Analytical modelling and quality improvementof a pet production train: A Real case study
Başlık çevirisi mevcut değil.
- Tez No: 758143
- Danışmanlar: PROF. LUCA FERRARINI, DR. SOROUSH RASTEGARPOUR
- Tez Türü: Yüksek Lisans
- Konular: Biyomühendislik, Elektrik ve Elektronik Mühendisliği, Bioengineering, Electrical and Electronics Engineering
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2021
- Dil: İngilizce
- Üniversite: Politecnico di Milano
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 86
Özet
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Özet (Çeviri)
POLYETHYLENE TEREPHTHALATE (PET) has proved its utility by being one of the most used thermoplastics in a wide range of applications from food to textile industry. The importance of the quality indices has become very popular among researchers and pushed them toward investigating different production technology in order to meet better final product characteristics, meanwhile maintaining other production properties. This dissertation aims at identifying a way to find the optimal setting of the process variables of a large PET production train in order to optimize the final product quality in terms of chemical properties and purity. In fact, a modeling framework is proposed able to discover operational inefficiencies hidden in the plant control settings and classic maneuvers, which may not be always optimal. Consequently, by detecting and correcting such an undesired behavior, the efficiency of the component under study will be increased. In this regard, the estimation and prediction of the key process variables and chemical properties of the final products are extremely important to enforce efficiency. However, it is also a very challenging task due to the strong dependency of the performance on disturbances and operating conditions. In this thesis, we tackle the problem to develop and properly tune an equivalent analytical reference model for the system, validated by various sets of real data recorded in different operational conditions. In particular, we focus on a real case study consisting of four chemical reactors in series. The quality of the model is then evaluated through an intensive sensitivity analysis based on simulations, in order to find the optimal setting of the process variables to minimize an objective function. In the end, the results show that the obtained optimal setting of process variables are able to operate the plant at a lower temperature level. In fact, it guarantees better color indices for final product with respect to the current condition, while preserving the chemical and process properties in their valid operational range.
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