Continuous protein recovery using a liquid–solid circulating fluidized bed ion exchange system: Modelling and experimental studies

In this study, a continuous protein recovery process using a Liquid–Solid Circulating Fluidized Bed (LSCFB) ion exchange system is described and a model with known kinetics has been developed. Experiments and computer simulations using Matlab? are conducted at different operating conditions. The effects of hydro‐dynamic parameters and kinetic parameters on the performance of the LSCFB ion exchange system are discussed. The model is shown to be applicable for the design of LSCFB ion exchange systems for protein recovery.     

Modeling biological nutrient removal in a liquid–solid circulating fluidized bed bioreactor

BACKGROUND: Both laboratory‐scale and pilot‐scale liquid–solid circulating fluidized bed (LSCFB) bioreactors have demonstrated excellent biological nutrient removal (BNR) from municipal wastewater. In this study, a model for the LSCFB for biological nutrient removal has been developed, calibrated, and validated using pilot‐scale experimental results. RESULTS: An efficient reactor arrangement predicted anoxic–anaerobic and aerobic biofilm thicknesses of 150–400 and 70–175 µm in the riser and downer, respectively. Furthermore, distribution of chemical oxygen demand (COD), NH₄‐N, NO_X‐N, and dissolved oxygen in the biofilm, as well as nutrients removed in the aerobic and anoxic zones, reflect nitrification, denitrification and enhanced biological phosphorus removal in the LSCFB. The model predicted both anoxic effluent and final effluent COD, SCOD, SBOD, NH₄‐N, NO₃‐N, TKN, TN, PO₄‐P, and TP were within the 95% confidence intervals of the experimental data. Model‐predicted simultaneous nitrification/denitrification occurring in the aerobic downer. CONCLUSION: This model developed for LSCFB using the AQUIFAS biofilm diffusion model successfully evaluated the process performance. It is an efficient tool for further research, design, and optimization of the fixed film bioreactor. Copyright © 2010 Society of Chemical Industry     

Performance evaluation of an ANN–GA aided experimental modeling and optimization procedure for enhanced synthesis of marine biosurfactant in a stirred tank reactor

BACKGROUND: An improved resilient back‐propagation neural network modeling coupled with genetic algorithm aided optimization technique was employed for optimizing the process variables to maximize lipopeptide biosurfactant production by marine Bacillus circulans. RESULTS: An artificial neural network (ANN) was used to develop a non‐linear model based on a 2⁴ full factorial central composite design involving four independent parameters, agitation, aeration, temperature and pH with biosurfactant concentration as the process output. The polynomial model was optimized to maximize lipopeptide biosurfactants concentration using a genetic algorithm (GA). The ranges and levels of these critical process parameters were determined through single‐factor‐at‐a‐time experimental strategy. Improved ANN‐GA modeling and optimization were performed using MATLAB v.7.6 and the experimental design was obtained using Design Expert v.7.0. The ANN model was developed using the advanced neural network architecture called resilient back‐propagation algorithm. CONCLUSION: Process optimization for maximum production of marine microbial surfactant involving ANN‐GA aided experimental modeling and optimization was successfully carried out as the predicted optimal conditions were well validated by performing actual fermentation experiments. Approximately 52% enhancement in biosurfactant concentration was achieved using the above‐mentioned optimization strategy. © 2012 Society of Chemical Industry     

Drying Kinetics Prediction of Solid Waste Using Semi‐Empirical and Artificial Neural Network Models

The drying process of organic solid waste is investigated, based on an experimental study involving its drying kinetics. The experiments were conducted in a thin‐layer fixed‐bed dryer under various operational conditions. The problem of selecting the best fit for solid waste moisture content as a function of time is addressed as well, using artificial neural network (ANN) models and four well‐known drying kinetics correlations commonly applied to biological materials. According to the statistical analysis employed, the simulations showed good results for the ANN, and the Overhults model provided optimum agreement with experimental data among all other models evaluated. Empirical correlations between the Overhults model parameters and the drying operational conditions using nonlinear regression techniques were determined.     

