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Focus on CO2 emission reduction through the creation of a local suppliers network.
The positive benefits of systemic acting/thinking/designing are given substance thanks to a greater traceability of materials and packaging components and their suppliers, as well as a higher quality of the entire supply-chain and a significant reduction of CO2 emissions and logistics costs.
Furthermore, these benefits result in a higher quality of the entire supply-chain, in a significant decrease of logistics costs as well as in the consequent reduction of CO2 emissions [2].
In order to go through a project with the SD approach, it is essential to start from the current state of the art and to make a peculiar observation of all the aspects necessary for the system to live (energy and material input) and of what occurs inside it (qualities and quantities data).
ACT LOCALLY: (a) using local materials it is possible to obtain a greater traceability of the resources used in industrial processes so as to ensure, by implication, a high quality finished product; (b) reduction of CO2 (actually its production is caused by a supply of raw materials and semi-finished products for packaging manufacturing which happens long away from the points of final packing); (c) reduction in energy consumption due to unnecessary transport. 4.

The X ray powder diffraction data of the products of different reductants at different sintering temperatures and holding time are shown in Fig. 1.
It shows that the reaction of carbothermal reduction of mullite had ended at this time.
When sintering time was 5h, the peak of α-Al2O3was the highest, showing the best reduction effect at this time.
Second, the carbothermal reduction of SiO2 occurred at about 1500°C or at a lower temperature.
Finally, when the reaction of carbothermal reduction of SiO2was complete, the carbothermal reduction of mullite occurred at about 1550°C or at a higher temperature, and the end temperature of carbothermal reduction of mullite is 1600°C.

ZrN(ZrO2)/Si2N2O composite materials were synthesized by in-situ carbothermal reduction-nitridation process, with zircon and carbon black as raw materials.
The carbothermal reduction-nitridation reaction process was also discussed.
This over-stoichiometry of carbon black was necessary to promote the carbothermal reduction-nitridation reaction.
(J/mol), lg(pCO/pQ)=-10408/T+6.30 (14) (J/mol), lg(pCO/pQ)=-11126/T+6.12 (15) (J/mol), lg(pCO/pQ)=-13046/T+6.88 (16) Fig. 4 shows the predomination region diagram for ZrO2-SiO2-C-N2 system plotted by the thermodynamic data as shown in formulas (14)-(16), with T as horizontal axis and lg(pCO/pQ) as vertical axis.
Che: Data handbook of inorganic compound thermodynamics (Northeastern University Press, Shenyang, 1993).

The result is to take 0.75 as the reduction coefficient of shear capacity.
The data of Table 2 could proof this conclusion.
Table 4 data show that, according to the literature [9], calculated the shear capacity is less than the experimental values, while the calculated values with the literature[8] are greater than the experimental values, the reason is that not considered the reinforcement effect of dead load level and degree of damage in the original beam.
Dead load levels on the shear strength reduction factor η to determine.
The reduction coefficient value of shear capacity of reinforced beams is researched.

This paper describes the technical problems for upgrading efficiency by core loss reduction on high speed motor.
These characteristic data is useful for magnetic characteristic analysis.
Design Technique – Vector Magnetic Characteristic Analysis In order to develop the high efficiency high speed motor, computer simulation technology is used for the design of core loss reduction by vector magnetic characteristic analysis as shown in Fig.11 It is very difficult to design the shape construction for core loss reduction on motor.
Fig. 12 Parameter for reduction of core loss.
Vector magnetic characteristics is useful and should be utilize for core reduction of motors.

It realizes screening and dimensionality reduction for primitive data through factor analysis(FA), decreases data redundancy regardless of related data, we make use of the nonlinear reflection ability and association learning ability of wavelet neural network, and proposes a model of pressure sensor temperature compensation.
(2)Divide the data into training data and test data
(5)Apply the trained neural network to inspect test data[4]. 5 Temperature Compensation of the Pressure Sensor 5.1 Calibration Data In experiment the PT14 piezoresistive pressure sensor and AD590 temperature transducer are used, their output are UP and Ut, as shown in table 1.
Random function generates the initial data center, extension constant and weights of output.
Table 4 Comparison with WNN Average iteration times Average training time [s] WNN 7 3.4218 WNN Based on FA 3 1.2597 In short, WNN based on FA improves the stability of pressure sensorand accuracy. 6 Summary For the sake of eliminating the temperature influence on the output of sensor, WNN based on FA is proposed, and the validity of the method is tested. it realizes screening and dimensionality reduction for primitive data through factor, decreases data redundancy regardless of related data, we make use of the nonlinear reflection ability and association learning ability of WNN, and propose a model of pressure sensor temperature compensation.

The GO and rGO samples structure were indentified from XRD data and SQUID data for magnetic characteristics.
By evaluating SQUID measurement data, the magnetic susceptibility value of sample was determined and plotted as shown in Fig. 2.
XRD characteristic of GO sampel and rGO sample with various reduction times at 200 oC.
Magnetic susceptibility characteristic of GO sample (black circle) and rGO sample (red fill-circle) evaluated from FC and ZFC SQUID data in H = 500 Oe.
Magnetic susceptibility characteristic of GO and rGO amples versus χ/T from FC SQUID data in H = 500 Oe and temperatures below 200 K.

Experimental equipments include: reduction furnace, reduction tube, electronic balance, silicon controlled voltage regulator, temperature controller, flowmeter and so on.
Fig 4 Experimentation of hydrogen reduction a Experiment conditions (1) hydrogen flow: 2L/min; (2) nitrogen flow: 2L/min(gas shield), 5L/min(reduction process); (3) weight: 50g; (4) corundum crucible: inner diameter: Φ49mm, external diameter: Φ54mm，high: 80mm.
b Experimental results and analysis Weightlessness curve and analysis of reducibility Ten times reduction experiments were proceed, weightlessness datas were written down every minute, Weight loss curve of iron ore is shown in the figure 5.
According to figure 5, the final reduction temperature of 0.18 mm ore was about 950 ℃， the 2μm ore was about 730℃, the reduction temperature of micron order ore droped about 100℃, compared with millimeter.
In the weightlessness experiment, the percent reduction was more than 95%.

Data acquisition is the key step for data warehouse.
Rhombus-dimension-reduction technology is adopted to decompose the dimensions in multi-dimension data storage so as to raise the computer answer speed of the search analysis, as shown in Fig. 3.
Data access tool includes query and reporting, OLAP, data mining etc.
Data mining subsystem organizes and executes the data mining task in the object-oriented form and its data sources are obtained from the data warehouse.
Metadata are data about data that describe the content, quality, condition, and other characteristics of data.

Cone data indicate that the incorporation of 1% GNP leads to a 12% reduction in the peak heat release rate compared to pure PMMA.
Measurements The X-ray diffraction (XRD) data were obtained at room temperature using a Rigaku Miniflex II Desktop X-ray diffractometer equipped with a Cu-Ka source (λ=1.5404Å).
The incorporation of 1% GNP leads to a 12% reduction in the peak heat release rate (PHRR) compared to pure PMMA.
Increasing the GNP loading brings about a gradual reduction in PHRR; the reductions are 16% and 19% at 2% and 3% loading, respectively.
The reason for the reduction in PHRR may be due to physical barrier effect caused graphene sheets.