We consider the partially linear multiplicative model with monotonic constraint for the analysis of positive response data. We propose a constrained least product relative error (LPRE) estimation procedure for the mod...We consider the partially linear multiplicative model with monotonic constraint for the analysis of positive response data. We propose a constrained least product relative error (LPRE) estimation procedure for the model by means of B-spline basis expansion. We have also established asymptotic properties of the proposed estimators under regularity conditions. We finally provide numerical simulations and a real data application to assess the finite sample performance of the developed methodology.展开更多
Shale gas, as an environmentally friendly fossil energy resource, has gained significant commercial development and shows immense potential. However, accurately predicting shale gas production faces substantial challe...Shale gas, as an environmentally friendly fossil energy resource, has gained significant commercial development and shows immense potential. However, accurately predicting shale gas production faces substantial challenges due to the complex law of decline, nonlinear and non-stationary features in production data, which greatly repair the robustness of current models in predicting shale gas production time series. To address these challenges and improve accuracy in production forecasting, this paper introduces a novel and innovative approach: a hybrid proxy model that combines the bidirectional long short-term memory(BiLSTM) neural network and random forest(RF) through deep learning. The BiLSTM neural network is adept at capturing long-term dependencies, making it suitable for understanding the intricate relationships between input and output variables in shale gas production.On the other hand, RF serves a dual purpose: reducing model variance and addressing the concept drift problem that arises in non-stationary time series predictions made by BiLSTM. By integrating these two models, the hybrid approach effectively captures the inherent dependencies present in long and nonstationary production time series, thereby reducing model uncertainty. Furthermore, the combination of BiLSTM and RF is optimized using the recently-proposed marine predators algorithm(MPA) to fine-tune hyperparameters and enhance the overall performance of the proxy model. The results demonstrate that the proposed BiLSTM-RF-MPA model achieves higher prediction accuracy and demonstrates stronger generalization capabilities by effectively handling the complex nonlinear and non-stationary characteristics of shale gas production time series. Compared to other models such as LSTM, BiLSTM, and RF, the proposed model exhibits superior fitting and prediction performance, with an average improvement in performance indicators exceeding 20%. This innovative framework provides valuable insights for forecasting the complex production performance of unconventional oil and gas reservoirs, which sheds light on the development of data-driven proxy models in the field of subsurface energy utilization.展开更多
Background:As the market demands change,SMEs(small and medium-sized enterprises)have long faced many design issues,including high costs,lengthy cycles,and insufficient innovation.These issues are especially noticeable...Background:As the market demands change,SMEs(small and medium-sized enterprises)have long faced many design issues,including high costs,lengthy cycles,and insufficient innovation.These issues are especially noticeable in the domain of cosmetic packaging design.Objective:To explore innovative product family modeling methods and configuration design processes to improve the efficiency of enterprise cosmetic packaging design and develop the design for mass customization.Methods:To accomplish this objective,the basic-element theory has been introduced and applied to the design and development system of the product family.Results:By examining the mapping relationships between the demand domain,functional domain,technology domain,and structure domain,four interrelated models have been developed,including the demand model,functional model,technology model,and structure model.Together,these models form the mechanism and methodology of product family modeling,specifically for cosmetic packaging design.Through an analysis of a case study on men’s cosmetic packaging design,the feasibility of the proposed product family modeling technology has been demonstrated in terms of customized cosmetic packaging design,and the design efficiency has been enhanced.Conclusion:The product family modeling technology employs a formalized element as a module configuration design language,permeating throughout the entire development cycle of cosmetic packaging design,thus facilitating a structured and modularized configuration design process for the product family system.The application of the basic-element principle in product family modeling technology contributes to the enrichment of the research field surrounding cosmetic packaging product family configuration design,while also providing valuable methods and references for enterprises aiming to elevate the efficiency of cosmetic packaging design for the mass customization product model.展开更多
Using the typical characteristics of multi-layered marine and continental transitional gas reservoirs as a basis,a model is developed to predict the related well production rate.This model relies on the fractal theory...Using the typical characteristics of multi-layered marine and continental transitional gas reservoirs as a basis,a model is developed to predict the related well production rate.This model relies on the fractal theory of tortuous capillary bundles and can take into account multiple gas flow mechanisms at the micrometer and nanometer scales,as well as the flow characteristics in different types of thin layers(tight sandstone gas,shale gas,and coalbed gas).Moreover,a source-sink function concept and a pressure drop superposition principle are utilized to introduce a coupled flow model in the reservoir.A semi-analytical solution for the production rate is obtained using a matrix iteration method.A specific well is selected for fitting dynamic production data,and the calculation results show that the tight sandstone has the highest gas production per unit thickness compared with the other types of reservoirs.Moreover,desorption and diffusion of coalbed gas and shale gas can significantly contribute to gas production,and the daily production of these two gases decreases rapidly with decreasing reservoir pressure.Interestingly,the gas production from fractures exhibits an approximately U-shaped distribution,indicating the need to optimize the spacing between clusters during hydraulic fracturing to reduce the area of overlapping fracture control.The coal matrix water saturation significantly affects the coalbed gas production,with higher water saturation leading to lower production.展开更多
