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In order to enhance the efficiency and benefits of the sludge anaerobic digestion process, K2FeO4 was added to a sludge anaerobic digestion system, and its effects on the system were comprehensively investigated. Results showed that sludge anaerobic digestion was greatly improved by adding 500 mg/L K2FeO4. Biogas and methane productions were increased by 26.6 and 28.4%, respectively. Sludge reduction, protein removal, and the conversion efficiency of dissolved organics were all improved. The mechanism revealed that the disintegration of sludge flocs, enhancement of protease activity, and decrease of soluble sulfide toxicity on microorganisms contributed to biogas production and sludge reduction. Biogas quality was improved, benefitting from the decreasing H2S content in biogas; as additionally, the cost of biogas desulfuration was reduced. In the biogas slurry treatment, the PO43--P concentrations were decreased by 39%, which also reduced the cost of the dephosphorization processes at certain extent.

Abstract Hydrothermal treatment (HT) is one of the efficient approaches for upgrading municipal solid waste (MSW). In the present study, emission characteristics of polycyclic aromatic hydrocarbons (PAHs) from hydrothermally treated municipal solid waste (H-MSW) combustion alone and H-MSW/coal co-combustion were investigated at different temperatures. The results showed that for all fuel combustion, the majority of PAHs were 3- or 4-ring PAHs. In addition, flue gas had the highest yields of PAHs followed by fly ash and bottom ash, while the ring number of dominated PAHs in fly ash was higher than those in flue gas and bottom ash. Compared to MSW, H-MSW combustion generated less PAHs at the value of 1131.95–7649.24 μg/g. The blending of H-MSW and coal reduced total PAH emissions and positive interactions were observed between H-MSW and coal during co-combustion. The toxicity equivalent quantity (TEQ) values of the PAHs from combustion were in the order MSW > H-MSW > H-MSW/coal, which was consistent with the total PAH emissions. The present study illustrated that significant reduction of PAH emissions and toxicity from combustion could be achieved by HT and the blending of H-MSW and coal.

Abstract Distributed energy system becomes increasingly popular due to high efficiency and low pollution emissions. When it operates following the electricity load, thermal energy storage system can be used to accommodate surplus cooling and heating and improve the energy efficiency. This study investigates the energy and economic performance of thermal storage systems for surplus cooling and heating in distributed energy system, considering the impacts of climates, building combinations and cooling/heating supply systems. Results show that the thermal storage system can improve the primary energy efficiency and is more useful when residential buildings adopt centralized heating and decentralized cooling systems. By varying climates and building combinations, the thermal storage system can improve the primary energy efficiency by 0.28%–3.69%. The economic performance is not promising with a long payback period. The main reason is that the overall utilization rate of the surplus energy is low (less than 45%), especially when the cooling/heating load is low continually during spring and autumn. Additionally, the effect of static and dynamic thermal storage model is analyzed. Results show that the static model would over-estimate the performance of the thermal storage system significantly and the dynamic model is recommended.

China’s steel industry is an energy-intensive sector. Synergistic reduction of emissions of CO2 and air pollutants (SO2, NOx, and PM2.5) in the steel industry has an important practical significance for climate change and air pollution control. According to the CO2 emission reduction intensity targets (CERO) and air pollutant emission targets (PERO) for 2020 and 2030, 28 types of energy-saving and emission reduction technologies (20 types of carbon reduction technology and eight types of air pollution end-of-pipe technology) were selected for examination, and a two-stage dynamic optimization model with collaborative implementation of PERO and CERO was built to assess the near future (2015–2020) and long-term (2020–2030) implementation plans for synergistic emissions reduction of CO2 and air pollutants. The results show that in the near future, the implementation of PERO will have a greater synergistic effect on CO2 emission reduction. CO2 emission reduction under PERO in 2020 will be 97 million tons (Mt) higher than that of CERO, an increase of nearly 26%. However, the effects of implementing CERO are better in the long run. Under CERO, the emission reductions of SO2, NOx, and PM2.5 in 2030 are 2.44 Mt, 1.47 Mt, and 0.86 Mt, respectively, and 7%, 4%, and 5% higher than the implementation of PERO. As far as marginal abatement cost is concerned, in the near future, the marginal abatement costs of CO2 and air pollutant equivalents are 1.06 yuan/kgCO2 and 133 yuan/kg pollution equivalent (pe) under PERO, which are 23% and 11% lower than that of CERO, while in the long run, the marginal abatement costs of CO2 and pollutant equivalents under CERO are 0.025 yuan/kgCO2 and 2.73 yuan/kgpe, about 96% and 95% lower than that of PERO.

