Volume 4, Issue 1

A three-layer feed forward neural network trained with a Levenberg–Marquardt batch error back propagation algorithm has been used to model the strong relationships between the boiling point of aliphatic alcohols and intermolecular forces consisting both in Van Der Waals forces and polar interactions, respectively. For that purpose, we use the multifunctional autocorrelation method to provide an appropriate topological description. Two types of descriptors are generated: the first is commonly used in QSARs and QSPRs modelling, it gives a general description of the whole of the molecule; the second is attributed to the local description of the group hydroxyl. In this we have turned our interests to the explanatory capacities of our methodology to explore the relative contribution and/or the contribution profile of the input factors compared to the size of the molecule. The initial data set is divided into different subsets in increasing order of values of boiling point. Then, we explore the good descriptive ability of the molecular descriptors calculated solely from the modified autocorrelation method to carry out a variable analysis and give information about features of the compounds responsible for their boiling points. This is made possible by comparing the regression coefficients for the established linear model, and by using the Garson weight portioning method for the ANN analysis.

An alumina sintering rotary kiln flame image retrieval method was put forward based on artificial neural network (ANN) and flame shape features. An effective flame shape descriptor was introduced, based on which the flame image recognitions were carried out using ANN. Then, a flame image retrieval algorithm was designed. Experiments were carried out on the prototype machine with the flame images sampled from an alumina sintering rotary kiln. The results indicate that the shape descriptors can effectively describe the flame shapes and the proposed flame image retrieval method can achieve both high accuracy and efficiency. This method can be of promising theoretical and practical value for alumina sintering rotary kiln management and surveillance.

An agitated two-phase flow is studied numerically and experimentally in a mixing vessel agitated with two six-blade Rushton turbines. In Computational Fluid Dynamics (CFD), the full Eulerian multiphase approach coupled with the standard turbulence model is performed to deal with two-phase flow. The impeller rotation was modelled by the Multiple Reference Frame (MRF) approach. The simulation was used to investigate the flow field, power and mixing time in single and two-phase cases. The results of the calculations have been verified with the data that was measured by particle image velocimetry (PIV) and planar laser-induced fluorescence (PLIF) technique. The predicted results show good agreement with the experimental data. The computational model presented in this study could be useful for explaining the two-phase flow patterns on the mixing process and extending the applications of multiphase stirred reactors.

This paper describes a robust support vector regression (SVR) methodology, which can offer superior performance for important process engineering problems. The method incorporates hybrid support vector regression and genetic algorithm technique (SVR-GA) for efficient tuning of SVR meta parameters. The algorithm has been applied for prediction of critical velocity of solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed SVR correlation noticeably improved prediction of critical velocity over a wide range of operating conditions, physical properties, and pipe diameters.

Experimental studies and CFD modeling on the effect of baffle V cut on power consumption, using a standard turbine agitator, has been presented. Modification was done by introducing a V cut in the baffle. The power consumption for the agitated vessel fitted with standard plain baffle is higher than that of the V cut baffle. Power characteristics for three different types of baffle combination were obtained within transition and turbulent regimes of the fluid flow in the agitated vessel. CFD simulations have been used to predict power consumption. The simulation results have been compared with the experimental data obtained on a pilot-scale set up.

The design of a fire-tube shell boiler consists of a bundle of tubes contained inside a shell. The heat transfer process from the combustion gas to the boiling water via the tube surface is extremely complicated as it involves combustion, convection, conduction, and boiling process. Severe boiling and evaporating processes take place outside the fire-tube shell boiler where the steam is generated. However, many processes such as the regeneration of absorbent for CO2 removal needs optimum steam consumption, which reflects its high costs for operation. Therefore, good control performance of steam pressure becomes important. This study is aimed at developing a control scheme to minimize the effect of over-firing on the fire-tube shell boiler which impacts on excessive fuel consumption. The Neural Network Predictive Controller (NNPC) is used in this work, using the optimization and neural toolboxes which are written in the MATLAB code and were compared with the PID controller for set-point tracking and disturbance rejection ability. The comparison reveals that NNPC gives an excellent alternative to PID controller due to the non-linearity of the fire-tube shell boiler system. Since NNPC can reduce the fuel consumption by minimizing actuator moves, the control of boiler and burners in this plant in Brunei is recommended to be upgraded to replace the existing PID controller.