The production capacity of shale oil reservoirs after hydraulic fracturing is influenced by a complex interplay involving geological characteristics,engineering quality,and well conditions.These relationships,nonlinea...The production capacity of shale oil reservoirs after hydraulic fracturing is influenced by a complex interplay involving geological characteristics,engineering quality,and well conditions.These relationships,nonlinear in nature,pose challenges for accurate description through physical models.While field data provides insights into real-world effects,its limited volume and quality restrict its utility.Complementing this,numerical simulation models offer effective support.To harness the strengths of both data-driven and model-driven approaches,this study established a shale oil production capacity prediction model based on a machine learning combination model.Leveraging fracturing development data from 236 wells in the field,a data-driven method employing the random forest algorithm is implemented to identify the main controlling factors for different types of shale oil reservoirs.Through the combination model integrating support vector machine(SVM)algorithm and back propagation neural network(BPNN),a model-driven shale oil production capacity prediction model is developed,capable of swiftly responding to shale oil development performance under varying geological,fluid,and well conditions.The results of numerical experiments show that the proposed method demonstrates a notable enhancement in R2 by 22.5%and 5.8%compared to singular machine learning models like SVM and BPNN,showcasing its superior precision in predicting shale oil production capacity across diverse datasets.展开更多
To improve the productivity of oil wells,perforation technology is usually used to improve the productivity of horizontal wells in oilfield exploitation.After the perforation operation,the perforation channel around t...To improve the productivity of oil wells,perforation technology is usually used to improve the productivity of horizontal wells in oilfield exploitation.After the perforation operation,the perforation channel around the wellbore will form a near-well high-permeability reservoir area with the penetration depth as the radius,that is,the formation has different permeability characteristics with the perforation depth as the dividing line.Generally,the permeability is measured by the permeability tester,but this approach has a high workload and limited application.In this paper,according to the reservoir characteristics of perforated horizontal wells,the reservoir is divided into two areas:the original reservoir area and the near-well high permeability reservoir area.Based on the theory of seepage mechanics and the formula of open hole productivity,the permeability calculation formula of near-well high permeability reservoir area with perforation parameters is deduced.According to the principle of seepage continuity,the seepage is regarded as the synthesis of two directions:the horizontal plane elliptic seepage field and the vertical plane radial seepage field,and the oil well productivity prediction model of the perforated horizontal well is established by partition.The model comparison demonstrates that the model is reasonable and feasible.To calculate and analyze the effect of oil well production and the law of influencing factors,actual production data of the oilfield are substituted into the oil well productivity formula.It can effectively guide the technical process design and effect prediction of perforated horizontal wells.展开更多
This present research work focuses on the valorization of pig droppings for production of biogas in mono digestion and co-digestion with proportions of cow dung from the urban commune of N’Zérékoré. It...This present research work focuses on the valorization of pig droppings for production of biogas in mono digestion and co-digestion with proportions of cow dung from the urban commune of N’Zérékoré. It was carried out in December 2020 in the Physics laboratory of the University of N’Zérékoré. The anaerobic digestion process took 25 days in an almost constant ambient temperature of 25˚C. Five digesters were loaded on 12/06/2020, two of which with 1 kg of pig dung and 1 kg of cow dung both in mono-digestion. The 3 other digesters in co-digestion with different proportions of pig manure and cow dung. The substrate in each digester is diluted in 2 liters of water, with a proportion of (1/2). The main results obtained are: 1) the evolution of the temperature and pH during digestion process, 2) the average biogas productions 0.61 liters for (D1);1.20 liter for (D2);1.65 liter for (D3);1.51 liter for (D4) and 1.31 liter for (D5). The cumulative amounts of biogas are respectively: D1 (7.95 liters), D2 (15.60 liters), D3 (21.50 liters), D4 (19.65 liters) and D5 (17.05 liters). The total cumulative production is 81.75 liters at the end of the process. The originality of this research work is that the proposed model examines the relation between the daily biogas production and the variation of temperature, pH and pressure. The combustibility test showed the biogas produced during the first week was no combustible (contains less than 50% methane). Combustion started from the biogas produced from the 15th day and it is from the 20th day that a significant amount of stable yellow/blue flame was observed. The results of this study show the combination of pig manure and cow dung presents advantages for optimal biogas production.展开更多
A households′production behavior directly influences the quality of the environment and determines the successful development of nature reserves.Meanwhile,the households′production behaviors are complicated by inter...A households′production behavior directly influences the quality of the environment and determines the successful development of nature reserves.Meanwhile,the households′production behaviors are complicated by interrelated factors,such as protection attitudes,resource endowment,and family wealth.This research evaluated households near the Crested Ibis National Nature Reserve in Shaanxi Province,acquiring data from 436 households around Yang County and Ningshan County in the south slope of Qinling Mountains,China.Based on the collected data,we developed a structural equation model to evaluate the coupling relationships among households′ protection attitudes,production behaviors,resource endowment,and family wealth.The results showed that:1) households with great resource endowment had more negative attitudes,probably due to their greater protection costs;2) the households with higher education levels had worse protection attitudes;3) the households with more family wealth were likely to use fewer fertilizers,pesticides,and firewood;4) the households with more resource endowment showed less production and management behaviors;5) the enhancement of households' attitudes improved production behaviors to protection the environment,but the effects were not statistically significant.Our results provide a basis for the government's protection policy making,exploring the effective management measures that are beneficial for both nature reserve management and community development.展开更多
Interpretative structural model(ISM) can transform a multivariate problem into several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical str...Interpretative structural model(ISM) can transform a multivariate problem into several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical structure model. To build an ISM of a production system, the partial correlation coefficient method is proposed to obtain the adjacency matrix, which can be transformed to ISM. According to estimation of correlation coefficient, the result can give actual variable correlations and eliminate effects of intermediate variables. Furthermore, this paper proposes an effective approach using ISM to analyze the main factors and basic mechanisms that affect the energy consumption in an ethylene production system. The case study shows that the proposed energy consumption analysis method is valid and efficient in improvement of energy efficiency in ethylene production.展开更多
