Iqbal M. Mujtaba

Performance of different types of controllers in tracking optimal temperature profiles in batch reactors

Abstract Performance of three different types of controllers in tracking the optimal reactor temperature profiles in batch reactor is considered hear. A complex exothermic batch reaction scheme is used for this purpose. The optimal reactor temperature profiles are obtained by solving optimal control problems off-line. Dual mode (DM) control with propotional-integral (PI) and proportional-integral-derivative (PID) and generic model control (GMC) algorithms are used to design the controllers to track the optimal temperature profiles (dynamic set points). Neutral network technique is used as the on-line estimator the amount of heat released by the chemical reaction within th GMC algorithm. The GMC controller coupled with a neural network based heat-release estimator is found to be more effective and robust than the PI and PID controllers in tracking the optimal temperature profiles to obtain the desired products on target.

Fresh water by reverse osmosis based desalination : Simulation and optimisation

Abstract The reverse osmosis (RO) desalination process to make fresh water from seawater has been studied here. First, a model for the process is developed. Sensitivity of different operating parameters (feed flow rate, feed pressure) and design parameters (internal diameter, total number of tubes) on the recovery ratio are studied via repetitive simulation. Finally, an optimisation framework for the process is developed so as to maximize the recovery ratio or a profit function using different energy recovery devices, subjectto general constraints. The optimal operating parameters (feed flow rate, feed pressure) and design parameters (internal diameter, total number of tubes) are determined by solving the optimisation problem using an efficient successive quadratic programming (SQP) based method. The optimal values for the decision variables depend on the constraints introduced, and are also sensitive to variations in water and energy prices, as well as feed concentration. The use ofthe emerging energy recovery devices is widely justified, reporting much higher reductions in operating costs than the traditional technology used for this purpose. Using a pressure exchanger device, it is possible to reduce energy consumption by up to 50%.

Optimal operation policies in batch reactors

Optimal operation policies in batch reactors are obtained using dynamic optimisation technique. Two different types of optimisation problems, namely, maximum conversion and minimum time problems are formulated and solved and optimal operation policies in terms of reactor temperature or coolant flow rate are obtained. A path constraint on the reactor temperature is imposed for safe reactor operation and an endpoint constraint on undesired waste production (by-product) is imposed to minimise environmental impact. Two different types of models are considered within the optimisation framework. The shortcut model allows determination of optimal reactor temperature profile to be used for detailed design of the reactor. The detailed model allows optimising operating conditions for an already designed batch reactors.

Simultaneous optimization of design and operation of multicomponent batch distillation column—single and multiple separation duties

A method is presented for the simultaneous optimization of a batch distillation column design and its operation, for single and multiple separation duties, each involving different multicomponent mixtures and complex operations with intermediate cuts. For operation structures selected a priori, the formulation presented permits the use of general distillation design and cost models. The objective function and constraints include capital and operating cost. In particular, the number of internal plates is optimized along with the most significant operating variables (recoveries in various cuts and reflux ratio profiles and times). The multiple duty formulation presented accounts for the different importance of each duty and setup time between batches. Application of the method to single duty multicomponent separation from the literature shows that significant profit improvements can be achieved within acceptable computing times. For multiple separation duties (two binary mixtures), the method clearly shows the importance of including allocation time to each duty and setup time for each batch in the objective function.

Simulation and Optimization of Full Scale Reverse Osmosis Desalination Plant

This paper focuses on steady state performance predictions and optimization of the Reverse Osmosis (RO) process utilizing a set of implicit mathematical equations which are generated by combining solution-diffusion model with film theory approach. The simulation results were compared with operational data which are in good agreement having relative errors of 0.71% and 1.02%, in terms of water recovery and salt rejection, respectively. The sensitivity of different operating parameters (feed concentration, feed flow rate and feed pressure) and design parameters (number of elements, spacer thickness, length of filament) on the plant performance were also investigated. Finally a non linear optimization framework to minimize specific energy consumption at fixed product flow rate and quality while optimizing operating variables (feed flow rate, feed pressure) and design parameters (height of feed spacer, length of mesh filament). Reduction in operating costs and energy consumption up to 50 % can be reached by using pressure exchanger as energy recovery device.

