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Chinese biochemical engineering is committed to supporting the chemical and food industries, to advance science and technology frontiers, and to meet major demands of Chinese society and national economic development. This paper r...
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Chinese biochemical engineering is committed to supporting the chemical and food industries, to advance science and technology frontiers, and to meet major demands of Chinese society and national economic development. This paper reviews the development of biochemical engineering, strategic deployment of these technologies by the government, industrial demand, research progress, and breakthroughs in key technologies in China. Furthermore, the outlook for future developments in biochemical engineering in China is also discussed.
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Global solar radiation information is the basis for many solar energy utilizations as well as for economic and environmental considerations. However, because solar-radiation changes, and measurements are sometimes not available, a...
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Global solar radiation information is the basis for many solar energy utilizations as well as for economic and environmental considerations. However, because solar-radiation changes, and measurements are sometimes not available, accurate global solar-radiation data are often difficult or impossible to obtain. Machine-learning models, on the other hand, are capable of conducting highly nonlinear problems. They have many potential applications and are of high interest to researchers worldwide. Based on 232 paper regarding to the machinelearning models for global solar radiation prediction, this paper provides a comprehensive and systematic review of all important aspects surrounding machine-learning models, including input parameters, feature selection and model development. The pros and cons of three input-parameter sources (observation data from a surface meteorological observation station, satellite-based data, numerical weather-predicting re-analyzed data) and three feature selection methods (filter, wrapped, embedded) are reviewed and analyzed in this paper. Using data pre-processing algorithms, output ensemble methods, and model purposes, seven classes of machinelearning models are identified and reviewed. Finally, the state of current and future research on machinelearning models to forecast the global solar radiation are discussed. This paper provides a compact guide of existing model modification and novel model development regarding predicting global solar radiation.
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Meteorological elements are important for various fields related to human activities, including scientific research. Using the Tibetan Areas of West Sichuan Province (TAOWS) as an example, this study examined the estimation method...
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Meteorological elements are important for various fields related to human activities, including scientific research. Using the Tibetan Areas of West Sichuan Province (TAOWS) as an example, this study examined the estimation methods for near-surface air temperature (T-a), vapour pressure deficit (VPD), and atmospheric pressure (P) and their distribution characteristics in areas with complex terrains and sparse stations. An improved satellite-based approach, combining an artificial neural network and inverse distance weighting (ANN-IDW), is proposed for estimating T-a and VPD with high-accuracy under all weather conditions from Moderate Resolution Imaging Spectroradiometer (MODIS) data. The data of 41 meteorological stations in TAOWS and its adjacent areas were used for the training and validation of the ANN-IDW. For T-a and VPD, the mean absolute errors (MAEs) of the ANN-IDW are 1.45 degrees C to 2.15 degrees C and 0.54 hPa to 0.87 hPa, respectively. Also, the detailed features of the distribution of the estimated T-a and VPD are prominent and closely related to the terrain. The accuracy of the method was also verified indirectly. In addition, the improved method based on the existing method was applied for estimating P. The results confirm that (1) the ANN-IDW is suitable for estimating T-a and VPD in areas with complex terrain and sparse stations under all weather conditions; (2) the improved method is more suitable for estimating P at high-elevation. Moreover, the distribution characteristics of meteorological elements in TAOWS were also analysed. These elements influence agricultural production and animal husbandry and have a high application value. The results further show that topography is the most important factor affecting the spatial distribution and complexity of meteorological elements over complex terrains, but the degree of influence of topography varies greatly across different seasons.
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Understanding the quantitative relationship between the effective thermal conductivity and the moisture content of a material is required to accurately calculate the envelope heat and mass transfer and, subsequently, the building ...