In order to solve the problem of modeling product configuration knowledge at the semantic level to successfully implement the mass customization strategy, an approach of ontology-based configuration knowledge modeling...In order to solve the problem of modeling product configuration knowledge at the semantic level to successfully implement the mass customization strategy, an approach of ontology-based configuration knowledge modeling, combining semantic web technologies, was proposed. A general configuration ontology was developed to provide a common concept structure for modeling configuration knowledge and rules of specific product domains. The OWL web ontology language and semantic web rule language （SWRL） were used to formally represent the configuration ontology, domain configuration knowledge and rules to enhance the consistency, maintainability and reusability of all the configuration knowledge. The configuration knowledge modeling of a customizable personal computer family shows that the approach can provide explicit, computerunderstandable knowledge semantics for specific product configuration domains and can efficiently support automatic configuration tasks of complex products.展开更多
With the increased competition of modern economy and globalization,consumer creation which based on the analysis of consumer behavior was more and more attentioned and respected by business.Based on the meaning and ch...With the increased competition of modern economy and globalization,consumer creation which based on the analysis of consumer behavior was more and more attentioned and respected by business.Based on the meaning and characteristics of agricultural product consumer creation,index system of value model of agricultural product consumer creation was put forward through analytical hierarchy process（AHP）.The weights of the indicators and related indicators of impact on the value were analyzed,and value models of agricultural product consumer creation were constructed to provide ideas for development of agricultural product consumer market and research of consumer value.Consumer creation was constructed to provide ideas for development of agricultural product consumer market and research of consumer value.展开更多
On basis of test information, the research performed analysis on water production function models of two crops, which indicated that water model of crops in whole growth stage and water model of crops indifferent grow...On basis of test information, the research performed analysis on water production function models of two crops, which indicated that water model of crops in whole growth stage and water model of crops indifferent growth stages have consistency as well as differences, providing references for optimization of irrigation water. Meanwhile, the research analyzed the deficiency of optimization on irrigation water for crops just by Jensen model.展开更多
The research performed analysis on causes of asymmetric information of agricultural product supply chain and made conclusion on operation mechanism and characteristics of supply chain based on asymmetric information. ...The research performed analysis on causes of asymmetric information of agricultural product supply chain and made conclusion on operation mechanism and characteristics of supply chain based on asymmetric information. Finally, the research detailed profit sharing of agricultural product supply chain in the context of asymmetric information and proposed suggestions, providing references of pricing and profit sharing of supply chains of agricultural products.展开更多
The PLDIM (Product Lifecycle Dynamic Information Model) is the most important part of the PLDM (Product Lifecycle Dynamic Model ) and it is the basis to create the information system and to implement PLM (Product Life...The PLDIM (Product Lifecycle Dynamic Information Model) is the most important part of the PLDM (Product Lifecycle Dynamic Model ) and it is the basis to create the information system and to implement PLM (Product Lifecycle Management). The information classification, the relationships among all information items, PLDIM mathematic expression, information coding and the 3D synthetic description of the PLDIM are presented. The information flow and information system structure based on the two information centers and Internet/Intranet are proposed, and how to implement this system for ship diesel engines are also introduced according to the PLDIM and PLM solutions.展开更多
This study constructs a function-private inner-product predicate encryption(FP-IPPE)and achieves standard enhanced function privacy.The enhanced function privacy guarantees that a predicate secret key skf reveals noth...This study constructs a function-private inner-product predicate encryption(FP-IPPE)and achieves standard enhanced function privacy.The enhanced function privacy guarantees that a predicate secret key skf reveals nothing about the predicate f,as long as f is drawn from an evasive distribution with sufficient entropy.The proposed scheme extends the group-based public-key function-private predicate encryption(FP-PE)for“small superset predicates”proposed by Bartusek et al.(Asiacrypt 19),to the setting of inner-product predicates.This is the first construction of public-key FP-PE with enhanced function privacy security beyond the equality predicates,which is previously proposed by Boneh et al.(CRYPTO 13).The proposed construction relies on bilinear groups,and the security is proved in the generic bilinear group model.展开更多
The net primary productivity(NPP) is an important indicator for assessing the carbon sequestration capacities of different ecosystems and plays a crucial role in the global biosphere carbon cycle. However, in the cont...The net primary productivity(NPP) is an important indicator for assessing the carbon sequestration capacities of different ecosystems and plays a crucial role in the global biosphere carbon cycle. However, in the context of the increasing frequency, intensity, and duration of global extreme climate events, the impacts of extreme climate and vegetation phenology on NPP are still unclear, especially on the Qinghai-Xizang Plateau(QXP), China. In this study, we used a new data fusion method based on the MOD13A2 normalized difference vegetation index(NDVI) and the Global Inventory Modeling and Mapping Studies(GIMMS) NDVI_(3g) datasets to obtain a NDVI dataset(1982–2020) on the QXP. Then, we developed a NPP dataset across the QXP using the Carnegie-Ames-Stanford Approach(CASA) model and validated its applicability based on gauged NPP data. Subsequently, we calculated 18 extreme climate indices based on the CN05.1 dataset, and extracted the length of vegetation growing season using the threshold method and double logistic model based on the annual NDVI time series. Finally, we explored the spatiotemporal patterns of NPP on the QXP and the impact mechanisms of extreme climate and the length of vegetation growing season on NPP. The results indicated that the estimated NPP exhibited good applicability. Specifically, the correlation coefficient, relative bias, mean error, and root mean square error between the estimated NPP and gauged NPP were 0.76, 0.17, 52.89 g C/(m^(2)·a), and 217.52 g C/(m^(2)·a), respectively. The NPP of alpine meadow, alpine steppe, forest, and main ecosystem on the QXP mainly exhibited an increasing trend during 1982–2020, with rates of 0.35, 0.38, 1.40, and 0.48 g C/(m^(2)·a), respectively. Spatially, the NPP gradually decreased from southeast to northwest across the QXP. Extreme climate had greater impact on NPP than the length of vegetation growing season on the QXP. Specifically, the increase in extremely-wet-day precipitation(R99p), simple daily intensity index(SDII), and hottest day(TXx) increased the NPP in different ecosystems across the QXP, while the increases in the cold spell duration index(CSDI) and warm spell duration index(WSDI) decreased the NPP in these ecosystems. The results of this study provide a scientific basis for relevant departments to formulate future policies addressing the impact of extreme climate on vegetation in different ecosystems on the QXP.展开更多
