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    Decision tree regression model to predict low-rank coal moisture content during convective drying process
    (Taylor & Francis Inc, 2020) Pekel, Engin; Akkoyunlu, Mehmet Cabir; Akkoyunlu, Mustafa Tahir; Pusat, Saban
    Coal is still a significant energy source for the world. Due to the utilization of low-rank coal, drying is a key issue. There are lots of attempts to develop efficient drying processes. The most prominent method seems as thermal drying. For thermal drying processes, the most important subject is the coal moisture content change with time. In this study, convective drying experiments were utilized to develop a new model based on decision tree regression method to predict coal moisture content. The developed model gives satisfactory results in prediction of instant coal moisture content with changing drying conditions. With the decision tree depth of six, the best test results were achieved as 0.056 and 0.802 for MSE and R-2 analyses, respectively.
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    Determination of Effective Parameters for Coal Moisture Content Determination Using a 'Design of Experiment' Method
    (Taylor & Francis Inc, 2018) Akkoyunlu, Mustafa Tahir; Pekel, Engin; Akkoyunlu, Mehmet Cabir; Pusat, Saban; Ozkan, Coskun; Kara, Selin Soner
    Drying of low rank coal is a very significant topic due to its high economic value. One of the most important parameter in the designing of a dryer is the exiting moisture content of the material at the specified conditions and time. Aim of this study was to utilize the design of experiment methodology to determine the most effective parameters on the moisture content of the dried coal. The investigated parameters were dryer bed height, coal particle size, inlet drying air velocity, inlet drying air temperature, inlet drying air relative humidity, exit drying air relative humidity, and time. The results showed that bed height and time were the most effective parameters, and inlet air relative humidity and inlet drying air velocity were less effective parameters. Additionally, particle size and inlet drying air temperature parameters were also significant. To attain more accurate results in the prediction of the exiting coal moisture content, only more influential parameters may be used. Therefore, the design of experiment methodology was a significant way of determining the effective parameters in the coal drying process.
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    Fragmentation of a Turkish Low Rank Coal During Fixed-bed Evaporative Drying Process
    (Taylor & Francis Inc, 2021) Pusat, Saban; Akkoyunlu, Mustafa Tahir; Erdem, Hasan Huseyin
    In the present study, a new approach was utilized to investigate the fragmentation of low rank coal during the thermal drying process. Drying experiments were conducted at different drying air temperatures (70, 100, 110 and 130 degrees C), drying air velocities (0.4 and 0.7 m/s), bed heights (100, 150 and 250 mm) and particle fractions (20-50, 28-50 and 35-50 mm) for a Turkish lignite (Konya-Ilgin lignite) in a fixed-bed dryer. Konya-Ilgin lignite was used in the heating sector. Particle sizes used in these experiments were coarser than the studies in the literature due to legal constraints. The effects of drying parameters on coal fragmentation were evaluated. The fragmentation increased as the drying air temperature and the velocity increased, and it decreased as the bed height and the particle size increased. Results of this study may be used in the design of a suitable dryer for low rank coal.
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    Moisture content estimation during fixed bed drying process with design of experiment and ANFIS methods
    (Inderscience Enterprises Ltd, 2019) Akkoyunlu, Mustafa Tahir; Pekel, Engin; Akkoyunlu, Mehmet Cabir; Pusat, Saban; Ozkan, Coskun; Kara, Selin Soner
    In this study, a two stage methodology was applied to predict the exit coal moisture content during the drying process. The first stage included a design of experiment (DoE) study which made easy to determine the significance levels of drying parameters. At the end of the DoE stage, it was determined that the most significant parameter was bed height, and the least significant parameter was exit air relative humidity. The second stage included an adaptive neuro-fuzzy inference system (ANFIS) method which was applied to estimate the exit coal moisture content at any time. The experimental studies were conducted with different levels of the parameters (air temperature, air velocity, bed height, particle size, and air relative humidity). At the end of the second stage, the applicability of the ANFIS in the estimation of the exit coal moisture content was showed with satisfying results. R2 value was increased from 0.465 to 0.842.
