Product family modeling technology for customized cosmetic packaging design based on basic-element theory

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.

A Well Productivity Model for Multi-Layered Marine and Continental Transitional Reservoirs with Complex Fracture Networks

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.

Production Capacity Prediction Method of Shale Oil Based on Machine Learning Combination Model

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.

Productivity Prediction Model of Perforated Horizontal Well Based on Permeability Calculation in Near-Well High Permeability Reservoir Area

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.

A Biogas Production Model from the Combination of Pig Manure and Cow Dung in N’Zérékoré City, Republic of Guine

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.

Configuration knowledge modeling of customizable products based on semantic web technologies

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.

Construction of Value Models of Consumer Creation for Agricultural Products Based on the Analytic Hierarchy Process(AHP)

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.

Consistency of Water Production Function Models of Two Crops

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.

The Operation Mechanism of Agricultural Products Supply Chain and Profit Allocation Model in the Context of 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.

Basic Soil Productivity of Spring Maize in Black Soil Under Long-Term Fertilization Based on DSSAT Model

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.

Hydrogen production via steam reforming of bio-oil model compounds over supported nickel catalysts

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%.

Technological Options to Ameliorate Waste Treatment of Intensive Pig Production in China:An Analysis Based on Bio-Economic Model

Ameliorating waste treatment by technological improvements affects the economic and the ecological-environment benefits of intensive pig production. The objective of the research was to develop and test a method to determine the technical optimization to ameliorate waste treatment methods and gain insight into the relationship between technological options and the economic and ecological effects. We developed an integrated bio-economic model which incorporates the farming production and waste disposal systems to simulate the impact of technological improvements in pig manure treatment on economic and environmental benefits for the case of a pilot farm in Beijing, China. Based on different waste treatment technology options, three scenarios are applied for the simulation analysis of the model. The simulation results reveal that the economic-environmental benefits of the livestock farm could be improved by reducing the cropland manure application and increasing the composting production with the current technologies. Nevertheless, the technical efficiency, the waste treatment capacity and the economic benefits could be further improved by the introduction of new technologies. It implies that technological and economic support policies should be implemented comprehensively on waste disposal and resource utilization to promote sustainable development in intensive livestock production in China.

A Feature-Based Parametric Product Modeling System in CIMS Environment

This paper proposes an approach of developing the feature based parametric product modeling system which is suitable for integrated engineering design in CIMS environment.The architecture of ZD--MCADII and the characteristics of its each module are introduced in detail. ZD--MCADII’s product data is managed by an object--oriented database management system OSCAR, and the product model is built according to the standard STEP. The product design is established on a unified product model, and all the product data are globally associated in ZD--MCADII. ZD--MCADII provides various design features to facilitate the product design, and supports the integrity of CAD, CAPP and CAM.

Analysis and calculation model of energy consumption and product yields of delayed coking units

Delayed coking is an important process consumption and light oil yield are important factors used to convert heavy oils to light products. Energy for evaluating the delayed coking process. This paper analyzes the energy consumption and product yields of delayed coking units in China. The average energy consumption shows a decreasing trend in recent years. The energy consumption of different refineries varies greatly, with the average value of the highest energy consumption approximately twice that of the lowest energy consumption. The factors affecting both energy consumption and product yields were analyzed, and correlation models of energy consumption and product yields were established using a quadratic polynomial. The model coefficients were calculated through least square regression of collected industrial data of delayed coking units. Both models showed good calculation accuracy. The average absolute error of the energy consumption model was approximately 85 MJ/t, and that of the product yield model ranged from 1 wt% to 2.3 wt%. The model prediction showed that a large annual processing capacity and high load rate will result in a reduction in energy consumption.

Disassemblability Modeling Technology of Configurable Product Based on Disassembly Constraint Relation Weighted Design Structure Matrix(DSM)

The current research of configurable product disassemblability focuses on disassemblability evaluation and disassembly sequence planning. Little work has been done on quantitative analysis of configurable product disassemblability. The disassemblability modeling technology for configurable product based on disassembly constraint relation weighted design structure matrix （DSM） is proposed. Major factors affecting the disassemblability of configurable product are analyzed, and the disassembling degrees between components in configurable product are obtained by calculating disassembly entropies such as joint type, joint quantity, disassembly path, disassembly accessibility and material compatibility. The disassembly constraint relation weighted DSM of configurable product is constructed and configuration modules are formed by matrix decomposition and tearing operations. The disassembly constraint relation in configuration modules is strong coupling, and the disassembly constraint relation between modules is weak coupling, and the disassemblability configuration model is constructed based on configuration module. Finally, taking a hydraulic forging press as an example, the decomposed weak coupling components are used as configuration modules alone, components with a strong coupling are aggregated into configuration modules, and the disassembly sequence of components inside configuration modules is optimized by tearing operation. A disassemblability configuration model of the hydraulic forging press is constructed. By researching the disassemblability modeling technology of product configuration design based on disassembly constraint relation weighted DSM, the disassembly property in maintenance, recycling and reuse of configurable product are optimized.

Shale gas production evaluation framework based on data-driven models

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.

Saline Water Irrigation Scheduling Through a Crop-Water-Salinity Production Function and a Soil-Water-Salinity Dynamic Model

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.

Model study on Bohai ecosystem 1. Model description and primary productivity

A Nutrient -Phytoplankton -Zooplankton（NPZD） type of ecological model is developed to reflect the biochemical process, and further coupled to a primitive equation ocean model, an irradiation model as well as a river discharge model to reproduce ecosystem dynamics in the Bohai Sea. Modeled primary production shows reasonable consistency with observations quantitatively and qualitatively; in addition, f-ratio is examined in detail in the first time, which is also within the range reported in other studies and reveals some meaningful insight into the relative contributions of ammonium and nitrate to the growth of phytoplankton in the Bohai Sea.

Extended Object Model for Product Configuration Design

This paper presents an extended object model for case-based reasoning （CBR） in product configuration design. In the extended object model, a few methods of knowledge expression are adopted, such as constraints, rules, objects, etc. On the basis of extended object model, case representation model for CBR is applied to product configuration design system. The product configuration knowledge can be represented by the extended object. The model can support all the processes of CBR in product configuration design, such as case representation, indexing, retrieving, and case revising. The presented model is an extension of the traditional object-oriented model by including the relationship class used to express the relation between the cases, constraints class used in the product configuration knowledge representation, index class used in ease retrieving, and solution class used in case revising. Therefore, the product configuration knowledge used in the product configuration design can be represented by using this model. In the end, a metering pump product configuration design system is developed on the basis of the proposed product configuration model to support customized products.

Modeling the formation of soluble microbial products(SMP) in drinking water biofiltration

Both a theoretical and an empirical model were developed for predicting the formation of soluble microbial products （SMP） during drinking water biofiltration. Four pilot-scale biofilters with ceramsite as the medium were fed with different acetate loadings for the determination of SMP formation. Using numerically simulated and measured parameters, the theoretical model was developed according to the substrate and biomass balance. The results of this model matched the measured data better for higher SMP formation but did not fit well when SMP formation was lower. In order to better simulate the reality and overcome the difficulties of measuring the kinetic parameters, a simpler empirical model was also developed. In this model, SMP formation was expressed as a function of fed organic loadings and the depth of the medium, and a much better fit was obtained.