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The simulated data showed equilibrium of ilmenite with precipitated phases formed at different temperature.
The initial stage of ilmenite reduction was fast because of iron oxide reduction by CO [7].
XRD data on 1000-1100°C are presented in Sharifah Aishah et. al. study [5].
The X was higher than the reduction at 5 wt. % that had reduction of 39.8%.
After 180 minutes of reduction, samples with 10 wt. % FeCl3 had a reduction of 69.0%.

It was found that the presence of aquatic worms in the reactor significantly improved the sludge reduction by increasing the total suspended solids (TSS) reduction (Table 1).
Accordingly, sludge reduction was 36–77% [6].
Table 1 Literature data of sludge reduction with worms in aerobic wastewater treatment processes Operation conditions Main results Controlsb References Pilot activated sludge system; 20 oC; domestic wastewater Ya=0.14 SVI ↓ Control Y=0.22 Wei et al. (2003) [3] TSS↓ 39-65% VSS↓ 0.5-6.3mg/mg worm/d Y=0.10-0.27 SVI↓ Control deducted in calculations Control Y=0.25-0.49 Liang et al. (2006)[7] TSS ↓ 48(±45)% No control Wei & Liu (2006)[8] VSS↓ 0.2-0.8mg/mg worm/d Effluent TP↑ Control deducted in calculations Huang et al. (2007)[9] Pilot activated sludge system; 18-23 oC; domestic wastewater COD↓ 18-67% Y=0.15 SVI↓ phosphate↑ Control COD↓ 20% Control Y=0.4 Rensink & Rulkens (1997)[10] Sludge disposal or TSS↓ 25-50% SVI↓ Control Ratsak (1996)[4] TSS↓ 36-77% The hydraulic load↑5-15% Hendrickx et al.(2009)[6] aY refers to the sludge yield (kgSS/kgCODremoved) bControls refer to the conventional biofilter (without worms) Although the presence of worms in the aerobic wastewater
It was found that the presence of earthworms in vermifilter significantly improved the sludge reduction, the BOD5 reduction, the COD reduction and the excess sludge stabilization was also enhanced by reducing the ratio of volatile suspended solids to suspended solids (VSS/SS).
Sludge reduction with a novel combined worm-reactor.

Introduction Dimensionality reduction is an efficient method to handle the complicated high dimensional data commonly met in our modern society and widely explored in data mining, machine learning, pattern recognition and etc.
locally linear embedding(lle) Given a data matrix ,where, represent the data points in the space.
Conclusion Dimension reduction is a very effective method to deal with high dimensional data and widely explored in pattern recognition, data mining, machine learning and etc.
LLE has been regarded as the classical non-linear dimension reduction algorithm because of this good performances on real data and artificial data.
Niyogi, Laplacian eigenmaps for dimensionality reduction and Laplacian eigenmaps for dimensionality reduction and data representation, Neural Computation 15 (2003) 1373-1396

FCA has been applied to a variety of fields such as data mining, artificial intelligence software engineering ,information retrieval, database management systems, software engineering, knowledge acquisition and other aspects.
Recently, there has been growing interest in knowledge reduction in FCA.
Conclusion The rough set theory and the concept lattice theory show combination and complement for data analysis.
Information-preserving hybrid data reduction based on fuzzy-rough techniques.
Lv, A heuristic knowledge-reduction method for decision formal contexts.

Plenty of basic research, including spot investigation and data collection, was carried out in this paper.
Through extensive field research and data collection, the present paper identifies 18 types of typical energy saving technologies in cement sector, takes daily 5000 tons production line as the benchmark, makes quantitative analysis of the emission reduction potentials and costs in cement sector based on the marginal emission reduction cost estimates, and thus proposes policy recommendations for technical development.
This study combines emission reductions and economic benefits, which is the emission reduction cost mentioned above.
It embodies the relationship between the emission reductions achieved by each emission reduction measure and the costs needed in per ton of CO2 emission reduction.
Discussion on cement sector CO2 emission reduction in China.

Experimental Result This experiment use 41 reservoir sandstone sample data for testing, as described above, each sample data has 12 feature parameters and 2 geophysical parameters of the porosity and permeability, the 12 feature parameters as input to Elman neural network for training to calculate the corresponding geophysical parameters.
There are 9 feature parameters as input data whose correlation degree is more than 0.8.
From the contrast curve (Fig. 1 and Fig.2), the calculation results are in good accordance with the real data, which reflects that the Elman neural networks with the hybrid dimensionality reduction have strong ability of learning and calculating.
In order to detect the effect of Elman neural networks with the hybrid dimensionality reduction, sample data sets were selected respectively using only the GRA and PCA dimensionality reduction as well as no dimension reduction to calculate the sandstone reservoir geophysical parameters, and then comparing the results of the above test methods with the results of Table 1.
Future work is to collect a large number of rock samples of slices identification and geophysical property analysis data for subsequent tests, to improve the calculation precision of hybrid dimensionality reduction with Elman neural network.

This is the process of chromium ore smelting reduction in a converter.
Chrome Ore Reduction Characteristics and Principle of Selection.
The data from Table 1 show that the presence of Al2O3 makes Fe reduction more difficult and the presence of MgO makes Cr reduction slightly hard.
The reseachers consider that various reduction laws should respond in the reduction of the natural chromite.
Chen: Thermodynamic Data Manual of Inorganic Matters (Northeastern University Publications, Shenyang 1993)

The metal and slag in sponge iron obtained from reduction was melted and separated.
The sponge iron obtained from reduction was melted and separated.
At 950℃,1000℃, the reduction experiment was carried out respectively, so as to determine the time of complete direct reduction.
Results and discussions Results and discussions of direct reduction Data of direct reduction is shown in Table3 and Fig2, in which, weight loss is the weight difference between re-concentration minerals and sponge iron.
Data of direct reduction experiment Sample # Temperature/℃ Holding time /h Weight of sponge iron /g Weight loss w/g 1 950 1.0 284.14 56.86 2 950 2.0 269.23 71.77 3 950 3.0 264.05 76.95 4 950 4.0 263.99 77.01 5 1000 0.5 290.55 50.45 6 1000 1.0 278.96 62.04 7 1000 1.5 270.82 70.18 8 1000 2.0 270.91 70.09 Fig2.

SSDP makes full use of both labeled and unlabeled data to construct the weight incorporating the neighborhood information of data.
The labeled data points are used to maximize the separability between different classes and the unlabeled data points are used to estimate the intrinsic geometric structure of the data.
This leads ones to consider the dimensionality reduction methods to represent the data in a lower dimensional space.
It performs dimensionality reduction by projecting the original high dimensional data into the low dimensional linear subspace spanned by the leading eigenvectors of the data’s covariance matrix.
In many practical applications of pattern classification and data mining, one often faces a lack of sufficient labeled data, since labeling data often requires expensive human labor.

It has conceived as a tool to conceptualize and analyze various types of data.
In Section 3, some new types of knowledge reduction, namely, -level distribution reduction and -level approximate reduction are proposed in set-valued information systems , moreover, properties and relationship between them are discussed.
That is to say is a distribution consistent reduction of.
In this paper, we have studied knowledge reduction of set-valued decision information systems based on tolerance relation, The distribution reduction is introduced.
Kumar: New techniques for data reduction in a database system for knowledge discovery applications.