Control of a hydrolyzer using neural-network based controller (original) (raw)
Related papers
Artificial Neural Network-based Neurocontroller for Hydropower Plant Control
TEM Journal
In this paper, the behavior of a system dynamics is represented where neuro-controller is designed, trained, and implemented. The development of the mathematical models is based on suggestions and recommendations from the literature issued by the working group of IEEE. According to the mathematical models, simulation is developed in Simulink software. MATLAB/Simulink software was used to represent the difference between the conventional PID controller and artificial neural network (ANN) neuro-controller. Nonlinear autoregressive-moving average (NARMA-L2) has been used for control simulation of the hydro-power plant (HPP) with neuro-controllers on one hand, and conventional PID control on the other hand.
Artificial neural network based modeling and controlling of distillation column system
International Journal of Engineering, Science and Technology, 2011
A Neural Network Internal Model Control (NN-IMC) strategy is investigated, by establishing inverse and forward model based neural network (NN). Further for developing the model has been selected suitable adaptive filter. Two types of NN-based inverse model (i.e. with and without disturbance input) were accurately simulated. The results indicated that the neural networks are capable to establish forward and inverse model rapidly from the couple of input-output open loop data of single distillation column binary system with a good root mean square error (RMSE). The simulation results revealed that NN-IMC with appropriate learning rate -momentum is capable to pursue the set-point changes and to reject the disturbance changes without steady state error or oscillations. NN-IMC with inverse model which contains disturbance input (modified NN-IMC) offer better performance than without it (conventional NN-IMC).
New Approach in Hydropower Plant Control Based on Neural Networks
Energija, ekonomija, ekologija
A new approach to efficient, faster, and intelligent hydropower plant (HPP) control, where constituent equipment is described with highly non-linear mathematical models based on the recommendation from the working group of IEEE on prime movers, is represented in this paper. HPP stability and high efficiency are important factors dependent on the dynamic changes in the energy system demands and the starting time of the plant because the obtained energy is very flexible to those changes in the energy system. This paper is shown and analysed the implementation of the artificial neural network-based controller with PID as an auxiliary controller which helped achieve better behaviour, faster plant stabilization, and operation. The benefits of new technologies and possibilities led to improvements in HPP control and faster system operation. This is achieved by using MATLAB® – Deep Learning Toolbox whereas the simulations are prepared in Simulink. Artificial Neural Networks (ANN) as a tech...
IJERT-Artificial Neural Network Based Modeling and Control of Bioreactor
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/artificial-neural-network-based-modeling-and-control-of-bioreactor https://www.ijert.org/research/artificial-neural-network-based-modeling-and-control-of-bioreactor-IJERTV3IS080128.pdf In this paper presents about control of Bioreactor useing Artificial Neural Network. bioreactor has become an active area of research in recent years. This is partially attributable to the fact that bioreactors can be extremely difficult to control. Their dynamic behavior is invariably non-linear and model parameters vary in an unpredictable manner. Accurate process models are rarely available due to complexity of the underlying biochemical processes. A feedback controller is needed to account for disturbances and time-varying behavior. Neural network based model predictive controller designed for the control of bioreactor. In the first step the neural network model of bioreactor is obtained by levenburg-marquard training the data for the training the network generated using mathematical model of bioreactor.
A very simple structure for neural network control of distillation
Journal of Process Control, 1995
This paper presents a novel approach for process control that uses neural networks to model the steadystate inverse of a process which is then coupled with a simple reference system synthesis to generate a multivariable controller. The control strategy is applied to dynamic simulations of two methanol-water distillation columns that express distinctly different behaviour from each other (one simulates a lab column, while the second simulates an industrial-scale high-purity column). A steady-state process simulation package was used to generate all the neural network training data. An efficient training algorithm based on a nonlinear least-squares technique was used to train the networks. The neural network modelbased controllers show robust performance for both setpoints and disturbances, and performed better than conventional feedback proportional-integral (PI) controllers with feedforward features.
Artificial Neural Network Based Inverse Model Control Of A Nonlinear Process
In process industries the non linear process control is a challenging and difficult task due to its non linear behavior, delays and time variation between inputs and outputs of system. Conical tank system is one such non linear process which is widely used in process industries due of its non linear shape and easy flow of liquid across its cross section area. As conical tank is inherently non linear it becomes difficult to model the linear plant equation for the same. The control of liquid level in conical tank is a complex and complicated task because of its constantly changing cross section area. So, Artificial Neural network (ANN) based controller is designed because of its ability to model non linear systems and its inverses. The Direct Inverse Control (DIC) designed using ANN is mainly dependent on the inverse response of the system which is difficult task to obtain it analytically. In this paper, ANN based DIC is trained by Levenberg Marquardt Back propagation algorithm and helps to obtain optimized response/performance of the system. The simulation results show that direct inverse control realize a good dynamic behaviour of interacting and non interacting conical tank system.
As a result of good modeling capabilities, neural networks have been used extensively for a number of chemical engineering applications such as sensor data analysis, fault detection and nonlinear process identi®cation. However, only in recent years, with the upsurge in the research on nonlinear control, has its use in process control been widespread. This paper intend to provide an extensive review of the various applications utilizing neural networks for chemical process control, both in simulation and online implementation. We have categorized the review under three major control schemes; predictive control, inverse-model-based control, and adaptive control methods, respectively. In each of these categories, we summarize the major applications as well as the objectives and results of the work. The review reveals the tremendous prospect of using neural networks in process control. It also shows the multilayered neural network as the most popular network for such process control applications and also shows the lack of actual successful online applications at the present time. q
Process Control Using a Neural Network Combined with the conventional PID Controllers
Transaction on Control, Automation and Systems Engineering, 2000
A neural controller for process control is proposed that combines a conventional multi-loop PID controller with a neural network. The concept of target signal based on feedback error is used for on-line learning of the neural network. This controller is applied to distillation column control to illustrate its effectiveness. The result shows that the proposed neural controller can cope well with disturbance, strong interactions, time delays without any prior knowledge of the process.
Design of neural controllers for various configurations of continuous bioreactor
2010
In the present study, the Neural network (NN) based controller design has been implemented for a non-linear continuous bioreactor process. Multilayer feed forward networks (FFNN) were used as direct inverse neural network (DINN) controllers as well as IMC based NN controllers. The training as well as testing database was created by perturbing the open loop process with pseudo random signals (PRS). For set point tracking; at an operating condition where the cell growth is substrate limited, the DINN controllers were designed for conventional turbidostat and nutristat configurations. DINN controllers performed effectively for set-point tracking. To address the disturbance rejection problems, which are very likely to be faced by the bioreactors, the IMC based neural control architecture was proposed with suitable choice of filter and disturbance transfer function. To assess the controllability of the various bioreactor configurations, like conventional turbidostat and nutristat & concentration turbidostat and nutristat, the offset or degree of disturbance rejection by the proposed IMC based NN controllers were utilized. The 'concentration turbidostat' using the feed substrate concentration as the manipulated variable was found to be the best control configuration among the continuous bioreactor configurations.
A novel analysis and design of a neural network assisted nonlinear controller for a bioreactor
International Journal of Robust and Nonlinear Control, 1999
A novel approach is presented for the analysis and the design of a controller for a bioreactor. It is based on the Model Reference Control theory, assisted by a neural network identifier. The control objectives specified in the paper require the controller to be a nonlinear one, however, it is shown that it is stable in the sense of bounded input bounded output and locally stabilizing in the sense of Lyapunov. The feasibility and the efficacy of the proposed approach are tested on the benchmark problem.