Increasing basic farmland soil productivity has significance in reducing fertilizer application and maintaining high yield of crops. In this study, we defined that the basic soil productivity （BSP） is the production...Increasing basic farmland soil productivity has significance in reducing fertilizer application and maintaining high yield of crops. In this study, we defined that the basic soil productivity （BSP） is the production capacity of a farmland soil with its own physical and chemical properties for a specific crop season under local environment and field management. Based on 22-yr （1990-2011） long-term experimental data on black soil （Typic hapludoll） in Gongzhuling, Jilin Province, Northeast China, the decision support system for an agro-technology transfer （DSSAT）-CERES-Maize model was applied to simulate the yield by BSP of spring maize （Zea mays L.） to examine the effects of long-term fertilization on changes of BSP and explore the mechanisms of BSP increasing. Five treatments were examined： （1） no-fertilization control （control）; （2） chemical nitrogen, phosphorus, and potassium （NPK）; （3） NPK plus farmyard manure （NPKM）; （4） 1.5 time of NPKM （1.5NPKM） and （5） NPK plus straw （NPKS）. Results showed that after 22-yr fertilization, the yield by BSP of spring maize significantly increased 78.0, 101.2, and 69.4% under the NPKM, 1.5NPKM and NPKS, respectively, compared to the initial value （in 1992）, but not significant under NPK （26.9% increase） and the control （8.9% decrease）. The contribution percentage of BSP showed a significant rising trend （P〈0.05） under 1.5NPKM. The average contribution percentage of BSP among fertilizations ranged from 74.4 to 84.7%, and ranked as 1.5NPKM〉NPKM〉NPK〉NPKS, indicating that organic manure combined with chemical fertilizers （I.5NPKM and NPKM） could more effectively increase BSP compared with the inorganic fertilizer application alone （NPK） in the black soil. This study showed that soil organic matter （SOM） was the key factor among various fertility factors that could affect BSP in the black soil, and total N, total P and/or available P also played important role in BSP increasing. Compared with the chemical fertilization, a balanced chemical plus manure or straw fertilization （NPKM or NPKS） not only increased the concentrations of soil nutrient, but also improved the soil physical properties, and structure and diversity of soil microbial population, resulting in an iincrease of BSP. We recommend that a balanced chemical plus manure or straw fertilization （NPKM or NPKS） should be the fertilization practices to enhance spring maize yield and improve BSP in the black soil of Northeast China.展开更多
Using a crop-water-salinity production function and a soil-water-salinity dynamic model, optimal irrigation scheduling was developed to maximize net return per irrigated area. Plot and field experiments were used to o...Using a crop-water-salinity production function and a soil-water-salinity dynamic model, optimal irrigation scheduling was developed to maximize net return per irrigated area. Plot and field experiments were used to obtain the crop water sensitivity index, the salinity sensitivity index, and other parameters. Using data collected during 35 years to calculate the 10-day mean precipitation and evaporation, the variation in soil salinity concentrations and in the yields of winter wheat and cotton were simulated for 49 irrigation scheduling that were combined from 7 irrigation schemes over 3 irrigation dates and 7 salinity concentrations of saline irrigation water (fresh water and 6 levels of saline water). Comparison of predicted results with irrigation data obtained from a large area of the field showed that the model was valid and reliable. Based on the analysis of the investment cost of the irrigation that employed deep tube wells or shallow tube wells, a saline water irrigation schedule and a corresponding strategy for groundwater development and utilization were proposed. For wheat or cotton, if the salinity concentration was higher than 7.0 g L-1 in groundwater, irrigation was needed with only fresh water; if about 5.0 g L-1, irrigation was required twice with fresh water and once with saline water; and if not higher than 3.0 g L-1, irrigation could be solely with saline water.展开更多
The steam reforming of four bio-oil model compounds（acetic acid,ethanol,acetone and phenol） was investigated over Ni-based catalysts supported on Al2O3 modified by Mg,Ce or Co in this paper.The activation process ca...The steam reforming of four bio-oil model compounds（acetic acid,ethanol,acetone and phenol） was investigated over Ni-based catalysts supported on Al2O3 modified by Mg,Ce or Co in this paper.The activation process can improve the catalytic activity with the change of high-valence Ni（Ni2O3,NiO） to low-valence Ni（Ni,NiO）.Among these catalysts after activation,the Ce-Ni/Co catalyst showed the best catalytic activity for the steam reforming of all the four model compounds.After long-term experiment at 700°C and the S/C ratio of 9,the Ce-Ni/Co catalyst still maintained excellent stability for the steam reforming of the simulated bio-oil（mixed by the four compounds with the equal masses）.With CaO calcinated from calcium acetate as CO2 sorbent,the catalytic steam reforming experiment combined with continuous in situ CO2 adsorption was performed.With the comparison of the case without the adding of CO2 sorbent,the hydrogen concentration was dramatically improved from 74.8% to 92.3%,with the CO2 concentration obviously decreased from 19.90% to 1.88%.展开更多
Increasing the production and utilization of shale gas is of great significance for building a clean and low-carbon energy system.Sharp decline of gas production has been widely observed in shale gas reservoirs.How to...Increasing the production and utilization of shale gas is of great significance for building a clean and low-carbon energy system.Sharp decline of gas production has been widely observed in shale gas reservoirs.How to forecast shale gas production is still challenging due to complex fracture networks,dynamic fracture properties,frac hits,complicated multiphase flow,and multi-scale flow as well as data quality and uncertainty.This work develops an integrated framework for evaluating shale gas well production based on data-driven models.Firstly,a comprehensive dominated-factor system has been established,including geological,drilling,fracturing,and production factors.Data processing and visualization are required to ensure data quality and determine final data set.A shale gas production evaluation model is developed to evaluate shale gas production levels.Finally,the random forest algorithm is used to forecast shale gas production.The prediction accuracy of shale gas production level is higher than 95%based on the shale gas reservoirs in China.Forty-one wells are randomly selected to predict cumulative gas production using the optimal regression model.The proposed shale gas production evaluation frame-work overcomes too many assumptions of analytical or semi-analytical models and avoids huge computation cost and poor generalization for numerical modelling.展开更多
文摘We consider the partially linear multiplicative model with monotonic constraint for the analysis of positive response data. We propose a constrained least product relative error (LPRE) estimation procedure for the model by means of B-spline basis expansion. We have also established asymptotic properties of the proposed estimators under regularity conditions. We finally provide numerical simulations and a real data application to assess the finite sample performance of the developed methodology.
