Development of Slag Management System

Development of Slag Management System

Kyei-Sing Kwong¹, James P. Bennett¹

1National Energy Technology Laboratory, US DOE 1450 SW. Queen Ave. Albany, OR 97321 USA Keywords: Slag Management, Coal Slag Viscosity, Gasification

Abstract

Degradation of refractory liners is one of the key factors limiting the service life of entrained flow slagging gasifiers, which is caused primarily by refractory/slag interactions. Slag originates from impurities in the carbon feedstock, typically coal and/or petcoke, which melt and coalesce during gasification, flowing over the refractory liner in the gasifier and interacting with it. Slagging gasifier operators attempt to minimize refractory degradation by controlling slag viscosity (and interactions) through the gasification process temperature. A computer model utilizing empirical and neural network calculations was developed to predict T100, T250, fluid temperature, and liquidus temperature of slag compositions based on chemistry and a newly developed “similarity index”.

Development of the model and its application in designing slags to minimize refractory degradation will be discussed.

Introduction

Gasification is a process of converting carbonaceous materials, water and oxygen into steam, carbon monoxide, hydrogen, carbon dioxide and methane in a high pressure and high temperature reaction vessel (gasifier). Non-volatile mineral impurities in the carbon feedstock are liquefied at gasification temperatures, forming corrosive slags. The slag attacks refractory linings used to insulate and protect the steel pressure vessel from the harsh operating conditions present in a gasifier. Slagging gasifiers are typically operated between about 1300 and 1550 °C and at pressures of 2.75 MPa (400 psi) or higher, with a mass flow rate of molten slag often exceeding 10 tons/hour [1, 2]. The slag composition and the gasifier operating temperature are significant factors affecting the refractory lining performance. The gasifier operating temperature can be optimized using the slag liquidus temperature, ash fusion temperature, and viscosity characteristics of the ash. The optimized operating temperature should be high enough to allow slag flow out the gasifier yet low enough to minimize refractory corrosion. Refractory service life can be improved if the operating temperature is lower than the slag liquidus temperature.

Slag liquidus temperature, ash fusion temperature, and viscosity characteristics of the ash are dependent on the slag composition.

Typically, more than 90 % of the mineral component of a coal ash slag contains oxides of silicon, aluminum, iron, and calcium. Minor constituents such as magnesium, potassium, sodium, titanium, and sulfur account for about 8 % of the mineral component; while trace constituents such as arsenic, cadmium, lead, mercury, and selenium, when combined make up less than 1 %

Advances in Molten Slags, Fluxes, and Salts: Proceedings of The 10th International Conference on Molten Slags, Fluxes and Salts (MOLTEN16) Edited by: Ramana G. Reddy, Pinakin Chaubal, P. Chris Pistorius, and Uday Pal TMS (The Minerals, Metals & Materials Society), 2016

of the total composition. Table I summarizes the average, maximum, and minimum oxide content of coal slags in the NETL slag management tool set’s viscosity database in weight percent. The NETL slag management tool set will be discussed later.

Table I. The composition range of slag constituents in the NETL slag management tool-set database in weight percent.

Weight % SiO2 Al2O3 FeO CaO MgO Na2O K2O MnO TiO2 P2O5 SO3

Average 42 23 11 15 4 1 1 0 1 0 1

Maximum 68 37 35 40 20 10 4 0.8 20 3 18

Minimum 19 10 1 1 0 0 0 0 0 0 0

Standard deviation

8 5 7 9 4 2 1 0.2 1.3 0.6 2

The fluidity of coal slags has a close relationship with the properties: T100, T250, Tcv, ash fusion fluid temperature and liquidus temperature. Coal slags consist primarily of a silicate network, where each silicon atom is tightly bonded to 4 oxygen atoms. Cations such as Na⁺, Ca²⁺, Mg²⁺, Fe²⁺ and K⁺ tend to depolymerize the silicate networks by forming non-bridging oxygen (NBO), O^-, and free oxygen O^2-. The degree of de-polymerization of coal slags can be characterized by the ratio of NBO to the number of tetrahedral-coordinated atoms (T), expressed as NBO/T [3]. In general, a higher index value means a lower viscosity coal slag.

