software also includes comprehensive databases on the physical state of degradation, including hundreds of illustrations, retrofit work details, cost, etc.

We investigated how a community of microbial decomposers adapted to a reference site responds to a sudden decrease in the water quality. For that, we assessed the activity and diversity of fungi and bacteria on decomposing leaves that were transplanted from a reference (E1) to a polluted site (E2), and results were compared to those from decomposing leaves either at E1 or E2. The two sites had contrasting concentrations of organic and inorganic nutrients and heavy metals in the stream water. At E2, leaf decomposition rates, fungal biomass, and sporulation were reduced, while bacterial biomass was stimulated. Fungal diversity was four times lower at the polluted site. The structure of fungal community on leaves decomposing at E2 significantly differed from that decomposing at E1, as indicated by the principal response curves analysis. Articulospora tetracladia, Anguillospora filiformis, and Lunulospora curvula were dominant species on leaves decomposing at E1 and were the most negatively affected by the transfer to the polluted site. The transfer of leaves colonized at the reference site to the polluted site reduced fungal diversity and sporulation but not fungal biomass and leaf decomposition. Overall, results suggest that the high diversity on leaves from the upstream site might have mitigated the impact of anthropogenic stress on microbial decomposition of leaves transplanted to the polluted site.

It has been known for a very long time that chemical energy may be converted into electrical energy by using biomass, considered a renewable energy source. In the study that is being presented here, an explanation and a presentation are offered on a one-of-a-kind hybrid system that generates dependable power and cooling by harnessing the chemical energy of biomass. An anaerobic digester takes in organic material and converts it into biomass by using the high-energy content of cow manure as fuel. The Rankin cycle is the primary engine that drives the system that produces energy, and its combustion-based byproducts are routed to an ammonia absorption refrigeration system in order to provide sufficient cooling for the process of pasteurizing and drying the milk. It is expected that solar panels might contribute to the production of sufficient amounts of power for necessary activities. The technical and financial facets of the system are both being investigated at the moment. In addition, the optimal working conditions are determined by employing a forward-thinking multi-objective optimization strategy. This method simultaneously raises the operational effectiveness to the greatest extent that is practically possible while simultaneously lowering both expenses and emissions. The findings indicate that under ideal conditions, the levelized cost of the product (LCOP), efficiency, and emission of the system are, respectively, 0.087 $/kWh, 38.2%, and 0.249 kg/kWh. The digester and the combustion chamber both have very high exergy destruction rates, with the digester having the highest rate and the combustion chamber having the second-highest rate among all of the system's components. This assertion is supported by every one of these components.

Coal power plants have been a major source of undesirable emissions. Despite the technological advancements in renewable energies, coal units are still in-service in many developed and developing countries due to their reliability, adequacy, and flexibility for power delivery. There are some promising technologies for cleaner operation during power production from coal, including supercritical boiler (SC) design and carbon capture and storage (CCS), however, the challenging in innovating effective methods is still open to expand the boundary of knowledge in this speciality. This paper introduces a novel and simple method for reducing CO2 emissions and improving the dynamic responses of a 600 MW SC coal power plant by Artificial Neural Network (ANN) technique. A wide-range data-driven feedforward ANN model has been identified and verified for the various operations recorded as closed-loop data-sets, which covers all situations of startup, once-through mode, and even emergency shutdown of the unit. The closed-loop SC plant model has been augmented with an inverse multivariable coordinate NN controller, developed by analogous learning algorithm to improve the plant automation. With precisely selected setpoints, as operational rules, of temperature, pressure, and earliest possible power demand signals, the automated SC plant has been capable to operate with lower coal consumption - and thus lower emissions – than the existing operation strategy during startup, normal operation, and emergency shutdown modes. The improvement in dynamic responses have been quantified through simulations with comparison with existing performance, which have resulted in an overall average reduction of 2.143 Kg/s in coal consumption.

Abstract The technology heterogeneity in the construction industry causes difficulties for an unbiased evaluation of safety performance. The non-parametric and two-hierarchy frontier data envelopment analysis (DEA) model was constructed according to the inputs and outputs of China’s provincial construction industry in 2017. Safety performance and its potential as well as undesirable output potential were analyzed from the aspect of technology gap and management efficiency. The results showed that the average safety performance in China's provincial construction industry was calculated to be 0.715, which was caused by technology gap and management efficiency. Northwest China has good performance in technology gap ratio and Central China performs well in management efficiency. Meanwhile, the strategies to improve safety performance in China's provincial construction industry were developed. In addition, potential of the safety technology gap and safety management efficiency took up 70.18% and 29.82% in terms of the undesirable output control. East, Central, South and Southwest China are the main areas needing to strengthen accident prevention practice. The findings can be used to help scholars understand the condition of safety performance in China's provincial construction industry, and provide guidelines to evaluate the performance in construction safety considering production technology heterogeneity.

