Legislación Comparada

In this chapter various statistical and Data Science techniques were presented to close the gap between the data that are available, and the requirements with regards to real time coal quality information of the Coal Value Chain. Specifi- cally, Excel files from SGS Laboratories, XRF Data, Material Movement Data, Stockpile Information files and the PI DCS system were captured, cleaned and stored in a standardised format for real time on-line processing.

All the data sources in isolation, although valuable, do not add the level of insight that can be extracted from combining them. A stacker simulation model that was developed and implemented to predict the properties of the heaps in all the stack yards utilising the XRF data in combination with the data form the SGS laboratories, the material movement files and the stockpile information files, was discussed. The stacker simulation model allows for the prediction of coal properties (including but not exclusive to ash) over the length of the heap, as well as the average ash percentage and standard error of the ash percentage for the heap. This information is used to do blend planning for the week, and is compared to the lab analysis data for validation purposes.

The output from the stacker simulation model was combined with the data from the material movement files, and the reclaimer data from the PI DCS system to calculate the reclaimed ash and standard deviation of the ash going to the gasification plant. This information is now used to develop a reclaiming strategy to minimise the impact of the heaps with unfavourable ash properties on the gasification factory. The data extracted from the various data sources at the SCS facilities have therefore been converted into valuable information that can be used for making informed decisions.

In the last section a design and analysis of computer experiments strategy has been applied to an existing FactSage computer model for predicting slag formation of the ash. The input to the slagging model consists of ash elemental properties that can be obtained from the XRF analyser. Due to the compo- sitional nature of the ash elements data, a space filling design appropriate for mixture experiments was required. Various dissimilarity measures appropriate to mixture experiments were compared using two design comparison measures. In addition, an optimisation strategy was presented for obtaining maximin de- signs. The resulting optimal dissimilarity measure (Divergence) and optimisa- tion strategy were applied to obtain a mixture design. The model runs were obtained for the design points, and a quadratic model was fitted to the data. The predicted results from the quadratic model compared very well to the

actual results, and it was therefore concluded that the design was appropriate for the model. This model can now be used to do real time on-line prediction of the slag formation of the ash.

In summary, in this chapter the real time coal quality analyses from an XRF analyser were summarised and integrated with various data sources from the Coal Supply Facility to provide information on the coal quality of each mine. In addition, simulation models were developed to generate informa- tion on the coal quality of each heap and the quality of the reclaimed coal sent to gasification. Finally, the novel application of distance measures other than Euclidean measures was introduced for space filling design for computer experiments in mixture variables.

In the next chapter a monitoring strategy for the Sasol Coal Gasification (SCG) plant will be developed and presented.

Chapter 3

Coal Gasification

The Sasol Coal Gasification (SCG) plant is a highly complex facility. The system consists of two separate facilities known as Gasification West and Gasi- fication East. Each facility consists of four trains, each containing between 10 and 11 gasifiers (see Figure 3.1). Each gasifier is equipped with instrumenta- tion which records online performance data on the gasifiers. The coal from the SCS facility discussed in Chapter 2 comprises the main feedstock to the SCG plant. The coal qualities therefore have a direct impact on the performance of the SCG plant. Monitoring and comparing the performance of the 84gasifiers are of utmost importance as the units supply the feedstock for the downstream units in the coal to liquids facility.

Producon Facility Train 1 GG1 GG10 Train 2 GG13 GG23 Train 4 GG25 GG35 Train 5 GG37 GG46 Train 1 GG1 GG10 Train 2 GG13 GG23 Train 4 GG25 GG35 Train 5 GG37 GG46 Phase 1 Phase 2 Phase 3 Phase 4 P ro d u c o n P ro ce ss es W e st Ea st

Figure 3.1: Gasification Overview

3.1

Coal Gasification Monitoring

The coal gasification plant is a unique facility as it is the largest coal to liquids facility in the world. A need was identified for a custom product that can facilitate the monitoring of the gasification facility specifically for engineering managers. This product should utilize the real time data, and convert it to information on the different layers of the production facility (i.e. Factory, Side of Factory, Production Train, Gasifier). Even though the conversion of data into the appropriate information may be technically involved, the results should be intuitive, and easy to interpret.

The developed product discussed in this chapter entails an efficient multi- variate process monitoring methodology for those process variables that govern gasifier performance. These variables can be divided into three groups, pro- duction, utility and stability variables. From a monitoring perspective there are two different but complimentary strategies that can be followed for pro- cess monitoring. The first strategy is a process driven (fundamental) approach

where information from the subject matter experts is utilized to specify for example the optimal operating ranges for the variables. A fundamental ap- proach will be discussed in Chapter 4. Alternatively adata driven (empirical)

approach can be followed where historical data are used to specify the opti-

mal operating ranges. These two approaches do overlap, and in Chapter 4 an integrated approach will be employed to develop a monitoring strategy.

Real time monitoring of the gasifier process variables is the primary ob- jective. The process data are captured in real time on a distributed control system (DCS), and may be captured at different time intervals for the differ- ent variables. From a data perspective some of the problems that need to be addressed entail the selection of an appropriate aggregation window, as well as an appropriate aggregation method for each process variable. The aggregation methods will be discussed in detail in Chapter 5.

As all ten gasifiers on one train receive the same coal, and are managed by the same operator it is expected that the performance will be similar. Any deviation of performance of a gasifier from the mean performance of all the gasifiers on the train can be an indication of mechanical problems. Therefore, it is important to evaluate the differences between the gasifiers on a train. This is a longer term approach, but should still be available in real time. In Section 3.4 Canonical Variate Analysis (CVA) biplots will be proposed as an approach to this problem.

In the current application the development of the empirical real time mon- itoring methodology follows three consecutive steps. Therefore, this chapter is divided into the following three sections:

• Section 3.2 - The selection of the reference set.

• Section 3.3 - The utilisation of the reference set for multivariate process monitoring using the PCA biplot.

• Section 3.4 - Using the CVA Biplot for monitoring.

Each section focuses on the specific topic, and to facilitate the flow of the discussion a methodology will sometimes be introduced and utilised before the relevant theoretical background is provided. For example, the PCA biplot is utilised in the section on reference set selection, but will only be discussed in detail in the following section on multivariate process monitoring using the PCA biplot.

The first aspect to be addressed in this chapter is to find the optimal oper- ating window and period of stable operation. Therefore, the expected behavior of the process needs to be specified. This is normally achieved by selecting a reference set of data from an historical time period where the process was run- ning stable and within expectation. The correct selection of the reference data set is crucial to the success of real time process monitoring. Generalized Or- thogonal Procrustes Analysis (GOPA) (Gower and Dijksterhuis, 2004) will be employed in this study for selecting the optimal reference set for the multivari- ate monitoring of the multiple identical production processes (i.e. gasifiers).