Artificial neural networks model for the prediction of steady state phenol biodegradation in a pulsed plate bioreactor

BACKGROUND: A recent innovation in fixed film bioreactors is the pulsed plate bioreactor (PPBR) with immobilized cells. The successful development of a theoretical model for this reactor relies on the knowledge of several parameters, which may vary with the process conditions. It may also be a time‐consuming and costly task because of their nonlinear nature. Artificial neural networks (ANN) offer the potential of a generic approach to the modeling of nonlinear systems. RESULTS: A feedforward ANN based model for the prediction of steady state percentage degradation of phenol in a PPBR by immobilized cells of Nocardia hydrocarbonoxydans (NCIM 2386) during continuous biodegradation has been developed to correlate the steady state percentage degradation with the flow rate, influent phenol concentration and vibrational velocity (amplitude × frequency). The model used two hidden layers and 53 parameters (weights and biases). The network model was then compared with a Multiple Regression Analysis (MRA) model, derived from the same training data. Further these two models were used to predict the percentage degradation of phenol for blind test data. CONCLUSIONS: The performance of the ANN model was superior to that of the MRA model and was found to be an efficient data‐driven tool to predict the performance of a PPBR for phenol biodegradation. Copyright © 2008 Society of Chemical Industry     

Modeling and optimization of electrospun PAN nanofiber diameter using response surface methodology and artificial neural networks

Response surface methodology (RSM) based on a three‐level, three‐variable Box‐Benkhen design (BBD), and artificial neural network (ANN) techniques were compared for modeling the average diameter of electrospun polyacrylonitrile (PAN) nanofibers. The multilayer perceptron (MLP) neural networks were trained by the sets of input–output patterns using a scaled conjugate gradient backpropagation algorithm. The three important electrospinning factors were studied including polymer concentration (w/v%), applied voltage (kV) and the nozzle‐collector distance (cm). The predicted fiber diameters were in agreement with the experimental results in both ANN and RSM techniques. High‐regression coefficient between the variables and the response (R² = 0.998) indicates excellent evaluation of experimental data by second‐order polynomial regression model. The R² value was 0.990, which indicates that the ANN model was shows good fitting with experimental data. Moreover, the RSM model shows much lower absolute percentage error than the ANN model. Therefore, the obtained results indicate that the performance of RSM was better than ANN. The RSM model predicted the 118 nm value of the finest nanofiber diameter at conditions of 10 w/v% polymer concentration, 12 cm of nozzle‐collector distance, and 12 kV of the applied voltage. The predicted value (118 nm) showed only 2.5%, difference with experimental results in which 121 nm at the same setting were observed. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Optimal design of liquid-solid circulating fluidized bed for continuous protein recovery

In recent years, liquid-solid circulating fluidized beds (LSCFBs) are being applied as a reactor system in a number of new applications. This study addresses optimal design of LSCFB system at the design stage for the continuous protein recovery. The operation of LSCFB system for continuous protein recovery is associated with several important objectives such as production rate and recovery of protein as well as the amount of ion exchange resin requirements, all of which need to be optimized simultaneously. In this study, an experimentally validated mathematical model was used to perform the multi-objective optimization of the LSCFB system at the design stage. In the optimization study, eight operating and design parameters were used as decision variables. These variables were chosen based on systematic sensitivity analysis of the system which showed complex interplay of the decision variables over the system performance indicators. Elitist non-dominated sorting genetic algorithm with its jumping gene adaptation (NSGA-II-aJG) was used to solve a number of two- and three-objective function optimization problems. The optimization resulted in Pareto optimal solutions, which provides a broad range of non-dominated solutions due to conflicting behavior of the decision variables on the system performance indicators. Compared to the optimization results obtained in the operating stage, the performance of the system was further improved at the design stage optimization as changes in physical dimensions of the LSCFB system can provide better performance than would have been possible by adjusting only the operating parameters.     

