FUZZYFCC: Fuzzy logic control of a fluid catalytic cracking unit (FCCU) to improve dynamic performance
Introduction
Fluid catalytic cracking (FCC) is an important oil refinery process, which converts high molecular weight oils into lighter hydrocarbon products. It consists of two interconnected gas–solid fluidized bed reactors: the riser reactor, where almost all the endothermic cracking reactions and coke deposition on the catalyst occur, and the regeneration reactor, where air is used to burn off the coke deposited on the catalyst. The heat produced is carried from the regenerator to the reactor by the catalyst. Thus, in addition to reactivating the catalyst, the regenerator provides the heat required by the endothermic cracking reactions. Industrial FCC units are designed to be capable of using a variety of feed stocks, including straight run distillates, atmospheric and vacuum residua and vacuum gas oils. They produce a range of products, which must adapt to seasonal, environmental, and other changing demand patterns. Since FCC units are capable of converting large quantities of heavy feed into valuable lighter products, any improvement in design, operation or control can result in substantial economic benefits (Bollas et al., 2003).
A fluidized catalytic cracking unit (FCCU) consists of reactor–regenerator, riser reactor, main fractionator, absorber–stripper–stabilizer, main air blower, wet gas compressor, etc. The FCCU converts heavy oil into a range of hydrocarbon products, including LPG, fuel gas, gasoline, light diesel, aviation kerosene, slurry oil, among which high octane number gasoline is most valuable. But their values are all market driven, so it is one of the control goals to maximize the production of one or more in different seasons. Since the catalyst circulates through a closed loop consisting of riser, regenerator and reactor, these three main parts are of particular interests both in industrial and research circles. Numerous papers have been published concerning different modelling approaches and control strategies for the FCC process, which deal with the strong interactions and many constrains from the operating, security and environmental point of view. The potential of yielding more market-oriented oil products, increasing production rate and stabilizing the operation become the major incentives to search for more accurate and practical models, high performance, and cost effective and flexible control strategies (Chunyang, Sohrab, & Arthur, 2003).
Section snippets
Optimization and control of FCC: a literature review
As known, FCC is the most important refinery unit. Since 1940s, a lot of modelling, optimization and control studies of the FCC have been realized. There is a rich literature about FCC, for example: Voerhies's reaction kinetics model, Fan and Fan's stability (steady state) specification of fluidized catalytic bed reactor. Optimization studies examples are: Schrake's linear programming with gradient algorithm, Savas's and Nicholson's local linearization of nonlinear model. Kurihara has used
Intelligent control of chemical process
Chemical processes include manufacturing phases such as, filling or emptying a reactor, heating or mixing a product. Intelligent control is a control system with the ultimate degree of autonomy in terms of self-learning, self-reconfigurability, reasoning, planning and decision making, and the ability to extract the most valuable information from unstructured and noisy data from any dynamically complex system and/or environment (Shoureshi, 1993).
Complex industrial processes such as a batch
FCC process modelling and control application: FUZZYFCC
The FCC Unit (Plant 7) is a UOP (an international supplier and licensor of process technologies, catalysts, adsorbents, process plants to the petroleum refining industries) design (FCC, 2001). The main parts of the FCCU are catalyst and fractionator. Catalyst circulation takes place in the catalyst part. The catalyst consists of reactor and regenerator units which are connected with each other with riser and stand-pipe. Catalyst in the catalyst circulation rises from riser to reactor and from
Results and discussions
There is no general and mathematically optimal solution for the control if plant (unit) to be controlled is complex and highly nonlinear. The modelling of the process and its solution become even more difficult if a sufficiently precise model is unknown or cannot be identified. It is well known however that in many cases a human can master the performance of such a plant using linguistic control algorithms that represent the operator knowledge and experience about the plant/unit by using
Conclusion
The following conclusions can be drawn from the application of fuzzy logic to the control of the FCC as described in this paper:
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A fuzzy logic-based control system was developed to estimate the variables in a FCCU. By careful selection of the input variables (here 10 variables) and designing the rules (here 30 rules) for the system and their statistical analysis, 97.53% of control accuracy can be obtained.
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The results were generally in compliance with empirical FCCU data. However, in most cases
Acknowledgement
Sakarya University is a partner of the Innovative Production Machines and Systems (I*PROMS) Network of Excellence (Contract No. 500273). http://ww.iproms.org <http://ww.iproms.org>.
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