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Using hybridized ANN-GA prediction method for DOE performed drying experiments
Drying Technology ( IF 2.7 ) Pub Date : 2020-04-15 , DOI: 10.1080/07373937.2020.1750027
Mehmet Cabir Akkoyunlu 1 , Engin Pekel 2 , Mustafa Tahir Akkoyunlu 3 , Saban Pusat 4
Affiliation  

Abstract 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.

中文翻译:

使用混合 ANN-GA 预测方法对 DOE 进行的干燥实验

摘要 煤炭是能源工业的重要组成部分,在能源生产设施中发挥着关键作用。水分是对煤炭有相当大影响的常见条件。长期以来,煤干燥一直是一个广泛领域中非常感兴趣的问题。煤干燥中的确定参数是通过实验获得的。高成本、时间限制和实验重复是实验工作中最常见的问题之一。使用带有实验的定性方法对于识别和表征煤干燥过程更有用。本文的目的是利用混合预测方法寻找煤干燥过程中的有效参数。遗传算法 (GA) 和人工神经网络 (ANN) 相互混合,以识别和表征煤干燥过程。GA-ANN 算法应用于煤的干燥过程来预测煤的水分,但它最初并没有提供一个像样的结果。随后,执行实验设计 (DoE) 方法以确定六个参数的主要影响。由于两个参数在统计上不显着,因此生成了两种情景。第一个场景排除空气相对湿度参数,第二个场景排除空气相对湿度和空气流速参数。在应用 DoE 方法之后,GA-ANN 在场景 2 中取得了不错的结果。但它起初并没有提供像样的结果。随后,执行实验设计 (DoE) 方法以确定六个参数的主要影响。由于两个参数在统计上不显着,因此生成了两种情景。第一个场景排除空气相对湿度参数,第二个场景排除空气相对湿度和空气流速参数。在应用 DoE 方法之后,GA-ANN 在场景 2 中取得了不错的结果。但它起初并没有提供像样的结果。随后,执行实验设计 (DoE) 方法以确定六个参数的主要影响。由于两个参数在统计上不显着,因此生成了两种情景。第一个场景排除空气相对湿度参数,第二个场景排除空气相对湿度和空气流速参数。在应用 DoE 方法之后,GA-ANN 在场景 2 中取得了不错的结果。第二种情景不包括空气相对湿度和空气流速参数。在应用 DoE 方法之后,GA-ANN 在场景 2 中取得了不错的结果。第二种情景排除了空气相对湿度和空气流速参数。在应用 DoE 方法之后,GA-ANN 在场景 2 中取得了不错的结果。
更新日期:2020-04-15
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