Band 7, Heft 1

This paper examines the application of a particle filtering-based optimization technique, the genealogical decision trees (GDT), to a finite horizon pump scheduling problem in a water distribution network. The GDT approach for trajectory tracking is first introduced, and a modified algorithm for minimization of costs during pump sequence optimization is then presented. Several variants of the algorithm are suggested, using the extended end constraint and neutrality. The performance of the optimization in various algorithm and parameter settings is examined in extensive simulations. It was observed that both the extended end constraint and neutrality improved the performance, however the deviation between solutions within a population and between different runs remained uncomfortably large. Finally, a comparison with a number of alternative up-to-date optimization techniques is provided. It was observed that the performance of GDT was adequate, compared with the best available approaches.

Dicarboxylic acid esters are one of the important classes of synthetic lubricants which exhibit good oxidation and thermal stability, low volatility, shear stability, high viscosity index and low toxicity. In this work, enzymatic synthesis of dioleyl adipate ester was performed in batch mode with a stirred tank reactor (STR). Immobilized Candida antarctica lipase B was used as the biocatalyst for the esterification reaction. Response surface methodology (RSM) based on a four-factor-five-level central composite rotatable design (CCRD) was employed to model and analyze the reaction. The effects of all investigated parameters including amount of enzyme (1-10%w/w), temperature (35-75oC), time (30-480 min) and impeller speed (1-500 rpm) were significant (P<0.0001). All factors positively affected the yield with reaction time having the most significant effect. The highest obtained conversion yield was 96.2% under experimental conditions of 60.0oC, 323.7 min, amount of enzyme 6.0%w/w of the substrates and 500 rpm impeller speed. Verification experiments exhibited good correspondence between actual yields and the values predicted by the statistical model. The predictive model was successfully applied for optimization of upscale synthesis of adipate ester.

The coefficient of performance (COP) for a water purification process integrated to an absorption heat transformer with energy recycling was optimized using the artificial intelligence. The objective of this paper is to develop an integrated approach using artificial neural network inverse (ANNi) coupling with optimization methods: genetic algorithms (GAs) and particle swarm algorithm (PSA). Therefore, ANNi was solved by these optimization methods to estimate the optimal input variables when a COP is required. The paper adopts two cases studies to accomplish the comparative study. The results illustrate that the GAs outperforms the PSA. Finally, the study shows that the GAs based on ANNi is a better optimization method for control on-line the performance of the system, and constitutes a very promising framework for finding a set of “good solutions”.

Adsorptive separations have gained considerable importance in process industry. A generic simulator has been developed in this work for Pressure Swing Adsorption (PSA) processes. Several simple and complex, conventional and unconventional PSA cycles have been studied using this simulator. Distinction has been made between PSA processes where a raffinate stream richer in the weakly adsorbed component as compared to the feed is the desired product as against processes where extract stream richer in the strongly adsorbed component is the desired product. These are termed as raffinate PSA and extract PSA respectively. Extract PSA is an unconventional process variation. The studies include several simple and complex PSA cycles for raffinate and extract PSA of industrial importance. The studies are aided by a generic simulator for all PSA process variations developed for the purpose. The simulator is equally applicable to Vacuum Swing Adsorption (VSA), Pressure Vacuum Swing Adsorption (PVSA), rapid cycle PSA processes, etc.

In this work a CFD-ANN model is developed to give the predictions of sieve tray point efficiency. The main objective has been to find the extent to which CFD can be used in combination with artificial neural network as a prediction tool for efficiencies of industrial trays. The model was tested against a wide range of tray geometries, operating conditions and binary systems of materials. CFD model was applied, as a virtual experiment tool for direct prediction of point efficiencies, using tray geometries and operating conditions for any binary system of liquids. The model results were in agreement to experimental data from literatures, shown that CFD-ANN model can be used as a powerful tool in distillation column design and analysis.

The paper proposes a multi-level approach for process synthesis using process superstructure optimisation. More specifically, we address the introduction of detailed reactor models to explore the effects of non-ideal reactor behaviour during the process synthesis activity after the initial screening of design candidates has been completed using ideal reactor models and high-level design trends extracted to narrow the design space to a few promising process structures. The approach focuses on heterogeneously catalysed gas-phase reaction systems due to their prominence in industrial practice and is illustrated with examples in styrene and acetic acid production.

The aim of this study is to demonstrate the comparison of an artificial neural network (ANN) and an adaptive neuro fuzzy inference system (ANFIS) for the prediction of the coefficient of performance (COP) for a water purification process integrated in an absorption heat transformer system with energy recycling. ANN and ANFIS models take into account the input and output temperatures for each one of the four components (absorber, generator, evaporator, and condenser), as well as two presures and LiBr+H2O concentrations. Experimental results are performed to verify the results from the ANN and ANFIS approaches. For the network, a feedforward with one hidden layer, a Levenberg-Marquardt learning algorithm, a hyperbolic tangent sigmoid transfer function and a linear transfer function were used. The best fitting training data set was obtained with three neurons in the hidden layer. On the validaton data set, simulations and experimental data test were in good agreement (R2>0.9980). However, the ANFIS model was developed using the same input variables. The statistical values are given in as tables. However, comparaison between two models shows that ANN provides better results than the ANFIS results. Finally this paper shows the appropriateness of ANN and ANFIS for the quantitative modeling with reasonable accuracy.

