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Production of SO2 and lime from phosphogypsum reduced With lignite in a nitrogen atmosphere Shaocong Zheng*, Feixiang Cheng, Zhijuan Wang (College of Chemistry and Chemical Engineering, Qujing Normal University, Qujing 655011, China) zhshcong2008@sina.com Keywords: Phosphogypsum, Lignite, Reduced decomposition, Sulfur dioxide and lime Abstract: In this study, using lignite as a reducer, the reaction characteristic of the reduction decomposition of phosphogypsum to SO2 and lime was investigated in a nitrogen atmosphere at different conditions.
A tube furnace was used to analyze the reductive decomposition process of PG mixed with lignite, and experimental data were collected to determine the optimum conditions for production SO2 and lime.
Data were collected to discuss the reaction mechanism.
Fig. 1 Schematic of the experimental apparatus 4．Results and Discussion 4.1 Effect of the Molar Proportions of Phosphogypsum and lignite Some earlier experimental data have shown that the mole ratio of PG and coal affected the decomposition process and the molar proportion of C:CaSO4=0.5-1.0 is suitable for the decomposition of phosphogypsum.

In the in-situ crystallization approach, metal salts and graphene oxide reduction co-occur.
CV curve of ITO in similar medium shows cathodic reduction.
Based on these CV curves, the cathodic reduction plateau initiates at approximately 0.6 volts on rGO and slightly more positively, around 0.7 volts, on bare ITO.
Availability of Data and Materials No data was used for the research described in the article.
Sheremet, Photochemistry dominates over photothermal effects in the laser-induced reduction of graphene oxide by visible light, Nat Commun 15 (2024) 9711

Next stage of loading was loaded (unloaded) after the data was collected.
Results Analysis of the Experiment This experiment data is selected every 0.4 m.
The data of composite grouting pile settlement, pile body parameters such as strain, stress and axial force were analyzed respectively, comparing the strain and the optical fiber strain data at the same time.
The reduction of pile stress fluctuates from the top of the pile to the bottom of the pile.
But the data needs to be modified with the strain gauge data.

The strain information obtained at sensor locations was used as the input data for ANNs.
The output or target data was the x and y coordinates of the centroid of the defects.
Strain data for 80% of the defect locations was randomly selected and used to train the ANN.
Neural network analysis An ANN may be described as a non-linear mapping function from multiple input data to a variety of output data.
The number of nodes used for the collection of strain data was varied as noted in the results section below.

Data sources Certain questions were designed in the questionnaire for each construction project risk.
Then sample data is achieved through the process of the result of questionnaire.
Data processing and test In the processing of gained sample data, factor analysis is applied to introduce the steps of extracting data using technical risk affecting the project (16 indicators) as examples
First, principal component analysis and the feasibility test should be used on original data.
The above test results show that the original data can be carried through factor analysis

Training data are trained to Model A to find classified positions.
It starts with providing testing data to Model A for classifying positions from RSS.
To obtain the best result, researchers [12, 13] suggest that these data need to perform the pre-process preparation such as data cleaning, data normalizing, data reduction, and etc.
Then, they are evaluated with testing data.
When training data are adequate, EFF can boost accuracy of every fundamental model as obviously seen since the amount of data are more than seven samples.

The traditional virtual assembly technology requires building a true three-dimensional scene of all the assembly data, leading to complex modeling, low sense of reality and efficiency of real-time interaction.
Experimental Results and Analysis In this experiment, assembly environment of a reduction gearbox is established with the goal to make its peephole cover registered at the position where it should be.
In the assembly of reduction gearbox, because of its multitudinous components, when assembly steps one by one are decomposed and presented in the display of the staff, they will operate correctly without the basis of training and assemble the parts correctly in perception of the real environment.

When investigating the stress, it was found that a larger radius leads to a reduction in stresses.
Complementary material data like yield strength and tensile strength were supplemented by calculations with JMatPro so that a classification of the calculated stresses to the bearable loads can be made.
By comparing the data, a statement can be made about the probability of crack formation and plastic deformation.
Key Findings In order to be able to make a statement about the influence of the radius on the stresses, the data from the stress calculations are dealt with first.
The chosen simplifications related to the reduction of the simulation time by using non-heat conducting components saved resources.

A good correlation, in terms of microstructural analysis, between the simulation data and the experimentally obtained results was obtained, confirming the reliability of the model.
Finally, results of the simulations are compared with experimental data.
Figure 1 Internal set-up of the centrifugal induction furnaces Boundary and initial conditions have been chosen based on available experimental data.
Figure 6a reports the results of the mathematical model, while the experimental data are shown in Figure 6b.
A good correlation, between the simulation data and the experimentally obtained results has been obtained, confirming the reliability of the model.

Problems centred on the non-availability of data.
Generally data was found to be sparse and lacking.
The primary data collected were aggregates per year of the data collected from the companies already mentioned in the methodology.
This greatly affected the data quality.
Opportunities for reduction of resource depletion and environmental pollution have been identified.