Recommendations

The Fuzzy controller presented has proved to be the most effective solution for the effective control of the superheater final steam temperature. It is recommended for field testing in this application and should significantly improve final steam temperature control, as well as avoid grid separations related to final steam temperature safe operating margins.

The MPC controller generally underperformed the Fuzzy controller, but has nonetheless outperformed the current PID controller. Should resource constraints allow for on-line testing of both proposed controllers before final implementation, the MPC controller has shown its ability to reduce the steam temperature’s deviation from setpoint and peak excursion values.

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Testing and simulation the performance of these two control techniques was carried out only based on one unit at Komati Power Station. Future work will therefore also include applying the techniques developed in this research to other units at the Station. This work will identify which control technique transfer more easily to other units.

The foundation to develop the two control techniques for other units has been given in this thesis. The first requirement would be to gather raw data from the remaining units. In gathering data, the researcher should consider the factors pointed out in Chapter 2. Scenarios should include a variety of operating conditions to ensure that, within the complete set, it contains independent and uncorrelated process measurements. It should also be noted that future research might explore more possibilities of gathering process data on-line. It could advocate for, and design techniques for impulse and step response testing to be done online for system identification. This is no small task considering that the entire power generating process has many interdependent systems. This can be motivated for based on the positive results that can be achieved as highlighted in the research done in this thesis.

With suitably chosen training data the researcher can apply the same model identification techniques used in Chapter 3. Expansion of the model can also be suggested for future work. Assumptions are made and the model complexity determined by the researcher’s findings during system analysis. This process continually evolves as well as new research and technology becoming available as time goes on. Modelling of the combustion processes is a large field of research on its own. It is an ideal area for focus to allow for more accurate modelled behaviour to be used for control purposes, not only for steam temperature control, but also many others.

Increasing model accuracy may include taking aspects like ash build up, coal quality and boiler efficiency into account on a real time basis. Other activities that affect the heat transfer rate could also be explored to better control these transient conditions. As mentioned before, in some cases the added complexity might not realise much of an improvement, and would have to be investigated case by case. Soot-blowing, as one example, causes significant combustion instability at Komati Power Station and could potentially add significant value to the control effort if it is adequately modelled and compensated for as a measured disturbance.

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During this research, installation of additional process measurements was not considered due to the hypothesis that a large performance increase could be obtained purely by applying more advanced control techniques. The research has clearly demonstrated this. This should not stop future researchers from identifying areas of improvement in process measurement that might be site specific. What comes to mind here specifically for Komati, is an additional measurement(s) of the final superheater stage at one or more points along the steam flow path. Currently the Measurement of CO gasses are also becoming mandatory. Installations are being carried out across all sites. This is a critical indication revealing much about combustion instability. The use of these and other measurements could drastically improve the performance of the final steam temperature controller and other combustion process controllers.

With model identification done the researcher can apply the same techniques provided in Chapter

4 to develop the controller for each unit. Within the framework given, the MPC controller can

easily be developed straight from the model. The Fuzzy controller requires further training to determine adequate response to steam flow acceleration. With the technique used during this research the degrees of freedom have been decreased and allow for training of the Fuzzy controller from base values derived from the model.

Within the two control techniques explored there remains room for expansion. It has to be acknowledged that the use of these and other more advanced control techniques, in the power industry in South Africa, is not widespread as also evident at Komati Power Station. It is paramount that these techniques are tested and adopted in the industry through proof of application before more complex versions are introduced into the industry. With future research, other features of the MPC controller, including more stringent constraint control and further compensation for non-linearities can be explored. The fuzzy controller itself can be expanded further to include typical human operator responses that are not limited to spraywater control, but may include the introduction of oil burner support or mill biasing.

There are also other topics related to the main steam temperature controller that fell outside the scope of this research topic, and can be considered for future focus. Optimization of the inner loop is currently not required. It can be explored further in future research topics especially in

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terms of robustness. This may include compensating the controller for changes in water temperature used to quench steam.

There also exist components of process automation and redundancy over just pure control when working with complex systems. Multiple measurements are available for this controller although not currently used. Measurements of water feed rate and temperature could be used to directly control the water flow rate along with the temperature at the superheater inlet. This could be used to provide more flexibility and robustness should one of these measurements fail while the system is in service, thereby avoiding shutdown, manual intervention or production delays. On the other side of automation and control lies manual intervention. In an ideal research environment this is usually not of much importance as manual intervention defeats the whole purpose of the control system. The power generation industry faces the reality that components may fail and, in some instances, require manual intervention while issues are being corrected. With complex systems and processes, manually manipulating a system, like final steam temperature control, can be critical but outside the ability of a human operator. The human operator is in charge of the entire system and cannot divert all focus to this intervention. Future research could investigate aiding an operator who is required to manually intervene. One such instance is in the event that an actuator has to be manually operated by the human operator. Keeping track of the behaviour of non-linear systems with large time constants can be challenging for a human operator with focus on multiple processes. Providing more information relating to the process states and the system’s predicted behaviour based on the models derived here could severely decrease the burden on the operators while they intervene.