Application of AI in Chemical Engineering

A major shortcoming of traditional strategies is the fact that solving chemical engineering problems due to the highly nonlinear behavior of chemical processes is often impossible or very difficult. Today, artificial intelligence (AI) techniques are becoming useful due to simple implementation, easy designing, generality, robustness and flexibility. The AI includes various branches, namely, artificial neural network, fuzzy logic, genetic algorithm, expert systems and hybrid systems. They have been widely used in various applications of the chemical engineering field including modeling, process control, classification, fault detection and diagnosis. In this chapter, the capabilities of AI are investigated in various chemical engineering fields

Study of power plant, carbon capture and transport network through dynamic modelling and simulation

The unfavourable role of CO₂ in stimulating climate change has generated concerns as CO₂ levels in the atmosphere continue to increase. As a result, it has been recommended that coal-fired power plants which are major CO₂ emitters should be operated with a carbon capture and storage (CCS) system to reduce CO₂ emission levels from the plant. Studies on CCS chain have been limited except a few high profile projects. Majority of previous studies focused on individual components of the CCS chain which are insufficient to understand how the components of the CCS chain interact dynamically during operation. In this thesis, model-based study of the CCS chain including coal-fired subcritical power plant, post-combustion CO₂ capture (PCC) and pipeline transport components is presented. The component models of the CCS chain are dynamic and were derived from first principles. A separate model involving only the drum-boiler of a typical coal-fired subcritical power plant was also developed using neural networks.The power plant model was validated at steady state conditions for different load levels (70-100%). Analysis with the power plant model show that load change by ramping cause less disturbance than step changes. Rate-based PCC model obtained from Lawal et al. (2010) was used in this thesis. The PCC model was subsequently simplified to reduce the CPU time requirement. The CPU time was reduced by about 60% after simplification and the predictions compared to the detailed model had less than 5% relative difference. The results show that the numerous non-linear algebraic equations and external property calls in the detailed model are the reason for the high CPU time requirement of the detailed PCC model. The pipeline model is distributed and includes elevation profile and heat transfer with the environment. The pipeline model was used to assess the planned Yorkshire and Humber CO₂ pipeline network.Analysis with the CCS chain model indicates that actual changes in CO₂ flowrate entering the pipeline transport system in response to small load changes (about 10%) is very small (<5%). It is therefore concluded that small changes in load will have minimal impact on the transport component of the CCS chain when the capture plant is PCC

A dynamic fuzzy model for a drum–boiler–turbine system

A nonlinear dynamic fuzzy model for natural circulation drum–boiler–turbine is presented. The model is derived from Åström–Bell nonlinear dynamic system and describes the complicated dynamics of the physical plant. It is shown that the dynamic fuzzy model gives in some appropriate sense accurate global nonlinear prediction and at the same time that its local models are close approximations to the local linearizations of the nonlinear dynamic system. This closeness is illustrated by simulation in various condition