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Understanding the quantitative relationship between the effective thermal conductivity and the moisture content of a material is required to accurately calculate the envelope heat and mass transfer and, subsequently, the building energy consumption. We experimentally analyzed the pore size distributions and porosities of common building materials and the influence of the moisture content on the effective thermal conductivity of building materials. We determined the quantitative relationship between the effective thermal conductivity and moisture content of building materials. The results showed that a larger porosity led to a more significant effect of the moisture content on the effective thermal conductivity. When the volumetric moisture content reached 10%, the thermal conductivities of foam concrete and aerated concrete increased by approximately 200% and 100%, respectively. The effective thermal conductivity increased rapidly in the low moisture content range and increased slowly in the high moisture content range. The effective thermal conductivity is related to the moisture content of the materials through an approximate power function. As the moisture content in the walls of a new building stabilizes, the effective thermal conductivity of normal concrete varies only slightly, whereas that of aerated concrete varies more significantly. The effective thermal conductivity of the material is proportional to the relative humidity of the environment. This trend is most noticeable when the wall material is aerated concrete.
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Cold thermal energy storage (CTES) is a cost-efficient storage approach for PV powered air-conditioning systems in tropical buildings. However, the feasibility and performance of different CTESs, including chilled water storage, i...
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Cold thermal energy storage (CTES) is a cost-efficient storage approach for PV powered air-conditioning systems in tropical buildings. However, the feasibility and performance of different CTESs, including chilled water storage, ice storage, PCM cooling storage, and building thermal storage, are still unclear for off-grid PV airconditioned buildings. In this study, an off-grid PV system with battery and CTES is proposed, including the operation strategy to manage the energy-storage process. To implement all CTES-types, a co-simulation model, which integrates the system model with the ARX (autoregressive with exogenous input) cooling-load model, is constructed to enable the interaction between supply- and demand-side. Based on the minimization of net present cost and the constraint of a certain required solar fraction, an optimization method is developed to find the optimal systemconfiguration. Moreover, an office, a hotel, and a residence are selected for a case study. The results indicate that CTES is feasible for higher solar fraction. The feasibility ranking of building type is (from high to low): residence, hotel, and office. In the most suitable case (residence), the maximum reduction of system costs (compared to battery-only system) is 49.76%, 41.77%, 44.31%, and 22.78%, respectively, for chilled-water storage, ice storage, PCM cooling storage, and building thermal storage. These values are 22.48%, 12.00%, 17.42%, 2.60% for the hotel, and 19.26%, 11.57%, 13.64%, 4.06% for the office. In addition, the sensitivity analysis shows that the increasing cost of both PV-array and CTES-device affects the feasibility of CTES negatively, while the increasing battery cost is a positive factor. Compared to the cost of the PV-array and the CTES-device, the battery cost represents the most significant effect-factor for feasibility.
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High-energy consumption of a district heating system is mainly caused by low-efficiency operation. Real-time regulation and control of the system in the operation stage has great energy-savings potential, and accurate load predict...
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High-energy consumption of a district heating system is mainly caused by low-efficiency operation. Real-time regulation and control of the system in the operation stage has great energy-savings potential, and accurate load prediction is the basis of system control and on-demand supply. The key for obtaining an accurate hourly heating load forecast value is to select reasonable input parameters. Therefore, this paper summarizes the methods of predicting heating loads and selecting input parameters, proposes the Eclat algorithm of association rules to obtain the best combination of input parameters, and introduces the Spearman parameter selection method for comparison; the Eclat-Support Vector Regression (E-SVR) and Spearman-Support Vector Regression (S-SVR) prediction models are established. The heating load data of a district in Xi'an, China is taken as an example, and the results show that the water supply temperature of the historical primary network was the most significant factor that affects the district ultrashort-term heating load prediction. The E-SVR prediction model has better performance than S-SVR with an R-2 value of 0.92, an accuracy improvement of 8.2%, and a root mean square error (RMSE) reduction of 28.1%. Single factor analysis of different prediction models showed that with an increase in the length of the prediction cycle, the influences of the historical water supply temperature and flow rate on the heating load decrease gradually, and the influence of outdoor temperature increases gradually. (C) 2020 Elsevier Ltd. All rights reserved.