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    Moisture content estimation during fixed bed drying process with design of experiment and ANFIS methods
    (Inderscience Enterprises Ltd, 2019) Akkoyunlu, Mustafa Tahir; Pekel, Engin; Akkoyunlu, Mehmet Cabir; Pusat, Saban; Ozkan, Coskun; Kara, Selin Soner
    In this study, a two stage methodology was applied to predict the exit coal moisture content during the drying process. The first stage included a design of experiment (DoE) study which made easy to determine the significance levels of drying parameters. At the end of the DoE stage, it was determined that the most significant parameter was bed height, and the least significant parameter was exit air relative humidity. The second stage included an adaptive neuro-fuzzy inference system (ANFIS) method which was applied to estimate the exit coal moisture content at any time. The experimental studies were conducted with different levels of the parameters (air temperature, air velocity, bed height, particle size, and air relative humidity). At the end of the second stage, the applicability of the ANFIS in the estimation of the exit coal moisture content was showed with satisfying results. R2 value was increased from 0.465 to 0.842.
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    A new empirical correlation to model drying characteristics of low rank coals
    (Inderscience Enterprises Ltd, 2017) Pusat, Saban; Akkoyunlu, Mustafa Tahir
    In the present study, drying of Konya-Ilgin lignite (a Turkish lignite) was modelled by using thin-layer drying models from the literature and a new empirical model. The required experimental works were carried out at different experimental conditions in a fixed bed dryer. The experimental conditions were 0.4 m/s, 0.7 m/s and 1.1 m/s drying air velocities, 70 degrees C, 100 degrees C and 130 degrees C drying air temperatures, 20 mm, 35 mm and 50 mm particle sizes and 80 mm, 130 mm and 150 mm bed heights. The performance of thin-layer drying models was evaluated by a statistical method. The results showed that the proposed model achieves better results than thin-layer drying models in the literature. Therefore, the proposed empirical model may be used to represent drying characteristics of any material.
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    A new empirical correlation to model drying characteristics of low rank coals
    (Inderscience Enterprises Ltd, 2017) Pusat, Saban; Akkoyunlu, Mustafa Tahir
    In the present study, drying of Konya-Ilgin lignite (a Turkish lignite) was modelled by using thin-layer drying models from the literature and a new empirical model. The required experimental works were carried out at different experimental conditions in a fixed bed dryer. The experimental conditions were 0.4 m/s, 0.7 m/s and 1.1 m/s drying air velocities, 70 degrees C, 100 degrees C and 130 degrees C drying air temperatures, 20 mm, 35 mm and 50 mm particle sizes and 80 mm, 130 mm and 150 mm bed heights. The performance of thin-layer drying models was evaluated by a statistical method. The results showed that the proposed model achieves better results than thin-layer drying models in the literature. Therefore, the proposed empirical model may be used to represent drying characteristics of any material.
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    Using hybridized ANN-GA prediction method for DOE performed drying experiments
    (Taylor & Francis Inc, 2020) Akkoyunlu, Mehmet Cabir; Pekel, Engin; Akkoyunlu, Mustafa Tahir; Pusat, Saban
    Coal is an important component in the energy industry and plays a key role in energy-producing facilities. Moisture is a common condition that has a considerable impact on coal. Coal drying has long been a question of great interest in a wide range of fields. Defining parameters in the coal drying is obtained by experiments. High costs, time constraints, and repetition of an experiment are one of the most frequently stated problems with experimental works. Using qualitative methods with experiments can be more useful for identifying and characterizing the coal drying process. The purpose of this article is finding the effective parameters in the coal drying process by using a hybridized prediction method. Genetic Algorithm (GA) and Artificial Neural Network (ANN) are hybridized with each other to identify and characterize the coal drying process. GA-ANN algorithm is applied to the coal drying process to predict the moisture of coal, but it does not provide a decent result at first. Later, the Design of Experiment (DoE) methodology is performed to determine the main effects of six parameters. Two scenarios are generated because two parameters are not statistically significant. The first scenario excludes the air relative humidity parameter, and the second scenario excludes the air relative humidity and the velocity of air parameters. Following the application of the DoE method, GA-ANN reaches decent results in scenario-2.