Dividing-wall column (DWC) is very attractive as the most energy saving alternative for conventional distillation columns. Hence, the main objective of this study is to develop general procedures for simulation and optimization of DWCs for industrial applications. For this, six examples involving typical industrial conditions are selected, and then the relative importance of various design variables of DWCs is examined through rigorous simulation using a commercial simulator, in order to gain insight into the design of such columns. The results show that vapor and liquid splits in the column have significant impact on the energy requirements of DWCs although their effect on condenser and reboiler duty is less pronounced within the optimum region. Further, simulation results show that DWC optimization does not need to include the variation of the number of stages as its effect on reducing the total annual cost is small. Based on the simulation and design results for the six examples, a simplified procedure for the quick design of DWCs using process simulators is proposed.

The process of soybean meal industrial drying requires a high consumption of energy, and it is usually performed in an indirect rotary dryer. A study aiming for the reduction of this energy consumption is vital for the competitiveness of this product. Thus, the purpose of this work was to develop a mathematical model of soybean meal drying, in permanent regime, in order to assess the optimal operational conditions of the dryer and its energetic performance. The developed model is based on balances of mass and energy, and using constitutive and empirical equations. The model validation was carried out through operational data. From this, the influences of the initial temperature, initial moisture, and relative humidity of air, as well as the dryer energetic performance, were investigated.

Obtaining an accurate and comprehensive multivariable mathematical model of the polymerization processes is of strategic importance to the control engineering purposes in the polymerization industry. These processes are typically nonlinear and it is difficult to apply traditional estimation techniques. This paper describes an approach based upon multivariable fuzzy-neural representation of the process model. A concrete model is constructed with the Sugeno fuzzy inference technique by applying the state space implementation in the local fuzzy rules for modeling the dynamic behavior of the polymer process. Such multivariable fuzzy-neural models of polymer quality could be used successfully for optimization and control of polymerization processes. A short example for such an implementation is included with additional results for the modeling of the main characteristic parameters of the polymer process.

The plate heat exchanger exhibits excellent heat transfer characteristic, which allows a very compact design with ease of maintenance and modification of heat transfer area by adding (or) removing plates. Constructional parameters such as flow path, trough angle and corrugation can affect the performance of plate heat exchangers by altering effectiveness (?) and number of transfer unit (NTU). Especially plate heat exchangers play a vital role in petroleum industries for wide range of temperature application. Hence, it was proposed to choose kerosene as cold fluid and hot water as hot fluid in this present investigation. A vertical type of plate heat exchanger, in which flow pattern is maintained as co-current, has been used to conduct the experimental runs. The numbers of plates in the plate heat exchanger used in the present study are 10. The number of flow channels (space maintained between two consecutive channels) allocated for both fluids are 9. Experimental runs have been conducted for different combinations of hot fluid flow rates and cold fluid flow rates for single phase flow, in which hot water is considered as hot streams and kerosene as cold fluid. The thermal performance of plate heat exchanger has been analyzed based on calculated parameters using experimental data set. A similar corrugated plate heat exchanger model having the same dimensions as that of the experimental one was developed with aid of CFD tool. The model was simulated at different operating conditions and compared with experimental results. The simulated results are in good agreement with experimental data. The percentage deviation between experimental results and simulation results for over all heat transfer coefficient is less than ±6%.

In this paper mathematical models are proposed to simulate the dynamic behaviour of the reduction of Cr(VI) with Fe(II) in a batch stirred tank. Two approaches have been adopted to represent systems in non-equilibrium and quasi-equilibrium conditions. The models derived reflect the changes of the relevant reactant species concentration based on the Oxidation Reduction Potential (ORP) of the system. An automated pilot plant has been designed and commissioned to carry out on-line/real time data acquisition and control for this Cr(VI) reduction process experimentally. Both the quasi-equilibrium and non-equilibrium models were validated experimentally. Simulated and on-line results indicate distinctive ORP profiles pattern for these two processes. The ORP profiles for non-equilibrium processes show a distinctive pattern which indicates the complete reduction of Cr(VI) in the batch sample.

This paper presents a systematic study on the homogeneously catalyzed reactive distillation (RD) process operated in both batch and continuous mode for the synthesis of ethyl acetate. In the first part, the fundamental model has been developed incorporating the reaction term within the model structure of the nonreactive distillation process. In case of batch rectifier, the process operation is simulated at the startup phase under total reflux condition for reaching the steady state. The open-loop process dynamics are also examined running the batch process at production phase under partial reflux condition.The concerned batch rectifier has the ability to provide high purity product. On the other hand, the continuous RD column exampled at the beginning of the second part produces relatively low quality product. In the present study, an attempt has been made to configure a feasible continuous column with improved product quality. In order to propose a RD setup, several simulation experiments have been performed for sensitivity analysis. An interesting phenomenon, the input multiplicity, is observed for both the batch as well as continuous column.