Development Of Fuzzy Dynamic Models: Application To Polymerization Systems

This work presents the development of dynamic models based in the fuzzy systems for polymerization processes. These reactions present a highly non linear dynamic behavior, thus making difficult the attainment of mathematical models for conventional methods. The solution copolymerization of methyl methacrylate and vinyl acetate in a continuous stirred tank reactor is used to illustrate the generation of the proposed model. Factorial planning was used to discriminate the process variables with higher impact on the system performance (effects) and they are used to built up a dynamic model based on the functional fuzzy relationship of Takagi-Sugeno type. These models present an excellent capacity to represent dynamic data. Moreover, they allow the inclusion of qualitative or operational information of the process. Gaussian membership functions are used for the fuzzy sets and model determination (rules number and model parameters) is obtained from the process database. The treatment of these data for the fuzzy model determination is carried out by means of algorithms of subtractive clustering and least squares. The kinetic parameters and reactor operating conditions are obtained from the literature and a mathematical model is considered as plant for generation of identification data. Fuzzy dynamic models showed satisfactory predictive capabilities, and may be an interesting alternative to attack problems of modeling in chemical processes.ABDELAZIM, T., MALIK, O.P., Identification of nonlinear systems by Takagi-Sugeno logic grey box modeling for real-time control (2005) Control Engineering Practice, 13, pp. 1489-1498ALEXANDRIDIS, A.P., SIETTOS, C.I., SARIMVEIS, H.K., BOUDOUVIS, A.G., BAFAS, G.V., Modelling of nonlinear process dynamics using Kohonen's neural networks, fuzzy systems and Chebyshev series (2002) Computers and Chemical Engineering, 26, pp. 479-486CERRADA, M., AGUILAR, J., COLINA, E., TITLI, A., Dynamical membership functions: An approach for adaptive fuzzy modeling (2005) Fuzzy Sets and Systems, 152, pp. 513-533CHEN, B., LIU, X., Reliable control design of fuzzy dynamic systems with time-varying delay (2003) Fuzzy Sets and Systems, pp. 1-26CHEN, Y., LIU, X., Modeling mass transport of propylene polymerization on Ziegler-Natta catalyst (2005) Polymer, 46, pp. 9434-9442CHIU, S., A cluster estimation method with extension to fuzzy model identification (1994) IEEE, pp. 1240-1245CHIU, S., Method and software for extracting fuzzy classification rules by subtractive clustering (1996) IEEE, pp. 461-465CONGALIDIS, J.P., RICHARDS, J.R., RAY, W.H., Feedforward and feedback control of a solution copolymerization reactor (1989) AIChe Journal, 35 (6), pp. 891-907. , JuneHABBI, H., ZELMAT, M., BOUAMAMA, B.O., A dynamic fuzzy model for a drum-boiler-turbine system (2003) Automatica, 39, pp. 1213-1219MANER, B.R., DOYLE III, F.J., Polymerization reactor control using autoregressiveplus volterra-based MPC (1997) AIChe Journal, 43 (7), pp. 1763-1784. , JulyPASSINO, K. M. and YURKOVICH, S. Fuzzy Control. Addison-Wesley- Longman, Menlo Park, CA,1998SALA, A., GUERRA, T.M., BABUSKA, R., Perspectives of fuzzy systems and control (2005) Fuzzy Sets and Systems, 156, pp. 432-444TAKAGI, T., SUGENO, M., Fuzzy identification of systems and its applications to modeling and Control (1985) IEEE Transactions on Systems, Man, and Cybernetics, 15, pp. 116-133ZADEH, L., Outline of a new approach to the analysis of complex systems and decision process (1973) IEEE Transactions on Systems, Man, and Cybernetics, 1, pp. 28-4

Development Of Dynamic Models And Predictive Control By Fuzzy Logic For Polymerization Processes