Evaluation of neural networks-based controllers in batch polymerisation of methyl methacrylate

The importance of batch reactors in todays process industries cannot be overstated. Thus said, it is important to optimise their operation in order to consistently achieve products of high quality while minimising the production of undesirables. In processes like polymerisation, these reactors are responsible for a greater number of products than other reactor types and the need for optimal operation is therefore greater. An approach based on an offline dynamic optimisation and online control strategy is used in this work to generate optimal set point profiles for the batch polymerisation of methyl methacrylate. Dynamic optimisation is carried out from which controller set points to attain desired polymer molecular end point characteristics are achieved. Temperature is the main variable to be controlled, and this is done over finite discrete intervals of time. For on-line control, we evaluate the performance of neural networks in two controllers used to track the derived optimal set points for the system. The controllers are generic model control (GMC), ([P.L. Lee, G.R. Sullivan, Generic model control, Comput. Chem. Eng. 12(6) (1998) 573-580]) and the neural network-based inverse model-based control (IMBC), ([M.A. Hussain, L.S. Kershenbaum, Implementation of an inverse model based control strategy using neural networks on a partially simulated exothermic reactor, Trans. IchemE 78(A) (2000) 299-311]). Although the GMC is a model-based controller, neural networks are used to estimate the heat release within its framework for on-line control. Despite the application of these two controllers to general batch reactors, no published work exists on their application to batch polymerisation in the literature. In this work, the performance of the neural networks within each controllers algorithm for tracking and setpoint regulation of the optimal trajectory and in robustness tests on the system is evaluated.

Optimal Operation of RO System with Daily Variation of Freshwater Demand and Seawater Temperature

Abstract An optimal operation policy of Reverse Osmosis (RO) based desalination system is developed in this work considering variations in freshwater demands with changing seawater temperature throughout the day. A storage tank is added to the system layout to provide additional operational flexibility and to ensure the availability of freshwater at all times. A steady state model for the RO process is developed and is linked with a dynamic model for the storage tank. The membrane modules are divided into two groups to add flexibility in operation to RO networks. The tank level and salinity are monitored dynamically by introducing path constraints in the optimization formulation. The total operating cost of the RO process is minimized in order to find the optimal operational variables at discreet time intervals.

Optimal operation of RO system with daily variation of freshwater demand and seawater temperature

Abstract The optimal operation policy of flexible RO systems is studied in this work. The design and operation of RO process is optimized and controlled considering variations in water demands and changing seawater temperature throughout the day. A storage tank is added to the system layout to provide additional operational flexibility and to ensure the availability of freshwater to customer at all times. A steady state model for the RO process is developed and linked with a dynamic model for the storage tank. The membrane modules are divided into a number of groups to add flexibility in operation to RO network. The total operating cost of the RO process is minimized in order to find the optimal layout and operating variables at discreet time intervals for three design scenarios.

Neural-network approach to dynamic optimization of batch distillation: Application to a middle- vessel column

A framework is proposed to optimize the operation of batch columns with substantial reduction of the computational power needed to carry out the optimization calculations. The proposed framework relies on the use of an artificial neural network (ANN) based process model to be employed by the optimizer. To test the viability of the framework, the optimization of a pilot-plant middle-vessel batch column (MVBC) is considered. The maximum-product problem is formulated and solved by optimizing the column operating parameters, such as the reflux and reboil ratios and the batch time. It is shown that the ANN based model is capable of reproducing the actual plant dynamics with good accuracy, and that the proposed framework allows a large number of optimization studies to be carried out with little computational effort.

Optimization of Batch Extractive Distillation Processes for Separating Close Boiling and Azeotropic Mixtures

Operating features and limitations of Batch Extractive Distillation (BED) processes separating close boiling and azeotropic mixtures are studied using dynamic model and optimization techniques. A general Multiperiod Dynamic Optimization (MDO) problem is presented for the separation of binary feed mixtures and the recovery of solvent. Product specifications are imposed as constraints on the operation which allows quantitative assessment of the performance (operation time, productivity, profit, etc.) of BED processes and decomposes the MDO problem into a series of independent Single-period Dynamic Optimization (SDO) problems. Two modes of solvent feeding are discussed: the batch mode and the semi-continuous mode . Also two strategies for the initial feed charge to the reboiler are discussed under the semi-continuous mode of solvent feeding. They am full feed charge and fractional feed charge strategies. Time sequenced reflux ratio and solvent feed rate, although imposing a path constraint on the operation, improve the performance of BED significantly. Using a maximum of two time intervals for each distillation Task (to separate products), conditions have been developed to tackle this constraint efficiently. These considerably ease the solution of the optimization problem. All these features are illustrated using typical close boiling and azeotropic mixtures.