A dynamic model of an industrial packed-bed multi-tubular reactor was developed to investigate performance of an industrial ethylene oxide (EO) reactor, conducting epoxidation of ethylene over a silver-based catalyst. The set of nonlinear kinetic rate equations for the catalytic oxidation process in the presence of ethylene dichloride (EDC) as a moderator was coupled with the governing heat and mass transfer equations along the packed bed. Catalyst deactivation was modelled as a nonlinear function of operating time and the equation was benchmarked against plant data for the period of operation. Our process model was compared with experimental data obtained from an industrial EO reactor. The model predictions were found to agree well with the plant data. The influences of operating parameters such as EDC level, reactant concentrations, reactor pressure, coolant temperature and the feed temperature on reactor performance were investigated. The variables having significant impact on work rate and selectivity were identified. The model was used to optimise the performance of ethylene oxide reactor for maximising work rate and selectivity.

Full scale biofilter systems are often subjected to variations in operational and environmental conditions. Occasional fluctuations in the flow rate, temperature, concentrations of process emissions, and pH drops are commonly encountered. In this work, a general transient biofilter model which incorporates axial dispersion effects, interactive kinetics, multi-component adsorption effects and oxygen limitations aspects have been used to study the dynamic behavior of biofilters to process upsets. Biofilter performance under sinusoidal and triangular perturbations of concentrations, flow rate, and media pH are presented. The results of this study will be useful in the designing and process control of biofilter systems that are exposed to varying operational conditions.

Biofouling is one of the most important problems associated with the application of membrane in bioprocessing. A strategy for membrane regeneration is the chemical cleaning of fouled membranes. The choice of chemical cleaning agent and operating parameters are critical to cleaning efficiency. In this work the cleaning efficiency of different cleaning agents for the removal of biofouling layer was studied. The cleaning agents include hydrogen peroxide (H2O2), sodium hydroxide (NaOH), hydrochloric acid (HCl), 1-propanol, urea and Ethylene Diamin Tetra Acetic acid (EDTA). The influences of different experimental variables on the cleaning efficiency were evaluated. Response surface methodology was employed to optimize the cleaning efficiency of the cleaning agents with three variables of concentration of cleaning agent, time of cleaning, and stirring speed for cleaning of GVHP microfiltration membrane fouled by E.coli in a dead end cell. To build the mathematical model and minimize the number of experiments for the design parameters, a response surface methodology with a face-centered central composite design (CCF) was defined. The adequacy of the calculated model for the response was confirmed by means of variance analysis and additional experiments. Predicted optimal conditions to achieve maximum flux recovery were elucidated. The validity of the response model was verified by a good agreement between predicted and experimental results.

The design of suspension mixing tanks is often embedded in empiricism due to insufficient understanding of the hydrodynamics of dense solid-liquid interaction. This is particularly limiting during the design of mixing tanks or batch reactor for expensive high quality products in the fine chemicals, biological and pharmaceutical processing industries. In this paper, a computational fluid dynamics (CFD)-assisted design approach has been employed to study the effectiveness of mixing tank geometrical configurations to suspend particles. Ten crucial variables were investigated and the effects of changing the value of these variables were documented. In addition, a multivariable study involving these ten variables was also conducted to investigate the most important variables among them. Finally, by capturing the essence of the results, a design heuristic was developed which can be applied across the process industry.

Polyethyleneterephtalate (PET) is one of the most popular plastic materials used for various purposes, mainly for producing of bottles. Recycling of this material is known, but the ratio of recycled material is too low. With the lowering of primary resources, the PET could be used as a secondary source of carbon and hydrocarbons. One of the ways of processing the waste PET is pyrolysis under the specific conditions, which leads to decomposition of this material with two main products. First, it is a mixture of organic acids – terephtalic acid and benzoic acid with a ratio about 10:1. Those acids can be used in the chemical industry; terephtalic acid can be used for producing of brand-new PET. The second product is solid carbon, which can be used for various purposes, for example as the active carbon. Several experiments were accomplished with this carbon material under various conditions of its activation. Conditions of these experiments could affect the specific surface area of active carbon up to 1200 square meters per gram, and also the pore volume as well as the pore diameter.

CFD simulation results of gas–solid fluidized bed using 2D Eulerian–Eulerian approach at high gas velocities integrating the kinetic theory of granular flow are presented. The range of gas velocities are from 0.35 to 2.19m/s. Predicted particle motion and bubble behavior in the bed are consistent with the literature data. Simulation results indicate that Arastoopour's drag model is the best choice for the current system. Laminar flow and turbulent models, standard k-? and Simonin, are compared at Ug = 2.19m/s. CFD results show that Simonin model can be used at high gas velocities, reasonably. Current model reduces simulations errors compared with previous works. The minimum error is 6.5% and the maximum error is 13% in lower and upper part of the bed in radial direction, respectively.