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Due to the ability to regulate target metabolic pathways globally and dynamically, metabolic regulation systems composed of transcription factors have been widely used in metabolic engineering and synthetic biology. This review in...
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Due to the ability to regulate target metabolic pathways globally and dynamically, metabolic regulation systems composed of transcription factors have been widely used in metabolic engineering and synthetic biology. This review introduced the categories, action principles, prediction strategies, and related databases of transcription factors. Then, the application of global transcription machinery engineering technology and the transcription factor-based biosensors and quorum sensing systems are overviewed. In addition, strategies for optimizing the transcriptional regulatory tools' performance by refactoring transcription factors are summarized. Finally, the current limitations and prospects of constructing various regulatory tools based on transcription factors are discussed. This review will provide theoretical guidance for the rational design and construction of transcription factor-based metabolic regulation systems.
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The estimation of forest aboveground biomass (AGB) using Landsat 8 operational land imagery (OLI) images has been extensively studied, but forest aboveground biomass (AGB) is often difficult to estimate accurately, in part due to ...
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The estimation of forest aboveground biomass (AGB) using Landsat 8 operational land imagery (OLI) images has been extensively studied, but forest aboveground biomass (AGB) is often difficult to estimate accurately, in part due to the multi-level structure of forests, the heterogeneity of stands, and the diversity of tree species. In this study, a habitat dataset describing the distribution environment of forests, Landsat 8 OLI image data of spectral reflectance information, as well as a combination of the two datasets were employed to estimate the AGB of the three common pine forests (Pinus yunnanensis forests, Pinus densata forests, and Pinus kesiya forests) in Yunnan Province using a parametric model, stepwise linear regression model (SLR), and a non-parametric model, such as random forest (RF) and support vector machine (SVM). Based on the results, the following conclusions can be drawn. (1) As compared with the parametric model (SLR), the non-parametric models (RF and SVM) have a better fitting performance for estimating the AGB of the three pine forests, especially in the AGB segment of 40 to 200 Mg/ha. The non-parametric model is more sensitive to the number of data samples. In the case of the Pinus densata forest with a sample size greater than 100, RF fitting provides better fitting performance than SVM fitting, and the SVM fitting model is better suited to the AGB estimation of the Pinus yunnanensis forest with a sample size of less than 100. (2) Landsat 8 OLI images exhibit superior accuracy in estimating the AGB of the three pine forests using a single dataset. Variables, such as texture and vegetation index variables, which can reflect the comprehensive reflection information of ground objects, play a significant role in estimating AGBs, especially the texture variables. (3) By incorporating the combined dataset with characteristics of tree species distribution and ground object reflectance spectrum, the accuracy and stability of AGB estimation of the three pine forests can be improved. Moreover, the employment of a combined dataset is also effective in reducing the number of estimation errors in cases with AGB less than 100 Mg/ha or exceeding 150 Mg/ha.
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Solar energy building applications are attracting increasing attention from researchers, engineers, businessmen and officials due to their significant benefits in sustainable development, such as energy saving, cost reduction and ...
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Solar energy building applications are attracting increasing attention from researchers, engineers, businessmen and officials due to their significant benefits in sustainable development, such as energy saving, cost reduction and environmental protection. Trombe wall, as a classical passive solar heating technique, has been studied for many years. A variety of concepts, methodologies and experiences have been developed during relevant research. Especially in recent years, numerous studies on Trombe wall have been published, which implies a rising attention to this technique. This review focuses on the classification, experimental assessment, modeling methods, and evaluation metrics for Trombe wall. In detail, nine types of Trombe walls are introduced according to their materials, structures and functions. Four experimental methods and two modeling methods of Trombe wall are discussed based on their functions, advantages, disadvantages, and applicability. Three aspects of evaluation metrics for Trombe wall are summarized in terms of technique, economy and environment. Moreover, the current and future research of Trombe wall are discussed at the end. The authors consider this article would be useful for their peers and can facilitate the technical development of Trombe wall.
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