Originally presented as the author's Thesis--Technische Hochschule Aachen, 2008. / Includes bibliographical references.

Thesis (M.S. in E.A.S.)--School of Earth and Atmospheric Sciences, Georgia Institute of Technology, 2004. Directed by Philip N. Froelich. / Includes bibliographical references (leaves 131-133).

No description available.

Thesis (M.S.)--Ohio University, November, 2008. / Title from PDF t.p. Includes bibliographical references.

Thesis (Ph.D.)--University of Wyoming, 2009. / Title from PDF title page (viewed on May 21, 2010). Includes bibliographical references (p. 50-63).

M.Sc. / This project considers the feasibility of using heated cooling water from coal-fired power stations within Eskom for the culturing of C/arias gariepinus (Sharptooth catfish) and Oreochromis mossambicus (Mozambique tilapia). The work includes: an overview of world wide trends in power plant thermal effluent aquaculture, the identification of suitable power stations for aquaculture, long term monitoring of the physical and chemical characteristics of the cooling water at selected power stations, including the effects of the cooling water on the growth response, food conversion, condition factor, survival rate and suitability for human consumption of the aforementioned fish species reared in the cooling water at Matla and Kriel Power Stations.

M.Ing. / Water gives life. It waters the fields of farmers; it nurtures the crops and stock of rural communities; it provides recreation for our children, our friends, our families; it supports our power generation, our mines, our industry, and the plants and animals that make up ecosystems. Water is the key to development and a good quality of life in South Africa. South Africa's water belongs to its people. It is the task of the South African Government to care for this water, to seek its fair distribution, and to facilitate its wise use for, amongst other things, social and economical development. Issues such as water resource management, use, protection, water services, etc., are presently governed by a number of policies, acts and regulations. All South Africans has a responsibility regarding the management of the country's resources. The supply of water to its entire people makes it extremely important to optimise the use of this scarce source. Access to water and water availability remains a key factor in ensuring the sustainability of development in Southern Africa. The coal fired power industry is a major user of natural resources; coal for fuel and water for steam generation as well as the cooling systems. It is estimated that 1.5% of the water abstracted in South Africa is used for power generation. The power industry receives its water mainly as abstraction from surface impoundments in the form of rivers and dams. Eskom, as a strategic user of water, is mindful of the importance of water to its business, as well as the development of the country. In addition to the interests of the government as the shareholder, Eskom recognises the legitimate interests, as stakeholders, of specific government departments, employees, consumers, suppliers, investors and lenders of capital, rating agencies, the media, policy and regulatory bodies, trade unions, non-governmental groups and local communities in its affairs. Eskom needs to ensure, through an effective water management strategy, that water is used wisely and effectively and that Eskom's impact on local water resources (surface and underground) is minimised. Eskom therefore has to manage water resources in a manner that will sustain the ecological integrity, support social development and ensure economic growth. Eskom has undertaken to benchmark the power generation industry, in co-operation with the DW AF, in a project aimed at developing the principles of water conservation and water demand management. In order to effectively manage water quality and quantity at Eskom's power stations, and to show Eskom's commitment with regard to water conservation and use, Eskom has compiled its own water and environmental policies.

Online monitoring supports plant reliability and performance management by providing real time information about the condition of equipment. However, the intricate geometries and harsh operating environment of coal fired power plant boilers inhibit the ability to do online measurements of all process related variables. A low-cost alternative lies in the possibility of using knowledge about boiler operation to extract information about its condition from standard online process measurements. This approach is evaluated with the aim of enhancing online condition monitoring of a boiler’s convective pass heat exchanger network by respectively using a soft sensor and a data-driven method. The soft sensor approach is based on a one-dimensional thermofluid process model which takes measurements as inputs and calculates unmeasured variables as outputs. The model is calibrated based on design information. The data-driven method is one developed specifically in this study to identify unique fault signatures in measurement data to detect and quantify changes in unmeasured variables. The fault signatures are initially constructed using the calibrated one-dimensional thermofluid process model. The benefits and limitations of these methods are compared at the hand of a case study boiler. The case study boiler has five convective heat exchanger stages, each composed of four separate legs. The data-driven method estimates the average conduction thermal resistance of individual heat exchanger legs and the flue gas temperature at the inlet to the convective pass. In addition to this, the soft sensor estimates the average fluid variables for individual legs throughout the convective pass and therefore provides information better suited for condition prognosis. The methods are tested using real plant measurements recorded during a period which contained load changes and on-load heat exchanger cleaning events. The cleaning event provides some basis for validating the results because the qualitative changes of some unmeasured monitored variables expected during this event are known. The relative changes detected by both methods are closely correlated. The data-driven method is computationally less expensive and easily implementable across different software platforms once the fault signatures have been obtained. Fault signatures are easily trainable once the model has been developed. The soft sensors require the continuous use of the modelling software and will therefore be subject to licencing constraints. Both methods offer the possibility to enhance the monitoring resolution of modern boilers without the need to install any additional measurements. Implementation of these monitoring frameworks can provide a simple and low-cost contribution to optimized boiler performance and reliability management.