This paper numerically investigated the effect of chamber aspect ratio and operating conditions on flow stability within a short form spray dryer without the inclusion of droplet injection. Extensive analysis using different mesh configurations led to a new finding on the effect of expansion ratio on flow stability. A larger expansion ratio produces a more stable flow, which is due to the limitation of the jet fluctuation by the outlet geometry constriction. However, the flow might not be completely steady as fluctuations are magnified at higher inlet velocities, which can be explained by the confined jet feed-back mechanism. Fast Fourier Transform (FFT) analysis of the computed instantaneous velocity at selected spatial locations in the chamber revealed the effect of the chamber expansion ratio on periodicity of the flow structure. It is observed that changes in the expansion ratio primarily affect the amplitude of the 'noise' region in the frequency spectrum.

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

The key intermediate for the manufacturing of a powerful explosive, HMX, is 3, 7-Dinitro-1, 3, 5, 7-tetraazabicyclo [3, 3, 1] nonane (DPT). It is being manufactured by the nitration of hexamine under mild acidic conditions and at low temperature yields of DPT in the initial stage, which upon further nitration gives HMX. The yield of DPT decides the productivity and economy of the HMX process. The present study's aim is to achieve the rate of formation of products and the stimulus of process parameters on the reaction for essentially enhancing the yield of DPT. The rate of formation of DPT has been studied in a continuous process under a wide envelope of residence times and temperatures. The rate expressions and activation energies of DPT and side products have been derived. The optimal temperature of reaction has been derived to obtain a higher yield of DPT at the lowest formation of side products. The selectivity of process has been enhanced by varying the concentration of limiting reagent.The qualitative and quantitative picture has been highlighted to realize the role of formation of DPT in HMX formation, and the study reveals that the yield of DPT and side products is sensitive toward the concentration of nitronium ion.

This paper proposes a method for the flexibility analysis in flowsheet simulation of solid processes. It provides the evaluation of a process with varying independent variables in terms of acceptance regarding the process demands. The existence of control variables for the operation of the process is considered as well as processes without this potential. The result is a plain information about the capability of the process to handle varying independent influencing variables. The method gives an approximation of the largest inscribed block-shaped volume within the solution space, spanned by the vector of the independent variables. So that for each variable, a range is given for which an allowed operating point can be achieved. The method is applied to a soil-washing process as an important example in the field of solids processes.

The implementation of model-based control and diagnostics suffer strongly from the fact that models deteriorate as a function of process and sensor deterioration. Also, changes in the raw material (i.e. wood) may occur and often the process control is not addressing these variations in reality. It is thus vital for the model system to be robust in the sense that it is transparent and easy for the operator to maintain. Robustness is essential in many parts of the system, including measurement, process model validation, the ability of the model to adapt to changes in the process, optimization algorithms, and of course the model itself. In this paper, we first show three real-life applications of the utilization of models for diagnostics and control. Thereafter conditions for on-line adaptation of the models are discussed. The challenges when designing such a system are in achieving operator confidence, filtering of misleading measured data, adaptation of process parameters when the process parameters change, and combining validation of measurements and process models. These challenges are met by using a combination of physical and statistical models and methods based on them such as model predictive control (MPC) and parameter estimation. The model should be maintained by a qualified engineer who should be able to explain the system to the operator so that it is understood and confidence can be maintained.

The aim of this work was to simulate in 1D, the spatio-temporal evolution of the moisture content, the temperature and the mechanical stress within a highly deformable and saturated product during convective drying. The hydro-thermal model, written in a fixed coordinate system, consisted of solid mass balance equation, moisture transfer diffusion/advection equation and heat transfer conduction/advection equation. These equations were coupled by the solid phase velocity terms due to hydric shrinkage. Convective boundary conditions completed this set of equations. The hydro-thermal model had been merged with a static mechanical model which was based on the hypothesis of an elastic behavior, of a plane deformation and of an ideal shrinkage. The hydro-thermo-mechanical model had been applied to a parallelepipedical potato sample. Its thickness was small compared to the other dimensions, in order to observe an unidirectional drying and a negligible shear stress case. The transport and equilibrium properties of the product required for the modelling were determined from previous experiments which were independent of the drying trials.