Per Lux and Flood’s definition of acid-base properties for oxides melts, the high quantity of silica or silicon oxide present in coal ash slags makes them acidic [4]. Basic oxides such as Na2O, CaO, MgO, K2O and FeO tend to react with silica, leading to lowered melting temperature and thinner (low viscosity) slags. Therefore, the ratio of the total amount of basic oxides to the amount of SiO2 is an important index for both liquidus temperature and slag viscosity.

Al2O3, an amphoteric (i.e. a compound that can act as an acid or base), is also an important oxide in coal ash slags. Phase diagrams (CaO-SiO2-Al2O3, MgO-SiO2-Al2O3 and FeO-SiO2, Al2O3) indicate that the ratio of SiO2/Al2O3 is also an important factor affecting slag liquidus temperatures. Slags containing principally Al2O3 and low basic oxide quantities tend to form mullite as their primary phase (first phase to precipitate from the melt during cooling), creating a slag of a high liquidus temperature. High Al2O3 and FeO content in a slag promote the formation of a spinel primary phase (FeAl2O4). Lower Al2O3 content with high CaO leads to the formation of slags with anorthite as their primary phase, resulting in slags with a low liquidus temperature.

In general, the best slags for gasification of coal should have a high liquidus temperature and low T100; moreover, the temperature difference between the two should be large enough to allow for easy control of the viscosity during gasification process.

Coal slags may contain solids phases at a gasifier’s operating temperature that do not melt or have time to melt during their residence in the gasifier. The presence of those solids in the gasifier slag affects the fluidity of the slag during gasification. In general, increasing the solid content of a slag increases its apparent viscosity. The liquidus temperature, a function of the slag composition, determines whether solids are present at the operating temperature.

Viscosity is the measure of the internal friction of a fluid during flow. The ease by which a molten coal ash slag flows from an entrained flow gasifier is directly related to slag viscosity.

Molten slag viscosity can be measured by many ways, with a rotating spindle being commonly used. Slag can exhibit Newtonian or non-Newtonian flow behavior, with the viscosity of a Newtonian fluid being independent of spindle’s shear rate. The viscosity of non-Newtonian fluids change as a function of shear rate and time; therefore, viscosity measurements of a typical non-Newtonian fluid can be considered accurate only when these experimental parameters are accounted for and reported. Depending on a coal slag’s composition and test temperature, coal slags typically behave as Newtonian fluids at high temperature and undergo a transition to non- Newtonian flow when solid content is high in it as solid phases precipitate at the lower end of the gasifier operating temperature range (generally, below Tcv).

Ash fusion testing is based on the position a pyramidal ash cone sample bending (deforming) due to increasing liquid content with increasing temperature in either an oxidizing or reducing atmosphere. The sample deformation measured during ash fusing testing is reported as four temperatures: initial deformation, softening, hemispherical and fluid temperatures. The major drawback associated with the ash fusion testing is the reliance on visual observation and operator subjectivity, leading to reproducibility issues between different laboratories.

Modeling Procedures

Figure 1 shows the modeling procedure followed in this study. First, slag viscosity and slag fusion temperatures data was collected and assembled into a database. The NETL slag viscosity database contains 262 records from sources published in the open literature, while the slag fluid temperature database (ash fusion information) containing around 2000 records from the USGS CoalQual database [5]. These slag constituents were used as the starting point for calculating corresponding liquidus temperatures through FactSage™ (version 6.4) thermodynamic calculations [6]. Results from these calculations were collected into a database of liquidus temperatures. The viscosity database was used to evaluate the performance of six empirical models and the FactSage™ slag viscosity module. In addition, viscosity, fluid temperature, and liquidus temperature databases were used to build similarity and neural network models.