Modeling and Optimization of Anode‐Supported Solid Oxide Fuel Cells on Cell Parameters via Artificial Neural Network and Genetic Algorithm

An artificial neural network (ANN) and a genetic algorithm (GA) are employed to model and optimize cell parameters to improve the performance of singular, intermediate‐temperature, solid oxide fuel cells (IT‐SOFCs). The ANN model uses a feed‐forward neural network with an error back‐propagation algorithm. The ANN is trained using experimental data as a black‐box without using physical models. The developed model is able to predict the performance of the SOFC. An optimization algorithm is utilized to select the optimal SOFC parameters. The optimal values of four cell parameters (anode support thickness, anode support porosity, electrolyte thickness, and functional layer cathode thickness) are determined by using the GA under different conditions. The results show that these optimum cell parameters deliver the highest maximum power density under different constraints on the anode support thickness, porosity, and electrolyte thickness.     

Dynamic modeling of a gas pressure control system for a gas‐assisted injection molding process

This study presents the development of dynamic models for gas injection pressure that may be implemented in the design of control systems for gas‐injection units. A nonlinear dynamic model was first derived and then verified by experimental measurements. This was done by using a laboratory‐built, gas‐assisted injection unit. The agreement between the prediction and measurement indicates that the present nonlinear dynamic model adequately predicts the dynamic behavior of gas injection pressure during the process. Although the resulting model is useful for understanding the behavior of the process and the effects of different process variables, its complexity may cause difficulties in a real control application. Therefore, a second‐order model based on the basic characteristics of the nonlinear model was proposed to approximate the gas injection pressure. In order to determine the model parameters, the algorithm of recursive least‐square system identification was employed. A comparison of simulated results of an identified model with experimental data showed that the model accurately predicted the transient behavior of gas injection pressure. Consequently, this low‐order model can be easily implemented into the control system design of a gas‐injection unit.     

Using artificial neural networks to model and interpret electrospun polysaccharide (Hylon VII starch) nanofiber diameter

Present work was aimed to develop an artificial neural networks (ANN) model to predict the polysaccharide-based biopolymer (Hylon VII starch) nanofiber diameter and classification of its quality (good, fair, and poor) as a function of polymer concentration, spinning distance, feed rate, and applied voltage during the electrospinning process. The relationship between diameter and its quality with process parameters is complex and nonlinear. The backpropagation algorithm was used to train the ANN model and achieved the classification accuracy, precision, and recall of 93.9%, 95.2%, and 95.2%, respectively. The average errors of the predicted fiber diameter for training and unseen testing data were found to be 0.05% and 2.6%, respectively. A stand-alone ANN software was designed to extract information on the electrospinning system from a small experimental database. It was successful in establishing the relationship between electrospinning process parameters and fiber quality and diameter. The yield of smaller diameter with good quality was favored by lower feed rate, lower polymer solution concentration, and higher applied voltage.     

Studies on Regime Transition,Operating Range and System Stability in a Liquid‐Solid Circulating Fluidized Bed

In the present work, the variations in the solids circulation rate and solids holdup were analyzed to study the behavior of a liquid‐solid circulating fluidized‐bed (LSCFB) regime. The results confirm the existence of two regions in the regime of LSCFB. A new concept of critical liquid velocity, j_lc, is proposed in the present work for demarcation between region 1 and region 2, which is found to be a constant value of about 1.3 u_t for all particles considered. The operating range of the LSCFB regime is obtained for the various particles and a correlation is developed from the data to estimate the maximum total liquid velocity. The predicted maximum liquid velocity was compared with the experimental values and found to be in good agreement within ±9 %. The effects of total liquid velocity, particle size and density on the stable operating range are discussed. Analysis of the experimental results shows that stable operation prevails both in region 1 and region 2.     