基金supported by Sichuan Natural Science Foundation (Grant No. 2023NSFSC0423)CNPC Innovation Found (Grant No. 2022DQ02-0207)+2 种基金Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202201510)supported by a grant from the Human Resources Development program (No. 20216110100070) of the Korea Institute of Energy Technology Evaluation and Planning (KETEP)funded by the Ministry of Trade, Industry, and Energy of the Korean Government。
文摘Shale gas, as an environmentally friendly fossil energy resource, has gained significant commercial development and shows immense potential. However, accurately predicting shale gas production faces substantial challenges due to the complex law of decline, nonlinear and non-stationary features in production data, which greatly repair the robustness of current models in predicting shale gas production time series. To address these challenges and improve accuracy in production forecasting, this paper introduces a novel and innovative approach: a hybrid proxy model that combines the bidirectional long short-term memory(BiLSTM) neural network and random forest(RF) through deep learning. The BiLSTM neural network is adept at capturing long-term dependencies, making it suitable for understanding the intricate relationships between input and output variables in shale gas production.On the other hand, RF serves a dual purpose: reducing model variance and addressing the concept drift problem that arises in non-stationary time series predictions made by BiLSTM. By integrating these two models, the hybrid approach effectively captures the inherent dependencies present in long and nonstationary production time series, thereby reducing model uncertainty. Furthermore, the combination of BiLSTM and RF is optimized using the recently-proposed marine predators algorithm(MPA) to fine-tune hyperparameters and enhance the overall performance of the proxy model. The results demonstrate that the proposed BiLSTM-RF-MPA model achieves higher prediction accuracy and demonstrates stronger generalization capabilities by effectively handling the complex nonlinear and non-stationary characteristics of shale gas production time series. Compared to other models such as LSTM, BiLSTM, and RF, the proposed model exhibits superior fitting and prediction performance, with an average improvement in performance indicators exceeding 20%. This innovative framework provides valuable insights for forecasting the complex production performance of unconventional oil and gas reservoirs, which sheds light on the development of data-driven proxy models in the field of subsurface energy utilization.
基金the Guangdong Planning Office of Philosophy and Social Science(Grant No.GD22XYS04).
文摘Background:As the market demands change,SMEs(small and medium-sized enterprises)have long faced many design issues,including high costs,lengthy cycles,and insufficient innovation.These issues are especially noticeable in the domain of cosmetic packaging design.Objective:To explore innovative product family modeling methods and configuration design processes to improve the efficiency of enterprise cosmetic packaging design and develop the design for mass customization.Methods:To accomplish this objective,the basic-element theory has been introduced and applied to the design and development system of the product family.Results:By examining the mapping relationships between the demand domain,functional domain,technology domain,and structure domain,four interrelated models have been developed,including the demand model,functional model,technology model,and structure model.Together,these models form the mechanism and methodology of product family modeling,specifically for cosmetic packaging design.Through an analysis of a case study on men’s cosmetic packaging design,the feasibility of the proposed product family modeling technology has been demonstrated in terms of customized cosmetic packaging design,and the design efficiency has been enhanced.Conclusion:The product family modeling technology employs a formalized element as a module configuration design language,permeating throughout the entire development cycle of cosmetic packaging design,thus facilitating a structured and modularized configuration design process for the product family system.The application of the basic-element principle in product family modeling technology contributes to the enrichment of the research field surrounding cosmetic packaging product family configuration design,while also providing valuable methods and references for enterprises aiming to elevate the efficiency of cosmetic packaging design for the mass customization product model.
文摘Using the typical characteristics of multi-layered marine and continental transitional gas reservoirs as a basis,a model is developed to predict the related well production rate.This model relies on the fractal theory of tortuous capillary bundles and can take into account multiple gas flow mechanisms at the micrometer and nanometer scales,as well as the flow characteristics in different types of thin layers(tight sandstone gas,shale gas,and coalbed gas).Moreover,a source-sink function concept and a pressure drop superposition principle are utilized to introduce a coupled flow model in the reservoir.A semi-analytical solution for the production rate is obtained using a matrix iteration method.A specific well is selected for fitting dynamic production data,and the calculation results show that the tight sandstone has the highest gas production per unit thickness compared with the other types of reservoirs.Moreover,desorption and diffusion of coalbed gas and shale gas can significantly contribute to gas production,and the daily production of these two gases decreases rapidly with decreasing reservoir pressure.Interestingly,the gas production from fractures exhibits an approximately U-shaped distribution,indicating the need to optimize the spacing between clusters during hydraulic fracturing to reduce the area of overlapping fracture control.The coal matrix water saturation significantly affects the coalbed gas production,with higher water saturation leading to lower production.
基金supported by the China Postdoctoral Science Foundation(2021M702304)Natural Science Foundation of Shandong Province(ZR20210E260).