Abstract Knocking combustion for spark-ignition engine is related to auto-ignition of a portion of the unburned fuel-air mixture. In this investigation, the detailed chemical kinetic mechanism was engaged to numerically study the auto-ignition characteristics of hydrogen-enriched methane under engine-relevant conditions. Compared with the experimental data, the USC Mech 2.0 mechanism obtained the closest agreement, and it was adopted in the sensitivity analysis, the rate of production (ROP) analysis and the reaction pathway analysis. Results showed that at high temperature and high pressure, the ignition delay times (IDs) of CH4/H2 fuel blends reduce significantly (by two orders of magnitude at most) with rising hydrogen fraction, but the decline rate is not so obvious (within 37.3%) at low temperature. The sensitivity analysis indicated that at high temperature the reaction (R1) and reactions (R2, R3) promote each other while at low temperature only the reaction (R3) unilaterally facilitates the reaction (R12). The ROP analysis implied that the decrease of IDs of methane by hydrogen addition is realized through increasing the H, O, and OH radical production. Interestingly, the IDs for CH4/H2 fuel blends show different trends at different temperature, which decrease (by 47.5% at most) at low temperature but increase (by 132.7% at most) at high temperature as the equivalence ratio rises. The sensitivity analysis showed that the ignition kinetics for CH4/H2 fuel blends more depend on the CH4 concentration at low temperature but oxygen concentration at high temperature. This investigation not only supplements the fundamental combustion studies of CH4/H2 fuel blends in elevated pressures, but also reveals the influence mechanism of hydrogen addition and equivalence ratio on the IDs of CH4/H2 fuel blends at high pressure. More importantly, it may offer fundamental insights for the control of knocking combustion of spark-ignition (SI) engine.

Abstract Two office layouts with high and low levels of thermal control were compared, respectively traditional cellular and contemporary open plan offices. The traditional Norwegian practice provided every user with control over a window, blinds, door, and the ability to adjust heating and cooling. Occupants were expected to control their thermal environment to find their own comfort, while air conditioning was operating in the background to ensure the indoor air quality. In contrast, in the British open plan office, limited thermal control was provided through openable windows and blinds only for occupants seated around the perimeter of the building. Centrally operated displacement ventilation was the main thermal control system. Users’ perception of thermal environment was recorded through survey questionnaires, empirical building performance through environmental measurements and thermal control through semi-structured interviews. The Norwegian office had 35% higher user satisfaction and 20% higher user comfort compared to the British open plan office. However, the energy consumption in the British practice was within the benchmark and much lower than the Norwegian office. Overall, a balance between thermal comfort and energy efficiency is required, as either extreme poses difficulties for the other.

Abstract In South Africa, the electricity mismatch of supply and demand has been of major concern. Additional to past problems, the 2008 electricity crisis made the solution crucial after its damaging consequences to the economy. The disagreement on the need and consequences of the continuous electricity price hikes worsens the situation. To contribute to the recent electricity debate, this paper proposes a time-varying price elasticity of demand for electricity; the sensitivity of electricity consumption to price fluctuations changes throughout the years. The main purpose of this study is the estimation of the price elasticity of electricity in South Africa during the period 1980–2005 by employing an advanced econometric technique, the Kalman filter. Apart from the decreasing effect of electricity prices to consumption (−71.8% in the 1990s and −94.5% in the 2000s in average), our results conclude to an important finding: the higher the prices (for example in the 1980s) the higher the sensitivity of consumers to price fluctuations. Thus, further increases of the electricity prices may lead to changes in the behaviour of electricity consumers, focusing their efforts on improving their efficiency levels by introducing demand-side management techniques or even turning to other sources of – cheaper – energy.

Abstract In developed countries, buildings are involved in almost 50% of total energy use and 30% of global green-house gas emissions. Buildings' operational energy is highly dependent on various building physical, operational, and functional characteristics, as well as meteorological and temporal properties. Besides physics-based building energy modeling, machine learning techniques can provide faster and higher accuracy estimates, given buildings' historic energy consumption data. Looking beyond individual building levels, forecasting buildings’ energy performance helps city and community managers have a better understanding of their future energy needs, and plan for satisfying them more efficiently. Focusing on an urban-scale, this study systematically reviews 70 journal articles, published in the field of building energy performance forecasting between 2015 and 2018. The recent literature have been categorized according to five criteria: 1. Learning Method, 2. Building Type, 3. Energy Type, 4. Input Data, and 5. Time-scale. The scarcity of building energy performance forecasting studies in urban-scale versus individual level is considerable. There is no study incorporating building functionality in terms of space functionality share percentages, nor assessing the effects of climate change on urban buildings energy performance using machine learning approaches and future weather scenarios. There is no optimal criteria combination for achieving the most accurate machine learning-based forecast, as there is no universal measure able to provide such global comparison. Accuracy levels are highly correlated with the characteristics of forecasting problems. The goal is to provide a comprehensive status of machine learning applications in urban building energy performance forecasting, during 2015–2018.