Numerical investigation on the hydrodynamics of an LSCFB riser

Analysis of fluid flow in a liquid-solid circulation fluidized bed (LSCFB) is necessary to understand its behavior under different operating parameters. In this work, ample parametric studies have been carried out numerically, which provides a view how an LSCFB operates under different operating parameters, and the numerical model has been validated using the experimental data. This study aims to get an insight of the behavior of LSCFB under different operating parameters, which include solids circulation rate, primary and auxiliary liquid velocity. In addition to this task, numerical modeling has also been carried out to predict the behavior of different particles with different densities upon fluidization in an LSCFB, which resolves the problem of experimentation with a wide spectrum of new particles that might have a wide variety of applications in an LSCFB. LSCFBs always involve high Reynolds number flow and dense solids concentration, which demands for proper modeling of the turbulent flow, liquid-solid interactions and particle-particle interactions. Kinetic theory based on Eulerian-Eulerian two-phase model is used to account for particle interactions and is applied to model the solids viscosity and solids pressure, which takes into account the particle-particle collisions.     

Application of gain scheduling for modeling the nonlinear dynamic characteristics of NO<Subscript><Emphasis Type="Italic">x</Emphasis></Subscript> emissions from utility boilers

A hierarchical gain scheduling (HGS) approach is proposed to model the nonlinear dynamics of NO _x emissions of a utility boiler. At the lower level of HGS, a nonlinear static model is used to schedule the static parameters of local linear dynamic models (LDMs), such as static gains and static operating conditions. According to upper level scheduling variables, a multi-model method is used to calculate the predictive output based on lower-level LDMs. Both static and dynamic experiments are carried out at a 360 MW pulverized coal-fired boiler. Based on these data, a nonlinear static model using artificial neural network (ANN) and a series of linear dynamic models are obtained. Then, the performance of the HGS model is compared to the common multi-model in predicting NO _x emissions, and experimental results indicate that the proposed HGS model is much better than the multi-model in predicting NO _x emissions in the dynamic process. This paper was presented at the 7 ^th China-Korea Workshop on Clean Energy Technology held at Taiyuan, Shanxi, China, June 25–28, 2008.     

Artificial Neural Network Modeling of Osmotic Dehydration Mass Transfer Kinetics of Fruits

Artificial neural network (ANN) models were developed for the prediction of transient moisture loss (ML) and solid gain (SG) in osmotic dehydration of fruits using process kinetics data from the literature. ANN models for ML and SG were developed based on data over a broad range of operating conditions and ten common processing variables: temperature and concentration of osmotic solution, immersion time, initial water and solid content of the fruit, porosity, surface area, characteristic length, solution-to-fruit mass ratio, and agitation level. The trained models were able to accurately predict the outputs with associated regression coefficients (r) of 0.96 and 0.93, respectively, for ML and SG. These ANN models performed much better than those obtained from linear multivariate regression analysis. The large number of process variables and their wide ranges considered along with their easy implementation in a spreadsheet make them very useful and practical for process design and control.     

AN EXPERIMENTAL STUDY OF LIQUID-PARTICLE FLOW IN CIRCULATING FLUIDIZED BED

A liquid-solid circulating fluidized bed (LSCFB) is operated at high liquid velocity, where particle entrainment is highly significant and between the conventional liquid fluidized bed and the dilute phase liquid transport regimes. LSCFB has potential applications in the fields of food processing, biochemical processing, and petrochemical and metallurgical processing. It is well known that the flow characteristics in a liquid-solid circulating fluidized bed are different from those of a conventional liquid-solid fluidized bed. The limited studies available in literature do not provide complete understanding of the flow structure in this typical regime.

In the present work, experiments were carried out in a 0.0762 m ID and 3 m height laboratory-scale liquid-solid circulating fluidized bed apparatus by using various solid particles and tap water as fluidizing medium. In the experimental setup, two distributors (specially designed) were used to monitor solid circulation rate in the riser. The effects of operating parameters, i.e., primary liquid flow rate in the riser (Up), solid circulation rate (G_s), and particle diameter (dp), were analyzed from the experimental data. Finally, a correlation was developed from the experimental data to estimate average solid holdup in the riser, and it was compared with present experimental and available data in the literature. They agree well with a maximum root-mean-square deviation of 7.83%.     