文摘The production capacity of shale oil reservoirs after hydraulic fracturing is influenced by a complex interplay involving geological characteristics,engineering quality,and well conditions.These relationships,nonlinear in nature,pose challenges for accurate description through physical models.While field data provides insights into real-world effects,its limited volume and quality restrict its utility.Complementing this,numerical simulation models offer effective support.To harness the strengths of both data-driven and model-driven approaches,this study established a shale oil production capacity prediction model based on a machine learning combination model.Leveraging fracturing development data from 236 wells in the field,a data-driven method employing the random forest algorithm is implemented to identify the main controlling factors for different types of shale oil reservoirs.Through the combination model integrating support vector machine(SVM)algorithm and back propagation neural network(BPNN),a model-driven shale oil production capacity prediction model is developed,capable of swiftly responding to shale oil development performance under varying geological,fluid,and well conditions.The results of numerical experiments show that the proposed method demonstrates a notable enhancement in R2 by 22.5%and 5.8%compared to singular machine learning models like SVM and BPNN,showcasing its superior precision in predicting shale oil production capacity across diverse datasets.
文摘To improve the productivity of oil wells,perforation technology is usually used to improve the productivity of horizontal wells in oilfield exploitation.After the perforation operation,the perforation channel around the wellbore will form a near-well high-permeability reservoir area with the penetration depth as the radius,that is,the formation has different permeability characteristics with the perforation depth as the dividing line.Generally,the permeability is measured by the permeability tester,but this approach has a high workload and limited application.In this paper,according to the reservoir characteristics of perforated horizontal wells,the reservoir is divided into two areas:the original reservoir area and the near-well high permeability reservoir area.Based on the theory of seepage mechanics and the formula of open hole productivity,the permeability calculation formula of near-well high permeability reservoir area with perforation parameters is deduced.According to the principle of seepage continuity,the seepage is regarded as the synthesis of two directions:the horizontal plane elliptic seepage field and the vertical plane radial seepage field,and the oil well productivity prediction model of the perforated horizontal well is established by partition.The model comparison demonstrates that the model is reasonable and feasible.To calculate and analyze the effect of oil well production and the law of influencing factors,actual production data of the oilfield are substituted into the oil well productivity formula.It can effectively guide the technical process design and effect prediction of perforated horizontal wells.
文摘This present research work focuses on the valorization of pig droppings for production of biogas in mono digestion and co-digestion with proportions of cow dung from the urban commune of N’Zérékoré. It was carried out in December 2020 in the Physics laboratory of the University of N’Zérékoré. The anaerobic digestion process took 25 days in an almost constant ambient temperature of 25˚C. Five digesters were loaded on 12/06/2020, two of which with 1 kg of pig dung and 1 kg of cow dung both in mono-digestion. The 3 other digesters in co-digestion with different proportions of pig manure and cow dung. The substrate in each digester is diluted in 2 liters of water, with a proportion of (1/2). The main results obtained are: 1) the evolution of the temperature and pH during digestion process, 2) the average biogas productions 0.61 liters for (D1);1.20 liter for (D2);1.65 liter for (D3);1.51 liter for (D4) and 1.31 liter for (D5). The cumulative amounts of biogas are respectively: D1 (7.95 liters), D2 (15.60 liters), D3 (21.50 liters), D4 (19.65 liters) and D5 (17.05 liters). The total cumulative production is 81.75 liters at the end of the process. The originality of this research work is that the proposed model examines the relation between the daily biogas production and the variation of temperature, pH and pressure. The combustibility test showed the biogas produced during the first week was no combustible (contains less than 50% methane). Combustion started from the biogas produced from the 15th day and it is from the 20th day that a significant amount of stable yellow/blue flame was observed. The results of this study show the combination of pig manure and cow dung presents advantages for optimal biogas production.
基金Under the auspices of Forestry Industry Research Special Funds for Public Welfare Projects(No.201004008)National Natural Science Foundation of China(No.71003007)Research Program of Food and Agriculture Organization(No.CHN/2011/077/LOA)
文摘A households′production behavior directly influences the quality of the environment and determines the successful development of nature reserves.Meanwhile,the households′production behaviors are complicated by interrelated factors,such as protection attitudes,resource endowment,and family wealth.This research evaluated households near the Crested Ibis National Nature Reserve in Shaanxi Province,acquiring data from 436 households around Yang County and Ningshan County in the south slope of Qinling Mountains,China.Based on the collected data,we developed a structural equation model to evaluate the coupling relationships among households′ protection attitudes,production behaviors,resource endowment,and family wealth.The results showed that:1) households with great resource endowment had more negative attitudes,probably due to their greater protection costs;2) the households with higher education levels had worse protection attitudes;3) the households with more family wealth were likely to use fewer fertilizers,pesticides,and firewood;4) the households with more resource endowment showed less production and management behaviors;5) the enhancement of households' attitudes improved production behaviors to protection the environment,but the effects were not statistically significant.Our results provide a basis for the government's protection policy making,exploring the effective management measures that are beneficial for both nature reserve management and community development.
基金Supported by the National Natural Science Foundation of China(61374166,6153303)the Doctoral Fund of Ministry of Education of China(20120010110010)the Fundamental Research Funds for the Central Universities(YS1404,JD1413,ZY1502)
文摘Interpretative structural model(ISM) can transform a multivariate problem into several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical structure model. To build an ISM of a production system, the partial correlation coefficient method is proposed to obtain the adjacency matrix, which can be transformed to ISM. According to estimation of correlation coefficient, the result can give actual variable correlations and eliminate effects of intermediate variables. Furthermore, this paper proposes an effective approach using ISM to analyze the main factors and basic mechanisms that affect the energy consumption in an ethylene production system. The case study shows that the proposed energy consumption analysis method is valid and efficient in improvement of energy efficiency in ethylene production.
基金The National Natural Science Foundation of China(No.70471023).