A model simulator NALD-2 has been developed to study the interactions of fluoride and aluminium in the Nalgonda Defluoridation Process, which principally involves the preferential adsorption of fluoride ions onto insoluble aluminium hydroxides that are formed from alum hydrolysis reactions and undergo precipitation. This model represents the defluoridation mechanism taking into account the charged behavior of the amphoteric aluminium hydroxide colloids, charged site densities, and fluoride complexation reactions in order to predict the extent of defluoridation and concentrations of dissolved and colloidal aluminium in the treated water as functions of pH, alum dosage and raw fluoride concentrations. Model validations were carried out using secondary data of Selvapathy & Arjunan (1995) for total residual aluminium in Nalgonda treated water. Slight variations in pH into the acidic range cause substantial increase in colloidal aluminium and hence pH and alum dosages are important control parameters to limit residual aluminium in treated water. The NALD-2 simulator helps in predicting optimum alum dosages for minimum residual aluminium in the treated water. It was concluded that the dosages of alum recommended in the Nalgonda method cannot bring down the residual Al below the permissible limit of 0.2mg/L in the treated water.

A dynamic model is developed to present chlorine decay in chlorine contact tank, and a single-input single-output (SISO) model that presents both chlorine dosing and decay process is developed in Simulink of Matlab software with considerations of the process disturbances of temperature and stagnant flow in the tank. A computational fluid dynamics (CFD) model of chlorine transport and decay in the tank is also developed with the use of mixture multiphase model to present the chlorine mixing and decay models in the tank. To optimally control free chlorine residual (FCR) concentration in the SISO system, a linear model predictive control (LMPC) is designed using the SISO system and LMPC control algorithm. The LMPC control objective is to regulate the optimal mass flow rates of gaseous chlorine to control the chlorine decay process inputs/outputs within the constraints. The results on the LMPC simulation using reference data from a real water plant show that the LMPC can control the FCR concentration in the tank within the constraint by regulating the optimal mass flow rates of gaseous chlorine. Commercial CFD software, FluentTM, has been used in this study to simulate the FCR distribution in the CCT channel based on the LMPC result.

Over decades, dynamic matrix control (DMC) was used as an advanced control strategy in processing industries that yields good performance, especially in the presence of uncertainties and undesirable noises. As the implementation of PID algorithm in digital platform is easier, an equivalent PID controller is synthesized where proportional gain (KC) of a controller is obtained by approximating a dynamic matrix control algorithm. Integral time (?i) and derivative time (?d) are obtained from process model and step response coefficients. The performances of the equivalent PID controller are tested on different process models (dynamics) and closed loop responses are analyzed. This performance is compared with the DMC-SISO response. The designed PID controller is robust and stable. Results obtained from a computer simulation are presented.

The paper aims at modelling linearly adsorbed solute breakthrough curves into chemically heterogeneous media at column scale. The model was developed according to the mixing-cell-in-series approach and provided a general expression of the reduced variance for either water tracer or linearly adsorbed solute into either chemically homogeneous or heterogeneous medium. According to the experimental investigation of Semra et al. (2008), medium activity was assumed discrete with a known mean capacity. The chemical heterogeneity scale was assessed by the inverse of the ratio of active grains number to the total grains one. Discussion of the heterogeneous model results was accomplished considering breakthrough curve moments. Moment analysis showed that respective effects of interaction and transport are no longer independent from each other even when adsorption is linear. For a constant mean capacity and a constant hydrodynamic dispersion, the effective dispersion, accounted for by the temporal reduced variance, increases linearly with the chemical heterogeneity scale and the breakthrough spreading increase is favoured by high capacities. Moreover, mathematical form of the reduced variance predicts asymmetric breakthrough curves even in the case of equilibrium assumption. Considering kinetics, the model shows additive but independent effects of kinetics and hydrodynamics as in the case of chemically homogeneous media; it shows also independent kinetic effects from heterogeneity. Finally, modelling results were coherent with experimental observations made by Semra et al. (2008).

Chromium in its trivalent and hexavalent form in the environment is toxic above certain concentrations. Cr(VI), which is more toxic than Cr(III), was chosen in this work and removed from aqueous solutions of potassium dichromate by adsorption onto physically and chemically activated carbons derived from Zea mays(corn or maize) cob, an agricultural waste product. Adsorption studies were done at the optimum pH of 2 and at the room temperature of 30°C. The aim of the work was to study the adsorption equilibrium and kinetic data with various isotherm and kinetic models. The best fit to the equilibrium data was provided by the 3-parameter Fritz-Schluender model followed by the 2-parameter Langmuir model. The adsorption isotherms were favorable and the adsorption on chemically activated carbons improved as the impregnation ratio was increased. Kinetic modeling showed that the fractional power model provided the best fit followed by the second-order Ritchie's equation. The adsorption of Cr(VI) on the activated carbons was film-diffusion controlled.

