A general approach to develop and assess models estimating coal energy content

The gross calorific value (GCV) is an important property defining the energy content of fuels such as coal. Many industries require the GCVs to accurately quantify and report the energy efficiency of the operations. Unfortunately, measuring the GCVs of coal can be a time-consuming and expensive process. Multiple researchers have therefore developed models to predict the GCVs based on more accessible variables such as proximate and ultimate compositions. This study presents a review of models available from literature as background to the problem statement. These models were developed for specific coal types from various geographical locations. Since the properties of coal differs between locations, it is questionable whether these models are applicable for coal from a new location. This limiting factor, along with the various options of existing models identifies the problem of this study: Will the literature models be applicable to a new coal dataset and which one would be best? A preliminary evaluation testing the application of existing literature models on new coal data showed significant discrepancies in the results. This evaluation demonstrated that the literature models perform differently on new data with errors ranging from 3.7% to 72.1%. The evaluation also shows that various approaches are used in the development of these models. The significant variance in results together with the different model attributes makes it challenging to objectively assess and compare suitable models. The evaluation findings necessitate the need to devise a general industry "best practice" approach to develop new models in a consistent way. Furthermore, it also requires a methodology to objectively compare the different model characteristics and subsequent results. A detailed literature study was conducted to determine the general steps required for model development. The literature study identified three common focus areas namely data preparation, model development and model validation. An additional focus area was also added to assess techniques to visually evaluate, assess and compare the various models. This fourth focus area introduces a new and practical method to ultimately identify the most suitable model to use on new coal data. The four focus areas identified in the literature study were combined to devise a methodology to use for the development and comparison of GCV models. The methodology layout follows the four focus areas from literature: data preparation, model development, model validation and comparison of results. Each focus area consists of several sub-steps based on the industry and academic best practices obtained from literature. The methodology was tested and verified by applying it to three different case studies. The case studies consist of coal data from South Africa, India and Alaska. A new GCV model is developed for each dataset and presented errors in the range of 0.26% to 2.63%. These results verifies the new methodology's ability to consistently deliver high quality models. The visualisation technique is further used to investigate and validate results. The original assessment combing available models and new unrelated data was repeated. The results show that there is a significant difference between model results obtained using model focus area / region specific data vs. unrelated data (0.26%−2.63% vs. 8.26%−72.3%). However, the new technique now allows the user to quickly assess model quality and accuracy. This ultimately enables the user to select the most appropriate model for a specific dataset. The study identified the need to objectively develop and compare the performance and applicability of GCV models. A wide literature survey was conducted to find academic and industry best practice techniques required to create a structured approach to develop new GCV models. The method to compare the models was applied to case studies and enabled the user to identify which model would be best in a practical and objective manner.

Grantor dc:publisher

North-West University (South Africa)

Year dc:date.issued

2019

Author dc:creator

• Van Aarde, C.

Advisor dc:contributor.advisor

• Kleingeld, M.

• Calorific value
• proximate analysis
• model development
• objective comparison
• visualisation

Language dc:language.iso

en

Identifier URI

0000-0003-1382-4562

OAI identifier oai:identifier

oai:repository.nwu.ac.za:10394/34274