利用 ANN 和 ANFIS 建立石油钻井污泥干燥工艺参数模型

IF 2.8 4区 工程技术 Q2 ENGINEERING, CHEMICAL Processes Pub Date : 2024-09-11 DOI:10.3390/pr12091948
Aytaç Moralar
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引用次数: 0

摘要

石油钻井污泥(PDS)是全球钻井活动中产生的最重要的废物之一。必须采用最具成本效益的方法来处理这种废物。本手稿的目的是对实验应用于 PDS 的干燥过程参数进行数学建模。为此,本研究采用了两种不同的人工智能技术:人工神经网络 (ANN) 和自适应神经模糊推理系统 (ANFIS)。这些方法用于预测参数。在计算中,输入包括不同数量(50 克、100 克和 150 克)和干燥时间的石油钻井泥浆,使用 120 瓦微波干燥功率。结果表明,在测试阶段,ANFIS 的判定系数(R2)和均方根误差(RMSE)分别为 0.999965 和 0.005425,而 ANN 的 R2 和 RMSE 分别为 0.999973 和 0.004774。对评估结果的分析表明,与干燥速率值相比,这两种方法对水分含量的预测都更接近实验值。
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Modeling Drying Process Parameters for Petroleum Drilling Sludge with ANN and ANFIS
Petroleum drilling sludge (PDS) is one of the most significant waste products generated during drilling activities worldwide. The disposal of this waste must be carried out using the most cost-effective methods available. The objective of this manuscript is to mathematically model the parameters of drying processes experimentally applied to PDS. For this purpose, this study employed two different artificial intelligence techniques: artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFISs). These methods were used to predict the parameters. In the calculations, the inputs included petroleum drilling mud with varying quantities (50 g, 100 g, and 150 g) and drying times, using a 120 W microwave drying power. The results indicated that the coefficient of determination (R2) and the root mean square error (RMSE) obtained during the test phase for ANFIS were 0.999965 and 0.005425, respectively, while for ANN, the R2 and RMSE were 0.999973 and 0.004774, respectively. Analysis of the evaluation results revealed that both methods provided predictions for moisture content that were closer to experimental values compared to drying rate values.
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来源期刊
Processes
Processes Chemical Engineering-Bioengineering
CiteScore
5.10
自引率
11.40%
发文量
2239
审稿时长
14.11 days
期刊介绍: Processes (ISSN 2227-9717) provides an advanced forum for process related research in chemistry, biology and allied engineering fields. The journal publishes regular research papers, communications, letters, short notes and reviews. Our aim is to encourage researchers to publish their experimental, theoretical and computational results in as much detail as necessary. There is no restriction on paper length or number of figures and tables.
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