Soft Sensors have been developed through phenomenological, empirical and hybrid modeling for quality variable predictions in various chemical processes. In this work a soft sensor based on an empirical model has been developed for the successful predictions of melt index (MI) in grade-changing polymerization of High Density Polyethylene (HDPE) processes. In order to capture the nonlinearity and grade-changing characteristics of the polymerization process efficiently, a recursive partial least squares (RPLS) update as well as a model bias update is applied to the process data successfully. Two schemes have been proposed: scheme-I and scheme-II. Scheme-I makes use of an arbitrary threshold value which selects one of the two update strategies according to the process requirement at a certain updating instance so as to minimize the relative root mean square error (RMSE). On the other hand, with the aim of preventing excessive RPLS update, scheme-II minimizes the number of RPLS update runs (NPR) while maintaining, increasing or sometimes reducing the RMSE obtained from scheme-I. Proposed schemes are compared with other strategies to exhibit their superiority.

In this paper, the effect of operating decisions on the design and energy consumption for inverted batch distillation is considered. In distillation, utility cost is a function of column vapour load (V). We present a different type (as compared to what is available in the existing literature) of optimisation problem formulation and show that for a fixed market demand scenario minimisation of V for a given N will not only minimise the utility cost (cooling water + energy cost) and the capital cost but will also maximise the profitability of the operation. Also, with several examples of binary separation, it is shown that the decision of operating policy (such as constant reboil ratio, time dependent reboil ratio, etc.) at the design stage can have a significant impact on the capital and utility cost and overall profitability. This will in turn have significant effect on global warming as savings in utility cost reflects savings in energy cost. For difficult separations, which are capital and energy intensive, 40% savings in capital cost, 60% savings in utility and 70% improvement in profitability are possible if operating decisions are taken at the design stage.

The present work has been carried out to characterize the dynamics of three-phase flow in cylindrical fluidized bed, run under homogeneous bubble flow and heterogeneous flow conditions using CFD (Computational Fluid Dynamics) simulation. The investigation has been done to study the flow pattern of three-phase fluidized bed along with parametric studies. The simulations were performed for air-water-glass beads in a fluidized bed of H = 0.6 m, Di = 0.1 m and ds = 0.05 m to study the flow pattern. Eulerian-Eulerian, three-phase simulations with k-? turbulence for liquid phase were carried out using the flow simulation software CFD-6.2.16, with a focus on characterizing the dynamics properties of gas-liquid-solid flows. Detail study of the flow pattern in three-phase fluidized bed has been carried out and the flow pattern has been presented in the form of contour plots. The results presented are useful for understanding the dynamics of gas-liquid-solid flows in fluidized bed and provide a basis for further development of CFD model for three phase systems.

Activated lignin, having a surface area of 1023 m2 g-1, has been prepared from lignin sulfate that was treated by 30% H2O2 and carbonized at 300°C in order to test the chromium (VI) adsorption from aqueous solution. The influence of contact time, pH, initial concentration of adsorbent and adsorbate and temperature on the adsorption capacity was investigated. The maximum removal of Cr(VI) was found to be 92.36% at pH=2 and a contact time of 80 min. Optimal concentration of lignin and Cr(VI) was found to be 3.8 gL-1 and 180mg L-1, respectively. The adsorption kinetics was examined with pseudo-first-order and pseudo-second-order equations. The analytical data fit well to the pseudo-second-order equation, and the rate of removal of chromium was found to speed up by increasing the temperature. Activation energy for the adsorption process was found to be 18.19 KJ mol-1. The Langmuir-Freundlich adsorption isotherm models were applied to describe the isotherm and its constants for the adsorption of Cr(VI) on lignin. These constants and correlation coefficients of the isotherm models were calculated and compared with each other. Results indicated that Cr(VI) uptake could be described by the Langmuir adsorption isotherm. The maximum adsorption capacity (qmax) of Cr(VI) on lignin was 75.75 mg g-1 at a temperature of 40°C. The dimensionless equilibrium parameter (RL) signified a favorable adsorption of Cr(VI) on lignin and was found to be between 0.0601 and 0.818 (0L<1). The thermodynamic parameters such as ?G°, ?S°, and ?H° were calculated and it has been found that the reaction was spontaneous and endothermic in nature. This study indicates that lignin has the potential to become an effective and economical adsorbent for the removal of Cr(VI) from waste water.

The correlation of the data for phase and chemical equilibrium in acetic acid-n-butanol-water-n-butylacetate is presented. The azeotropic properties, the topological structure and the existence of a reactive azeotrope are discussed. The data are correlated by Hayden-O'Connell second virial coefficients and NRTL. Chemical equilibrium constant has a strong influence on the reactive azeotrope.