Figure 1. A flow chart representing the modeling procedures used in this study to suggest an optimal gasifier operating temperature range.

A slag management tool set was developed to suggest a gasifier’s operating temperature from the prediction result for T100, T250, fluid, and liquidus temperatures determined by the NETL models (similarity and neural network). This tool set allows users to review referenced records in similarity modeling and to verify the model’s prediction. This tool set also allows advanced users to select, modify, or input these values if users prefer a value different from the predicted one.

The slag management tool set is not a black box - it allows users to review the reference records, provides detailed information about each model’s predicted performance, explains some of the terminology used in calculations, adopts to a specific users’ database, and allows advanced users to adjust predictions. This paper focuses on the performance of empirical and NETL similarity models on coal slag viscosity. NETL similarity models will be discussed later.

Viscosity models

Many empirical viscosity models have been published in the open literature, with each based on their own experimental data and regression analysis. Quite often, users use an empirical model without fully understanding its limitations and performance. Many of these empirical models are generally not amenable to extrapolation outside the range of the original experimental data. Coal slags have a wide range of compositions containing different solids and solid quantities, depending on their chemistry, temperature, the oxygen partial pressure of the gasifier, and the residence time in the gasifier (short residence times do not allow system equilibrium to be achieved in a system). Because of the way literature models were developed, it is unlikely that a single model will yield good predictions for every coal slag. Several empirical models were selected for inclusion in the viscosity model being developed based on their ability to predict coal slag properties; which were the following: Browning, Urbain, Kalmanovitch, silica ratio, Riboud, and Watt-Fereday [7, 8]. An experimental slag viscosity database was created using charts or tables published in the literature or in reports. A computer program was written to track

data curves in a chart and convert them to digital format. The predicted slag viscosities of these empirical models were then compared with the slag viscosity database created using published information.

FactSage™ has a viscosity module which directly relates slag chemistry viscosity to the structure of the melt, and the structure in turn is calculated from the thermodynamic description of the melt using the Modified Quasichemical Model [9]. The FactSage™ model requires very few optimized parameters to fit the experimental data for pure oxides and selected binary and ternary systems. The viscosities of multicomponent melts and glasses are then predicted by the model within experimental error limits without using any additional parameters [9].

Similarity models as developed and used by NELT do not use mathematic regression methods, instead being based on a large database, with predictions made based on data from similar sample chemistries in the database. A figure-of-merit based on chemical composition, silica ratio, optical basicity and NBOT was adopted to define similarity between reference and target samples. Using this approach, predicted performance was improved over an earlier approach based solely on a slag’s chemical composition. Given the experimental uncertainty and errors during slag viscosity measurements, a “regional” group fit within a reasonable temperature range is adopted rather than the “best” fit. A regional group fit means that three similar referenced samples from the database were selected for making comparison within a range of 50 °C from a predicted slag chemistry, then the best regional group fit samples were used to yield temperature predictions for specific viscosities (such as T100 and T250). The prediction performance was improved using this approach versus a simple look-up of the “best” individual fit values.

Gasifier users need an accurate and reliable model to predict viscosity and temperature relationships for different slag compositions. It is, however, impossible to know which models are reliable without extensive testing. In addition, the size of a database or datasets is closely related to the prediction performance of a model. High-temperature viscosity measurements are both time and resource intensive, with a large investment required in order to build a reasonably sized database.

The performance index of models

In order to know which model perform best, an error index term was developed to define the model’s accuracy in ^oC.

