• Energy Research
• 2021-2025close
• CN close
• BR close
• Energy close

Abstract Accurate power forecasting is of great importance to the turbine control and predictive maintenance. However, traditional physics models and statistical models can no longer meet the needs of precision and flexibility when thermal power plants frequently undertake more and more peak and frequency modulation tasks. In this study, the recurrent neural network (RNN) and convolutional neural network (CNN) for power prediction are proposed, and are applied to predict real-time power of turbine based on DCS data (recorded for 719 days) from a power plant. In addition, the performances of two deep learning models and five typical machine learning models are compared, including prediction deviation, variance and time cost. It is found that deep learning models outperform other shallow models and RNN model performs best in balancing the accuracy-efficient trade-off for power prediction (the relative prediction error of 99.76% samples is less than 1% in all load range for test 216 days). Moreover, the influence of training size and input time-steps on the performance of RNN model is also explored. The model can achieve remarkable performance by learning only 30% samples (about 216 days) with 3 input time-steps (about 60 s). Those results of the proposed models based on deep-learning methods indicated that deep learning is of great help to improve the accuracy of turbine power prediction. It is therefore convinced that those models have a high potential for turbine control and predictable maintenance in actual industrial scenarios.

Abstract In this study, the compatibility and corrosion behavior of carbon steel, stainless steel, aluminum and copper in contact with blends composed of biodiesel (consisting of soybean oil, beef tallow and swine lard) and petrodiesel were evaluated. Static immersion tests (total, partial and crevice) and exposure in vapor phase were carried out for 2160 h at 50 °C. Our main findings indicate that fuel blends influence the corrosion behavior, being observed a less corrosive attack on the materials, except for copper that presented an anomalous corrosion behavior, with a clear trend towards lower corrosion rates. Carbon steel and stainless steel in total, partial and crevice tests presented surface morphology with slightly changes, while carbon steel exposed in vapor phase showed corrosive attack. Copper presented the highest corrosion rates in partial (9.5273 μm/y), total (9.1484 μm/y) and crevice (6.6178 μm/y) tests in the B7 blend, respectively; and the lowest corrosion rates in total (0.0547 μm/y) for the B15 blend; in partial (0.4926 μm/y) and crevice (0.0182 μm/y) tests for the B30 blend. Among the materials evaluated, copper showed higher influence on biodiesel oxidative stability. Aluminum exhibited good compatibility and did not show any compound formed on its surface without presenting any corrosion.

Abstract The effects of partial premixing on soot formation were investigated both experimentally and numerically in counterflow diffusion flames (CDFs) of ethylene, propane and ethylene/propane binary mixtures. The partial premixing ratio was maintained at relatively low levels so as to minimize the hydrodynamic effects; while the chemical effects of partial premixing on soot formation can be highlighted. The experimental results and simulations consistently showed that partial premixing notably increased soot formation in ethylene flames; while it slightly reduced the soot formation in propane flames, suggesting that partial premixing played different chemical roles in the soot formation process of ethylene and propane flames. Kinetic analyses revealed that partial premixing showed opposing effects on propargyl formation of ethylene and propane flames; this in turn led to the opposing effects on the formation of benzene and polycyclic aromatic hydrocarbons (PAH), and finally on the soot inception process. For partial premixing in propane/ethylene mixtures, the observed synergistic effects with propane added to ethylene CDFs disappeared, indicating a chemical cross-linking effect between partial premixing and ethylene/propane mixture. In addition, the present soot model showed satisfactory performance in reproducing the mismatch between PAH and soot formation tendencies in ethylene and propane CDFs. This result implies that great care is needed when using PAH concentration as an indicator of sooting tendency of different fuels. The present modeling results provided new insights into the chemical role of partial premixing in the soot evolution process of ethylene, propane and their blends. Further investigations towards the partial premixing effects on soot formation of larger liquid hydrocarbons such as diesel surrogates are required.

Abstract China's carbon emissions have been ranking first in the world. This study filled in the gaps in research, decomposed carbon intensity from the perspective of time, space and industry. A decoupling effort model based on factor decomposition models was constructed to analyze the driving factors of carbon emissions and economic decoupling, which builded a foundation for achieving sustainable economic development. Using the Logarithmic Mean Divisia Index method (LMDI), the paper measured the carbon emission intensity of 29 provinces and cities in China from 1998 to 2019, and decomposed the decoupling effect between GDP and carbon emission on the basis of factor decomposition by tapio. The results showed that: (1) Carbon intensity declined first, then rise lightly, and finally declined steadily. For the primary industry and the tertiary industry, the carbon intensity declined steadily, while the carbon intensity increased accordingly to the overall carbon intensity. In terms of spatial evolution, the regional differences between different provinces decreased correspondingly. (2) The cumulative contribution rates of these three effects, i.e., technological progress, industrial structure and regional scale were 106.3299%, −15.1486% and 8.8188%, respectively. There were obvious differences of these cumulative contribution rates of carbon intensity among different provinces. (3) From the perspective of industrial, technological progress effect is the largest contribution for carbon intensity in the secondary industry. The Industrial structure effect mainly affects the primary and tertiary industries; and no significant difference in regional scale effect. (4) The decoupling effect gradually improved, and technological progress has played an absolute leading role in promoting the decoupling effect. Based on the research results, the key policy recommendation are put forward as follows: (1) Further improve the technological level and support clean technology enterprises. (2) Promote industrial upgrading in backward industrial provinces (3) Promote regional assistance and the introduction of high-quality foreign investment.

Abstract Based on the design concept of a fourth-generation smart pipe network system, this paper innovatively proposes a new TOTS (Two-supply/One-return, triple pipe structure) arrangement method for district heating systems. Moreover, to accurately predict the heat loss due to the pipeline operation of the multi-pipe system, based on the multipole calculation method, a new heat loss theoretical analytical model for the TOTS was created; additionally, a corresponding three-dimensional numerical simulation model was established, which was analyzed and numerically solved. The results showed that in comparison with thermal loss data measured by Danfoss et al., the above analytical and numerical models have a high accuracy, and the deviation is within 2%. Additionally, through calculations, it was found that the distance between the heating pipes is an important factor that affects the total heat loss from the new multi-control heating system and the actual heat exchange between pipes.