A 1 MWe multi-field solar thermal power plant comprising parabolic trough collectors and linear Fresnel reflectors was proposed and studied in the literature for Jodhpur, India. This power plant runs without a fossil fuel backup and thermal energy storage. It operates at part-load due to daily and seasonal variations of the direct normal irradiance, causing a reduction in annual power generation and capacity factor. The steam turbine usually operates at a constant pressure. The present study simulates the power plant using a sliding pressure operation strategy and compares the annual performance with a constant pressure strategy. The simulations were performed in TRNSYS for the 1 MWe multi-field power plant proposed at Jodhpur, India. The simulation showed an improvement of 3.83 % in annual electricity production using sliding pressure compared to the constant pressure operation strategy.
This paper analyzes China’s provincial low-carbon efficiency using the super-SBM model, GML index, and ridge regression. Results reveal that enhancing energy structure, promoting renewable energy, and increasing carbon sequestration benefit the efficiency. Eastern and western regions’ renewable energy use significantly impacts efficiency, while vegetation construction is vital in central and northeastern regions. Efficiency initially decreased, hitting a low in 2011-2012, then rose since 2013. Spatially, efficiency declines from periphery to center, with the order: east, west, northeast, and central. Technological progress notably influences low-carbon efficiency. Findings inform carbon mitigation strategies, guiding China’s path to carbon neutrality.
The waterlogging disaster has become a major threat to the sustainable and resilient development of cities. However, there is still a lack of unified accounting for waterlogging damage, particularly ignoring its environmental loss. In this paper, we developed an emergy-based footprint to assess the synthetic economic and environmental impact of urban waterlogging. The results show that the average waterlogging footprint under different return periods is 4.43E +19sej. The waterlogging footprint of each sector sorted from largest to smallest is transportation (35.47%), commerce (27.48%), environment (17.79%), industry (13.02%), residential (3.97%), infrastructure (2.26%), indicating that the impact of waterlogging on the environment is noticeable. The emergy-based waterlogging footprint can provide a useful metric for quantifying the potential and indirect losses embodied in the waterlogging processes. This paper may provide a useful tool for urban waterlogging disaster risk and loss assessment.
China’s wastewater treatment plants (WWTPs) have consumed a large amount of electricity, which is threatening the sustainable development of regions with severe energy scarcity. In this paper, we developed a novel framework for evaluating the technical efficiency of WWTPs and identifying the key pathways to save electricity and improve treatment efficiency. First, multiple regional initial and integral electricity scarcity risks were investigated based on the proposed electricity stress index (ESI). Then, an index system covering two inputs (scale and electricity consumption) and six outputs (COD, BOD, SS, NH3-N, TN and TP pollutant removal volumes) was constructed to assess the technical efficiencies of 3776 WWTPs by introducing the multiple electricity scarcity risks into data envelopment analysis (DEA). The results showed that the original average technical efficiency score of investigated samples was 0.340, of which only 28 samples were relatively effective. The remaining WWTPs had different levels of input excesses and over 60% electricity overcapacity, indicating that the substantial potential for technical efficiency improvement and electricity saving. Moreover, regional electricity scarcity risks differed significantly and the technical efficiency changed significantly considering ESI. This paper may present a useful tool for the technical efficiency assessment of WWTPs.
The carbon mitigation for water supply system has become a hot issue concerning carbon peaking and carbon neutrality goals of cities. This study aimed to establish an integrated water-carbon nexus model to account the carbon emissions of urban water supply system. First, the physical-virtual water inventory was established covering local water intake, water transfer, and virtual water imports. Then, the water-carbon nexus model was constructed to simulate the carbon emissions of each stage along the whole water supply chain. Regarding physical water supply, the carbon emissions of pumping station and valve combinations, and water delivery routes were accounted while those from virtual water imports embodied in water-intensive products were calculated as well based on the multi-regional input-output (MRIO) analysis. Finally, taking Beijing-Tianjin-Hebei region as a case, we conducted a scenario analysis in context of regional water resource planning and carbon mitigation goals during 2020-2030. This study may provide a novel integrated water supply optimization framework for coordinating regional water resource management and carbon mitigation activities.
Energy and water are intertwined in the urban water cycle and need to be coordinated for an integrated resource utilization and more resilient structure. This paper presented a multi-objective optimization model for the energy-water nexus in urban water cycle targeting less energy consumption and more water sustainability with a case study of Beijing. The findings show that the total energy consumption of the social water cycle is 389.831 billion kWh, the maximum of energy consumption in water use processes is water use process. The least energy consumption is the water supply process. Based on the ecological network analysis method, a social water cycle network with 19 flow paths of 7 nodes is constructed. Based on the non-dominated sorting genetic algorithm II (NSGA-II), a multi-objective optimization of energy-water nexus in the urban water cycle was performed to achieve the coordinated goal of water sustainability and energy consumption. The findings may help promote the urban water system management and improve its sustainability.
Traditional building automation controllers are having low performance in dealing with non-linear phenomena. In recent years, model predictive control (MPC) has become a notable control algorithm for building automation system capable of handling non-linear processes. Performance of model-based controllers, such as MPC, is depending on reasonably accurate process models. For a building using baseboard radiator heater, a non-linear model is a more reliable representation of heat distribution system. Therefore, this study aims to present a non-linear gray-box model for a residential building connected to the local district heating network that is equipped with radiator heat emitters. The model is supposed to forecast the indoor air temperature as well as the radiator secondary return temperature. The model is validated using measurements collected from a building in Västerås, Sweden. In addition to a better accuracy, another motivation behind using a non-linear heating circuit model is to enhance its generalization performance. With the added benefits of accuracy and generalization, this model is expected to extend practical MPC implementation for such buildings.
The Euro 3 Koreans heavy-duty diesel engines produce the high NOx pollutant and harmful for human body. The effective method to reduce nitrogen oxide (NOx) pollutions is the selective catalytic reduction (SCR) system. This study deals with problems in the urea evaporation and decomposition process in heavy duty diesel engine with 12.000cc. The ammonia quantity and quality were sampled at the catalyst inlet using a 18 gas sensor, and samples of NOx from each urea injector were compared by experiments and simulations using STAR-CCM+ software. The results elucidate the saturation phenomena, vaporization phenomena, urea distribution patterns, and NOx reduction efficiency value urea injectors.
This paper presents a predictive probabilistic approach (PPA) for the optimal sizing of new distributed generation capacities in support of the main grid to respond to a fraction of the total load during the supply current interruption duration defined in using renewable-based microgrid assets. The model is used to simulate network failures by disabling the network for a certain time step. The load profile can be changed during network interruptions to represent a critical load. The generation capacity of the microgrid must be able to generate the value in grid-connected mode while supporting a critical load during a grid outage. The probability of loss of load characterizes the adequacy and energy not supplied represents the estimated reliability and the associated cost. The approach allows all technologies in the model to be evaluated, both during grid-connected mode and during grid outages when technologies can continue to power critical loads as part of the microgrid.
North Rhine-Westphalia (NRW) is the industrial center of Germany and one of the most important industrial locations in Europe. It is a key location for the energy-intensive basic materials industry like the production of steel and non-ferrous metals, (petro)chemicals, cement and lime, bricks, glass and ceramics, and paper. Around 20 % of NRW’s total greenhouse emissions derive from industrial processes. By 2045, industry must achieve climate-neutrality, which requires a massive transformation effort. Technologically, this needs large-scale utilization of green hydrogen, carbon management, consequent circular economy, and climate-neutral production of process heat. Furthermore, various adjustments to the policy framework are essential.