Where:

N = Number of calculate times

T = Temperature (^oC) at 50 to 500 P with a step increment of 50 P (P: poise) 𝐸𝑟𝑟𝑜𝑟 =∑^𝑣=500_𝑣=50 |𝑇𝐸𝑥𝑝− 𝑇_{𝑀𝑜𝑑𝑒𝑙}|

𝑁

Exp = Experiment value Model = Prediction value V = Constant viscosity

The number of calculations was used in determining the model error because each record may not cover the whole slag’s viscosity range from 50 to 500 P. Note that the calculated error term is for only a set of viscosity-temperature record from a slag. The slag viscosity database has 262 records (each record contains information for a slag); therefore, the distribution of prediction error for the 262 slags predicted by models can be calculated and normalized. The above performance index is designed for industry users so they may understand the average of error prediction for a specific temperature.

Results and Discussions Coal slag viscosity

Table II shows the results of prediction for six empirical and FactSage’s modified quasichemical models. This table indicates variability between the predictions of the different models, and that the silica ratio and Kalmanovitch models perform better than other models. The table also illustrates the need for models with improved predictions of slag flow behavior than those that exist in the literature.

Table II. The prediction error distribution of viscosity and temperature for six empirical and FactSage™ models (in percentage)

Error

(°C) Browning Urbain Kalmanovitch Silica

Ratio Riboud Watt-

Fereday FactSage™

0-40 35.88 6.87 45.04 52.29 3.05 12.98 26.56

40-80 21.76 24.43 22.52 20.99 10.69 31.68 18.36

80-120 18.70 32.44 13.36 11.45 32.44 24.05 20.31

>120 23.66 36.26 19.08 15.27 53.82 31.30 34.77

The Similarity Model Developed by NETL

Three similarity modeling versions were developed to predict slag viscosity based on the assumption that similar slags should have similar physical properties, including viscosity. Note that the definition of “similar slags” affects the accuracy of a slag viscosity prediction. Also note that most slags contain at least 10 oxides, making the definition of a “similar slag” complex and subjective. The development of a model was a self-study process from trial results based on multiple dataset calculation using a target sample selected from one of 262 slag viscosity curves.

The calculations generated using the target sample was treated as experimental data, and the other 261 data set records treated as reference samples in a database or knowledge base to make

a prediction for the target sample. Using this approach, the accuracy of each trial can be computed by the above performance index after multiply calculations. Table III lists prediction made using the four similarity approaches.

Four similarity modeling approaches are discussed concisely as follows:

1) Find similar slag, and then predict temperature at various viscosities from a known calculated knowledge base.

2) Rank the temperature at a specific constant viscosity, then determine the best fit regions using three consecutive samples to predict the temperature at a specific constant viscosity.

3) Rank the temperature at a specific constant viscosity from a group of three consecutive similar slags, then determine best fit region for predicting the temperature at specific constant viscosity

3+) More similarity indexes were used and the definition of “region” was modified by a temperature range (three samples within 50 °C), not a group of three consecutive samples.

Table III The prediction error distribution of viscosity and temperature for NETL similarity models (in percentage)

Error (°C) Version 1 Version 2 Version 3 Version 3⁺

0-40 48.24 47.66 58.78 66.1

40-80 23.14 27.73 22.52 16

80-120 16.08 11.33 8.02 9.5

>120 12.55 13.28 10.69 8.4

Comparing Table III and II, it is evident that version 3⁺ of the similarity model developed by NETL can yield improved accuracy predictions of coal slag viscosity. Currently, this slag management system is being implanted and evaluated at a commercial gasifier user.

Conclusions

A viscosity database was assembled from literature data which contains 262 records and used to compare and evaluate six empirical models from the literature against a modified quasichemical model. Similarity and neural network models were developed to improve performance

predictions of viscosity versus temperature based on slag composition. Results indicated that similarity and neural network models could yield improved viscosity versus temperature predictions than empirical models or the modified quasichemical model. Coal ash fluid temperature and liquidus temperature databases were also collected and used to predict fluid and liquidus temperature by similarity and neural network models. This slag management model was developed with a goal of improving the service life of refractory lining in gasifiers and to improve control of the gasification processes through improved predictions of T100, T250, fluid temperature, and liquidus temperature.

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