Modelling and control of crystallization process; Resource-Efficient Technologies; Vol. 3, iss. 1 : TECHNOSCAPE 2016: International Conference on Separation Technologies in Chemical, Biochemical, Petroleum and Environmental Engineering

Modelling and control of crystallization process; Resource-Efficient Technologies; Vol. 3, iss. 1 : TECHNOSCAPE 2016: International Conference on Separation Technologies in Chemical, Biochemical, Petroleum and Environmental Engineering

Detalhes bibliográficos
Parent link:Resource-Efficient Technologies: electronic scientific journal/ National Research Tomsk Polytechnic University (TPU).— , 2015-.— 2405-6537
Vol. 3, iss. 1 : TECHNOSCAPE 2016: International Conference on Separation Technologies in Chemical, Biochemical, Petroleum and Environmental Engineering.— 2017.— [P. 94–100]
Autor principal: Jha S. K.
Outros Autores: Karthika S., Radhakrishnan T. K.
Resumo:Title screen
Batch crystallizers are predominantly used in chemical industries like pharmaceuticals, food industries and specialty chemicals. The nonlinear nature of the batch process leads to difficulties when the objective is to obtain a uniform Crystal Size Distribution (CSD). In this study, a linear PI controller is designed using classical controller tuning methods for controlling the crystallizer outlet temperature by manipulating the inlet jacket temperature; however, the response is not satisfactory. A simple PID controller cannot guarantee a satisfactory response that is why an optimal controller is designed to keep the concentration and temperature in a range that suits our needs. Any typical process operation has constraints on states, inputs and outputs. So, a nonlinear process needs to be operated satisfying the constraints. Hence, a nonlinear controller like Generic Model Controller (GMC) which is similar in structure to the PI controller is implemented. It minimizes the derivative of the squared error, thus improving the output response of the process. Minimization of crystal size variation is considered as an objective function in this study. Model predictive control is also designed that uses advanced optimization algorithm to minimize the error while linearizing the process. Constraints are fed into the MPC toolbox in MATLAB and Prediction, Control horizons and Performance weights are tuned using Sridhar and Cooper Method. Performances of all the three controllers (PID, GMC and MPC) are compared and it is found that MPC is the most superior one in terms of settling time and percentage overshoot.
Idioma:inglês
Publicado em: 2017
Assuntos:
труды учёных ТПУ
электронный ресурс
кристаллизация
оптимальное управление
химическая промышленность
ПИ-регуляторы
контроллеры
Acesso em linha:http://earchive.tpu.ru/handle/11683/50278
Formato: Recurso Electrónico Capítulo de Livro
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=572520

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