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This is discussed using data from literature and an alternative approach to identify the process stability window is given.
Introduction Weight reduction in the transport is a critical factor for an immediate reduction of climate impact, especially since significant amounts of electricity still is fossil-based. [1] Similarly, a capability increase, such offered by semisolid metal processing, processes may improve process yields and allow more efficient designs allowing for weigh reduction and resource efficiency for more sectors than the transport industry. [2, 3] The control of solid fraction and stable operations are critical for the production of high quality parts independent on processing route [4, 5].
Conventional stability analysis for semisolid processing In the current paper data from Payandeh et al [6] will be re-analysed with respect to the processing window to better understand the chemical segregation and deviations in solid fractions observed.
Methodology The data used in this analysis was published by Payandeh et al. [6] with the chemical compositions shown in Table 1a, for the alloys and in Table 1b for the liquid and solid portions of the slurry Table 1 a.
The alloy composition from Payandeh et al. [6] Base alloy, xAlB Liquidus temperature Alloy Si Fe Mg Al 1 1.69 0.8 0.39 97.12 646 2 2.49 0.8 0.4 96.31 641 3 3.67 0.75 0.41 95.17 636 4 4.56 0.75 0.4 94.29 629 ThermoCalcTM and the TCAL6 database was used to calculate the evolution of solid fraction for both the base alloy and the remaining liquid portion of the melt and to produce data to assess the slopes of the curves.

Some useful data for both materials are shown in Table 2.
These data, plus the length of the packed powder, allowed the determination of particle volume fraction Vp.
Data for pure tin and Al-Si alloy.
The data for the infiltration height versus P can be satisfactorily fitted by means of Darcy´s law [6]: () 1 2 P V tk2 h p Δ⋅ −⋅ ⋅⋅ = μ (1) where Vp is the particle volume fraction, μ the viscosity of the alloy and k is the permeability of the compact.
Data for threshold pressure and infiltration rate for the graphite preforms infiltrated with pure tin and Al-12Si alloy are shown in Table 3.

The Labview software (National Instruments) and a PCI-MIO-16E-4 I/O module were used for generating and applying the potential program as well as acquiring data such as current intensities.
M/ligand (1:1) stoichiometry was found from analytical data (Table 2).
Two oxidation (A', B') and two reduction (C', D') peaks were observed for Co(PAN)@NaY/CT.
By combining surface analysis and spectroscopic data it was possible to confirm that the CoPAN can be encapsulated in the Y supercages zeolite, without damage to the original matrix or loss of its crystallinity.
Azevedo for collecting the powder diffraction data and Dr.

Other data of the are related in Table 1.
Table 1 - Geometric data related to the kiln analyzed.
The temperature data was collected in a 15-minute interval, totaling 234 time intervals.
The storage and management of collected data was done via LabView software.
According to the data acquisition system,T0= 299.06 K.

The environmental benefit of subsequent reduction of sulfide in the tailings from 3.7 to 0.2% on AMD generation potential is quantified through a combination of methodologies including MPA, ANC, NAPP, NAG and microbial testing.
Sulfide reduction from 4 to 1% reduces AMD generation.
Further reduction below 1% S yields diminishing returns.
Mass separation in a two-stage flotation for separation of active components from gangue NAF Chalco Pyrite Gangue 1kg 0.984kg 0.892kg 0 1 2 3 4 5 6 7 8 9 - - - 0 36 9 3.7% 2.20% % 1.10% 0.22 % NAG pH NAF PAF NAPP [kg H2SO4/ton] Fig. 2 shows the proportional mass flows over 2 stages for the same experimental data.
However, a reduction in the sulfur content of the sample greatly reduces the MPA, and thus the NAPP.

Table 1 shows data measured at THDi_beforeTR for each phase.
These data measured by using a reliable power meter (RPM) connected with phase wire and neutral wire.
0.297 Table 1 shows data measured at THDi_before TR at Figure 1.
These data generated by “Makmal Komputer 3” and before harmonic elimination process.
Table 2 shows data after divided by 2 between computer center_P and center_Q.

The resistivity data were fit to a p type model involving only doubly ionized oxygen vacancies, holes, and accidental acceptor impurities, and it decreased as P(O2) and temperature increased.
The former is consistent with a reduction reaction that produces oxygen vacancies, as expected for a compound that as Schottky disorder as its preferred intrinsic ionic disorder, and free electrons as Eq. 1:
(2) If the reduction is the major source of defects in the most highly reducing condition, then as Eq. 3 and Eq. 4
Reduction with the formation of oxygen vacancies causes no conceptual difficulties, but oxidation by either Eq. 5 or Eq. 6 should be difficult
The data in Fig.1 have been found to be very good agreement with the theory, so we think it’s the p-type conductivity.

The model takes the types, sites and sizes of DG as decision variables, economy and environment as the objective function, particle swarm algorithm is proposed to solve the multi-objective optimization problem .A case study based on data from offshore oilfield is presented to verify the advantages of the improved optimal sizing method.
Mohamed F, Koivo H present a generalized formulation to determine the optimal operating strategy and cost optimization scheme as well as the reduction of the emissions for a MicroGrid [4].
Based on a large number of historical data ,the wind speed obeys two-parameter Weibull distribution approximately, the probability density function as fomula(1) (1) Where v is wind speed, m/s. k is the shape parameter, k =1.8~2.3 in general; c is the scale parameter which reflects the average wind speed, distribution probability density function shows as fomula(2) (2) Where range of F(V) is [0,1]，dividing it into N parts， generating random number from 0 to1,then convert it to random numbers as equation (3) (3) Where, n=1,2…N，U is a random number in the range
Objective Function System Economic Model .Under low-carbon economy, it aims at achieving minimum economic cost annually, realizing new energy saving and emission reduction significantly, meanwhile ensuring system operate reliabily.
Conclusion In this study, considering carbon price volatility agrees to actuality, renewable energy can realize energy saving and emission reduction, PSO algorithm can obtain optimal solution to minimize the total cost .

In the process of soil cultivation, well-used plough components, surface of the spade plow, have the property of drag reduction.
That provides a reliable basis for drag reduction design of soil cutting and cultivating parts of bulldozing or shoving and carrying type.
(2) Considering processing error and the convenience of comparing resistance properties of different macro soil engaging surfaces, alignment data of parabolic soil engaging surfaces of three model moldboards are re-measured and curve fitting is done into cubic polynomial form.
Resistance signals converted to electrical signals by sensors are transmitted to data collection box of strain Test System and then are put into the computers to be recorded and shown and processed.
The resistance data are the average value of three repeated experiments.

Computations Input Data.
According to experimental data [7,8] the minimal value of shear modulus GPa 1)0,( 1 =TCfm was accepted.
iii) The magnetostriction of austenite becomes negligibly small on approach to Curie temperature due to the reduction of magnetization value.
In particular, the magnetically induced deformation of axially compressed specimen was measured and the reduction of deformation by factor ~ 0.3 on stress variation from MPa 15− to MPa 60− was observed.9 This reduction was conceivably attributed to the transformation of residual fragments of martensite structure "surviving" under the -60 MPa stress.
However, the stress exceeding the value of 10 MPa can substantially reduce magnetostriction and the reduction must be taken into account for the correct interpretation of experimental data.