The integrated gasification combined cycle (IGCC) is the most environmentally friendly coal-fired power generation technology that offers near zero green house gas emissions. This technology has higher thermal efficiency compared to conventional coal-fired power generation plants and uses up to 50% less water. This work involves the optimization of IGCC power plants by applying process integration techniques to maximize the use of energy available within the plant. The basis of this project was the theoretical investigations which showed that optimally designed and operated IGCC plants can achieve overall thermal efficiencies in the regions of 60%. None of the current operating IGCC plants approach this overall thermal efficiency, with the largest capacity plant attaining 47%. A common characteristic in most of these IGCC plants is that an appreciable amount of energy available within the system is lost to the environment through cold utility, and through plant irreversibility to a smaller extent. This work focuses on the recovery of energy, that is traditionally lost as cold utility, through application of proven process integration techniques. The methodology developed comprises of two primary energy optimization techniques, i.e. pinch analysis and the contact economizer system. The idea behind using pinch analysis was to target for the maximum steam flowrate, which will in turn improve the power output of the steam turbine. An increase in the steam turbine power output should result in an increase in the overall thermal efficiency of the plant. The contact economizer system is responsible for the recovery of low potential heat from the gas turbine exhaust en route to the stack to heat up the boiler feed water (BFW). It was proven in this work that a higher BFW enthalpy results in a higher overall efficiency of the plant. A case study on the Elcogas plant illustrated that the developed method is capable of increasing the gross efficiency from 47% to 55%. This increase in efficiency, however, comes at an expense of increased heat exchange area required to exchange the extra heat that was not utilized in the preliminary design. / Dissertation (MEng)--University of Pretoria, 2011. / Chemical Engineering / unrestricted

Due to global warming, there is an escalated need to move towards cleaner energy solutions. Almost 85% of South Africa's electric energy is provided via Eskom's conventional coal-fired power plants. Globally, coal-fired power plants have a significant share in the power generation energy mix and this will be the case over the next 20 years. A study, aligned with the aspiration of improving the thermal efficiency of the coal-fired power plants, was initiated, with a focus on the accuracy of energy accounting. The goal is that: if we can accurately quantify efficiency losses, the effort can be prioritized to resolve the inefficiencies. Eskom's thermal accounting tool, the STEP model, was reviewed against relevant industry standards (BS 2885, BS EN 12952-15, IEC 60953-0/Ed1) to evaluate the model uncertainty for losses computed via standard correlations. Relatively large deviations were noted for the boiler radiation, turbine deterioration and make-up water losses. A specific review of OEM (Original Equipment Manufacturer) heat rate correction curves was carried out for the determination of turbine plant losses, where these curves were suspected to have high uncertainty, especially when extrapolated to points of significant deviation from design values. For an evaluated case study, the final feed water correction curves were adjusted based on an analysis done with the use of power plant thermodynamic modelling tools namely: EtaPro Virtual Plant® and Steam Pro®. A Python® based computer model was developed to separately propagate systematic (instrument) and combined uncertainties (including temporal) through the STEP model using a numerical technique called sequential perturbation. The study revealed that the uncertainties associated with thermal efficiency, heat rate and individual thermal losses are very specific to the state of operations, as demonstrated by individual unit performance and the power plant's specific design baseline performance curves. Whilst the uncertainties cannot be generalized, a methodology has been developed to evaluate any case. A 3600 MWe wet-cooled power plant (6 x 600 MWe units) situated in Mpumalanga was selected to study the impact of uncertainties on the STEP model outputs. The results from the case study yielded that the thermal efficiency computed by the “direct method”, had an instrument uncertainty of 0.756% absolute (abs) versus the indirect method of 0.201% abs when computed at the station level for a 95% confidence interval. For an individual unit, the indirect efficiency uncertainty was as high as 0.581% abs. A study was conducted to find an optimal resolution (segment size) for the thermal performance metrics to be computed, by discretizing the monthly data into smaller segment sizes and studying the movement of the mean STEP model outputs and the temporal uncertainty. It was found that the 3-hour segment size is optimal as it gives the maximum movement of the mean of performance metrics without resulting in large temporal uncertainties. When considering the combined uncertainty (temporal and instrument uncertainty) at a data resolution of 1 minute and segment size of 3 hours, the “direct method”, had a combined thermal efficiency uncertainty of 0.768% abs versus the indirect method of 0.218% abs when computed at the station level for a 95% confidence interval. This would mean that the temporal uncertainty contribution to the combined uncertainty is 2.915% for the “direct method” and 14.919% for the “indirect method” of the above-stated uncertainties. The term “STEP Factor” can be used synonymously with effectiveness (percentage of the actual efficiency relative to the target efficiency). For the case evaluated, the mean “indirect method” STEP Factor at the station level moved from 86.698% (using monthly aggregated process data) to 86.135% (when discretized to 3-hour segments) which is roughly a 0.189% abs change in the station's thermal efficiency. This would appear fairly small on the station's overall efficiency but had a significant impact on the evaluation of the STEP Factor losses and the cost impact by the change in the plant efficiency, e.g. the final feed water STEP Factor loss at a unit level moved from 2.6% abs to 3.5% abs which is significant for diagnostic and business case motivations. Later the discrepancy between the direct STEP Factor and indirect STEP Factor were investigated as the uncertainty bands did not overlap as expected. The re-evaluation of the baseline component performance data resulted in the final feed water and the condenser back-pressure heat rate correction curves being adjusted. The exercise revealed that there could be potentially be significant baseline performance data uncertainty. The corrected indirect STEP Factor instrument uncertainty was now found to be 0.468% abs which translates to 0.164% abs overall efficiency. The combined uncertainty was corrected to 0.485% abs at a 3-hour segment size which translates to 0.171% abs overall efficiency. It has been deduced that the figures stated above are case-specific. However, the models have been developed to analyse any coal-fired power plant at various operating conditions. Furthermore, the uncertainty propagation module can be used to propagate uncertainty through any other discontinuous function or computer model. Various recommendations have been made to improve: the model uncertainty of STEP, data acquisition, systematic uncertainty, temporal uncertainty and baseline data uncertainty.