Statistical Approaches to Optimize the System to Reduce Sediment Accumulation in Ballast Tanks

Öz

Experimental studies, which are essential methods in engineering, are powerful techniques in terms of designing structures, optimization, and improvement of design. However, the results illustrated by experimental studies evaluating the effect of multiple factors on measures of performance may not be interpreted as statistically significant. In addition, the limited resources (time, labor and financial resources, etc.) are among the most important limitations of the experimental studies. In this study, three main factors that determine the working conditions of the pneumatic system developed to reduce the sediment accumulation in the ballast tanks of the ships are considered and the effect of these factors on sediment reduction is examined statistically. With the classical statistical approaches applied within the study, the experimental data could not be interpreted in a meaningful way at the desired level. For this reason, instead of interpreting the relationships in the data set obtained from the experimental set through a statistical model, it was determined to reveal the relationship between the variables directly through the data. Therefore, although the number of data in the dataset is limited, the Artificial Neural Networks approach been applied (ANN). The number of input data in the ANN structure set greatly affects the accuracy of this approach. Although it is theoretically possible to increase the data set to an infinite number in the experimental study in question, this is not applicable due to resource limitations, particularly time and labor. Because of that, another application is created when forming the ANN model, considering the purpose of the experimental study. Some of the experimental data is used in the training phase of the ANN model, and the ANN model is provided to suggest an optimal working condition. Experimental results not included in the ANN training phase are used to experimentally compare the optimal working condition proposed by the model. In this study, ANN is used as a tool for optimization and the optimum operating condition suggested by the model provides the best result in terms of sediment accumulation reduction among all experimental data. In this study, the problems experienced in the statistical interpretation of the experimental study results for the optimization of the operating parameter of the system developed to reduce the sediment accumulation in the ballast tanks of the ships and the approach applied when using Artificial Neural Networks (ANN) are discussed.

Anahtar Kelimeler

• ANN
• Ballast sediment
• Ballast tank

Balast Tanklarında Sediman Birikimini Azaltacak Sistemin Optimizasyonuna Yönelik İstatistiksel Yaklaşımlar

Öz

Deneysel çalışmalar mühendislik alanında sistem ve yapıların tasarımı, optimizasyonu, tasarımın iyileştirilmesi faaliyetlerinin vazgeçilmez ögelerindendir. Ancak özellikle birden çok faktörün performans ölçümleri üzerindeki etkisinin değerlendirilmesi amacıyla yapılan deneysel çalışmalarda elde edilen bulguların işaret ettiği sonuçlar, istatistiksel olarak anlamlı bir şekilde yorumlanamayabilir. Buna ek olarak, kaynakların (zaman, iş gücü ve maddi kaynaklar vb.) kısıtlı oluşu ise deneysel çalışmaların en önemli sınırlılıkları arasında yer almaktadır. Bu çalışmada, gemilerin balast tanklarında sediman birikimini azaltmak için geliştirilen pnömatik sistemin çalışma koşullarını belirleyen üç temel faktör ele alınmış ve bu faktörlerin sedimanın azaltılmasına etkisi istatistiksel açıdan irdelenmiştir. Çalışma kapsamında uygulanan klasik istatistiksel yaklaşımlarla, deneysel veriler istenilen düzeyde anlamlı bir şekilde yorumlanamamıştır. Bu nedenle deney setinden elde edilen veri kümesindeki ilişkileri istatistiksel bir model üzerinden yorumlamak yerine doğrudan veri üzerinden değişkenler arasındaki ilişkinin ortaya çıkarılması değerlendirilmiştir. Bunun için her ne kadar veri kümesindeki veri sayısı sınırlı olsa da Yapay Sinir Ağları (YSA) yaklaşımı uygulanmıştır. YSA girdi setinde veri sayısı bu yaklaşımın doğruluk ölçüsü (accuracy) üzerinde büyük bir öneme sahiptir. Söz konusu deneysel çalışmada veri setinin sonsuz sayıya çıkarılması teorik olarak mümkün olsa da kaynak kısıtları nedeniyle bunun gerçekleştirilmesi uygulanabilir değildir. Bu nedenle YSA modeli oluşturulurken deneysel çalışmanın amacı dikkate alınmış ve farklı bir yaklaşım uygulanmıştır. Deney verisinin bir kısmı YSA modelinin eğitim aşamasında kullanılmış, YSA modelinin optimum bir çalışma koşulu önermesi sağlanmıştır. YSA eğitim aşamasına dahil edilmemiş olan deneysel sonuçlar ise model tarafından önerilen optimum çalışma koşulunun deneysel olarak karşılaştırılmasında kullanılmıştır. Söz konusu çalışmada YSA optimizasyon için bir araç olarak kullanılmış ve model tarafından önerilen optimum çalışma koşulu, tüm deney verileri arasında sediman birikimini azaltılması açısından en uygun sonucu vermiştir. Bu çalışmada, gemilerin balast tanklarında sediman birikimini azaltmak için geliştirilen sistemin işletme parametresinin optimizasyonu için yapılan deneysel çalışma sonuçlarının istatistiksel olarak yorumlanmasında yaşanan sorunlar ve YSA kullanılırken uygulanan yaklaşıma yer verilmiştir.

Anahtar Kelimeler

• YSA
• Balast sedimanı
• Balast tankı

Kaynakça

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