The major issues in anaerobic treatment are discharge standard accomplished by post-treatment facilities. Model tanks have been designed for post treatment of anaerobic effluents. Tracer studies by KMnO4 have been employed on model tanks to know hydrodynamics and mixing inside the tanks. The existence and arrangement of baffles in the tanks and higher tracer density can result in much more complex flow pattern which includes turbulent mixing, dead zones and short circuiting. The objective of the paper is to present the result of a numerical study undertaken to investigate the flow inside the reactor and effect of hydraulic parameters on the extent of mixing. The computational fluid dynamics (CFD) study was also performed on the velocity distribution within the model tanks. Thus, several 2D steady-state and time-variable numerical simulations were performed with in the model tanks geometry. Tracer transport was analyzed in case of burst of concentration. The CFD study shows how velocity, pressure and their vector notations were useful to predict the dead spaces inside the tanks which affect the mixing behavior inside the tank.

Hydrogen atom transfer (HAT) underlies free-radical chain-breaking by phenolic compounds. Using linear free energy relationship (LFER) analysis, proton transfer kinetics was hypothesized as a surrogate rate process for HAT. Phenol red is a probe that is easily oxidized to highly absorbing specie by hydroxyl ion. Absorbance decay of oxidized phenol red was induced by incremental proton transfer from a model phenolic (resorcinol). Global best-fit kinetics profile of resorcinol approximates a mono-exponential decay model (R2 = 0.991) as a limiting law. Proton transfer rate constant (Kptt) versus concentration reveal the utility of the slope (?aoc) of the linear plot (r2= 0.990) as a sensitive predictor of phenolic antioxidant capacity. Superior antioxidant capacity profile of a polyphenol-rich dietary supplement: Garcinia kola seed extract, optimally obeyed a mixed linear/mono-exponential decay equation. Model robustness and selectivity for phenolics was achieved by specifying mathematical constraints as acceptance criteria. The method is more biologically relevant for chain-breaking antioxidants than free-radical-based assays because it captures antioxidant structure-function relationships. Further validation studies, using structurally diverse polyphenols, are warranted to ascertain general utility of the kinetic assay for achieving quality by design (QbD) in phenolic dietary supplement products.

The potential impacts of additional flow and organic loads resulting from the production of cellulosic ethanol on an existing integrated newsprint mill were simulated in this paper. It was found that depending on the ethanol production rate and the existing spare capacity for additional biochemical oxygen demand (BOD), treatment plant modifications may be required. In terms of operating costs, it was found that nutrients use could increase by 50% to 150%, while aeration flow could increase by 5% to 140% depending on the desired level of dissolved oxygen in the aeration basin. Significant increases in polymer use for mixed sludge dewatering could result due to additional biosolids production. Additional capital costs for air blowers could also be necessary unless the mill has existing spare capacity. It was also found that ethanol recovery efficiency and production rate had little impact on BOD removal up to a certain ethanol production level, and that the impact on operating costs decreased with increasing recovery.

This paper presents the results of numerical modeling of temperature distribution and flow pattern in a block radiator used in power transformer cooling system. Each block radiator consists of eighteen plane radiators which are parallel together and a typical power transformer (like a 30MVA) has 6 radiator blocks in each side which means it has 12 blocks totally. The numerical study using fluent software has been conducted to find the explanation of low cooling efficiency in this especial power transformer currently used in industry. Our main desire is the study of relation between radiator block characteristics and cooling behavior of system which can be used for its optimization in the future studies. The results indicate that recirculation occurs whenever pressure increase at the end of a radiator block that consequently prevents the enough oil flow through the last radiator of block. Experimental data taken from company and technical data extracted from transformer documents have been used for model calibration and results verification.

Modeling for hydrocarbon methane was carried out during the warm-up period. A one-dimensional model based on its catalytic combustion reaction was formed. The mass and energy balance equations derived for the gas and the solid phase are a set of partial differential equations. All these equations are solved using Backward Implicit Scheme. The variation of gas concentration, temperature and solid temperature along the length of the converter during the warm-up period was analysed. Also, the effects on time while changing various parameters—like inlet gas temperature, catalyst loading, surface area, and converter length—for methane conversion, were studied.

The complex flow patterns induced in fluidized bed catalytic reactors and the competing parameters affecting the mass and heat transfer characteristics makes the design of such reactors a challenging task to accomplish. The models of such a process rely heavily on predictive empirical correlations for mass and heat transfer coefficients. Unfortunately, published empirical based correlations have the common shortcoming of low-prediction efficiency compared with experimental data. In this work, an artificial neural network approach is used to capture the reactor characteristics in terms of heat and mass transfer based on published experimental data. The developed ANN-based heat and mass transfer coefficients relations were used in a conventional FCR model and simulated under industrial operating conditions. The hybrid model predictions of the melt-flow index and the emulsion temperature were compared to industrial measurements as well as published models. The predictive quality of the hybrid model was superior to other models. This modeling approach can be used as an alternative to conventional modeling methods.