Comparison of Estimation Capabilities of the Artificial Neural Network with the Wavelet Neural Network in Lipase-Catalyzed Synthesis of Triethanolamine-Based Esterquats Cationic Surfactant

In this study, estimation capabilities of the artificial neural network (ANN) and the wavelet neural network (WNN) based on genetic algorithm were investigated in a synthesis process. An enzymatic reaction catalyzed by Novozym 435 was selected as the model synthesis process. The conversion of enzymatic reaction was investigated as a response of five independent variables; enzyme amount, reaction time, reaction temperature, substrates molar ratio and agitation speed in conjunction with an experimental design. After training of the artificial neurons in ANN and WNN, using the data of 30 experimental points, the products were used for estimation of the response of the 18 experimental points. Estimated responses were compared with the experimentally determined responses and prediction capabilities of ANN and WNN were determined. Performance assessment indicated that the WNN model possessed superior predictive ability than the ANN model, since a very close agreement between the experimental and the predicted values was obtained.     

Modeling of glycolysis of flexible polyurethane foam wastes by artificial neural network methodology

The glycolysis process as a useful approach to recycling flexible polyurethane foam wastes is modeled in this work. To obtain high quality recycled polyol, the effects of influential processing and material parameters, i.e. process time, process temperature, catalyst‐to‐solvent (Cat/Sol) and solvent‐to‐foam (Sol/Foam) ratios, on the efficiency of the glycolysis reaction were investigated individually and simultaneously. For the continuous prediction of process behavior and interactive effects of parameters, an artificial neural network (ANN) model as an efficient statistical‐mathematical method has been developed. The results of modeling for the criteria that determine the glycolysis process efficiency including the hydroxyl value of the recycled polyol and isocyanate functional group conversion prove that the adopted ANN model successfully anticipates the recycling process responses over the whole range of experimental conditions. The Cat/Sol ratio showed the strongest influence on the quality of the recycled polyol among the studied parameters, where the minimum hydroxyl value was obtained at a medium amount of the assigned ratio. For the consumed polyurethane foam, a higher value of this ratio led to an increase in the hydroxyl value and isocyanate conversion. © 2015 Society of Chemical Industry     

Artificial Neural Network Modeling of Osmotic Dehydration Mass Transfer Kinetics of Fruits

Artificial neural network (ANN) models were developed for the prediction of transient moisture loss (ML) and solid gain (SG) in osmotic dehydration of fruits using process kinetics data from the literature. ANN models for ML and SG were developed based on data over a broad range of operating conditions and ten common processing variables: temperature and concentration of osmotic solution, immersion time, initial water and solid content of the fruit, porosity, surface area, characteristic length, solution-to–fruit mass ratio, and agitation level. The trained models were able to accurately predict the outputs with associated regression coefficients (r) of 0.96 and 0.93, respectively, for ML and SG. These ANN models performed much better than those obtained from linear multivariate regression analysis. The large number of process variables and their wide ranges considered along with their easy implementation in a spreadsheet make them very useful and practical for process design and control.     

Analysis of the influence of atmospheric plasma spray (APS) parameters on adhesion properties of alumina-titania coatings

Alumina-13 wt% titania wear resistant coatings were deposited using the Atmospheric Plasma Spray (APS) process under several processing conditions. Coating adhesion was then measured locally on cross sections by the indentation test and results were correlated with process variables. In order to identify the most influential factors on adhesion, artificial intelligence was used. The analysis was based on an Artificial Neural Network (ANN) taking into account training and test procedures to predict the dependences of measured property on experimental conditions. This study pointed out primarily that adhesion was largely sensitive to parameters that modified the in-flight particle characteristics (i.e. velocity and temperature). These effects were quantitatively demonstrated and predicted with an optimized neural network structure.