Ridvan Berber

Control of Batch Reactors: A Review

Because of the structural change in industry from mass production to the production of small amount of various products with high added value, batch processing has come to the fore in the nineties. In the competitive atmosphere of today, batch chemical reactors are pinpointed as where better performance can be obtained through better process control. Due to their intrinsic characteristics, however, the control of batch reactors involves complex control functionality beyond the well-established norms for continuous processes, and constitutes a challenging area. This paper reviews the progress in the field of batch reactor control. Particular developments in model based control that have been applied to batch reactors, theoretical and experimental experience reported in literature are addressed. Some main directions of current activities are highlighted and suggestions for further research are given.

Effect of inert solid particles at low concentrations on gas–liquid mass transfer in mechanically agitated reactors

Abstract Volume referred mass transfer coefficients k L a were determined for the slurries of 6 solids in water and n -butanol in a mechanically agitatted reactor of standard configuration. The measurements were carried out under variation of power input, gas flow rate, and solids concentration. It is observed that inert solid particles used increased by a drastically compared to the case with pure water only. On the other hand, the same inert solid particles with the same solid volume, exhibited conflicting behaviour in suspensions of n -butanol.

Dynamic modeling, sensitivity analysis and parameter estimation of industrial yeast fermenters

Abstract Reliable simulation of industrial scale fermenters is a challenging problem that needs to be solved for effective operation and control of such nonlinear and complex systems. Contrary to the available studies, which mostly rely on laboratory data, this work addresses the development and test of a rigorous dynamic model incorporating two phases and transport terms for industrial fermenters used for bakers yeast production. The influence of various parameters on model behaviour is determined by sensitivity analysis; and inspired by these results, those parameters that strongly effect the biomass production are identified by a parameter estimation technique. Comparison of the model predictions to two sets of actual process data for fed-batch and batch operating conditions verify the validity of model. The results provide valuable insights into the reliable simulation of large scale fermenters, and also bring an effective tool for further studies on optimization and control of such reactors.

Dynamics of an activated sludge process with nitrification and denitrification: Start-up simulation and optimization using evolutionary algorithm

Abstract An alternative but beneficial way of achieving nitrification and denitrification in activated sludge systems is to apply consecutive aeration and non-aeration periods in a single reactor. In this paper, dynamic modeling and start-up simulation in order to establish the continuous operation mode in such systems are first presented. Newly developed ASM3 is applied to the modeling of microbiological processes in the aeration tank and a 10 layer settling model is adapted to the settling tank. Then, the constrained optimization problem is tackled by an evolutionary algorithm, and the optimum aeration schedule for minimum energy consumption is identified. The constraint handling is achieved by three comparative methods using rejection of infeasibles, penalizing infeasibles and tournament selection without using any penalty parameters. The results indicate that the suggested optimum operation strategy may be economically beneficial in all methods but the third one, which is proposed by Deb [Deb, K. (2000). An efficient constraint handling method for generic algorithms. Computer Methods in Applied Mechanics and Engineering , 186 , 311–338] gives better solutions.

Off-line image analysis for froth flotation of coal

Froth flotation is an effective process for separating sulphur and fine minerals from coal. Such pre-cleaning of coal is necessary in order to reduce the environmental and operational problems in power plants. The separation depends very much on particle surface properties, and the selectivity can be improved by addition of a reagent. Image analysis can be used to determine the amount of reagent, by using the relation between surface properties and froth bubble sizes. This work reports some improvements in the efficiency of the image analysis, and in determination of bubble diameter distribution towards developing froth-based flotation models. Ultimate benefit of the technique would allow a pre-determined reagent addition profile to be identified for controlling the separation process.

Optimization of feeding profile for baker's yeast production by dynamic programming

Abstract An optimal substrate feeding for an industrial scale fed-batch fermenter is determined through iterative dynamic programming in order to maximize the cell-mass production and to minimize the ethanol formation. An experimentally validated rigorous dynamic model comprises constraints in the optimization problem. A new objective function is proposed to accommodate the competing requirements of maximum yeast production and minimum ethanol formation. The objective function is maximized with iterative dynamic programming with respect to the sugar feed rate. Results prove the effectiveness of dynamic programming for solving such high-dimensional and nonlinear optimization problems, and the resulting optimal policy indicates that considerable increase in yeast production in fed-batch fermenters can be achieved while minimizing the undesired by-product, ethanol.

Dynamic simulation of a distillation column separating a multicomponent mixture

Abstract In this work, a dynamic model was developed to simulate the transient behaviour of a pilot scale, continuous, siagewise, 15 plate distillation column separating a mixture of ethanol, iso-propanol and isobutanol. The accuracy of the predictions was assessed by transient response data collected during experiments under step changes in reflux ratio, feed flow rate and feed composition. Results demonstrated the feasibility of simulating multicomponent distillation under unsteady state conditions with fairly good success. Further improvements were suggested for a better accuracy

An artificial neural network model for the effects of chicken manure on ground water

In the areas where broiler industry is located, poultry manure from chicken farms could be a major source of ground water pollution, and this may have extensive effects particularly when the farms use nearby ground water as their fresh water supply. Therefore the prediction the extent of this pollution, either from rigorous mathematical diffusion modeling or from the perspective of experimental data evaluation bears importance. In this work, we have investigated modeling of the effects of chicken manure on ground water by artificial neural networks. An ANN model was developed to predict the total coliform in the ground water well in poultry farms. The back-propagation algorithm was employed for training and testing the network, and the Levenberg-Marquardt algorithm was utilized for optimization. The MATLAB 7.0 environment with Neural Network Toolbox was used for coding. Given the associated input parameters such as the number of chickens, type of manure pool management and depth of well, the model estimates the possible amount of total coliform in the wells to a satisfactory degree. Therefore it is expected to be of help in future for estimating the ground water pollution resulting from chicken farms.

A software for parameter estimation in dynamic models

A common problem in dynamic systems is to determine parameters in an equation used to represent experimental data. The goal is to determine the values of model parameters that provide the best fit to measured data, generally based on some type of least squares or maximum likelihood criterion. In the most general case, this requires the solution of a nonlinear and frequently non-convex optimization problem. Some of the available software lack in generality, while others do not provide ease of use. A user-interactive parameter estimation software was needed for identifying kinetic parameters. In this work we developed an integration based optimization approach to provide a solution to such problems. For easy implementation of the technique, a parameter estimation software (PARES) has been developed in MATLAB environment. When tested with extensive example problems from literature, the suggested approach is proven to provide good agreement between predicted and observed data within relatively less computing time and iterations.

Dynamic simulation and quadratic dynamic matrix control of an industrial low density polyethylene reactor

In this study, a reliable rigorous dynamic model of an industrial low density polyethylene (LDPE) reactor, one of the two 250 litre reactors at an LDPE plant in a petrochemical complex, is developed. The model predicts temperature profile, conversion and some of the polymer properties such as weight average molecular weight, and initiator composition. The predictions are in quite satisfactory agreement with actual plant data. Considering the multi-input, multi-output characteristics of the plant and interaction between control loops, dynamic matrix control algorithm with a quadratic programming routine is applied to the system through simulation. Simulation results showing the performance of the quadratic dynamic matrix control are presented, and are compared to the PI control employed in the plant. The results obtained from this algorithm show promise for a better control of the system, particularly for set point tarcking, compared to the conventional control currently employed.