This work presents the development of a predictive hybrid controller (PHC) based in fuzzy systems for polymerization processes. These reactions have typically a highly non linear dynamic behavior, thus making the performance of controllers based on conventional internal models to be poor or to require a lot of effort in controller tuning. The solution copolymerization of methyl methacrylate and vinyl acetate in a continuous stirred tank reactor is used to illustrate the performance of the proposed controller. It is introduced the development of a methodology for the design of the predictive controller based on functional fuzzy dynamic models of Takagi-Sugeno type. These models present an excellent capacity to represent dynamic data and this feature is explored in the proposed hybrid controller. Moreover, they allow the inclusion of qualitative or operational information of the process. Gaussian membership functions are used for the fuzzy sets and model determination (rules number and model parameters) is obtained from the process database. The treatment of these data for the fuzzy model determination is carried out by means of algorithms of subtractive clustering and least squares. The kinetic parameters and reactor operating conditions are obtained from the literature and a mathematical model is considered as a virtual plant for data generation and process identification. The modeling by the fuzzy approach showed to have a good potential for the processes representation. The PHC controller was compared to the dynamic matrix controller (DMC) to the regulatory and servo problems. The obtained results showed that the proposed control is robust and it requires less computational time than the conventional predictive controllers, being an interesting alternative to attack control problems in complex chemical processes.ABDELAZIM, T., MALIK, O.P., Identification of nonlinear systems by Takagi-Sugeno logic grey box modeling for real-time control (2005) Control Engineering Practice, 13, pp. 1489-1498ALEXANDRIDIS, A.P., SIETTOS, C.I., SARIMVEIS, H.K., BOUDOUVIS, A.G., BAFAS, G.V., Modelling of nonlinear process dynamics using Kohonen's neural networks, fuzzy systems and Chebyshev series (2002) Computers and Chemical Engineering, 26, pp. 479-486CERRADA, M., AGUILAR, J., COLINA, E., TITLI, A., Dynamical membership functions: An approach for adaptive fuzzy modeling (2005) Fuzzy Sets and Systems, 152, pp. 513-533CHEN, B., LIU, X., Reliable control design of fuzzy dynamic systems with time-varying delay (2003) Fuzzy Sets and Systems, pp. 1-26CHIU, S., A cluster estimation method with extension to fuzzy model identification (1994) IEEE, pp. 1240-1245CHIU, S., Method and software for extracting fuzzy classification rules by subtractive clustering (1996) IEEE, pp. 461-465CONGALIDIS, J.P., RICHARDS, J.R., RAY, W.H., Feedforward and feedback control of a solution copolymerization reactor (1989) AIChe Journal, 35 (6), pp. 891-907. , JuneDOUGHERTY, D., COOPER, D.A., Practical Multiple Model Adaptive Strategy for Multivariable Model Predictive Control (2003) Control Engineering Practice, 11, pp. 649-664GUIAMBA, I.R.F., MULHOLLAND, M., Adaptive Linear Dynamic Matrix Control Applied to an Integrating Process (2004) Computers and Chemical Engineering, 28, pp. 2621-2633HABBI, H., ZELMAT, M., BOUAMAMA, B.O., A dynamic fuzzy model for a drum-boiler-turbine system (2003) Automatica, 39, pp. 1213-1219HAERI, M., BEIK, H.Z., Application of Extended DMC for Nonlinear MIMO Systems (2005) Computers and Chemical Engineering, 29, pp. 1867-1874MANER, B.R., DOYLE III, F.J., Polymerization reactor control using autoregressive-plus volterra-based MPC (1997) AIChe Journal, 43 (7), pp. 1763-1784. , JulyPARK, M., RHEE, H., Property Evaluation and Control in a Semibatch MMA/MA Solution Copolymerization Reactor (2003) Chemical Engineering Science, 58, pp. 603-611PASSINO, K.M., YURKOVICH, S., (1998) Fuzzy Control, , Addison-Wesley-Longman, Menlo Park, CARAMASWAMY, S., CUTRIGHT, T.J., QAMMAR, H.K., Control of a Continuous Bioreactor Using Model Predictive Control (2005) Process Biochemistry, 40, pp. 2763-2770ROSS, T.J., (2004) Fuzzy Logic with Engineering Applications, , John Wiley & Sons Ltd, Second EditionSALA, A., GUERRA, T.M., BABUSKA, R., Perspectives of fuzzy systems and control (2005) Fuzzy Sets and Systems, 156, pp. 432-444SANTOS, L.O., AFONSO, P.A., CASTRO, J.A., OLIVEIRA, N.M., BIEGLER, L.T., On-line Implementation of Nonlinear MPC: An Experimental case study (2001) Control Engineering Practice, 9, pp. 847-857SCHNELLE, P.D., ROLLINS, D.L., Industrial Model Predictive Control Technology as Applied to Continuous Polymerization Processes (1998) ISA Transactions, 36 (4), pp. 281-292SILVA, J. E. L. Simulação e Controle Preditivo Linear (com Modelo de Convolução) e Não-Linear (com Modelo Baseado em Redes Neurais Artificiais) de Colunas Recheadas de Absorção com Reação Química. MSc. Thesis, DESQ/FEQ/UNICAMP, Campinas, São Paulo, Brazil, 1997;TAKAGI, T., SUGENO, M., Fuzzy identification of systems and its applications to modeling and Control (1985) IEEE Transactions on Systems, Man, and Cybernetics, 15, pp. 116-133TOLEDO, E.C., Modelagem, V., Simulação e Controle de Reatores Catalíticos de Leito Fixo. DSc. Thesis (1999) DPQ/FEQ/UNICAMP, , Campinas, São Paulo, BrazilZADEH, L., Outline of a new approach to the analysis of complex systems and decision process (1973) IEEE Transactions on Systems, Man, and Cybernetics, 1, pp. 28-4