The Comparison of Artificial Intelligence and Traditional Approaches In FCCU Modeling

FCCU (Fluid Catalytic Cracking Unit) is a part of oil refinery production process whereby valuable products such as gasoline, LPG (Liquid Petroluem Gas), diesel are manufactured in a short period of time. The objective of this paper is to find the most robust model by comparing the models of FCCU that are developed using different methodologies. The models of FCCU are developed by using Artificial Neural Network (ANN), Fuzzy Logic, Neuro-Fuzzy, and traditional methodology. In this paper, the criteria used for measuring the performance of different models is root mean squared error (RMSE). The models are applied to the real data obtained from TUPRAS (Turkish Petroleum Refineries Corporation)-FCCU. Kurihara (1967) model is used as the traditional model for comparing with intelligence modeling techniques. Finally, the Fuzzy Neural Network (FNN) model was found as the model with the minimum RMSE. Qwicknet 2.23, MATLAB 6.5, and Neuro-solutions 4.1 softwares have been used for the construction of ANN, fuzzy, and neuro-fuzzy models, respectively

Bir petrol rafineri ünitesinde(FCCU) bulanık modelleme ve kontrol

Bu tezin, veri tabanı üzerinden yayınlanma izni bulunmamaktadır.Bulanık Modelleme ve Kontrol, FCC Ünitesi, Bulanık Gruplama, Endüstriyel Prosesler Bu çalışmada, Petrokimya sanayinin en önemli parçası olan rafineri sistemi içinde bulunan FCC ünitesinin bulanık modellemesi yapılarak bulanık kontrol kuralları türetilmiştir. FCC ünitesi, rafineri sistemi içinde bulunan en önemli birimdir. Bunun sebebi, çok girdili ve çok çıktık yüksek derecede lineer olmayan, iç geri beslemeli, değişkenlerin arasında güçlü ilişkileri olan, zamanla değişen, dağılmış parametreli ve doğasında önemli derecede belirsiz davranışlara sahip olmasından kaynaklanmasıdır. Matematiksel modeller bu sistemi eksik tanımlamaktadır. Bundan dolayı, FCC ünitesi, bulamk mantığın konusu olabilecek özelliklere sahip bulunmaktadır. Sistemde bulunan Reaktör ve Rejeneratörün yanında aynştıncı kolonda dahil edilerek modelleme çalışması yapılmıştır. Genelde bulanık modelleme, nümerik yapı altında bulanık ilişki denklemleri ve dilsel yaklaşım ile yapılabilmektedir. Bu çalışmada, sonuncusu araştırılıyor. Bulanık modellemede, bu sistemin tanımlanması için sistematik bir metot Icullanılmaktadır. Bu metot bulanık gruplama algoritmasıdır. Bulamk gruplama algoritmasının, çeşitli problemleri bulunmaktadır. Bu problemlere 4. Bölümde değinilmiş ve çözümleri verilmiştir. Sistem için yapılan bulanık gruplama sonucunda 13 grup bulunmuştur. Yani 13 kural türetilmiştir. Sistemin bulanık modelinin oluşturulması kontrol için en önemli aşamayı oluşturmaktadır. Bilindiği gibi, modele göre, kontrol ve optimizasyon çalışması yapılmaktadır. Oluşturulan bulanık modelde Mamdani çıkarım metodu kullanılmıştır. Sonuçlar Bölüm 4 ve eklerde verilmektedir. Sonuçta, bulanık modelden elde edilen benzinin oktan-varil miktarı ile gerçek sistemden elde edilen benzinin oktan-varil miktarı karşılaştırılmış ve birbirlerine çok yakın değerlerin olduğu görülmüştür.Key Words: Fuzzy Modeling and Control, FCCU, Fuzzy Clustering, Industrial Processes In this study, fuzzy modeling of FCCU in the refinery system as the most important part of Petrochemical Industry was carried out and fuzzy control rules was generated. FCCU is the most important Unit in the refinery system. From a system point of view, the FCCU is a higly nonlinear system with MIMO (Multi input-multi output), internal feedback, strongly coupling, time-varying, distributed parameter, significantly uncertain behaviours in nature. Mathematical Models is not enough to identify this system. On the occasion of this, FCCU has a characteristic specifications as a subject of Fuzzy Logic. Modelling study has been made by including also Fractionator as an addition to Reactor and Regenerator in FCC system. Generally, the fuzzy modeling may be carried out in two ways, namely, the fuzzy relation equations under a numerical framework and the linguistic approach. In this study, only the latter is being investigated. In the fuzzy modeling, a systematic method is used to identify this system. This method is named as fuzzy clustering algorithm. Fuzzy clustering algorithm has various problems. These problems has been discussed in the 4th chapter. Using this algorithm, we obtain 13 clusters for this system. That is, 13 rules have been generated. Constituting fuzzy model of this system is the crucial phase. As known, According to the model, control and optimization has been done. The fuzzy model of this system was used with Mamdani inference system. The results has been given 4th chapter and appendixes. Based upon the results of the study, on comparing the gasoline octane-barrel amount obtained from the fuzzy model and the real system and nearly the same results was found

FUZZY MODELING APPLICATION OF FLUID CATALYTIC CRACKING UNIT (FCCU) OF A PETROLEUM REFINERY (TÜPRAŞ)

In this study, fuzzy modeling of FCCU in the refinery system as the most important part of Petrochemical Industry was carried out. FCCU is the most important unit in the refinery system as it is a higly nonlinear system with MIMO (Multi input-multi output), internal feedback, strongly coupling, time-varying, distributed parameter, significantly uncertain behaviours in nature. Mathematical Models is not enough to identify this system. On the occasion of this, FCCU has a characteristic specifications as a subject of Fuzzy Logic

The Comparison of Artificial Intelligence and Traditional Approaches In FCCU Modeling

FCCU (Fluid Catalytic Cracking Unit) is a part of oil refinery production process whereby valuable products such as gasoline, LPG (Liquid Petroluem Gas), diesel are manufactured in a short period of time. The objective of this paper is to find the most robust model by comparing the models of FCCU that are developed using different methodologies. The models of FCCU are developed by using Artificial Neural Network (ANN), Fuzzy Logic, Neuro-Fuzzy, and traditional methodology. In this paper, the criteria used for measuring the performance of different models is root mean squared error (RMSE). The models are applied to the real data obtained from TUPRAS (Turkish Petroleum Refineries Corporation)FCCU. Kurihara (1967) model is used as the traditional model for comparing with intelligence modeling techniques. Finally, the Fuzzy Neural Network (FNN) model was found as the model with the minimum RMSE. Qwicknet 2.23, MATLAB 6.5, and Neuro-solutions 4.1 softwares have been used for the construction of ANN, fuzzy, and neuro-fuzzy models, respectively