文摘In order to solve the problem of modeling product configuration knowledge at the semantic level to successfully implement the mass customization strategy, an approach of ontology-based configuration knowledge modeling, combining semantic web technologies, was proposed. A general configuration ontology was developed to provide a common concept structure for modeling configuration knowledge and rules of specific product domains. The OWL web ontology language and semantic web rule language （SWRL） were used to formally represent the configuration ontology, domain configuration knowledge and rules to enhance the consistency, maintainability and reusability of all the configuration knowledge. The configuration knowledge modeling of a customizable personal computer family shows that the approach can provide explicit, computerunderstandable knowledge semantics for specific product configuration domains and can efficiently support automatic configuration tasks of complex products.
基金Supported by Rural Development Research Center in Sichuan(2009CR2110921)~~
文摘With the increased competition of modern economy and globalization,consumer creation which based on the analysis of consumer behavior was more and more attentioned and respected by business.Based on the meaning and characteristics of agricultural product consumer creation,index system of value model of agricultural product consumer creation was put forward through analytical hierarchy process（AHP）.The weights of the indicators and related indicators of impact on the value were analyzed,and value models of agricultural product consumer creation were constructed to provide ideas for development of agricultural product consumer market and research of consumer value.Consumer creation was constructed to provide ideas for development of agricultural product consumer market and research of consumer value.
文摘On basis of test information, the research performed analysis on water production function models of two crops, which indicated that water model of crops in whole growth stage and water model of crops indifferent growth stages have consistency as well as differences, providing references for optimization of irrigation water. Meanwhile, the research analyzed the deficiency of optimization on irrigation water for crops just by Jensen model.
基金Supported by S&T Development Strategy Program of Tianjin(15ZLZLZF00210)S&T Development Strategy Program of Tianjin(15ZLZLZF00390)~~
文摘The research performed analysis on causes of asymmetric information of agricultural product supply chain and made conclusion on operation mechanism and characteristics of supply chain based on asymmetric information. Finally, the research detailed profit sharing of agricultural product supply chain in the context of asymmetric information and proposed suggestions, providing references of pricing and profit sharing of supply chains of agricultural products.
文摘The PLDIM (Product Lifecycle Dynamic Information Model) is the most important part of the PLDM (Product Lifecycle Dynamic Model ) and it is the basis to create the information system and to implement PLM (Product Lifecycle Management). The information classification, the relationships among all information items, PLDIM mathematic expression, information coding and the 3D synthetic description of the PLDIM are presented. The information flow and information system structure based on the two information centers and Internet/Intranet are proposed, and how to implement this system for ship diesel engines are also introduced according to the PLDIM and PLM solutions.
基金National Key Research and Development Program of China(2021YFB3101402)National Natural Science Foundation of China(62202294)。
文摘This study constructs a function-private inner-product predicate encryption(FP-IPPE)and achieves standard enhanced function privacy.The enhanced function privacy guarantees that a predicate secret key skf reveals nothing about the predicate f,as long as f is drawn from an evasive distribution with sufficient entropy.The proposed scheme extends the group-based public-key function-private predicate encryption(FP-PE)for“small superset predicates”proposed by Bartusek et al.(Asiacrypt 19),to the setting of inner-product predicates.This is the first construction of public-key FP-PE with enhanced function privacy security beyond the equality predicates,which is previously proposed by Boneh et al.(CRYPTO 13).The proposed construction relies on bilinear groups,and the security is proved in the generic bilinear group model.
基金supported by the National Natural Science Foundation of China (U2243227)。
文摘The net primary productivity(NPP) is an important indicator for assessing the carbon sequestration capacities of different ecosystems and plays a crucial role in the global biosphere carbon cycle. However, in the context of the increasing frequency, intensity, and duration of global extreme climate events, the impacts of extreme climate and vegetation phenology on NPP are still unclear, especially on the Qinghai-Xizang Plateau(QXP), China. In this study, we used a new data fusion method based on the MOD13A2 normalized difference vegetation index(NDVI) and the Global Inventory Modeling and Mapping Studies(GIMMS) NDVI_(3g) datasets to obtain a NDVI dataset(1982–2020) on the QXP. Then, we developed a NPP dataset across the QXP using the Carnegie-Ames-Stanford Approach(CASA) model and validated its applicability based on gauged NPP data. Subsequently, we calculated 18 extreme climate indices based on the CN05.1 dataset, and extracted the length of vegetation growing season using the threshold method and double logistic model based on the annual NDVI time series. Finally, we explored the spatiotemporal patterns of NPP on the QXP and the impact mechanisms of extreme climate and the length of vegetation growing season on NPP. The results indicated that the estimated NPP exhibited good applicability. Specifically, the correlation coefficient, relative bias, mean error, and root mean square error between the estimated NPP and gauged NPP were 0.76, 0.17, 52.89 g C/(m^(2)·a), and 217.52 g C/(m^(2)·a), respectively. The NPP of alpine meadow, alpine steppe, forest, and main ecosystem on the QXP mainly exhibited an increasing trend during 1982–2020, with rates of 0.35, 0.38, 1.40, and 0.48 g C/(m^(2)·a), respectively. Spatially, the NPP gradually decreased from southeast to northwest across the QXP. Extreme climate had greater impact on NPP than the length of vegetation growing season on the QXP. Specifically, the increase in extremely-wet-day precipitation(R99p), simple daily intensity index(SDII), and hottest day(TXx) increased the NPP in different ecosystems across the QXP, while the increases in the cold spell duration index(CSDI) and warm spell duration index(WSDI) decreased the NPP in these ecosystems. The results of this study provide a scientific basis for relevant departments to formulate future policies addressing the impact of extreme climate on vegetation in different ecosystems on the QXP.