Bamboo was a popular material substituting for wood, especially for one-off commodity in China. In order to recover energy and materials from waste bamboo, the basic characteristics of bamboo pyrolysis were studied by a thermogravimetric analyzer. It implied that the reaction began at 190∼210 °C, and the percentage of solid product deceased from about 25% to 17% when temperature ranged from 400 °C to 700 °C. A lab-scale fluidized-bed furnace was setup to research the detailed properties of gaseous, liquid and solid products respectively. When temperature increased from 400 °C to 700 °C, the mass percent of solid product decreased from 27% to 17% approximately, while that of syngas rose up from 19% to 35%. When temperature was about 500°C, the percentage of tar reached the top, about 31%. The mass balance of these experiments was about 93%∼95%. It indicated that three reactions involved in the process: pyrolysis of exterior bamboo, pyrolysis of interior bamboo and secondary pyrolysis of heavy tar.

Co-generation of energy derived from human movement is not new. Intentionally accumulating energy, from mass urban-mobility, provides opportunities to re-purpose power. However, when mass-mobility is predictable, yet not harnessed, this highlights critical gaps in application of interdisciplinary knowledge. This research highlights a novel application of geostatistical modelling for the built environment with the purpose of understanding where energy harvesting infrastructure should be located. The work presented argues that advanced Geostatistical methods can be implemented as an appropriate method to predict probability distribution, density, clustering of populations and mass-population mobility patterns from large-scale online distributed and heterogeneous data sets published by the Australian Urban Research Infrastructure Network. Where clear urban spatio-behavioural relationships of density and movement can be predicted – understanding such patterns supports cross-disciplinary city planning and decision-making. A data-informed – predictive spatial decision-making framework is proposed – facilitating the endeavour of cogenerating kinetic human energy within a prescribed space. This novel proposition could further sustainability strategies for compact living for cities such as in Perth, Western Australia which is increasingly economically and geographically pressured to densify. This research argues that surveillance data elucidate a capacity to interpret and understand impacts of densification strategies, efficacy of CCTV networks in existing and emerging cities.

In order to solve the problem of dynamic reconfiguration of distribution network connected with distributed generation (DG) and electric vehicle (EV), a dynamic reconfiguration method of distribution network based on time series symbolized segmentation is proposed. Firstly, the daily load curve of the distribution network accessed to distributed energy and electric vehicles is predicted. The time series of load is symbolized and the segmentation effect is evaluated by information entropy to determine the time division scheme. Then the model is established with the minimum daily operating loss cost as the objective function, and the distribution network is dynamically reconfigured according to the time division. The particle swarm optimization algorithm (PSO) based on branch group search is proposed to solve the model. Finally, the simulation calculation is carried out by using the IEEE33 node system as an example. The results show that the proposed method can reasonably divide the load time series, so that the daily opHrating loss cost of the distribution network after dynamic reconfiguration is reduced.

Abstract This study newly introduces a complementary cooperative algorithm considering generative adversarial network (GAN)-Conditional Latent Space (CLS) combined with bidirectional long short-term memory (BLSTM) for improved and efficient lithium-ion rechargeable battery state prediction. The GAN-CLS algorithm, which is an advanced method of GAN, can generate corresponding images from an input label description. Long short-term memory (LSTM) is a specific recurrent neural network (RNN) architecture that can predict sequences more accurately than conventional RNNs. In terms of battery state prediction, the combination of two methods (GAN-CLS and LSTM) surely provides more improved and efficient rechargeable battery state prediction in contrast to conventional state predictors. The procedure of this study is as follows. First, we propose methods to enhance the data from battery charge/discharge by converting prepared data to images; then, the GAN-CLS method is used to generate corresponding battery data from previous images. Subsequently, the generated data is used to train the BLSTM model. Finally, the trained model is used to predict the battery state. By various experiments and verification, it is concluded that the proposed study can be a good solution for rechargeable battery state prediction (reduction of the time cost 50 times in modeling and 20 times in train/test, provision of a more accurate prediction mean square error (MSE) smaller than 0.0025 and the average MSE less than 0.0013).