Control of pH in Fed-batch Neutralisation Processes

Model Based Controller and Linear Cascade Controller Applied to Fed Batch pH Process

Abstract: The present study aims at bringing out the best features of model‐based control, linear cascade control when applied to highly non‐linear systems like pH‐controlled fed‐batch processes. For these processes, control of pH by conventional Proportional‐Integral‐Derivative controller fails to provide satisfactory performance, because of the extreme non‐linearity in the pH dynamics. In the present study, for a fed‐batch neutralization process, a non‐linear control law has been derived for the model‐based Proportional Integral controller. Typical problems in process control like sampling, delay and perturbations in model parameters are addressed in this study using model‐based control. The simulation results show the superior performance and robustness of the model‐based controller and linear cascade controller over that of the conventional Proportional Integral controller.

Real Time Control of pH in Fed‐Batch Process

Abstract: The objective of this work is to implement the linear, non linear model based and linear cascade controllers to control pH in a fed batch neutralisation process in real time and compare the perform...

Time-Optimal Control of Chemical Neutralization Process

Abstract: The existing heat and power generation technologies involve the utilization of chemically treated water. Water treatment produces alkaline and acidic wastewaters, which must be neutralized. The paper considers the problem of optimal control of the chemical neutralization of acidic wastewater by means of lime water. The installation for neutralization consists of a tank and a recycling pipeline. The mathematical model of chemical neutralization comprises systems of nonlinear differential equations in parabolic partial derivatives to take into account the chemical reaction between the interacting components placed in the recycling pipeline or in the tank. The controlling action is alkali inflow rate at the inlet of the recycling-pump. The optimization problem is formulated as a problem of time-optimal control. It is shown that the time-optimal process of chemical neutralization is also optimal from the point of view of the accuracy of approaching to the neutral state.

Nonlinear control of pH processes using the strong acid equivalent

Abstract: Given the general structure of the nonlinear dynamic model, consisting of material balances and chemical equilibria equations, a new approach has been developed for the design of nonlinear controllers for pH processes. It consists of defining an alternative equivalent control objective which is linear in the states and using a linear control law in terms of this new control objective. Computer simulations evaluate the performance of this control methodology in the presence of disturbances and model uncertainty

Fuzzy self-tuning PI control of pH in fermentation

Abstract: Controlling the pH value of small-scale laboratory fermentation processes with a conventional PI controller is difficult. This is due to the highly non-linear response of the pH to the addition of acid or base, the time-varying behaviour of the microorganism, various reactor volumes, and the buffer capacity of the system. It is also very difficult for a biochemist without a control engineering background to adjust the PI parameters of the controller when a fermentation process is changed. This article describes the development and implementation of a fuzzy self-tuning PI controller for a low-cost laboratory pH control system with digital peristaltic pumps. The controller was designed to have a wide range of applications in batch, fed-batch and continuous processes in a laboratory reactor of a working volume ranging from 2 to 15 l. The essential idea is to tune the controller gains on-line by means of a parameter that results from a fuzzy inference mechanism. The tuning of the controller gains is based on the response of the pH system. The controller was first tested in simulations and then experimentally on 2- and 7-l reactors. This experimental validation confirmed that the pH controller is capable of controlling the pH value of a fermentation process within the required range of ±0.05 pH units. The self-tuning capability makes the controller robust with respect to the varying buffer capacity and the working volume of the fermenter.

Linearizing feedforward-feedback control of pH processes based on the Wiener model

Abstract: This paper examines the control of pH processes based on the Wiener model construct (a dynamic linear element representing the mixing dynamics of the process in series with a static nonlinearity representing the titration curve) Conditions under which the pH process behaves like an exact Wiener system are examined Linearization by output transformation using both the true inverse of the titration curve and an estimate of the inverse is employed to make the pH process appear linear enabling the application of a linear feedback (PI) controller Although many others have utilized an identified nonlinearity for linearizing feedback control of pH processes, much less work has been done on using the nonlinearity for linearizing feedforward control Here, a simple linearizing feedforward controller is proposed based on a current estimate of the inverse titration curve Simulated closed-loop results demonstrate the superiority of the linearizing feedforward–feedback strategy versus linearizing feedback only, when the inverse titration curve is accurately estimated

Fuzzy predictive control of highly nonlinear pH process

Abstract: This paper proposes a new approach to predictive control of highly nonlinear processes based on a fuzzy model of the Takagi-Sugeno form. Standard Model Based Predictive Control (MBPC) methods use linear process models and are therefore unable to deal with strong process nonlinearities. But, the advantage of linear MBPC is in implementation due to fast optimization algorithms and guaranteed convergence within each time sample. In our approach, step responses for different operating points are extracted on-line from the nonlinear fuzzy model and a modified linear DMC algorithm is used. In this way, all the advantages of both fuzzy modeling of nonlinear processes and DMC control are accomplished. For performance evaluation of this simple but efficient approach, a nonlinear pH-process is used.

Strategies for modeling and control of nonlinear chemical processes using neural networks

Abstract: A nonlinear predictive control framework is presented, in which nonlinear processes are modeled using neural networks. Several important issues concerning the modeling of nonlinear processes using neural networks are treated, with the emphasis placed on the convergence of neural networks to desired steady states. For nonlinear process predictive control where the neural network model is employed, an important case is examined. A typical nonlinear process, pH control problem, is taken as a case study to demonstrate the proposed approach, some significant results are given.