基金supported by the National 973 Program of China (2011CB100501)the National 863 Program of China(2013AA102901)+1 种基金the Special Fund for Agro-Scientific Research in the Public Interest, China (201203077)the Science and Technology Project for Grain Production, China (2011BAD16B15)
文摘Increasing basic farmland soil productivity has significance in reducing fertilizer application and maintaining high yield of crops. In this study, we defined that the basic soil productivity （BSP） is the production capacity of a farmland soil with its own physical and chemical properties for a specific crop season under local environment and field management. Based on 22-yr （1990-2011） long-term experimental data on black soil （Typic hapludoll） in Gongzhuling, Jilin Province, Northeast China, the decision support system for an agro-technology transfer （DSSAT）-CERES-Maize model was applied to simulate the yield by BSP of spring maize （Zea mays L.） to examine the effects of long-term fertilization on changes of BSP and explore the mechanisms of BSP increasing. Five treatments were examined： （1） no-fertilization control （control）; （2） chemical nitrogen, phosphorus, and potassium （NPK）; （3） NPK plus farmyard manure （NPKM）; （4） 1.5 time of NPKM （1.5NPKM） and （5） NPK plus straw （NPKS）. Results showed that after 22-yr fertilization, the yield by BSP of spring maize significantly increased 78.0, 101.2, and 69.4% under the NPKM, 1.5NPKM and NPKS, respectively, compared to the initial value （in 1992）, but not significant under NPK （26.9% increase） and the control （8.9% decrease）. The contribution percentage of BSP showed a significant rising trend （P〈0.05） under 1.5NPKM. The average contribution percentage of BSP among fertilizations ranged from 74.4 to 84.7%, and ranked as 1.5NPKM〉NPKM〉NPK〉NPKS, indicating that organic manure combined with chemical fertilizers （I.5NPKM and NPKM） could more effectively increase BSP compared with the inorganic fertilizer application alone （NPK） in the black soil. This study showed that soil organic matter （SOM） was the key factor among various fertility factors that could affect BSP in the black soil, and total N, total P and/or available P also played important role in BSP increasing. Compared with the chemical fertilization, a balanced chemical plus manure or straw fertilization （NPKM or NPKS） not only increased the concentrations of soil nutrient, but also improved the soil physical properties, and structure and diversity of soil microbial population, resulting in an iincrease of BSP. We recommend that a balanced chemical plus manure or straw fertilization （NPKM or NPKS） should be the fertilization practices to enhance spring maize yield and improve BSP in the black soil of Northeast China.
基金Project supported by the National Natural Science Foundation of China (Nos. 50339030 and 90202001).
文摘Using a crop-water-salinity production function and a soil-water-salinity dynamic model, optimal irrigation scheduling was developed to maximize net return per irrigated area. Plot and field experiments were used to obtain the crop water sensitivity index, the salinity sensitivity index, and other parameters. Using data collected during 35 years to calculate the 10-day mean precipitation and evaporation, the variation in soil salinity concentrations and in the yields of winter wheat and cotton were simulated for 49 irrigation scheduling that were combined from 7 irrigation schemes over 3 irrigation dates and 7 salinity concentrations of saline irrigation water (fresh water and 6 levels of saline water). Comparison of predicted results with irrigation data obtained from a large area of the field showed that the model was valid and reliable. Based on the analysis of the investment cost of the irrigation that employed deep tube wells or shallow tube wells, a saline water irrigation schedule and a corresponding strategy for groundwater development and utilization were proposed. For wheat or cotton, if the salinity concentration was higher than 7.0 g L-1 in groundwater, irrigation was needed with only fresh water; if about 5.0 g L-1, irrigation was required twice with fresh water and once with saline water; and if not higher than 3.0 g L-1, irrigation could be solely with saline water.
基金supported by the National Natural Science Foundation of China(No.51274066,51304048)the National Key Technology R&D Program of China(No.2013BAA03B03)the National Science Foundation for Post-doctoral Scientists of China(No.2013M541240)
文摘The steam reforming of four bio-oil model compounds（acetic acid,ethanol,acetone and phenol） was investigated over Ni-based catalysts supported on Al2O3 modified by Mg,Ce or Co in this paper.The activation process can improve the catalytic activity with the change of high-valence Ni（Ni2O3,NiO） to low-valence Ni（Ni,NiO）.Among these catalysts after activation,the Ce-Ni/Co catalyst showed the best catalytic activity for the steam reforming of all the four model compounds.After long-term experiment at 700°C and the S/C ratio of 9,the Ce-Ni/Co catalyst still maintained excellent stability for the steam reforming of the simulated bio-oil（mixed by the four compounds with the equal masses）.With CaO calcinated from calcium acetate as CO2 sorbent,the catalytic steam reforming experiment combined with continuous in situ CO2 adsorption was performed.With the comparison of the case without the adding of CO2 sorbent,the hydrogen concentration was dramatically improved from 74.8% to 92.3%,with the CO2 concentration obviously decreased from 19.90% to 1.88%.
基金funded by National Natural Science Foundation of China(52004238)China Postdoctoral Science Foundation(2019M663561).
文摘Increasing the production and utilization of shale gas is of great significance for building a clean and low-carbon energy system.Sharp decline of gas production has been widely observed in shale gas reservoirs.How to forecast shale gas production is still challenging due to complex fracture networks,dynamic fracture properties,frac hits,complicated multiphase flow,and multi-scale flow as well as data quality and uncertainty.This work develops an integrated framework for evaluating shale gas well production based on data-driven models.Firstly,a comprehensive dominated-factor system has been established,including geological,drilling,fracturing,and production factors.Data processing and visualization are required to ensure data quality and determine final data set.A shale gas production evaluation model is developed to evaluate shale gas production levels.Finally,the random forest algorithm is used to forecast shale gas production.The prediction accuracy of shale gas production level is higher than 95%based on the shale gas reservoirs in China.Forty-one wells are randomly selected to predict cumulative gas production using the optimal regression model.The proposed shale gas production evaluation frame-work overcomes too many assumptions of analytical or semi-analytical models and avoids huge computation cost and poor generalization for numerical modelling.