The present work attempts to study the flow dynamics of Geldart group A and group B particles in a circulating fluidized bed (CFB) riser with intruded exits using computational fluid dynamics (CFD) techniques. In this work, three different designs of riser exit geometries were studied using a 2D transient Eulerian approach with kinetic theory of granular flow in a commercial CFD simulation package. The three exits geometries, namely conventional exit, intruded curved and straight exits were designed and simulated. The various factors like solid volume fraction, pressure drop, and solid velocity were studied for both group A and group B particles. Results indicate that intruded exits have certain advantages like low riser pressure, uniform particle distribution, etc., over the conventional design because fluid and particle comes out from the core of the riser where there is no wall effect and particles have higher momentum concentration.

The effect of the bend angle on the unsteady developing turbulent air flow through oscillating circular-sectioned curved pipes with the various angles of 180°, 135° and 90° was investigated numerically. The bends had a diameter of 106 mm and a curvature radius ratio of 6.0 with long, straight upstream and downstream sections. Results of the mean velocity and static pressure were obtained at a Reynolds number of 31200 and at various longitudinal stations. The velocity of the primary flow was illustrated in the form of contour map and vector diagram. From the inlet plane of the three oscillating bends to the angle of 45°, the velocity fields in 180°, 90° and 135° bends are similar. The high velocity regions, however, occur near the upper and lower parts in 90° and 180° bends, respectively.

Combustion of Liquefied Petroleum Gas (LPG) in household kitchens results in the production of several harmful gases including Carbon Monoxide (CO) and Nitrogen Oxides (NOx). In the present work, an attempt has been made to investigate the effect of such gases on the percent oxygen saturation of blood and a nonlinear exponential model has been proposed based on reaction kinetics to quantify the amount of carboxyhaemoglobin and methaemoglobin formed in blood due to such exposures using a non-invasive technique. The model parameters have been estimated by using the experimental data obtained from exposure of the individuals to different concentrations of CO and NOx. Non-linear regression technique has been used in MATLAB® to optimise the objective function. The model has been validated for %COHb concentration in blood and corresponding data for ambient CO concentration reported in literature. The proposed model can be used to find the concentration of methaemoglobin and carboxyhaemoglobin formed in the blood for low exposures.

This paper is focused on the design of a Model Predictive Control (MPC) algorithm to control and optimize the methanol synthesis in a Simulated Moving Bed (SMB) reactor. First, the advantages that can be obtained when the process in carried out in this reactor configuration are summarized; then, the control algorithm is described. A simplified model based on Artificial Neural Networks (ANN) is used to calculate the control actions. The influence of the tuning parameters of the algorithm is studied by means of mathematical simulation, thus resulting in the best controller configuration. Finally, examples of the performance of the controlled system are provided, thus demonstrating the effectiveness of the proposed tool.

Recent surge in interest in biomass conversion in fluidized bed calls for an analysis of the information available on mathematical modeling of this attractive process. Such an analysis could provide a useful base for the development of mathematical tools for design or optimization of fluidized bed gasifiers. This paper analyzes the current reaction kinetics and its application in modeling pyrolysis and gasification of biomass in fluidized beds. This review identifies the knowledge gaps and relevance of different modeling approaches to design and operation of gasifier plants.

Process control in the field of chemical engineering has always been a challenging task for the chemical engineers. Hence, the majority of processes found in the chemical industries are non-linear and in these cases the performance of the linear models can be inadequate. Recently a promising alternative modelling technique, artificial neural networks (ANNs), has found numerous applications in representing non-linear functional relationships between variables. A feedforward multi-layered neural network is a highly connected set of elementary non-linear neurons. Model-based control techniques were developed to obtain tighter control. Many model-based control schemes have been proposed to incorporate a process model into a control system. Among them, model predictive control (MPC) is the most common scheme. MPC is a general and mathematically feasible scheme to integrate our knowledge about a target, process controller design and operation, which allows flexible and efficient exploitation of our understanding of a target, and thus produces optimal performance of a system under various constraints. The need to handle some difficult control problems has led us to use ANN in MPC and has recently attracted a great deal of attention. The efficacy of the neural predictive control with the ability to perform comparably to the non linear neural network strategy in both set point tracking and disturbance rejection proves to have less computation expense for the neural predictive control. The neural network model predictive control (NNMPC) method has less perturbations and oscillations when dealing with noise as compared to the PI controllers.

Critical comments on results of the CFD simulation of the impeller power input in a cylindrical baffled vessel under turbulent regime of flow of agitated liquid.

The study deals with analysis of the process characteristics of agitated system with a pitched blade impeller and radial baffles (impeller power input and impeller pumping capacity) under turbulent regime of flow of agitated batch. Original experimental data are compared with results of CFD simulation in a pilot plant mixing system published in literature.