Predictive functional control for the temperature control of a chemical batch reactor

A predictive functional control (PFC) technique is applied to the temperature control of a pilot-plant batch reactor equipped with a mono-fluid heating/cooling system. A cascade control structure has been implemented according to the process sub-units reactor and heating/cooling system. Hereby differences in the sub-units dynamics are taken into consideration. PFC technique is described and its main differences with a standard model predictive control (MPC) technique are discussed. To evaluate its robustness, PFC has been applied to the temperature control of an exothermic chemical reaction. Experimental results show that PFC enables a precise tracking of the set-point temperature and that the PFC performances are mainly determined by its internal dynamic process model. Finally, results show the performance of the cascade control structure to handle different dynamics of the heating/cooling system

Applications of recurrent neural networks in batch reactors. Part II: Nonlinear inverse and predictive control of the heat transfer fluid temperature

Although nonlinear inverse and predictive control techniques based on artificial neural networks have been extensively applied to nonlinear systems, their use in real time applications is generally limited. In this paper neural inverse and predictive control systems have been applied to the real-time control of the heat transfer fluid temperature in a pilot chemical reactor. The training of the inverse control system is carried out using both generalised and specialised learning. This allows the preparation of weights of the controller acting in real-time and appropriate performances of inverse neural controller can be achieved. The predictive control system makes use of a neural network to calculate the control action. Thus, the problems related to the high computational effort involved in nonlinear model-predictive control systems are reduced. The performance of the neural controllers is compared against the self-tuning PID controller currently installed in the plant. The results show that neural-based controllers improve the performance of the real plant.Publicad

Samopodešavajuće prediktivno funkcionalno upravljanje temperaturom egzotermičkog šaržnog reaktora

In this paper we study a self-adaptive predictive functional control algorithm as an approach to control of temperature in an exothermic batch reactor. The batch reactor is situated in a pharmaceutical company in Slovenia and is used in the production of medicines. Due to mixed discrete and continuous inputs the reactor is considered as a hybrid system. The model of the reactor used for the simulation experiment is explained in the paper. Next, we assumed an exothermic chemical reaction that is carried out in the reactor core. The dynamics of the chemical reaction that comply with the Arrhenius relation have been well documented in the literature and are also summarized in the paper. Furthermore, the online recursive least-squares identification of the process parameters and the self-adaptive predictive functional control algorithm are thoroughly explained. We tested the proposed approach on the batch reactor simulation example that included the exothermic chemical reaction kinetic model. The results suggest that such implementation meets the control demands, despite the strongly exothermic nature of the chemical reaction. The reference is suitably tracked, which results in a shorter overall batchtime. In addition, there is no overshoot of the controlled variable (temperature in the reactor core), which yields a higher-quality production. Finally, by introducing a suitable discrete switching logic in order to deal with the hybrid nature of the batch reactor, we were able to reduce switching of the on/off valves to minimum and therefore relieve the wear-out of the actuators as well as reduce the energy consumption needed for control.U članku se analizira samopodešavajući algoritam prediktivnog funkcionalnog upravljanja kao pristup upravljanju temperaturom egzotermičkog šaržnog reaktora. Šaržni se reaktor nalazi u jednoj slovenskoj farmaceutskoj tvrtki gdje se koristi za proizvodnju medikamenata. Budući da su ulazi u rektor i kontinuirani i diskretni, reaktor je promatran kao hibridni sustav. U članku je opisan model reaktora korišten za simulacije. Nadalje, pretpostavljeno je da se u jezgri reaktora odvija egzotermička reakcija. Opis dinamike kemijske reakcije Arrheniusovim jednadžbama dobro je dokumentiran u literaturi, pa je u članku dan samo kratki pregled. Posebno detaljno opisana je metoda najmanjih kvadrata za procjenu parametara modela te samopodešavajući agoritam prediktivnog funkcionalnog upravljanja. Predloženi pristup upravljanju provjeren je simulacijom na šaržnom reaktoru koji uključuje kinetički model egzoterničke kemijske reakcije. Simulacijski rezultati ukazuju da predloženo upravljanje ispunjava tražene zahtjeve, unatoč jakoj egzotermičkoj naravi kemijske reakcije. Zadane su reference dobro praćene, što rezultira skraćenjem trajanja šaržnog procesa. Osim toga, nepostojanje nadvišenja u temperaturi jezgre reaktora osigurava veću kakvoću proizvodnje. Na koncu, uvođenjem prikladne logike prekapčanja za prilagodbu hibridnoj naravi šaržnog reaktora moguće je značajno smanjiti prekapčanje dvopoložajnih ventila što ima za posljedicu smanjenje njihova trošenja i uštedu u potrošnji energije

