Fuzzy control of a nylon polymerization semi-batch reactor
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An experimental and theoretical approach on real-time control and monitoring of the apparent viscosity by fuzzy-based control
2022, Journal of Petroleum Science and EngineeringA model-based approach to quality monitoring of a polymerization process without online measurement of product specifications
2017, Computers and Industrial EngineeringCitation Excerpt :The quality planning of any product is effective if the variables or parameters directly related to their end-use properties (quality variables) can be measured or estimated (Alzghoul & Lofstrand, 2011; Freidina, Botvinnik, & Dvornikova, 2014). On-line monitoring, analysis and control are capable of improving product quality in both manufacturing (Singh, Gernaey, & Gani, 2009) and continuous (Lima, Maciel Filho, Embiruçu, & Maciel, 2007; Lima, Manenti, Maciel Filho, Embiruçu, & Wolf Maciel, 2009; Lima et al., 2010; Wakabayashi, Embiruçu, Fontes, & Kalid, 2009) processes through timely measurement. Delayed measurements, low sampling rates and missing data problems also affect other process analysis technologies not only associated with the product quality control itself (Aghabozorgi, Shirkhorshid, & Wah, 2015; Ali & Pievatolo, 2016; Baraldi, Maio, Rigamonti, Zio, & Seraoui, 2015; Ben-Arieh & Gullipalli, 2012; Cook, Harrison, Rouse, & Zhu, 2012; Haomin et al., 2014; Lee & Park, 2006).
Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor
2016, ISA TransactionsCitation Excerpt :Studies on fuzzy model based control include fuzzy model identification [20] for alternate forms of fuzzy models [21] incorporating handling of uncertainties [22] and adaptation approaches [23]. Application of fuzzy logic control to batch and semi batch chemical and bio-chemical processes has been reported to track the variable set point trajectories based on process or operator knowledge [24–31]. Data-driven fuzzy modeling approaches have been proposed based on clustering approaches [32] where the operating data is analyzed and partitioned into clusters and used in combination with Takagi–Sugeno (TS) fuzzy models, or through online fuzzy rule formulation, simplification, combination and redundancy elimination based on different techniques [33].
Control of a heat exchanger using neural network predictive controller combined with auxiliary fuzzy controller
2015, Applied Thermal EngineeringFuzzy logic control of a reverse flow reactor for catalytic oxidation of ventilation air methane
2014, Control Engineering PracticeCitation Excerpt :Fuzzy logic controllers (FLCs) constructed by type-1 fuzzy sets (T1-FS) provide an effective way to control systems of high nonlinearities (Zadeh, 1965). Since the pioneering work of Mamdani (Mamdani & Assilian, 1975), this kind of controller has been successfully implemented in many areas such as industrial process control (Park & Cho, 2005), robot (Lin & Lewis, 2003; Takeuchi, Nagai, and Enomoto 1988; Thongchai, Suksakulchai, Wilkes, & Sarkar, 2000), traffic signal control (Wei, Zhang, Mbede, Zhang, & Song, 2001), reactor control (Sheikhzadeh, Trifkovic, & Rohani, 2008; Wakabayashi, Embiruçu, Fontes, & Kalid, 2009; Wu & Pai, 2009) and so on. However, the type-1 fuzzy logic systems (T1-FLSs) were limited in handling systems with great uncertainties (Castillo & Melin, 2012).
A fuzzy-split range control system applied to a fermentation process
2013, Bioresource TechnologyCitation Excerpt :A simpler alternative is the application of fuzzy logic based controllers (FC). The advantages of fuzzy logic based controllers are simplicity, implementation easiness, robustness, and the ability to deal with complex nonlinear relationships using even imprecise, incomplete and noisy data (Wakabayashi et al. 2009; Eker and Torun, 2006; Silva et al., 2012). Sagüés et al. (2007) successfully implemented fuzzy controllers to control a biomass gasification process.