Large amount of heavy metal ions disposed in wastewater by various industries causes environmental pollution and toxicity in waste water. Many naturally available materials were used as adsorbents for the removal of heavy metal ions, but they are not effective because of low surface area. Novel nano materials are used as adsorbents in the removal of metal ions to treat potable water. In the present work nano sized large surface area (80.425 m2/g) Zinc Oxide (ZnO) was synthesized by gel combustion method and used as an adsorbent for the removal of cadmium (II) (Cd2+) from aqueous solution. The nano ZnO and Cd adsorbed ZnO were characterized by XRD, the additional Cd peaks in XRD pattern confirms the Cd adsorption. SEM analysis confirmed the adsorption of Cd2+ by change in the morphology of adsorbent. Percentage purity of the element and the presence of the Cd were characterized by EDAX. Adsorption of Cd2+ on ZnO was confirmed by varying parameters like initial concentration, pH and adsorbent dosage. Cadmium concentration in the solution was analyzed using Atomic Absorption Spectrometer (AAS). Increase in the initial concentration of Cd solution, decreases the percentage of adsorption, where as increase in the adsorbent dosage and pH of the solution increases the adsorption of Cd2+ on ZnO. Synthesized nano ZnO shows maximum of 95% Cd adsorption at lower initial concentration. Experimental data fits Langmuir and Freundlich adsorption isotherm models.

In this paper, Smith predictor based parallel cascade control strategy is proposed for enhanced control of continuous bioreactor. The scheme consists of secondary loop and primary loop. The secondary loop controller is designed based on the direct synthesis method. Then, the overall primary loop process model is identified using least square method. A delay compensation strategy is incorporated in the primary loop and the corresponding primary loop delay compensated controller is designed based on the identified primary loop model. For comparison, a simple parallel cascade control and a simple feedback control is also applied to the continuous bioreactor. From the simulation studies, it is observed that delay compensated control strategy provides enhanced closed loop performances compared to that of the simple parallel cascade control and simple feedback control. To analyze the performance in the presence of uncertainties, robustness analysis has been carried out based on sensitivity and complementary sensitivity functions.

This paper describes the use of the Box–Behnken experimental design to optimize the extraction parameters for producing maximum yield and total flavonoid content (TFC) from S. commune mycelia extract. The range of the independent variables, such as methanol-water concentration (0-100% v/v), extraction temperature (25-65°C), and mixing rate (0-250 rpm) were identified by a first set of single factor experiments. The actual values of the independent variables coded at three-level were selected based on the results of single factor experiments. Results showed that 3.0% extraction yield and 1.1 g% TFC were obtained under extraction condition of 84.6% (v/v) methanol-water concentration, extraction temperature 40.5°C, and mixing rate 162 rpm, respectively. In such condition, the error percentage between experimental and predicted data was 0.11 to 0.82%, which was less than 1%, thus indicating that the suitability of the models developed in optimizing the extraction conditions. The extraction mechanism was also investigated using a parabolic diffusion model, which described the washing and diffusion-controlled step. The proposed empirical model was valid with MSE value less than 10%.

The global shortage of resources and the intention to reduce CO2 emissions from fossil sources has triggered new approaches in the wastewater industry. Better ways are sought for to manage sewage sludge and to exploit its value as a secondary resource. However, we see a lack of tools to assess the appropriate treatment strategy which leads to imperfect process choices and designs. Here we present (a) a modeling approach and a simulation tool that allows the systematic evaluation of different sewage sludge treatment options and (b) a test case study for a mid-sized waste water treatment plant. The sludge model is discussed and tested for consistency with alternative approaches. In the case study, the direct conversion to electricity in a Rankine cycle is shown to be superior to biogas production or dried sludge export based on an energy cost analysis. The development presented is believed to enable more efficient waste-to-energy conversion and to contribute to sustainable resource management.

This paper proposes a method, namely MDKS (Kennard-Stone algorithm based on Mahalanobis distance), to divide the data into training and testing subsets for developing artificial neural network (ANN) models. This method is a modified version of the Kennard-Stone (KS) algorithm. With this method, better data splitting, in terms of data representation and enhanced performance of developed ANN models, can be achieved. Compared with standard KS algorithm and another improved KS algorithm (data division based on joint x - y distances (SPXY) method), the proposed method has also shown a better performance. Therefore, the proposed technique can be used as an advantageous alternative to other existing methods of data splitting for developing ANN models. Care should be taken when dealing with large amount of dataset since they may increase the computational load for MDKS due to its variance-covariance matrix calculations.

Owing to the wide utilization of heat exchangers in industrial processes, their cost minimization is an important target for both designers and users. Traditional design approaches are based on iterative procedures which gradually change the design and geometric parameters until given heat duty and set of geometric and operational constraints are satisfied.Although well proven, this kind of approach is time consuming and may not lead to cost effective design. The present study explores the use of non-traditional optimization technique: calledParticle swarm optimization (PSO), for design optimization of shell and tube heat exchangers from economic point of view. The optimization procedure involves the selection of the major geometric parameters such as tube diameters, tubelength, bafflespacing, number of tube passes, tubelayout, type of head, baffle cutetc and minimization of total annual cost is considered as design target. The presented PSO technique is conceptually simple, has only a few parameters and is easy to implement.Furthermore, the PSO algorithm explores the good quality solutions quickly, giving the designer more degrees of freedom in the final choice with respect to traditional methods. The methodology takes into account the geometric and operational constraints typically recommended by design codes. Three different case studies are presented to demonstrate the effectiveness and accuracy of proposed algorithm . The PSO method leads to a design of a heat exchanger with a reduced cost of heat exchanger as compare to cost obtained by previously reported GA approach.

An accurate semi batch process model should be a nonlinear dynamic model. Neural networks are suitable for modelling nonlinear dynamics and can be used for developing empirical models of semi batch processes. Multi stage neural network based modelling of the polymerization reactor described by Chylla and Haase, is illustrated in this paper. The process is divided into three regions namely heat up period, feed period and hold period and neural model is developed for each stage. This method of multi stage modelling captures the dynamics of the process accurately for the semi batch process. At different stages respective neural model is active based on the period of operation.