Shaping of molecular weight distribution using b-spline based predictive probability density function control

Issues of modelling and control of molecular weight distributions (MWDs) of polymerization products have been studied under the recently developed framework of stochastic distribution control, where the purpose is to design the required control inputs that can effectively shape the output probability density functions (PDFs) of the dynamic stochastic systems. The B-spline Neural Network has been implemented to approximate the function of MWDs provided by the mechanism model, based on which a new predictive PDF control strategy has been developed. A simulation study of MWD control of a pilot-plant styrene polymerization process has been given to demonstrate the effectiveness of the algorithms

Applications of recurrent neural networks in batch reactors. Part I: NARMA modelling of the dynamic behaviour of the heat transfer fluid

This paper is focused on the development of nonlinear models, using artificial neural networks, able to provide appropriate predictions when acting as process simulators. The dynamic behaviour of the heat transfer fluid temperature in a jacketed chemical reactor has been selected as a case study. Different structures of NARMA (Non-linear ARMA) models have been studied. The experimental results have allowed to carry out a comparison between the different neural approaches and a first-principles model. The best neural results are obtained using a parallel model structure based on a recurrent neural network architecture, which guarantees better dynamic approximations than currently employed neural models. The results suggest that parallel models built up with recurrent networks can be seen as an alternative to phenomenological models for simulating the dynamic behaviour of the heating/cooling circuits which change from batch installation to installation.Publicad

Temperature modelling and model predictive control of a pilot-scale batch reaction system

The temperature control equipment on a pilot scale batch reaction system located at EAFIT University in Medelln, Colombia, is modeled and a new controller is designed aiming at using it in the reactor current PLC-based control system. Some mathematical models are developed from experimental data to describe the system behavior and using them several model based predictive controllers are designed. The simplest, yet reliable, model obtained is an ARX polynomial model of order (1,1,1) that yields a four states ane model for which an explicit MPC was calculated. This controller has a reduced mathematical complexity and can probably be used directly on the existing control system.Preprin

PNNARMA model: an alternative to phenomenological models in chemical reactors

This paper is focused on the development of non-linear neural models able to provide appropriate predictions when acting as process simulators. Parallel identification models can be used for this purpose. However, in this work it is shown that since the parameters of parallel identification models are estimated using multilayer feed-forward networks, the approximation of dynamic systems could be not suitable. The solution proposed in this work consists of building up parallel models using a particular recurrent neural network. This network allows to identify the parameter sets of the parallel model in order to generate process simulators. Hence, it is possible to guarantee better dynamic predictions. The dynamic behaviour of the heat transfer fluid temperature in a jacketed chemical reactor has been selected as a case study. The results suggest that parallel models based on the recurrent neural network proposed in this work can be seen as an alternative to phenomenological models for simulating the dynamic behaviour of the heating/cooling circuits.Publicad

Methodology of Supervision by Analysis of Thermal Flux for Thermal Conduction of a Batch Chemical Reactor Equipped with a Monofluid Heating/Cooling System

We present the thermal behavior of a batch reactor to jacket equipped with a monofluid heating/cooling system. Heating and cooling are provided respectively by an electrical resistance and two plate heat exchangers. The control of the temperature of the reaction is based on the supervision system. This strategy of management of the thermal devices is based on the usage of the thermal flux as manipulated variable. The modulation of the monofluid temperature by acting on the heating power or on the opening degrees of an air-to-open valve that delivers the monofluid to heat exchanger. The study shows that the application of this method for the conduct of the pilot reactor gives good results in simulation and that taking into account the dynamics of the various apparatuses greatly improves ride quality of conduct. In addition thermal control of an exothermic reaction (mononitration) shows that the consideration of heat generated in the model representation improve the results by elimination any overshooting of the set-point temperature

Kinetic Intersections as a Source of Information on Rate Coefficients

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